HK1259947A1 - Methods and systems for disease monitoring and assessment - Google Patents

Description

Methods and systems for disease monitoring and assessment

Cross-referencing

This application claims priority to PCT patent application No. PCT/CN2015/094425, filed 11/12/2015, which is incorporated herein by reference in its entirety.

Background

The health or well-being of a subject may be determined by the physical attributes of the subject and the environment in which the subject is located. For example, a subject may be infected with a disease if the subject is exposed to a high concentration of a given virus at their workplace. As another example, a subject may be exposed to a virus when in proximity to another individual carrying the virus, which may result in the subject becoming infected with the disease.

Conventional methods and systems for diagnosing and/or treating disease conditions may have a number of disadvantages. For example, such systems and methods may not be able to obtain a relationship in space and time of the subject's environment and the subject's characteristics (displacement). If a subject is exposed to a high concentration of a pathogen, the subject may not be aware of the exposure and may not be looking for measures to prevent the onset of any potential disease condition. Furthermore, methods for diagnosing and treating a subject may not accurately determine the point in time at which the subject is exposed to a given pathogen. Such information may be important in identifying the type of pathogen to which the subject is exposed and providing targeted therapy.

Disclosure of Invention

Risk assessment and monitoring of disease can be a critical part of disease management. However, risk assessment and monitoring of a disease may both rely on relatively isolated data sets that do not account for multiple items such as identity (identity), physiological state, a given geographic location or multiple geographic locations. Thus, there can be a great deal of inaccuracy in both risk assessment and disease monitoring, which can lead to misdiagnosis of disease, underestimation or overestimation of risk, and ultimately a broader spread of disease than would otherwise occur. This is especially the case with infectious diseases such as influenza or other pathogenic diseases that can cause epidemics. Therefore, there is a need for a fast and accurate method and system for risk assessment and disease monitoring. Accurate determination of the location and/or source of an epidemic and knowledge of the prevalence of an epidemic in real time may allow individuals and medical professionals to take faster preventive and/or therapeutic action when an epidemic occurs at that location.

A need has been recognized herein for a fast and accurate method and system for risk assessment and disease monitoring. Accurate determination of the location and/or source of an epidemic and knowledge of the prevalence of an epidemic in real time may allow individuals and medical professionals to take faster preventive and/or therapeutic action when an epidemic occurs at that location.

The present disclosure provides methods and systems for risk assessment and monitoring of diseases. In some cases, the evaluation and/or monitoring includes analysis that takes into account a geographic location or multiple geographic locations. Such analysis may also take into account one or more quantitative measurements of biomarkers (quantitative measures). Further, the methods and systems described herein can be used to obtain disease information regarding: regression and/or progression of the disease, and/or a trend associated with the disease at a geographic location, and/or a plurality of geographic measurements. Such information may be provided to the user on an electronic display of the electronic device and may be used to take preventive and/or therapeutic action against the analyzed disease.

One aspect of the present disclosure provides a method of providing a user with an assessment of risk of contracting at least one disease. The method includes receiving a search query of a user over a network, wherein the search query includes information related to at least any two of an identity, a geographic location, and a physiological state of the user; and processing the search query with the aid of a computer processor to identify one or more tags that are available for searching in the disease database. The disease database may comprise indications of the at least one disease; disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations; subject information selected from two or more of an identity, a geographic location, and a physiological state of each of a plurality of subjects; and one or more associations between the at least one disease, disease progression information, and subject information. The method further comprises searching a disease database using the one or more tags to identify the at least one disease and the disease progression information; and providing the user with an assessment of the risk of contracting the at least one disease based on the disease progression information.

In some embodiments, the user is provided with an assessment of the risk of contracting the at least one disease on a graphical user interface on an electronic display of an electronic device. In some embodiments, the electronic device is a portable electronic device. In some embodiments, the graphical user interface is provided by a mobile computer application. In some embodiments, the information relates to the identity, geographic location, and physiological state of the user. In some embodiments, the evaluation is provided via a notification or alert on a network. In some embodiments, providing an assessment to the user comprises providing the user with one or more suggested preventative measures to reduce the rate of progression of the at least one disease at the geographic location.

In some embodiments, the indication of the at least one disease comprises identification information of at least one virus, at least one bacterium, and/or at least one protozoan. In some embodiments, the at least one virus is human immunodeficiency virus i (hiv i), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, EB (Epstein-Barr) virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, or rubella virus. In some embodiments, the at least one bacterium is bordetella pertussis (bordetella pneumoniae), Chlamydia pneumoniae (Chlamydia), Chlamydia trachomatis (Chlamydia trachomatis), Campylobacter jejuni (Campylobacter jejuni), helicobacter pylori (helicobacter pylori), Borrelia (Borrelia) bacteria, Mycoplasma pneumoniae (Mycoplasma pneumae), Mycobacterium tuberculosis (Mycobacterium tuberculosis), Haemophilus influenzae (Haemophilus influenzae), Streptococcus pyogenes (Streptococcus pyelonensis), Streptococcus pneumoniae (Streptococcus pneoniae), Clostridium tetani (Clostridium tetrientati), Treponema pallidum (treponepallidum), trypanosoma (trypanosoma streptococcum), Yersinia viridae (Yersinia toxoplastica), or Yersinia viridis. In some embodiments, the at least one protozoan is Plasmodium (Plasmodium) or Leishmania donovani (Leishmania donovani).

In some embodiments, the identity comprises at least one of a name, an age, and a gender of the user. In some embodiments, the physiological state comprises at least one of heart rate, blood pressure, cough frequency, cough intensity, sneeze frequency, sneeze intensity, chest tightness level, nasal obstruction level, body temperature, sweat level, weight, height, respiratory rate, blood pressure, nerve conduction velocity, lung volume (lung capacity), urine production rate, defecation frequency, presence of swollen lymph nodes, and biochemical profile of body fluid (biochemical profile) of the user.

In some embodiments, the geographic location is a continent, an island, an archipelago, a city/town/village, a county/county, a grade/county, a district/downtown, a province, a state/nation, a region (territory), a political region, a country, and/or a group of countries. In some embodiments, the geographic location is an area within the continent, the island, the archipelago, the city/town/village, the county/county, the grade city/county, the district/religion, the province, the state/nation, the region, the administrative district, the country, and/or the set of countries.

Another aspect of the present disclosure provides a method for monitoring at least one disease in a subject. The method includes processing a biological sample obtained directly from the subject at a plurality of time points to identify one or more biomarkers in the biological sample, and obtaining quantitative measurements of at least a subset of the one or more biomarkers at the plurality of time points. Each of the one or more biomarkers is indicative of the presence of the at least one disease in the subject, and the processing is performed with nucleic acid amplification of each biological sample having a sample volume of less than or equal to about 1 milliliter (mL), and the nucleic acid amplification is performed for a period of time of less than or equal to about 10 minutes. The method further comprises processing the quantitative measurements with the aid of a computer processor to determine disease information indicative of progression or regression of the at least one disease in the subject; and generating an output of the disease information. In some embodiments, the at least one disease is monitored at a fixed geographic location.

In some embodiments, each biological sample is obtained directly from the subject and processed without subjecting the biological sample to purification to isolate the one or more biomarkers. In some embodiments, the biological sample comprises whole blood. In some embodiments, the biological sample comprises saliva. In some embodiments, the biological sample comprises urine. In some embodiments, the biological sample comprises sweat. In some embodiments, the biological sample is processed without extracting nucleic acids from the biological sample.

In some embodiments, the nucleic acid amplification comprises Polymerase Chain Reaction (PCR). In some embodiments, the nucleic acid amplification comprises reverse transcription polymerase chain reaction (RT-PCR). In some embodiments, processing a biological sample comprises providing a reaction vessel comprising a given biological sample of the biological sample and reagents necessary to perform nucleic acid amplification; and subjecting the given biological sample to nucleic acid amplification under conditions sufficient to produce an amplification product indicative of the presence of the one or more biomarkers. In some embodiments, the reagent comprises a polymerase. In some embodiments, the reagent comprises one or more primers having a sequence complementary to the one or more biomarkers. In some embodiments, the nucleic acid amplification comprises reverse transcription performed in parallel with deoxyribonucleic acid (DNA) amplification. The reagents may include a reverse transcriptase, a DNA polymerase, and a primer set of ribonucleic acid (RNA) indicative of the at least one disease.

In some embodiments, processing the quantitative measurements comprises comparing the quantitative measurements for the plurality of time points to a reference to identify progression or regression of the at least one disease in the subject. In some embodiments, the one or more biomarkers comprise a nucleic acid. In some embodiments, the nucleic acid is derived from a virus. In some embodiments, the virus is human immunodeficiency virus i (hiv i), human immunodeficiency virus ii (hivii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, epstein barr virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, or rubella virus. In some embodiments, the nucleic acid is derived from a bacterium. In some embodiments, the bacterium is bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, haemophilus influenzae, streptococcus pneumoniae, streptococcus pyogenes, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii or yersinia pestis. In some embodiments, the nucleic acid is derived from a protozoan. In some embodiments, the protozoan is plasmodium or leishmania donovani.

In some embodiments, each biological sample is treated in a time period of less than or equal to about 5 minutes. In some embodiments, each biological sample is treated in a time period of less than or equal to about 2 minutes. In some embodiments, each biological sample is treated in a time period of less than or equal to about 1 minute. In some embodiments, each biological sample is treated in a time period of less than or equal to about 0.5 minutes.

In some embodiments, the sample volume is less than or equal to about 0.5 mL. In some embodiments, the sample volume is less than or equal to about 0.1 mL. In some embodiments, the sample volume is less than or equal to about 0.01 mL.

In some embodiments, generating the output comprises providing the disease information to a user on a graphical user interface of an electronic display. In some embodiments, the graphical user interface is provided by a mobile computer application. In some embodiments, the user is the subject. In some embodiments, the user is a healthcare professional. In some embodiments, generating the output comprises transmitting the disease information to a remote data storage unit.

In some embodiments, the method further comprises providing a questionnaire (quetonnaire) to the subject to assess the geographic location and/or physiological state of the subject; and identifying the at least one disease from the results of the questionnaire. In some embodiments, the questionnaire is provided to the subject on a user interface of an electronic device. In some embodiments, the user interface is provided by a mobile computer application. In some embodiments, the method further comprises obtaining an association between the results of the questionnaire and the at least one disease.

Another aspect of the present disclosure provides a method for monitoring at least one disease. The method includes receiving disease information for each of a plurality of subjects over a network. For a given subject of the plurality of subjects, the disease information is generated by: processing biological samples obtained directly from a given subject at a plurality of time points to identify one or more biomarkers in the biological samples, wherein each of said one or more biomarkers is indicative of the presence of said at least one disease in the given subject, and wherein said processing is performed with nucleic acid amplification of each biological sample having a sample volume of less than or equal to about 1 milliliter (mL) and a time period of less than about 10 minutes; obtaining quantitative measurements of at least a subset of the one or more biomarkers at the plurality of time points; and processing the quantitative measurements with the aid of a computer processor to determine the disease information, wherein the disease information indicates progression or regression of the at least one disease in a given subject. The method further comprises assembling the disease information in a storage location; processing the aggregated disease information to identify trends in the disease at a given geographic location and/or across multiple geographic locations; and generating an output indicative of the trend.

In some embodiments, each biological sample is obtained directly from the subject and processed without subjecting the biological sample to purification to isolate the one or more biomarkers. In some embodiments, the biological sample comprises whole blood. In some embodiments, the biological sample comprises saliva. In some embodiments, the biological sample comprises urine. In some embodiments, the biological sample comprises sweat. In some embodiments, the biological sample is processed without extracting nucleic acids from the biological sample.

In some embodiments, the nucleic acid amplification comprises Polymerase Chain Reaction (PCR). In some embodiments, the nucleic acid amplification comprises reverse transcription polymerase chain reaction (RT-PCR). In some embodiments, processing a biological sample comprises providing a reaction vessel comprising a given biological sample of the biological sample and reagents necessary to perform nucleic acid amplification; and subjecting the given biological sample to nucleic acid amplification under conditions sufficient to produce an amplification product indicative of the presence of the one or more biomarkers. In some embodiments, the reagent comprises a polymerase. In some embodiments, the reagent comprises one or more primers having a sequence complementary to the one or more biomarkers. In some embodiments, the nucleic acid amplification comprises reverse transcription performed in parallel with deoxyribonucleic acid (DNA) amplification. The reagents may include a reverse transcriptase, a DNA polymerase, and a primer set of ribonucleic acid (RNA) indicative of the at least one disease.

In some embodiments, processing the quantitative measurements comprises comparing the quantitative measurements for the plurality of time points to a reference to identify progression or regression of the at least one disease in the subject. In some embodiments, the one or more biomarkers comprise a nucleic acid. In some embodiments, the nucleic acid is derived from a virus. In some embodiments, the virus is human immunodeficiency virus i (hiv i), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, epstein barr virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTL V), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, or rubella virus. In some embodiments, the nucleic acid is derived from a bacterium. In some embodiments, the bacterium is bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, haemophilus influenzae, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, streptococcus pneumoniae, streptococcus pyogenes, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii and yersinia pestis. In some embodiments, the nucleic acid is derived from a protozoan. In some embodiments, the protozoa are plasmodium and leishmania donovani.

In some embodiments, each biological sample is treated in a time period of less than or equal to about 5 minutes. In some embodiments, each biological sample is treated in a time period of less than or equal to about 2 minutes. In some embodiments, each biological sample is treated in a time period of less than or equal to about 1 minute. In some embodiments, each biological sample is treated in a time period of less than or equal to about 0.5 minutes.

In some embodiments, the sample volume is less than or equal to about 0.5 mL. In some embodiments, the sample volume is less than or equal to about 0.1 mL. In some embodiments, the sample volume is less than or equal to about 0.01 mL.

In some embodiments, generating the output comprises providing the trend to a user on a graphical user interface of an electronic display. In some embodiments, the graphical user interface is provided by a mobile computer application. In some embodiments, the user is a given subject of the plurality of subjects. In some embodiments, the user is a healthcare professional. In some implementations, generating the output includes storing the trend in a storage location. In some embodiments, generating the output includes providing a notification or warning to a user regarding the trend. In some embodiments, the biological sample is processed on a designated point-of-care device of a plurality of point-of-care devices.

In some embodiments, generating the output includes providing an update regarding the trend. In some embodiments, the update indicates an increased prevalence of the at least one disease. In some embodiments, the update indicates a decreased prevalence of the at least one disease. In some embodiments, the trend of the disease is in a given geographic location. In some embodiments, each of the plurality of subjects is located at the given geographic location. In some embodiments, the trend of the disease is across multiple geographic locations. In some embodiments, each of the plurality of subjects is located at a given geographic location of the plurality of geographic locations.

Another aspect of the disclosure provides a non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, implements a method for providing a user with an assessment of risk of contracting at least one disease. The method comprises receiving a search query of a user over a network, the search query comprising information relating to at least any two of an identity, a geographic location and a physiological state of the user; the search query is processed with the aid of a computer processor to identify one or more tags that are available for searching in a disease database. The disease database comprises indications of the at least one disease; disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations; subject information selected from two or more of an identity, a geographic location, and a physiological state of each of a plurality of subjects; and one or more associations between the at least one disease, disease progression information, and subject information. The method further comprises searching a disease database using one or more tags to identify the at least one disease and the disease progression information; and providing the user with an assessment of the risk of contracting the at least one disease based on the disease progression information.

Another aspect of the disclosure provides a non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, implements a method for providing a user with an assessment of risk of contracting at least one disease. The method comprises processing a biological sample obtained directly from the subject at a plurality of time points to identify one or more biomarkers in the biological sample; and obtaining quantitative measurements of at least a subset of the one or more biomarkers at the plurality of time points. Each of the one or more biomarkers is indicative of the presence of the at least one disease in the subject, and the processing is performed with nucleic acid amplification of each biological sample having a sample volume of less than or equal to about 1 milliliter (mL) and a time period of the nucleic acid amplification of less than or equal to about 10 minutes. The method further comprises processing the quantitative measurements with the aid of a computer processor to determine disease information indicative of progression or regression of the at least one disease in the subject; and generating an output of the disease information.

Another aspect of the disclosure provides a non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, implements a method for providing a user with an assessment of risk of contracting at least one disease. The method includes receiving disease information for each of a plurality of subjects over a network. For a given subject of the plurality of subjects, the disease information is generated by: processing biological samples obtained directly from a given subject at a plurality of time points to identify one or more biomarkers in the biological samples, wherein each of said one or more biomarkers is indicative of the presence of said at least one disease in the given subject, and wherein said processing is performed with nucleic acid amplification of each biological sample having a sample volume of less than or equal to about 1 milliliter (mL) and a time period of less than about 10 minutes; obtaining quantitative measurements of at least a subset of the one or more biomarkers at the plurality of time points; and processing the quantitative measurements with the aid of a computer processor to determine disease information, wherein the disease information indicates progression or regression of the at least one disease in the given subject. The method further comprises assembling the disease information in a storage location; processing the aggregated disease information to identify trends in the disease at a given geographic location and/or across multiple geographic locations; and generating an output indicative of the trend.

Another aspect of the disclosure provides a non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, performs any of the methods described above or elsewhere herein.

Another aspect of the disclosure provides a computer system that includes one or more computer processors and a computer-readable medium coupled thereto. The computer-readable medium contains machine-executable code that, when executed by the one or more computer processors, performs any of the methods described above or elsewhere herein.

In some aspects, the present disclosure relates to providing a user with an assessment of the risk of contracting at least one disease while traveling. The present disclosure further relates to optimizing a trip.

In one of some aspects, the present disclosure relates to a method of providing a user with an assessment of risk of contracting at least one disease, the method comprising: (a) receiving a search query of a user over a network, the search query including information about a destination and optionally one or more route points; (b) processing the search query with the aid of a computer processor to identify one or more geo-location tags associated with the destination and optionally one or more route points for searching in a disease database, wherein the disease database contains disease progression information indicating progression or regression of the at least one disease at one or more geo-locations including the destination; (c) searching the disease database using the one or more geo-location tags to identify the at least one disease and the disease progression information; and (d) based on the disease progression information identified in (c), providing the user with an assessment of the risk of contracting the at least one disease at the destination and, in some cases, at the one or more route points.

In some embodiments, the user may be provided with an assessment of the risk of contracting the at least one disease on a graphical user interface on an electronic display of an electronic device.

In some implementations, the electronic device can be a portable electronic device.

In some embodiments, the graphical user interface may be provided by a mobile computer application.

In some embodiments, the search query may further include an identity and/or a physiological state of the user.

In some embodiments, the search query may include a starting location of the user.

In some embodiments, the evaluation may be provided via a notification or alert on the network.

In some embodiments, providing the user with the assessment may include providing the user with one or more suggested preventative measures to reduce the rate of progression of the at least one disease at the destination and/or route point.

In some implementations, providing the user with the assessment can include suggesting that the user avoid proceeding to the destination.

In some embodiments, providing the user with the assessment may include suggesting that the user avoid passing at least one of the one or more route points.

In some embodiments, providing the user with the assessment may include suggesting that the user travel to a different destination.

In some embodiments, the database may further comprise indications of the at least one disease.

In some embodiments, the indication of the at least one disease comprises identification information of at least one virus, at least one bacterium, and/or at least one protozoan.

In some embodiments, the at least one virus may be selected from human immunodeficiency virus i (hiv i), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, EB (Epstein-Barr) virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTL V), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, zika virus, Middle East Respiratory Syndrome (MERS) virus, herpes virus, hepatitis b virus, hepatitis c virus, hepatitis e virus (Epstein-bar), hepatitis e-virus, hepatitis b-virus (MERS) virus, hepatitis, Yellow fever virus, rift valley fever virus, chikungunya fever virus, enterovirus, coxsackievirus and norovirus.

In some embodiments, the at least one bacterium may be selected from the group consisting of bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, haemophilus influenzae, streptococcus pyogenes, streptococcus pneumoniae, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii, yersinia pestis, and salmonella.

In some embodiments, the at least one protozoan may be selected from the group consisting of plasmodium and leishmania donovani.

In some embodiments, the identity may include at least one of a name, an age, and a gender of the user.

In some embodiments, the physiological state may include at least one of heart rate, blood pressure, cough frequency, cough intensity, sneeze frequency, sneeze intensity, chest tightness level, nasal obstruction level, body temperature, sweat level, weight, height, respiratory frequency, blood pressure, nerve conduction velocity, lung volume, urine production rate, defecation frequency, presence of enlarged lymph nodes, and biochemical spectrum of body fluid of the user.

In some embodiments, the method may further comprise providing a total risk of infection with the at least one disease for travel to the destination via the route points.

In some embodiments, the search query may further include information regarding travel to the destination via the route points.

In some embodiments, the itinerary may include a time to reach each route point or destination, a time to leave each route point or origin, and/or a time to stop at each route point.

In some embodiments, providing the user with an assessment of the risk of contracting the at least one disease in (d) may further comprise taking into account the journey.

In another of the described aspects, the present disclosure relates to a method of providing a user with an assessment of risk of contracting at least one disease, the method comprising: (a) receiving a search query of a user through a network, the search query including information about a departure place and a destination selected by the user; (b) processing the search query with the aid of a computer processor and a travel cost data structure to (i) identify a route from the origin to the destination within the travel cost data structure, and (ii) determine one or more route points along the route, wherein the one or more route points include at least the origin and the destination, and wherein the travel cost data structure contains travel costs between a geographic location and an adjacent geographic location; (c) using the one or more route points to search a disease database containing disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations (including the destination and/or the one or more route points) to identify the at least one disease and the disease progression information; and (d) providing the user with an assessment of the risk of contracting the at least one disease at the destination and/or along the route based on the disease progression information identified in (c).

In some embodiments, the travel cost may include one or more items selected from the group consisting of travel time, travel expense, travel comfort, dwell time, predictability, safety, punctuality, and combinations thereof.

In some embodiments, the travel cost may include two or more items selected from the group, the two or more items being a weighted combination.

In some implementations, the travel cost data structure may be a weighted graph that includes the geographic locations as vertices and travel costs between neighboring geographic locations as weighted boundaries.

In some implementations, the travel cost data structure may be a table that includes geographic locations in columns and rows and travel costs between adjacent geographic locations in cells.

In some implementations, the method may further include creating a trip based on the route.

In some embodiments, in (b), a route from the origin to the destination within the travel cost data structure may be generated by employing a routing algorithm on the travel cost data structure.

In some embodiments, the routing algorithm is selected from a, Dijkstra, BFS, DFS, Greedy, and combinations thereof.

In another of some aspects, the present disclosure is directed to a method of providing a user with a trip to a destination, the method comprising: (a) receiving a search query of a user through a network, the search query including information about a departure place and a destination selected by the user; (b) processing the search query with the aid of a computer processor and a travel cost data structure to (i) identify a route from the origin to the destination within the travel cost data structure, and (ii) determine a plurality of route points along the route, wherein the plurality of route points includes at least the origin and the destination, and wherein the travel cost data structure contains travel costs between a geographic location and an adjacent geographic location; (c) searching a disease database containing disease progression information indicating progression or regression of the at least one disease at one or more geographic locations using each of the plurality of route points to identify the at least one disease and the disease progression information associated with route points of the plurality of route points; (d) based on the disease progression information identified in (c), (i) determining a risk of contracting the at least one disease, and (ii) optimizing the travel cost data structure by adjusting travel costs between a geographic location associated with the route point and an adjacent geographic location based on the risk; (e) repeating (b) through (d) as needed to generate an optimal route, wherein the optimal route reduces the risk of infecting the at least one disease; and (f) generating a trip for the user using the optimal route in (e).

In some implementations, the itinerary can be provided to the user on a graphical user interface on an electronic display of an electronic device.

In some embodiments, providing the user with the itinerary may further include providing the user with an assessment of a risk of contracting at least one disease.

In another of some aspects, the present disclosure is directed to a method of providing a user with a trip to a destination, the method comprising: (a) receiving a search query of a user through a network, the search query including information about a departure place and a destination selected by the user; (b) processing the search query with the aid of a computer processor and a travel cost data structure to (i) identify a plurality of routes leading from the origin to the destination within the travel cost data structure, and (ii) determine, for each of the plurality of routes, a plurality of via points along the route, wherein the plurality of via points includes at least the origin and the destination, and wherein the travel cost data structure contains travel costs between a geographic location and an adjacent geographic location; (c) for each of the plurality of routes, conducting a search in a disease database containing disease progression information indicating progression or regression of the at least one disease at one or more geographic locations using each of the plurality of route points to identify the at least one disease and the disease progression information associated with the route points of the plurality of route points; (d) based on the disease progression information identified in (c), for each of the plurality of routes, (i) determining a risk of contracting the at least one disease along the route, and (ii) optimizing the travel cost data structure by adjusting travel costs between geographic locations associated with the route points and adjacent geographic locations based on the risk; (e) repeating (b) through (d) as needed to generate an optimal route, wherein the optimal route results in a lowest travel cost among the plurality of routes; and (f) generating a trip for the user using the optimal route in (e).

In another of some aspects, the present disclosure relates to a method for optimizing a travel cost data structure comprising a plurality of geographic locations and a travel cost data structure between adjacent geographic locations, the method comprising: (a) using each of the plurality of geographic locations to search in a disease database to identify at least one disease and disease progression information associated with geographic locations of the at least plurality of geographic locations, the disease database comprising disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations; (b) based on the at least one disease and disease progression information identified in (a), (i) determining a risk of contracting the at least one disease, and (ii) optimizing the travel cost data structure by adjusting travel costs between the each and all of the plurality of geographic locations based on the risk; and (c) repeating (a) through (b) until all of the plurality of geographic locations are traversed, thereby optimizing the travel cost data structure.

In another of some aspects, the present disclosure is directed to a method of providing a user with a trip to a destination using an optimized travel cost data structure, the method comprising: i. receiving a search query of a user through a network, the search query including information about a departure place and a destination selected by the user; processing, with the aid of a computer processor and an optimized travel cost data structure, the search query to identify an optimal route from the origin to the destination within the travel cost data structure; generating a trip for the user using the optimal route in ii.

In some embodiments, the method further comprises (a) searching a disease database containing disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations including the destination using each of the one or more route points to identify the at least one disease and the disease progression information; and (b) providing the user with an assessment of the risk of contracting the at least one disease at the destination or along the route based on the disease progression information identified in (a).

In some embodiments, providing the user with an assessment of the risk of contracting the at least one disease in (b) may further comprise taking into account the journey.

Other aspects and advantages of the present disclosure will become apparent to those skilled in the art from the following detailed description, which shows and describes only illustrative embodiments of the disclosure. As will be realized, the disclosure is capable of other and different embodiments and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

Is incorporated by reference

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

Drawings

The novel features believed characteristic of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also referred to herein as "figures"), of which:

FIG. 1 is a workflow of an exemplary method for assessing risk associated with an infectious disease;

FIG. 2 is a workflow of an exemplary method for monitoring a disease in a subject;

FIG. 3 is a workflow of an exemplary method for monitoring a disease;

FIG. 4 is a schematic illustration of an exemplary computer control system that may assist in implementing the methods described herein; and is

Fig. 5A-5G are schematic illustrations of multiple views of an exemplary computer application that can be used in accordance with the methods described herein.

Detailed Description

While various embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. For example, the term "a cell" includes a plurality of cells, including mixtures thereof.

As used herein, the term "about," in the context of a particular use, generally refers to a range that is 15% greater or less than the stated numerical value. For example, "about 10" would include a range of 8.5 to 11.5.

As used herein, the terms "amplification" and "nucleic acid amplification" are used interchangeably and generally refer to the generation of one or more copies of a nucleic acid or "amplification product". The term "reverse transcription amplification" generally refers to the production of deoxyribonucleic acid (DNA) from ribonucleic acid (RNA) by the action of a reverse transcriptase.

As used herein, the term "geographic location" generally refers to a particular location on the earth or other celestial body. The geographic location may be described in any suitable manner, including using geographic coordinates (e.g., latitude and longitude); using the name of a geographic region (e.g., continent, island, archipelago, region of a particular country, region of a particular continent, region of a particular country, state/province, city/town/village, etc., regions associated with a geographic feature, such as a body of water, a mountain, a desert, a plain, a rainforest, etc.); using names of places, such as city/town/village, county/county, prefecture/county, district/teaching area, province, state/nation, region, administrative district, country, and/or a group of countries (e.g., european union, united kingdom); using one or more demographic characteristics (e.g., having a certain population, race, etc.) and using a name of a particular landmark, such as a building, school, workplace, shopping center, cell center, religious organization, hospital, health clinic, mobile unit, humanitarian assistance, home, or group of homes (e.g., residential district, apartment district, dormitory, etc.). In some cases, a geographic location may be identified by its PM2.5 value, which is a measure of the amount of particles in the air at that geographic location that are up to 2.5 microns in size (e.g., diameter).

Further, in some cases, the geographic location may be automatically determined by the electronic device via, for example, the ability to access a global navigation satellite system such as the Global Positioning System (GPS), the global navigation satellite system (GLONASS), the Indian Regional Navigation Satellite System (IRNSS), the beidou navigation satellite system (BDS), galileo (european satellite navigation system), and the like.

Alternatively, the geographic location may be automatically determined by the electronic device via any of a variety of geographic positioning techniques other than global navigation satellite system, such as multi-point positioning of wireless signals, global system for mobile communications (GSM), location-based services for mobile devices, Wi-Fi based positioning, hybrid positioning systems, and the like.

As used herein, the term "identity" generally refers to a classification (e.g., gender, age group, race group, disease group, etc.) that describes the subject or a particular group to which the subject belongs. Non-limiting examples of such classifications include the name of the subject (e.g., one or more of first name, last name, nickname, etc.), the age of the subject (e.g., included within a particular age range), and the social/biological gender (gender/six) (e.g., male, female, those with both sexual characteristics, etc.). In some cases, identity is provided by a biometric measurement metric, such as a fingerprint unique to a particular individual, a retinal scan, voice recognition, and a nucleic acid sequence or combination of nucleic acid sequences.

As used herein, the term "nucleic acid" generally refers to a polymeric form of nucleotides of any length (deoxyribonucleotides (dntps) or ribonucleotides (rNTP)) or analogs thereof. The nucleic acid can have any three-dimensional structure and can perform any known or unknown function. Non-limiting examples of nucleic acids include DNA, RNA, coding or non-coding regions of a gene or gene fragment, locus(s) determined by linkage analysis, exons, introns, messenger RNA (mrna), transfer RNA, ribosomal RNA, short interfering RNA (sirna), short hairpin RNA (shrna), micro RNA (mirna), ribozymes, cDNA, recombinant nucleic acids, branched nucleic acids, plasmids, vectors, DNA of any sequence isolated, RNA of any sequence isolated, nucleic acid probes, and primers. The nucleic acid may comprise one or more modified nucleotides, such as methylated nucleotides and nucleotide analogs. Modification of the nucleotide structure, if present, may be performed before or after nucleic acid assembly. The nucleotide sequence of a nucleic acid may be interrupted by non-nucleotide components. The nucleic acid may be further modified after polymerization, for example by conjugation or binding to a reporter agent.

As used herein, the term "physiological state" generally refers to a set of one or more measures indicative of the physical condition of a subject. The physiological state can consist of any collection of such measures, non-limiting examples of which include height, weight, heart rate, sneezing frequency, intensity of sneezing, frequency of coughing, intensity of coughing, level of nasal obstruction, level of chest tightness, blood pressure, body temperature, level of sweating, nerve conduction velocity, breathing frequency, lung volume, rate of urine production, frequency of defecation, presence of swollen lymph nodes, biochemical spectrum of bodily fluids (e.g., blood biochemical spectrum, urine biochemical spectrum, saliva biochemical spectrum, etc.), and skin moisture content.

As used herein, the term "reaction mixture" generally refers to a composition comprising reagents necessary to accomplish nucleic acid amplification (e.g., DNA amplification, RNA amplification), non-limiting examples of such reagents include primer sets specific for a target RNA or target DNA, DNA produced by reverse transcription of RNA, DNA polymerase, reverse transcriptase (e.g., for reverse transcription of RNA), suitable buffers (including zwitterionic buffers), cofactors (e.g., divalent and monovalent cations), dntps, and other enzymes (e.g., uracil-DNA glycosidase (UNG), and the like). In some cases, the reaction mixture may further comprise one or more reporter agents.

As used herein, the term "tag" generally refers to a word or string of characters of a search query that can be identified and used to search in a database with the aid of a computer processor. In some cases, a word or string equivalent to a tag is stored in the database to be searched, where the tag is recognized by the computer processor during the search as a member of the database. A "geographic location tag" is a "tag" that is related to a geographic location as described elsewhere herein.

As used herein, the term "target nucleic acid" generally refers to a nucleic acid molecule having a nucleotide sequence in an initial population of nucleic acid molecules, wherein it is desired to determine the presence, amount, and/or sequence of the nucleic acid molecule, or a change in one or more of these. The target nucleic acid can be any type of nucleic acid, including DNA, RNA, and the like. As used herein, "target ribonucleic acid (RNA)" generally refers to a target nucleic acid that is an RNA. As used herein, "target deoxyribonucleic acid (DNA)" generally refers to a target nucleic acid that is DNA. In some cases, the target nucleic acid can be indicative of one or more diseases.

As used herein, the term "subject" generally refers to an entity or medium having information that is detectable or detectable. The subject may be a human or an individual. The subject can be a vertebrate, such as a mammal (e.g., a human, a dog, or a cat) or an avian. Non-limiting examples of mammals include murines, simians, humans, farm animals (e.g., cows, chickens, horses, pigs, sheep, etc.), sport animals, and pets (e.g., dogs, cats, hamsters, rats, mice, guinea pigs, ferrets, etc.).

The present disclosure provides point of care (POC) systems for testing and analysis that may improve the detection and management of infectious diseases in a variety of situations, such as in dense situations, situations where laboratory facilities are scarce and resources are limited, or in remote areas where there is a delay in the receipt of laboratory results and patient follow-up may become complicated. The POC methods and systems of the present disclosure may make the healthcare facility more capable of providing sample-to-answer (sample-to-answer) results to patients during a single visit. Furthermore, the POC methods and systems of the present disclosure can enhance risk assessment and/or monitoring of disease from a geographic perspective due to the availability of fast communication networks, including wireless and satellite networks. POC devices capable of rapid communication over one of these networks can transmit data to a remote computer (e.g., a computer server) that can aggregate data that can be searched by users and/or used for disease risk assessment, disease monitoring, and disease management.

In one aspect, the present disclosure provides a method of providing a user with an assessment of risk of contracting at least one disease. The method includes receiving a search query of a user over a network, the search query including information related to at least any two of an identity, a geographic location, and a physiological state of the user. The search query is then processed by means of a computer to identify one or more tags that are available for searching in the disease database. The disease database may comprise indications of the at least one disease; disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations; subject information selected from two or more of an identity, a geographic location, a health state, and a physiological state of each of a plurality of subjects; and/or one or more associations between the at least one disease, disease progression information, and subject information. Additionally, the method includes searching a disease database using the one or more tags to identify the at least one disease and disease progression information, and providing the user with an assessment of risk of contracting the at least one disease based on the disease progression information. In some cases, the search query includes information about all three of the user's identity, geographic location, and physiological state. Typically, the user is a human.

A user's search query may be provided to an electronic device that transmits the search query over a network for processing by a computer processor. Non-limiting examples of electronic devices include personal computers (laptop computers, desktop computers, video game control panels), portable electronic devices (e.g., mobile phones (e.g., smart phones capable of running mobile applications (apps), etc.), tablet computers, pagers, calculators, portable video game control panels, portable music players (e.g., iPod (r))^TMEtc.). Further, the computer processor may be a component of a remote computer system that is networked with the electronic device. The network may be the internet, the internet and/or an extranet, or an intranet and/or extranet in communication with the internet. In some cases, the network is a cellular telephone network that communicates with the internet. In some cases, the remote computer system is part of a distributed computing network (e.g., a "cloud" network) that contains the remote computer system and, in some cases, the electronic device.

The disease database may be stored in a computer memory of a computer system, including the exemplary computer systems described elsewhere herein. Further, the disease database may be updatable as the database may be periodically updated, including in real time. As discussed above, the disease database contains indications of at least one disease. Non-limiting examples of such indications include identification of the disease (e.g., disease name), identification of at least one pathogen associated with the disease (e.g., bacterial pathogens (including bacteria as described elsewhere herein), identification of viral pathogens (including viruses as described elsewhere herein), identification of at least one symptom associated with the disease, and biochemical profiles associated with the disease (e.g., biochemical profile of a bodily fluid, biochemical profile of a tissue sample).

As discussed above, the disease database further comprises disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations. Such information may include the incidence of the at least one disease at one or more geographic locations; longitudinal incidence of the at least one disease at one or more geographic locations; mortality of the at least one disease at one or more geographic locations; longitudinal mortality of the at least one disease in one or more geographic regions; and/or the incidence of one or more symptoms associated with the at least one disease in one or more geographic regions. In some cases, the disease database may contain multiple types of disease progression information.

The disease database further comprises subject information selected from two or more of an identity, a geographic location, and a physiological state of each of the plurality of subjects. Such information may be provided statically to the database (e.g., via one or more data sets available at fixed points in time) or may be made in real-time, whereby subject data is continually added to the database from the user in communication with the database. Real-time updates may be provided to the disease database from input data received by multiple users of the disease database. In some cases, the subject information may be the same type of information that is relevant to at least two of the identity, geographic location, and/or physiological state of the user making the search query.

As discussed above, the disease database further comprises one or more associations between the at least one disease, disease progression information, and subject information. Such association includes associations between multiple disease database components. For example, the subject information may include data indicating that a plurality of subjects in a particular proximity segment have a relatively high heart rate. The disease progression information may indicate that the incidence of a particular disease increases over time in proximate segments of subjects having relatively high heart rates. Thus, in this example, the disease database may also contain associations between subjects with relatively high heart rates in the neighborhood and increasing disease incidence among the individuals in the neighborhood. Any suitable combination of disease, disease progression information, and subject information can be used to generate the association. In some cases, the disease database comprises a plurality of associations between diseases, disease progression information, and subject information of the disease database.

In addition, a search can be conducted in the disease database using the one or more tags to identify at least one disease and disease progression information. In processing, the computer processor may identify tags in the user's search query and find those tags stored in the disease database. The label may be a component of the at least one indicator of disease and/or a component of the disease progression information.

Based on the disease progression information identified from the disease database, the user may be provided with an assessment of the risk of contracting the disease. The assessment may include a qualitative assessment of risk (e.g., "low" risk, "elevated" risk, "high" risk; displayed by a particular color (e.g., green for relatively low risk, yellow for "elevated" risk, red for "high" risk)), and/or a quantitative assessment of risk (e.g., expressed as a percentage of likelihood of contracting at least one disease, a score of likelihood of contracting at least one disease, etc.). In the case where quantitative measurements are provided for evaluation, the quantitative measurements may be calculated using one or more calculation algorithms. In some cases, the disease progression information retrieved during the disease database search may be used in the calculation. Further, in some cases, providing the user with the assessment includes providing the user with one or more suggested preventative measures to reduce the rate of progression of the at least one disease in the geographic location. Such preventive measures include seeking immunization against the disease (in the case of pathogenic diseases), taking a predictive (predictive) drug (e.g., an immunostimulant, such as vitamin C) that inhibits infection and/or progression of the disease, avoiding a particular geographic location; wearing personal protective equipment (e.g., gloves, masks, shoe covers, hair nets, respirators, etc.) at a particular geographic location; enhancing personal hygiene measures (e.g., increasing frequency of hand washing, increasing use of hand sanitizer, etc.).

A Graphical User Interface (GUI) may be used to provide a user with an assessment of the risk of contracting at least one disease. The GUI may be a component of an electronic display of an electronic device, such as a computer system or other type of electronic device described elsewhere herein. In some cases, the electronic display may include a resistive or capacitive touch screen. The GUI may contain one or more graphical elements, such as text, images, and/or video. The arrangement of the one or more graphical elements may be adjusted to suit a given output. The arrangement of the one or more graphical elements may be adjusted statically or dynamically to suit a given output.

The GUI may be provided on an electronic display (including a display of a device containing a computer processor). In some cases, the electronic device is a portable electronic device as described elsewhere herein. Further, the GUI may contain text, graphics, and/or audio components. The GUI may be provided on an electronic display (including a display of a device containing a computer processor). Further, in some cases, the evaluation is provided via a notification or alert on the network. Such notifications or alerts may be provided to the electronic device described herein, including through textual information, through email, through social media, and/or through applications available on the electronic device. Further, the notification or alert provided to the user may prompt the user to take a medical action with respect to the at least one disease.

A workflow 100 outlining an exemplary implementation of the method is shown in fig. 1. As shown in fig. 1, a 25 year old beijing user with a severe cough provides (110) a search query to an electronic device, such as a smartphone or tablet computer (e.g., through an application installed on the electronic device). The search query contains the terms "severe cough", "age 25" and "beijing, china" and is transmitted (120) over a network (e.g., the internet) to a remote computer system containing a computer processor and a disease database as described herein. The remote computer system may be included as part of a distributed computing network, such as a cloud network. The computer processor processes (130) the search query to identify "severe cough", "age 25" and "beijing" as tags useful for searching in the disease database, and then searches (140) the disease database for the tags. In this disease database, "severe cough" and "beijing" are associated with the H1N1 influenza virus. The disease database contains information on the disease progression of the H1N1 influenza virus, which progresses faster and faster, and is relevant to subjects in the beijing age group of 25-40 years. A search of the disease database identified (140) the disease as H1N1 influenza virus and it progressed more and more rapidly in the beijing age group 25-40 years old. A quantitative assessment of the user's risk of contracting H1N1 influenza is generated (150) by a computer processor and transmitted over the internet to the user's electronic device. The electronic device displays (160) the quantitative assessment on a GUI disposed on a display thereof, and also displays a qualitative color indicating the relative likelihood of the user being infected with H1N1 influenza. In some cases, the GUI also displays (170) a recommendation to the user that he or she should wash his or her hands often and wear a mask covering his or her nose and mouth to avoid infection with the H1N1 flu.

In another aspect, the present disclosure provides a method for monitoring at least one disease in a subject. The method includes processing a biological sample obtained from a subject at a plurality of time points to identify one or more biomarkers in the biological sample and obtaining quantitative measurements of at least a subset of the one or more biomarkers at the plurality of time points. Each of the one or more biomarkers may be indicative of the presence of at least one disease in the subject. Further, the processing can be performed using nucleic acid amplification of each biological sample, the nucleic acid amplification having a sample volume of less than or equal to about 1 milliliter (mL) and the nucleic acid amplification having a time period of less than or equal to about 10 minutes. The method further includes processing the quantitative measurements with the aid of a computer processor to determine disease information indicative of progression or regression of the at least one disease in the subject, and generating an output of the disease information. In some cases, the at least one disease is monitored at a fixed geographic location or multiple geographic locations.

In some cases, the disease information is transmitted to a remote data storage unit. The computer processor may be a component of a computer system that communicates with the remote data storage unit over a network, including any type of network described elsewhere herein (e.g., a distributed computer network such as a cloud network). Further, the remote data storage unit may include any type of data storage medium described elsewhere herein. In some cases, generating the output of the disease information may include providing the disease information to a user on a GUI of an electronic display. The electronic display may be of an electronic device, including a portable electronic device, including the types of electronic devices described elsewhere herein.

Further, the method may further comprise providing a questionnaire to the subject to assess the geographic location and/or physiological state of the subject; and identifying the at least one disease from the results of the questionnaire. For example, a subject may be asked to provide information about one or more physiological states as described elsewhere herein, as well as information about their current geographic location. The results of the questionnaire can be used to determine an identity of the at least one disease (e.g., based on data about the disease associated with the input physiological state and geographic location), and then can be used to determine disease information. In some cases, the results of the questionnaire can be used to search a disease database and identify the at least one disease and/or disease progression information.

In some cases, the method further comprises obtaining one or more associations between the results of the questionnaire and the at least one disease. Non-limiting examples of such associations include the prevalence and/or progression or regression of at least one disease in a subject identifiable by the information submitted in the questionnaire. Such associations may be used to assess the risk of infection of the subject with the at least one disease, which may be identified by the information submitted in the questionnaire. In some cases, the determined associations are stored in a database for future use and comparison with other analyses of the subject biological sample. In addition, the results of the questionnaire can also be used to guide the selection of target-specific primers for the amplification reaction. In identifying a disease using a questionnaire, target-specific primers (e.g., primers that exhibit sequence complementarity to nucleic acids derived from a pathogenic genome) can be selected for nucleic acid amplification during processing of a biological sample.

Further, the questionnaire can be provided to the subject on a user interface (e.g., GUI) of the electronic device, and in some cases, can be used for machine learning purposes. The questionnaire results can be stored on the electronic device that receives the questionnaire answers from the user, or can be transmitted for storage to a remote data storage unit. Machine learning can aid in future processing of biological samples, processing of quantitative measurements, analysis of disease information indicative of progression or regression of a disease state, and can also provide information on assessments among multiple subjects. In some cases, the questionnaire may be provided to the subject on an electronic display of an electronic device, including a portable electronic device as described elsewhere herein. In some cases, the questionnaire is provided to the subject via a mobile application (e.g., an "app").

A workflow 200 outlining an exemplary implementation of the method is shown in fig. 2. As shown in fig. 2, biological samples are obtained from a subject at multiple time points (210). A biological sample having a volume of about 0.1mL is provided to a thermal cycler and subjected to thermal cycling in the presence of amplification reagents (e.g., primers, reverse transcriptase, DNA polymerase, nucleotides, etc.) to reverse transcribe and amplify (e.g., by RT-PCR) nucleic acids (e.g., biomarkers) indicative of the H1N1 influenza virus. Nucleic acid amplification was completed in less than 10 minutes. H1N1 influenza virus specific primers can be used during nucleic acid amplification for targeted amplification of nucleic acids. The amplicons were identified (230) as indicative of the H1N1 influenza virus and the amount of amplicon generated for each biological sample was obtained. In some cases, the amount of amplicon is obtained (240), e.g., by a real-time amplification reaction, during amplification. A questionnaire is provided (250) to a subject (e.g., via an application installed on an electronic device) in parallel or at different points in time via a GUI on an electronic display of the electronic device, e.g., a smartphone or tablet computer. The questionnaire asks the user to provide his or her location as well as height, weight and recent blood pressure readings. The subjects entered their location "Beijing" and provided height 1.82 meters (m), weight 80kg and blood pressure readings of 128mm Hg systolic pressure/82 mm Hg diastolic pressure. By searching a remote disease database, the electronic device identifies (260) the H1N1 influenza virus as a disease associated with the information provided by the subject in the questionnaire. The results of the questionnaire can also be used to select targeting primers for processing (220) the biological sample by nucleic acid amplification.

Using the amount of amplicon obtained from the biological sample (e.g., a quantitative measurement) and the identified H1N1 influenza virus information obtained from the questionnaire, the amount of amplicon obtained from the biological sample is processed (270) with the aid of a computer processor to obtain disease information indicative of the progression or regression of the H1N1 influenza virus in the subject. For example, a computer processor may analyze the amplicon data and determine any trend in amplicon amount over time. For example, an increase in amplicon associated with the H1N1 influenza virus over time may indicate progression of the H1N1 influenza virus in the subject, while a decrease in amplicon associated with the H1N1 influenza virus over time may indicate regression of the H1N1 influenza virus in the subject. Once disease information indicative of progression or regression is obtained, the disease information is output (280) on a GUI of an electronic device, which may be, for example, an electronic device used by the subject to provide answers to a questionnaire. In some cases, the disease information is also stored in storage locations of computer systems of a distributed computing network (e.g., a cloud network).

In another aspect, the present disclosure provides a method for monitoring at least one disease. The method includes receiving disease information for each of a plurality of subjects over a network. For a given subject in the plurality of subjects, generating disease information by processing biological samples obtained from the given subject at a plurality of time points to identify one or more biomarkers in the biological samples. Each of the one or more biomarkers may be indicative of the presence of the at least one disease in a given subject. The processing can be performed using nucleic acid amplification of each biological sample, the nucleic acid amplification having a sample volume of less than or equal to about 1 milliliter (mL) and the nucleic acid amplification having a time period of less than about 10 minutes. Further, generating disease information further comprises obtaining quantitative measurements of at least a subset of the one or more biomarkers at a plurality of time points; and processing the quantitative measurements with the aid of a computer processor to determine disease information. The disease information is typically indicative of the progression or regression of the at least one disease in a given subject. In addition, the method includes aggregating disease information in a storage location and processing the disease information aggregated in the storage location to identify a trend of the disease at a given geographic location or across multiple geographic locations, and then generating an output indicative of the trend.

The network can be any suitable network, including the types of networks described herein (e.g., the internet, an extranet, an intranet, a cloud network, etc.). In some cases, the received disease information is transmitted by an electronic device, non-limiting examples of which are described elsewhere herein. The electronic device may be a portable electronic device, including the types of portable electronic devices described elsewhere herein.

The disease trend for a given geographic location may be related to any suitable number of variables and/or considerations. For example, the trend may describe the prevalence of at least one disease at the geographic location or locations over a plurality of time points. In such a case, a positive trend may indicate progression of at least one disease at the geographic location or locations, while a negative trend may indicate regression of at least one disease at the geographic location or locations. In another example, the trend may describe the prevalence of one or more symptoms of at least one disease at the geographic location or locations over a plurality of time points. In such cases, a positive trend may indicate the progression of the symptoms, thereby indicating the progression of the at least one disease, while a negative trend may indicate the regression of the symptoms, thereby indicating the regression of the at least one disease at the geographic location or locations.

Generating the output indicative of the trend may also include storing the trend in a storage location. Any suitable electronic data storage/memory format (including those described elsewhere herein) may be used to store the output. In some cases, generating the output indicative of the trend may further include providing the trend to a user on a GUI of the electronic display. The electronic display may pertain to electronic devices, including portable electronic devices, including the electronic devices described elsewhere herein. Further, generating an output indicative of the trend may also include providing a notification or warning to the user regarding the trend. Such notifications or alerts may be provided to the user by an electronic device, including a portable electronic device as described elsewhere herein. In some cases, the notification or alert may be provided to the user through a text message, an email, through social media, through a mobile application, or through any other suitable form of electronic communication. Further, in some cases, the output indicative of the trend may include providing an update regarding the trend. The update may be indicative of an increase or decrease in prevalence of the at least one disease. An increase or decrease in prevalence of the at least one disease can be determined by comparing the obtained disease information to disease information obtained in a previous analysis.

Fig. 3 shows a workflow 300 outlining an exemplary implementation of the method. As shown in fig. 3, a computer system receives (310) influenza H1N1 virus disease information for each of a plurality of subjects over a network (e.g., the internet). Generating disease information for a given subject in the plurality of subjects by processing samples obtained directly from the given subject at a plurality of time points. During processing, a biological sample having a volume of about 0.1mL is provided to the thermal cycler and subjected to thermal cycling in the presence of amplification reagents (e.g., primers, reverse transcriptase, DNA polymerase) to reverse transcribe and amplify (e.g., by RT-PCR) nucleic acids (e.g., biomarkers) indicative of the H1N1 influenza virus. Nucleic acid amplification was completed in less than 10 minutes. H1N1 influenza virus specific primers can be used during nucleic acid amplification for targeted amplification of nucleic acids. The amplicons were identified as indicative of the H1N1 influenza virus of the subject, and the amount of amplicon generated for each biological sample was obtained. In some cases, the amount of amplicon is obtained during amplification, e.g., by a real-time amplification reaction. Further, the processing of the biological sample can be obtained by a designated point-of-care device of a plurality of point-of-care devices, particularly where the subject is located in a geographic location.

Using the amount of amplicon obtained from the biological sample (e.g., a quantitative measurement), the amount of amplicon obtained from the biological sample is processed by means of a computer processor to obtain disease information indicative of the progression or regression of the H1N1 influenza virus in a given subject. In some cases, the computer processor is a component of an electronic device used to transmit disease information to a computer system. Further, for example, an increase in amplicon associated with the H1N1 influenza virus over time may indicate progression of the H1N1 influenza virus in the subject, while a decrease in amplicon associated with the H1N1 influenza virus over time may indicate regression of the H1N1 influenza virus in the subject.

Once disease information indicative of progression or regression is obtained, the disease information obtained from the plurality of subjects is aggregated (320) into a memory of a computer system. The aggregated disease information may then be processed (330) by means of a computer processor of a computer system to identify trends in H1N1 in beijing (e.g., a given geographic location) or in cities with 1,000,000 or more populations in china (e.g., multiple geographic locations). Where a disease trend in Beijing is generated, the geographic locations of the plurality of subjects may be Beijing. In the case where disease trends at multiple geographic locations are desired, the subject may be a subject at a given geographic location of the multiple geographic locations (e.g., a city in china having over 1,000,000 population). After identifying the trend, an output of the trend is generated and displayed to the user on a GUI of the electronic display. The electronic display can pertain to an electronic device, such as a portable electronic device (e.g., a smartphone, a tablet computer, etc.) as described elsewhere herein.

The example shown in fig. 3 may be repeated for any number of cycles to provide updates regarding the trends. The updated disease information may be processed and provided to the user on a GUI of the electronic device. In some cases, this update may indicate an increase or decrease in prevalence of H1N1 influenza in cities in beijing or china having over 1,000,000 populations. To determine an increase or decrease in prevalence of H1N1 influenza, processing of updated disease information may include comparison with disease information obtained from previous analyses. Such disease information may be aggregated and stored in a storage location, including a storage location of a computer system.

Various aspects described herein include the assessment of disease, including risk assessment of infection with at least one disease and/or monitoring of at least one disease. The at least one disease may be any disease desired for analysis. In some cases, the disease is an infectious disease. In some cases, the infectious disease may be associated with a pathogenic agent, such as a pathogen. Pathogens include living and non-living species, non-limiting examples of which include microorganisms (microbism), microorganisms (microbe), viruses, bacteria, archaea (archaeum), protozoa, protists, fungi, and plants. The pathogen may comprise a nucleic acid that may encode, for example, a genome of the pathogen. Such nucleic acids can serve as biomarkers indicative of a disease associated with the pathogen. Identification and quantification of nucleic acid biomarkers can be used to generate information about a particular disease, including disease progression or regression information as described elsewhere herein.

In some cases, the at least one disease is identifiable by a virus. Non-limiting examples of viruses that can identify related diseases include human immunodeficiency virus i (hiv i), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus (e.g., influenza a, influenza b, influenza c, H1N1, H2N2, H3N2, H7N7, H1N2, H7N9, H9N2, H7N2, H7N3, H10N7, or H5N1 virus), hepatitis a virus, hepatitis b virus, hepatitis c virus (e.g., with a RNA-HCV virus), hepatitis d virus, hepatitis e virus, hepatitis g virus, measles virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, polio virus, simplex virus, smallpox virus, adenovirus (e.g., 55 adenovirus, herpes zoster virus), herpes virus (e.g., HPV), herpes virus (HPV), Human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, Zika virus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus, rift valley fever virus, chikungunya fever virus, enterovirus, coxsackie virus. Nucleic acids derived from viruses can be used as biomarkers that can be identified and quantified.

In some cases, the at least one disease is identifiable by a bacterium. Non-limiting examples of bacteria from which related diseases can be identified include Bordetella pertussis, Chlamydia pneumoniae, Chlamydia trachomatis, Campylobacter jejuni, Haemophilus influenzae, helicobacter pylori, Borrelia bacteria, Mycoplasma pneumoniae, Mycobacterium tuberculosis, Streptococcus pneumoniae, Streptococcus pyogenes, Clostridium tetani, Treponema pallidum, Trypanosoma cruzi, Toxoplasma gondii and Yersinia pestis. Nucleic acids derived from bacteria can be used as biomarkers that can be identified and quantified. In some cases, the at least one disease is identifiable by a protozoan. Non-limiting examples of protozoa from which related diseases can be identified include Plasmodium and Leishmania donovani. Nucleic acids derived from protozoa can be used as biomarkers that can be identified and quantified.

Further, in various aspects of the disclosure, a biological sample is obtained from a subject. Any suitable biological sample comprising nucleic acids may be obtained from a subject. The biological sample may be a solid substance (e.g., biological tissue) or may be a fluid (e.g., biological fluid). Solid samples can be homogenized in a homogenizing fluid so that they can be manipulated by fluid processing. In general, a biological fluid may include any fluid associated with a living organism. Non-limiting examples of biological samples include whole blood (or components of whole blood-e.g., white blood cells, red blood cells, platelets, plasma) obtained from any anatomical location (e.g., tissue, circulatory system, bone marrow) of a subject, skin, heart, lung, kidney, exhaled breath, bone marrow, feces, semen, vaginal fluid, interstitial fluid derived from tumor tissue, breast, pancreas, cerebrospinal fluid, tissue, throat swab, biopsy, placental fluid, amniotic fluid, liver, muscle, smooth muscle, bladder, gall bladder, colon, intestine, brain, cavity fluid, sputum, pus, microbiota (micropipota), meconium, breast milk, prostate, esophagus, thyroid, serum, saliva, urine, gastric juice and digestive fluid, tears, ocular fluid, sweat, mucus, cerumen, oil, glandular secretions, sweat, mucous membrane, and the like, Spinal fluid, hair, nails, skin cells, plasma, nasal swab or nasopharyngeal wash, spinal fluid, cord blood, emphatic fluid, and/or other waste or body tissue.

The biological sample may be obtained from the subject by any suitable route. Non-limiting examples of routes for obtaining a biological sample directly from a subject include: access the circulatory system (e.g., intravenous or intra-arterial access via a syringe or other needle), collection of secreted biological samples (e.g., stool, urine, sputum, saliva, etc.), surgical routes (e.g., biopsy), swabs (e.g., oral swab, oropharyngeal swab), pipetting, and expiration. In some cases, a biological sample may be obtained directly from a subject and subsequently processed without subjecting the biological sample to purification to isolate the biomarker. For example, when the biomarker is a nucleic acid, the biological sample may be processed without extracting the nucleic acid from the biological sample. As another example, a biological sample may be processed without bleaching, sample purification, and/or sample extraction.

In some aspects of the disclosure, biological samples are obtained from a subject at multiple time points. The biological sample may be obtained from the subject at any suitable number of time points depending, for example, on the period of time in which it is desired to monitor the disease. For example, a biological sample can be obtained from a subject 2, 3, 4, 5, 6, 7, 8, 9, 10, or more times. Further, the time points may be regularly spaced over a period of time (e.g., a one-day interval, a one-week interval, a two-week interval, a one-month interval, a one-quarter interval, a one-year interval, etc.) or may be irregularly spaced over a period of time. In some cases, the interval selected depends on the period of time over which it is desired to monitor the disease and/or any information known about the disease being monitored prior to or during sample collection.

In various aspects of the disclosure, a biological sample is processed using nucleic acid amplification. Processing of a biological sample obtained from a subject can include amplifying nucleic acid biomarkers of the biological sample. The nucleic acid biomarker can be a disease-associated nucleic acid, including nucleic acids of disease-associated pathogens. For example, the nucleic acid biomarker can be a nucleic acid (including nucleic acids of viruses described herein), a bacterial nucleic acid (including nucleic acids of bacteria described herein), and a protozoan nucleic acid (including nucleic acids of protozoans described herein).

In various aspects of the disclosure, the amount of biological sample processed using nucleic acid amplification can vary depending on, for example, the availability of the biological sample from the subject, the type of nucleic acid amplification used for processing, the capacity of the device (e.g., a thermocycler, a point of care device as described elsewhere herein, etc.) used to hold the biological sample for processing. In some cases, relatively small sample sizes may be processed, which may help to make point-of-care processing feasible and/or minimize the amount of biological sample that needs to be obtained from a subject. The minimal need for biological sample amounts can improve subject compliance by minimizing the time required to obtain a biological sample and/or minimizing any discomfort associated with biological sample acquisition.

As used herein, a sample volume can be used to describe the amount of a given biological sample that is processed using nucleic acid amplification. Typically, the volume of the biological sample treated using nucleic acid amplification is less than or equal to about 1mL, but can be greater than 1mL when desired. In some examples, the volume of the biological sample treated with nucleic acid amplification is less than or equal to about 0.75mL, less than or equal to about 0.5mL, less than or equal to about 0.25mL, less than or equal to about 0.1mL, less than or equal to about 0.075mL, less than or equal to about 0.050mL, less than or equal to about 0.010mL, less than or equal to about 0.0075mL, less than or equal to about 0.005mL, less than or equal to about 0.001mL, or less. In some examples, the volume of the biological sample treated using nucleic acid amplification is about 0.9mL, 0.8mL, 0.7mL, 0.6mL, 0.5mL, 0.4mL, 0.3mL, 0.2mL, 0.1mL, 0.09mL, 0.08mL, 0.07mL, 0.06mL, 0.05mL, 0.04mL, 0.03mL, 0.02mL, 0.01mL, 0.009mL, 0.008mL, 0.007mL, 0.006mL, 0.005mL, 0.004mL, 0.003mL, 0.002mL, or 0.001mL or less.

In various aspects of the present disclosure, processing of a biological sample may include providing a reaction vessel containing a given biological sample of the biological sample and reagents necessary to perform nucleic acid amplification. The given biological sample and reagent may be components of a reaction mixture contained by the reaction vessel. Once provided to the reaction vessel, the one or more nucleic acid biomarkers of a given biological sample are subjected to nucleic acid amplification under conditions sufficient to produce an amplification product of the nucleic acid biomarker. Because the amplification product is at least a partial copy of the one or more nucleic acid biomarkers, the amplification product is indicative of the presence of the one or more nucleic acid biomarkers in the biological sample.

Any suitable reaction vessel may be used for nucleic acid amplification. In some cases, the reaction vessel comprises a body that can comprise an inner surface, an outer surface, an open end, and an opposing closed end. Furthermore, the reaction vessel may comprise a lid. The lid may be configured to contact the body at its open end such that contact is made to close the open end of the reaction vessel. In some cases, the lid is permanently connected to the reaction vessel such that it remains attached to the reaction vessel in the open and closed configurations. In some cases, the lid is removable such that the lid is separated from the reaction vessel when the reaction vessel is opened. In some cases, the reaction vessel may be sealed, e.g., hermetically sealed.

The reaction vessels may be of different sizes, shapes, weights and configurations. The reaction vessel may be of regular or irregular shape. In some examples, the reaction vessel is circular, oval tubular, rectangular, square, diamond, annular, oval, and/or triangular. In some cases, the closed end of the reaction vessel may have a tapered, rounded, or flat surface. Non-limiting examples of reaction vessel types include tubes, wells, capillaries, cartridges, cups, centrifuge tubes, or pipette tips. The reaction vessel may be constructed of any suitable material, non-limiting examples of which include glass, metal, plastic, and combinations thereof.

In some cases, the reaction vessel is part of a group of reaction vessels. A set of reaction vessels may be particularly useful for automating the process and/or processing multiple samples simultaneously. For example, the reaction vessel may be a well of a microplate consisting of a plurality of wells. In another example, the reaction vessel can be held in a well of a thermal block of a thermal cycler, wherein the thermal block of the thermal cycler comprises a plurality of wells, each well capable of receiving a reaction vessel. A group consisting of reaction vessels may comprise any suitable number of reaction vessels. For example, a group may comprise at least 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 25, 35, 48, 96, 144, 384 or more reaction vessels. The reaction vessel sections of a set of reaction vessels may also be individually addressed by a fluid handling apparatus so that the fluid handling apparatus can correctly identify a reaction vessel and dispense the appropriate fluid substance into that reaction vessel. The fluid handling device may be used to automate the addition of fluid substances to the reaction vessel.

In some cases, the reaction vessel may contain multiple hot zones. The hot zones within the reaction vessel may be achieved by exposing different regions of the reaction vessel to different temperature cycling conditions. For example, the reaction vessel may comprise an upper hot zone and a lower hot zone. The upper thermal zone may be capable of receiving a biological sample and reagents necessary to obtain a reaction mixture for nucleic acid amplification. The reaction mixture may then be subjected to a first thermal cycling protocol. After the desired number of cycles, for example, the reaction mixture may slowly, but continuously, leak from the upper hot zone to the lower hot zone. In the lower hot zone, the reaction mixture is then subjected to a second thermal cycling regime of the desired number of cycles, which is different from the cycling regime in the upper hot zone. Such a strategy may be particularly useful when using nested PCR to amplify nucleic acids. In some cases, the hot zone may be created within the reaction vessel by means of a heat-sensitive layered material within the reaction vessel. In such a case, heating of the heat-sensitive layered material may be used to release the reaction mixture from one hot zone to the next. In some cases, the reaction vessel comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or more thermal zones.

Reagents necessary for nucleic acid amplification include one or more primers having sequence complementarity to one or more nucleic acid biomarkers and a polymerase (e.g., polymerase) capable of mediating nucleic acid synthesis in a template-directed manner. The one or more primers may be directed to a DNA biomarker and/or a ribonucleic acid (RNA) biomarker, depending on the particular biomarker being analyzed and the nucleic acid amplification protocol used. The one or more primers can be designed to target the sequence of a nucleic acid biomarker known to be associated with the disease under study, wherein the nucleic acid biomarker generates an amplicon indicative of the presence of the nucleic acid marker in a particular biological sample by amplification of the one or more primers.

In some cases, the reagents necessary for nucleic acid amplification include a polymerase, such as a DNA polymerase. Any suitable DNA polymerase can be used, including commercially available DNA polymerases. Non-limiting examples of DNA polymerases include Taq polymerase, Tth polymerase, Tli polymerase, Pfu polymerase, VENT polymerase, DEEPVENT polymerase, EX-Taq polymerase, LA-Taq polymerase, Expand polymerase, Sso polymerase, Poc polymerase, Pab polymerase, Mth polymerase, Pho polymerase, ES4 polymerase, Tru polymerase, Tac polymerase, Tne polymerase, Tma polymerase, Tih polymerase, Tfi polymerase, Platinum Taq polymerase, Hi-Fi polymerase, Tbr polymerase, Tfl polymerase, Pmutubo polymerase, Pyrobest polymerase, Pwo polymerase, KOD polymerase, Bst polymerase, Sac polymerase, Klenow fragment, and variants, modifications, and derivatives thereof.

Any type of nucleic acid amplification reaction can be used to amplify the nucleic acid and generate an amplification product. Further, amplification of nucleic acids can be linear, exponential, or a combination thereof. Amplification may be emulsion-based or may be non-emulsion-based. Non-limiting examples of nucleic acid amplification methods include reverse transcription (e.g., reverse transcription PCR (RT-PCR)), primer extension, Polymerase Chain Reaction (PCR), Ligase Chain Reaction (LCR), helicase dependent amplification, asymmetric amplification, rolling circle amplification, and Multiple Displacement Amplification (MDA). Amplification in the case where the nucleic acid is deoxyribonucleic acid (DNA) may use any DNA amplification method. Non-limiting examples of DNA amplification methods include Polymerase Chain Reaction (PCR), variations of PCR (e.g., real-time PCR, allele-specific PCR, assembly PCR, asymmetric PCR, digital PCR, emulsion PCR, dial-out PCR, helicase-dependent PCR, nested PCR, hot-start PCR, inverse PCR, methylation-specific PCR, miniprimer PCR, multiplex PCR, nested PCR, overlap-extension PCR, thermal asymmetric staggered PCR (thermal asymmetric interleaved PCR), descending PCR), and Ligase Chain Reaction (LCR). In some cases, DNA amplification is linear. In some cases, DNA amplification is exponential. In some cases, DNA amplification is achieved using nested PCR, which can increase the sensitivity of detecting amplified DNA products.

In the case of RNA biomarkers, nucleic acid amplification can include reverse transcription of the RNA biomarker in parallel with DNA amplification (e.g., RT-PCR nucleic acid amplification) in the presence of reverse transcriptase (e.g., HIV-1 reverse transcriptase, M-MLV reverse transcriptase, AMV reverse transcriptase, telomerase reverse transcriptase, and variants, modification products, and derivatives thereof), DNA polymerase, and a primer set for the RNA biomarker. In such nucleic acid amplification reactions, an RNA primer of the primers that targets an RNA biomarker hybridizes to the RNA biomarker, and the RNA biomarker is reverse transcribed into a DNA product complementary to the RNA by the action of a reverse transcriptase. The second primer in the primer set can then hybridize to the DNA product and be extended by the action of a DNA polymerase to generate a double stranded DNA product indicative of an RNA biomarker in the biological sample. The double-stranded DNA product may then be further amplified (perhaps by other primers in the primer set) to produce additional double-stranded DNA product. In some cases, reverse transcription and DNA amplification performed in parallel may be performed in a single reaction mixture within a single reaction vessel without purification and/or removal of the reaction mixture from the reaction vessel. In such cases, the reverse transcriptase, DNA polymerase, primer set, and given biological sample may be provided in a single reaction mixture in the reaction vessel.

Nucleic acid amplification may be isothermal or subjected to thermal cycling. Thermal cycling may be performed with the aid of a thermal cycler. Any suitable thermal cycler may be used. In some cases, a thermocycler is a component of a point of care device that processes a biological sample obtained from a subject. In addition, many nucleic acid amplification reactions comprise one or more primer extension reactions that produce amplification products. During the primer extension reaction, the double-stranded nucleic acid is denatured into single strands (if necessary), the primer is hybridized to one or both of the single strands, and the primer is extended in a template-directed manner by the action of a polymerase (e.g., DNA polymerase, reverse transcriptase). The primer extension reaction can include a cycle of incubating the nucleic acid to be amplified at a denaturation temperature for a denaturation duration and incubating the nucleic acid to be amplified at an extension temperature for an extension duration.

The denaturation temperature may vary depending on, for example, the particular biological sample being treated, the particular nucleic acid biomarkers being analyzed in the biological sample, the reagents used, and/or the desired reaction conditions. For example, the denaturation temperature can be from about 80 ℃ to about 110 ℃. In some examples, the denaturation temperature can be from about 90 ℃ to about 100 ℃. In some examples, the denaturation temperature can be from about 90 ℃ to about 97 ℃. In some examples, the denaturation temperature can be from about 92 ℃ to about 95 ℃. In other examples, the denaturation temperature can be about 80 ℃, 81 ℃, 82 ℃, 83 ℃, 84 ℃, 85 ℃, 86 ℃, 87 ℃, 88 ℃, 89 ℃, 90 ℃, 91 ℃, 92 ℃, 93 ℃, 94 ℃, 95 ℃, 96 ℃, 97 ℃, 98 ℃, 99 ℃ or 100 ℃.

The duration of denaturation can vary depending on, for example, the particular biological sample being treated, the particular nucleic acid biomarkers being analyzed in the biological sample, the reagents used, and/or the desired reaction conditions. For example, the duration of denaturation can be less than or equal to about 300 seconds, 240 seconds, 180 seconds, 120 seconds, 90 seconds, 60 seconds, 55 seconds, 50 seconds, 45 seconds, 40 seconds, 35 seconds, 30 seconds, 25 seconds, 20 seconds, 15 seconds, 10 seconds, 5 seconds, 2 seconds, or 1 second. For example, the duration of denaturation can be no more than 120 seconds, 90 seconds, 60 seconds, 55 seconds, 50 seconds, 45 seconds, 40 seconds, 35 seconds, 30 seconds, 25 seconds, 20 seconds, 15 seconds, 10 seconds, 5 seconds, 2 seconds, or 1 second.

The extension temperature may vary depending on, for example, the particular biological sample being treated, the particular nucleic acid biomarker being analyzed in the biological sample, the reagents used, and/or the desired reaction conditions. For example, the extension temperature may be about 30 ℃ to about 80 ℃. In some examples, the extension temperature may be about 35 ℃ to about 72 ℃. In some examples, the extension temperature may be about 45 ℃ to about 65 ℃. In some examples, the extension temperature may be about 35 ℃ to about 65 ℃. In some examples, the extension temperature may be about 40 ℃ to about 60 ℃. In some examples, the extension temperature may be about 50 ℃ to about 60 ℃. In other examples, the extension temperature can be about 35 ℃, 36 ℃, 37 ℃, 38 ℃, 39 ℃, 40 ℃, 41 ℃, 42 ℃, 43 ℃, 44 ℃, 45 ℃, 46 ℃, 47 ℃, 48 ℃, 49 ℃, 50 ℃, 51 ℃, 52 ℃, 53 ℃, 54 ℃, 55 ℃, 56 ℃, 57 ℃, 58 ℃, 59 ℃, 60 ℃, 61 ℃, 62 ℃, 63 ℃, 64 ℃, 65 ℃, 66 ℃, 67 ℃, 68 ℃, 69 ℃, 70 ℃, 71 ℃, 72 ℃, 73 ℃, 74 ℃, 75 ℃, 76 ℃, 77 ℃, 78 ℃, 79 ℃ or 80 ℃.

The extension duration may vary depending on, for example, the particular biological sample being processed, the particular nucleic acid biomarker being analyzed in the biological sample, the reagents used, and/or the desired reaction conditions. For example, the extension duration may be less than or equal to 300 seconds, 240 seconds, 180 seconds, 120 seconds, 90 seconds, 60 seconds, 55 seconds, 50 seconds, 45 seconds, 40 seconds, 35 seconds, 30 seconds, 25 seconds, 20 seconds, 15 seconds, 10 seconds, 5 seconds, 2 seconds, or 1 second. For example, the extension duration may be no more than 120 seconds, 90 seconds, 60 seconds, 55 seconds, 50 seconds, 45 seconds, 40 seconds, 35 seconds, 30 seconds, 25 seconds, 20 seconds, 15 seconds, 10 seconds, 5 seconds, 2 seconds, or 1 second.

In some aspects of the disclosure, a biological sample may undergo multiple cycles of a primer extension reaction. Any suitable number of cycles may be performed. For example, the number of cycles performed may be less than about 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, or 5 cycles. The number of cycles performed can depend on, for example, the number of cycles necessary to obtain a detectable amplification product (e.g., the cycle threshold (Ct)). For example, the number of cycles necessary to obtain a detectable amplification product can be less than about or about 100 cycles, 75 cycles, 70 cycles, 65 cycles, 60 cycles, 55 cycles, 50 cycles, 40 cycles, 35 cycles, 30 cycles, 25 cycles, 20 cycles, 15 cycles, 10 cycles, or 5 cycles. Further, in some cases, a detectable amount of an amplifiable product can be obtained at a cycle threshold (Ct) of less than 100, 75, 70, 65, 60, 55, 50, 45, 40, 35, 30, 25, 20, 15, 10, or 5.

In some cases, a biological sample may undergo multiple series of primer extension reactions. Individual ones of the plurality of series may comprise a plurality of cycles of a particular primer extension reaction characterized by, for example, particular denaturing and extension conditions as described elsewhere herein. Typically, each individual series is distinct from at least one other individual series in the plurality of series, e.g., in terms of denaturing conditions and/or extension conditions. For example, a single series may be different from another single series of the plurality of series with respect to any one, two, three, or all four of denaturation temperature, denaturation duration, extension temperature, and extension duration. Further, the plurality of series may include any number of individual series, for example, at least about or about 2, 3, 4, 5, 6, 7, 8, 9, 10 or more individual series.

For example, a plurality of series of primer extension reactions can include a first series and a second series. The first series, for example, can comprise more than ten cycles of a primer extension reaction, wherein each cycle of the first series comprises (i) incubating the reaction mixture at about 92 ℃ to about 95 ℃ for no more than 30 seconds, followed by (ii) incubating the reaction mixture at about 35 ℃ to about 65 ℃ for no more than about one minute. The second series, for example, can comprise more than ten cycles of a primer extension reaction, wherein each cycle of the second series comprises (i) incubating the reaction mixture at about 92 ℃ to about 95 ℃ for no more than 30 seconds, followed by (ii) incubating the reaction mixture at about 40 ℃ to about 60 ℃ for no more than about 1 minute. In this particular example, the first and second series differ in their extension temperature conditions. However, this example is not intended to be limiting as any combination of different extension and denaturation conditions can be used.

An advantage of performing multiple series of primer extension reactions may be that the multiple series of methods produce a detectable amount of amplification product indicative of the presence of a nucleic acid biomarker in a biological sample at a lower cycle threshold than a single series of primer extension reactions under comparable denaturing and extension conditions. The use of multiple series of primer extension reactions can reduce this cycling threshold by at least about or about 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% compared to a single series under comparable denaturing and extension conditions.

In addition, the biological sample may be preheated prior to performing the primer extension reaction. The temperature (e.g., pre-heating temperature) and duration (e.g., pre-heating duration) of pre-heating the biological sample can vary depending on, for example, the particular biological sample being analyzed. In some examples, the biological sample can be preheated for no more than about 60 minutes, 50 minutes, 40 minutes, 30 minutes, 25 minutes, 20 minutes, 15 minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6 minutes, 5 minutes, 4 minutes, 3 minutes, 2 minutes, 1 minute, 45 seconds, 30 seconds, 20 seconds, 15 seconds, 10 seconds, or 5 seconds. In some examples, the biological sample can be preheated at a temperature of about 80 ℃ to about 110 ℃. In some examples, the biological sample can be preheated at a temperature of about 90 ℃ to about 100 ℃. In some examples, the biological sample can be preheated at a temperature of about 90 ℃ to about 97 ℃. In some examples, the biological sample can be preheated at a temperature of about 92 ℃ to about 95 ℃. In other examples, the biological sample can be preheated at a temperature of about or at least about 80 ℃, 81 ℃, 82 ℃, 83 ℃, 84 ℃, 85 ℃, 86 ℃, 87 ℃, 88 ℃, 89 ℃, 90 ℃, 91 ℃, 92 ℃, 93 ℃, 94 ℃, 95 ℃, 96 ℃, 97 ℃, 98 ℃, 99 ℃, or 100 ℃.

In various aspects including the processing of biological samples by nucleic acid amplification, the time required to complete the process varies depending on, for example, the amount of biological sample to be processed, the capabilities of the equipment used for the process, and the amount of any biomarkers present in the sample. Typically, processing of a biological sample by nucleic acid amplification is completed in less than or equal to about 10min, however it may take longer depending on the particular processing strategy. In some examples, processing the biological sample by nucleic acid amplification is completed in about 0.1min to about 10 min. In some examples, processing the biological sample by nucleic acid amplification is completed in about 0.5min to about 10 min. In some examples, processing the biological sample by nucleic acid amplification is completed in about 1min to about 10 min. In some examples, processing the biological sample by nucleic acid amplification is completed in about 0.5min to about 5 min. In some examples, treating the biological sample by nucleic acid amplification is accomplished in a time less than or equal to about 9min, less than or equal to about 8min, less than or equal to about 7min, less than or equal to about 6min, less than or equal to about 5min, less than or equal to about 4min, less than or equal to about 3min, less than or equal to about 2min, less than or equal to about 1min, less than or equal to about 0.75min, less than or equal to about 0.5min, less than or equal to about 0.1min, or less.

As described elsewhere herein, aspects of the present disclosure include obtaining quantitative measurements of one or more biomarkers at multiple time points. Quantitative measurements can include absolute amounts (e.g., mass, molar amount, volume, concentration) and/or relative amounts (e.g., relative mass (e.g., mass percent), molar percent, volume percent) of the biomarkers in the biological sample. In some cases, a quantitative measurement may include a set of values (e.g., a set of quantities spanning multiple time points of analysis). Further, as also described elsewhere herein with respect to various aspects, the quantitative measurements are processed to determine disease information including disease information indicative of disease progression or regression. Any desired type of processing may be accomplished. Processing can include, for example, comparing the quantitative measurements for the plurality of time points to a reference to identify progression or regression of the disease in the subject. Such a reference may include an amount or relative amount of a biomarker that is associated with a health state (e.g., in the absence of disease) and/or at a time point that is different from a time point of the plurality of time points analyzed. In some cases, comparisons can be made between quantitative measurements at multiple time points, which can be used to determine the progression or regression of the disease over the analyzed multiple time points. Comparisons between the analyzed multiple time points can be used to generate updates to trends obtained from processing disease information indicative of progression or regression of the disease.

Additional reagents may be added to the amplification reaction mixture to help provide quantitative measurements of nucleic acid biomarkers in the processed biological sample. In some cases, such reagents include a reporter agent that produces a detectable signal, the presence or absence of which is indicative of the presence of an amplification product, and thus the presence of a given nucleic acid biomarker in the biological sample being analyzed. The intensity of the detectable signal may be proportional to the amount of amplification product, and thus the amount of nucleic acid biomarker in a given biological sample. For example, where the RNA biomarker is processed by reverse transcription performed in parallel and amplification of DNA obtained from the reverse transcription, the reagents necessary for both reactions may be included in the amplification reaction mixture, and may also include a reporter agent that produces a detectable signal indicative of the presence of amplified DNA product, and thus of the RNA biomarker. In some cases, the reporter enables real-time amplification methods useful for obtaining quantitative measurements during nucleic acid amplification, including real-time PCR for DNA amplification.

The reporter agent may be covalently or non-covalently linked to the nucleic acid, including the amplification product. Non-limiting examples of non-covalent attachment include ionic interactions, van der waals forces, hydrophobic interactions, hydrogen bonding, and combinations thereof. In some cases, a reporter can be bound to the initial reactant and a change in the level of the reporter can be used to detect the amplification product. In some cases, the reporter may be detectable (or undetectable) only while nucleic acid amplification is in progress. In some cases, an optically active dye (e.g., a fluorescent dye) can be used as a reporter. Non-limiting examples of dyes include SYBR Green, SYBR blue, DAPI, propidium iodide (propidium iodide), Hoeste, SYBR gold, ethidium bromide, acridine, proflavine, acridine orange, acridine yellow, fluorescent coumarin (fluorocoumanin), ellipticine, daunomycin, chloroquine, distamycin D, chromomycin, hominium bromide (hominium), mithramycin, ruthenium polypyridyl (ruthenium polypyridyl), amphenicol (anthramycin), phenanthridine and acridine, ethidium bromide, propidium iodide, hexidium iodide (hexidium iodide), ethidium dihydride, ethidium homodimer-1 and ethidium homodimer-2, ethidium nitride (ethidium monozide) and ACMA, Hoechst 33258, Hoechst 33342, Hoechst 34580, Hoechst 897, AAD, stilbene monohydramine, O3, Tokyo 3, O3, oxo-O751, oxo-O3, oxo-O, TOTO-1, TOTO-3, JOJO-1, LOLO-1, BOBOBO-3, PO-PRO-1, PO-PRO-3, BO-PRO-1, BO-PRO-3, TO-PRO-1, TO-PRO-3, TO-PRO-5, JO-PRO-1, LO-PRO-1, YO-PRO-1, YO-PRO-3, PicoGreen, OliGreen, RiboGreen, SYBR gold, SYBR Green I, SYBR Green II, SYBR DX, SYTO-40, -41, -42, -43, -44, -45 (blue), SYTO-13, -16, -24, -21, -23, -12, -11, -20, -22, -15, -14, SYBR, -25 (green), SYTO-81, -80, -82, -83, -84, -85 (orange), SYTO-64, -17, -59, -61, -62, -60, -63 (Red), Fluorescein Isothiocyanate (FITC), tetramethylrhodamine isothiocyanate (TRITC), rhodamine, tetramethylrhodamine, R-phycoerythrin, Cy-2, Cy-3, Cy-3.5, Cy-5, Cy5.5, Cy-7, Texas Red (Texas Red), Phar-Red, Allophycocyanin (APC), Sybr green I, Sybr green II, Sybr gold, CellTracker green, 7-AAD, ethidium dimer I, ethidium dimer II, ethidium homodimer III, ethidium bromide, eosin, green umbelliferyl fluorescent protein, erythrosine, coumarin, methylcoumarin, pyrene, malachite green, stilbene, fluorescein, cascade blue (cascade blue), dichlorotriazinylamine fluorescein, dansyl chloride, fluorescent lanthanide metal complexes (e.g., fluorescent lanthanide metal complexes including europium and terbium), carboxytetrachlorofluorescein, 5-and/or 6-carboxyfluorescein (FAM), 5- (or 6-) iodoacetamidofluorescein, 5- { [2 (and 3) -5- (acetylmercapto) -succinyl ] amino } fluorescein (SAMSA-fluorescein), lissamine rhodamine B sulfonyl chloride, 5-and/or 6 carboxyrhodamine (ROX), 7-amino-methyl-coumarin, 7-amino-4-methylcoumarin-3-acetic acid (AMCA), BODIPY fluorophore, 8-pyrenemethoxy-1, trisodium 3, 6-trisulfonate, 3, 6-disulfonic acid-4-amino-naphthalimide, phycobiliprotein, AlexaFluor 350, 405, 430, 488, 532, 546, 555, 568, 594, 610, 633, 635, 647, 660, 680, 700, 750, and 790 dyes, DyLight 350, 405, 488, 550, 594, 633, 650, 680, 755, and 800 dyes, or other fluorophores.

In some cases, the reporter may be a sequence-specific oligonucleotide probe that is optically active when hybridized to the amplification product. The use of oligonucleotide probes can improve the specificity and sensitivity of detection due to the sequence-specific binding of the probe to the amplification product. The probe can be attached to any optically active reporter (e.g., dye) described herein, and can further include a quencher capable of blocking the optical activity of the associated dye. Non-limiting examples of probes that can be used as a reporter include TaqMan probes, TaqMan Tamara probes, TaqMan MGB probes, or Lion probes.

In some cases, the reporter can be an RNA oligonucleotide probe that includes an optically active dye (e.g., a fluorescent dye) and a quencher positioned adjacent to each other on the probe. The close proximity of the dye to the quencher can block the optical activity of the dye. The probe can bind to the target sequence to be amplified. Once the probe is cleaved during amplification (e.g., by exonuclease activity of the DNA polymerase), the quencher separates from the dye, and the free dye regains its optical activity, which can then be detected.

In some cases, the reporter may be a molecular beacon. Molecular beacons include, for example, a quencher attached to one end of an oligonucleotide in a hairpin conformation. At the other end of the oligonucleotide is an optically active dye, e.g., a fluorescent dye. In the hairpin configuration, the optically active dye and the quencher are in sufficiently close proximity that the quencher is able to block the optical activity of the dye. However, once hybridized to the amplification product, the oligonucleotide assumes a linear conformation and hybridizes to the target sequence on the amplification product. Linearization of the oligonucleotide results in separation of the optically active dye from the quencher, allowing optical activity to recover and be detected. Sequence specificity of the molecular beacon for the target sequence on the amplification product can improve specificity and sensitivity of detection.

In some cases, the reporter may be a radioactive species. Non-limiting examples of radioactive species include¹⁴C、¹²³I、¹²⁴I、¹²⁵I、¹³¹I、Tc99m、³⁵S or³H。

Non-limiting examples of enzymes that can be used as reporters include alkaline phosphatase, horseradish peroxidase, I2-galactosidase, alkaline phosphatase, β -galactosidase, acetylcholinesterase, and luciferase.

Detection of the amplification product by the reporter agent may be accomplished by any suitable means of detection. The particular type of detection method used may depend, for example, on the particular amplification product, the type of reaction vessel used for amplification, other reagents in the reaction mixture, and the particular type of reporter used. Non-limiting examples of detection methods include optical detection, spectroscopic detection, electrostatic detection, electrochemical detection, and the like. Optical detection methods include, but are not limited to, fluorimetry and ultraviolet-visible light absorption. Spectroscopic detection methods include, but are not limited to, mass spectrometry, Nuclear Magnetic Resonance (NMR) spectroscopy, and infrared spectroscopy. Electrostatic detection methods include, but are not limited to, gel-based techniques, such as gel electrophoresis. Electrochemical detection methods include, but are not limited to, electrochemical detection of amplification products after high performance liquid chromatography separation of the amplification products.

In aspects of the present disclosure, information such as trends, quantitative measurements of biomarkers in biological samples, disease information, and/or updates or warnings thereof, is provided to a user. As described elsewhere herein, information may be provided to a user through a GUI on an electronic display of an electronic device. In some cases, the user is a subject from whom a biological sample is obtained and analyzed. In other cases, the user may be a healthcare professional. Non-limiting examples of healthcare professionals include medical personnel, clinicians (e.g., doctors, practicing nurses (PACs), nurses, medical assistants, physiotherapists, medical trainees, medical technicians), laboratory personnel (e.g., hospital laboratory technicians, research scientists, medical scientists), clinical monitors of clinical trials, personnel of hospitals or medical systems, health insurance company personnel, pharmaceutical company personnel, public health workers, humanitarian aid workers, or other personnel in the healthcare industry. In some cases, the GUI may be a GUI of an application run by the electronic device. When the electronic device is a portable device (e.g., a smartphone, a portable music player, a tablet computer, etc.), the application may be a mobile application ("app") that may run on the portable device. Mobile applications include software designed to run and/or display on mobile devices.

Further, in some cases, the information provided to the user may be provided in a report that may be displayed via a user interface, such as a GUI of the electronic device (including a GUI of a mobile application). Such a report may contain any number of required elements, non-limiting examples of which include information regarding: subject (e.g., gender, age, race, health status, etc.), raw data, processed data (e.g., graphical display, e.g., graph, chart, data sheet, data summary), quantitative measurements, disease information, associations between disease information and questionnaire results, disease trend information, diagnostic information, prognostic information, recommendations for future action, recommendations for disease treatment, recommendations for disease prevention, and combinations thereof. Further, the report may be stored in an electronic database, such as a disease database, so that the report may be accessed for comparison with future reports.

Exemplary mobile applications that run on an electronic device with a touch screen and that can help implement aspects of the present disclosure are schematically illustrated in fig. 5A-5G. Referring to fig. 5A, when executing a mobile application, the application (e.g., mobile application) may provide a welcome screen 500. The welcome screen 500 may include one or more graphical elements 501 (e.g., company logo, user photo, etc.) and/or welcome information 502 (e.g., application name, user welcome, slogan, trademark, etc.). After the welcome screen 500 is displayed, the application then displays a login screen 510, the login screen 510 may contain one or more graphical elements 511 as well as input fields for login information 512 (e.g., a username, email, or other identification string) and a password 513. After the user enters the login 512 and password 513 information, the user clicks the submit button 514 to enter the application.

After entering the correct login information 512 and password 513 into the login screen 510, the application then displays a main screen 520 schematically illustrated in fig. 5B. The home screen 520 may contain a location name 521 that may be entered into an input field (not shown) by a user or may be automatically obtained through the GPS function of the electronic device running the mobile application. The main screen 520 may also contain a graphical summary 522 of the disease data (e.g., temperature at the location, temperature difference from different locations, prevalence of disease at the location, PM2.5 level at the location, weather information, etc.). A more comprehensive numerical display 524 of the disease data summarized in the graphical summary 522 may also be provided. Based on the disease data 522 summarized on the home screen and/or any other data, the application generates or retrieves disease recommendation information 523 presented to the user. The disease recommendation information may include recommended disease treatment and/or prevention measures for the user to take. In addition, the home screen also includes a navigation portion 525 that includes graphical buttons (520, 530, 540, 550, and 560 corresponding to screens 520, 530, 540, 550, and 560 as described herein) that each guide (route) a user to another screen within the mobile application.

Upon clicking the button 530 of the navigation section 525, the mobile application displays a note entry screen 530 schematically illustrated in fig. 5C. The user is presented with a plurality of symptoms (e.g., "symptom a," "symptom B," and "symptom C") on the annotation input screen 530, where each symptom has an option button 532. Although only three symptom options are shown in fig. 5C, any number of related symptoms may be presented to the user. For each symptom, the user selects the appropriate button (buttons "1", "2", or "3" next to each symptom). For example, symptom a may be the frequency of sneezing per hour (where each button next to symptom a represents the frequency of sneezing per hour), symptom B may be the location of pain (where each button next to symptom B represents the location/type of pain (e.g., headache, sore throat, general pain, etc.), and symptom C may be body temperature (where each button next to symptom C represents a particular body temperature). After entering the appropriate symptom information into the annotation input screen 530, the mobile application processes the symptom information and provides disease suggestion information 531. The disease suggestion information 531 may be expanded as (populated) disease suggestion information 523 in the main screen 520. Additionally, the annotation input screen 530 may also include a button 533 that the user may click to share the entered symptom information on social media. In addition, the comment input screen 530 may also include a navigation portion 525.

Upon clicking the button 540 of the navigation portion 525, the mobile application displays a disease origin screen 540, schematically illustrated in fig. 5D. On the disease origin screen 540, the user is presented with buttons 542 ("a", "B", "C", "D"), each having a possible origin 541 of the one or more diseases. Although only four buttons are shown in FIG. 5D, any suitable number of buttons may be displayed. Upon clicking the button, the user is presented with a box 543 that provides more information about the origin of the disease. For example, button "a" of buttons 542 may correspond to a sink (sink). Upon clicking button "a," the user is presented with a box 543 with more details about how the sink can be a source of disease (e.g., disease infection). Additionally, the screen 540 may also contain up-to-date test results 544 from disease source testing (e.g., by processing samples obtained from a particular source) and/or survey results 545 provided by the user of the mobile application as to what source they detected the disease. In addition, the disease origin screen 540 may also include a navigation portion 525.

Upon clicking on the button 550 of the navigation portion 525, the mobile application displays a social media screen 550, schematically illustrated in FIG. 5E. Social media screen 550 displays a number of other users of the mobile application that the user has added to the "friends" list. For each added user, a photo or other avatar is displayed with the username (e.g., "username 1," "username 2," "username 3," and "username 4") 551. Each added user entry may also include a "comfort" button 552 and/or a "like" button 553. When the mobile application identifies that the added user may have a disease, the user of the mobile application may click on the "comfort" button 552 to send a message to the added user regarding their disease (e.g., a recovery health message, a comfort message, etc.). When the mobile application identifies that the added user is likely to be healthy, the user of the mobile application may click on the "like" button 553 to recognize the positive physiological state of the added user. The social media screen 550 may include any number of added users and may be displayed on several pages (e.g., accessible by sliding the screen or clicking on navigation buttons). In addition, the social media screen 550 may also include a navigation portion 525.

Upon clicking on the button 560 of the navigation section 525, the mobile application displays a user information screen 560 schematically illustrated in fig. 5F. The user information screen 560 may include a photograph or other avatar 561 provided by the user and available in social media for use on the other user's social media screen. The user information screen 560 may also display a user name 562. User information buttons 563 (buttons "A", 570, "C", and "D") may also be displayed. These buttons may be used to access a variety of screens, including accessing personal disease monitoring history (e.g., as described elsewhere herein), accessing annotation input history, accessing messages received from other users through social media (e.g., comfort messages, like messages as described above with respect to social media screen 550), reviewing and editing user information (e.g., username, avatar, location, gender, age, physiological information, etc.), and may also be used to access information for obtaining disease monitoring material. The user information screen 560 may also include disease information buttons 564 that each provide the user with access to information about a disease or group of diseases. Buttons 564 may also include buttons to view prevalence of a particular disease or group of diseases in multiple geographic locations and/or worldwide. In addition, the user information screen 560 may also include a navigation portion 525.

Upon clicking on button 570 of user information screen 560, the mobile application displays a test information screen 570, schematically illustrated in FIG. 5G. The test information screen 570 may include a new test information portion 571 that allows a user to associate disease monitoring tests with their profile. This portion may include a "scan" button 572 that accesses a camera of the electronic device (if present) and identifies a barcode imaged by the camera and associated with a material (e.g., a consumable) associated with disease monitoring. Instead of scanning, the section also contains an input field 573 in which the user can enter a barcode or other type of identifying information. In addition, the test information screen may also be used as an order for materials (orderform) necessary for disease monitoring. In such a case, the mobile application may display a material reservation portion 574 whereby the user is presented with buttons 575, each representing an address previously associated with the user. After clicking on the appropriate address, the user may complete the subscription on a further screen (not shown). Alternatively, the address information may be entered into the field 576 and then further processed. In addition, the test information screen 570 may also include a navigation portion 525.

Point-of-care devices, as used herein, generally refer to devices adapted to operate at or near a location at which a biological sample is obtained from a subject. The point of care device may be portable and/or capable of being moved to a position proximate to the subject or to a position of the subject. Further, the point of care device may be capable of processing a biological sample and/or obtaining one or more quantitative measurements of a biomarker. The data from the point-of-care device may be analyzed by a computer processor on the point-of-care device or may be transmitted over a network to a remote computer system that receives and further processes the data (e.g., generates quantitative measurements of one or more biomarkers, determines disease information, determines trends, etc.). The processed data may be sent back to the point-of-care device over a network or to a different electronic device to be displayed to the user. Further, in some aspects of the disclosure, including those comprising obtaining biological samples from a plurality of subjects, the biological sample from a given subject can be processed in a given point-of-care device of a plurality of point-of-care devices. For example, monitoring for a disease may include monitoring for a disease in a subject located in a plurality of geographic locations. At a given geographic location of the plurality of geographic locations, a biological sample obtained from a subject at the given geographic location may be processed using a point of care device.

In addition, the point-of-care device can comprise a reaction vessel that can receive a biological sample from the subject and any reagents necessary for nucleic acid amplification. The point-of-care device may also contain a heater and/or cooling system to regulate the temperature during nucleic acid amplification. Further, the point of care device can include a detector that detects a signal indicative of a biomarker in the biological sample. Such a signal can be used to provide a quantitative measurement of the biomarker in the sample. The detector and its detection modality (modality) may be any suitable detector/detection modality, including the types of detectors described elsewhere herein. In some cases, the point-of-care device may include on-board circuitry and/or a computer processor that may be used to receive data from a remote computer system over a network, and/or to process quantitative measurements, process disease information, generate trends, provide updates, provide warnings/notifications.

In some aspects, the present disclosure relates to providing a user with an assessment of risk of contracting at least one disease while traveling and/or optimizing a trip.

In one of some aspects, the present disclosure provides a method of providing a user with an assessment of risk of contracting at least one disease. The method may include receiving a search query of a user over a network, the search query may include information related to a destination and optionally one or more route points. With the aid of a computer processor, the search query may be processed to identify one or more geo-location tags associated with the destination and optionally one or more route points for searching in the disease database. The disease database may contain disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations. The one or more geographic locations may include a destination. The method may further include searching a disease database using the one or more geo-location tags to identify the at least one disease and disease progression information. The method may further include, based on the identified disease progression information, providing the user with an assessment of the risk of contracting at least one disease at the destination and, in some cases, at one or more route points.

As used herein, the term "destination" refers to a geographic location to which a user is heading or planning to head as described in this disclosure. The destination may be a geographic location as described elsewhere herein. Alternatively or additionally, the destination may be an entity associated with a geographic location as described elsewhere herein. For example, a destination may be a building, a commercial location (such as a restaurant, retail store, department store, mall, office building, bank, etc.), tourist attraction, public facility, transportation hub (such as a train station, airport, long distance bus stop, ferry, etc.), etc., so long as such destination may be associated with a geographic location as described elsewhere herein. A destination is associated with a geographic location if the destination can be manually or automatically identified as being located at the geographic location, or its location relative to the geographic location can be manually or automatically determined. In some embodiments, the destination may be associated, with the aid of a computer processor, with a geographic location tag that may be used for searching in a disease database.

As used herein, the term "route point" refers to a temporary destination at which a passenger may stop before moving to the next or final destination. All restrictions on the destination are applicable to the route points. For example, a route point is associated with a geographical location if the route point may be manually or automatically identified as being located at the geographical location, or its location relative to the geographical location may be manually or automatically determined. Although the term "temporary" is used to define a route point, it should not be construed as a specific limitation on the duration of a passenger's stay in transit at the route point. For example, the passenger may remain at the waypoint for less than 10 minutes, 20 minutes, 30 minutes, 1 hour, 2 hours, 3 hours, 5 hours, 12 hours, 1 day, 2 days, 5 days, or more than 5 days, or any duration in between these values. In some embodiments, route points may be associated, with the aid of a computer processor, with geographic location tags that may be used to search in a disease database. In some embodiments, mentioning one or more route points includes mentioning a reference to a start point and/or a destination.

The user's search query may be provided to an electronic device that transmits the search query over a network for processing by a computer processor as described elsewhere herein. Additionally, the computer processor may be a component of a remote computer system that is networked with the electronic device. The network may be a network as described elsewhere herein, such as the internet, an internet and/or an extranet, an intranet and/or extranet in communication with the internet, a cellular telephone network in communication with the internet, or a "cloud" network.

The disease database may be any disease database as described elsewhere herein that includes disease progression information as described elsewhere herein. The disease progression information indicates progression or regression of the at least one disease at one or more geographic locations. As described above, such information may include morbidity, longitudinal morbidity, mortality, longitudinal mortality, and/or morbidity of one or more symptoms associated with at least one disease at one or more geographic locations.

In some embodiments, the user may be provided with an assessment of the risk of contracting at least one disease on a Graphical User Interface (GUI) as described elsewhere herein. For example, the GUI may be a component of an electronic display of an electronic device as described elsewhere herein. In some embodiments, the electronic device may be a portable electronic device. In some embodiments, the graphical user interface may be provided by a mobile computer application.

In some embodiments, the search query may further include an identity and/or a physiological state of the user. The identity and physiological state may be any identity and physiological state as described elsewhere herein. For example, the identity may include at least one of a name, age, and gender of the user; the physiological state may include at least one of a heart rate, a blood pressure, a cough frequency, a cough intensity, a sneeze frequency, a sneeze intensity, a chest tightness level, a nasal congestion level, a body temperature, a sweat level, a weight, a height, a breathing frequency, a blood pressure, a nerve conduction velocity, a lung volume, a urine production rate, a defecation frequency, a presence of enlarged lymph nodes, and a biochemical spectrum of a body fluid of the user.

In some embodiments, the search query may include the user's origin. As used herein, the term "origin" refers to a geographic location at which a user starts or a planned trip starts as described in this disclosure. The origin may be a geographic location as described elsewhere herein. Alternatively or additionally, the origin may be an entity associated with a geographic location as described elsewhere herein. For example, a destination may be a building, a commercial location (such as a restaurant, retail store, department store, mall, office building, bank, etc.), a tourist attraction, a public facility, a transportation hub (such as a train station, airport, long distance bus stop, ferry, etc.), etc., so long as such a departure location may be associated with a geographic location as described elsewhere herein. An origin is associated with a geographic location if the origin can be manually or automatically identified as being located at the geographic location, or its position relative to the geographic location can be manually or automatically determined. In some embodiments, the origin may be associated, with the aid of a computer processor, with a geo-location tag that may be used for searching in a disease database.

Alternatively, the origin may be determined automatically by the electronic device via, for example, access to the capabilities of a global navigation satellite system such as the Global Positioning System (GPS), the global navigation satellite system (GLONASS), the Indian Regional Navigation Satellite System (IRNSS), the beidou navigation satellite system (BDS), galileo (european satellite navigation system), and the like. The electronic device may be any electronic device as described elsewhere herein. For example, the electronic device may be a personal computer, a portable electronic device (such as a mobile phone), a tablet computer, or the like.

Alternatively, the origin may be determined automatically by the electronic device via any of a variety of geolocation techniques other than global navigation satellite systems, such as multi-point location of wireless signals, global system for mobile communications (GSM), location-based services for mobile devices, Wi-Fi based location, hybrid location systems, and so forth.

In some embodiments, the assessment may be provided via a notification or alert on a network as described elsewhere herein. For example, such notifications or alerts may be provided to the electronic devices described herein, including by text message, by email, by social media, and/or by an application available on the electronic device.

In some embodiments, providing an assessment to the user may include providing the user with one or more suggested preventative measures to reduce the rate of progression of the at least one disease at the destination and/or route point. Such precautions may be any of those described elsewhere herein. For example, such precautions may be seeking immunization against the disease, taking a predictive drug that inhibits infection and/or progression of the disease, avoiding travel to a particular geographic location (including a destination and/or route point); changing modes of transportation (such as avoiding one or more modes of transportation that result in a higher risk of contracting a disease); wearing a personal protective device at a particular geographic location (including a destination and/or a route point); personal hygiene measures are enhanced. In some embodiments, providing the user with the assessment may include suggesting that the user avoid proceeding to the destination. In some embodiments, providing the user with the evaluation may include suggesting that the user avoid passing at least one of the one or more route points. In some embodiments, providing the user with an assessment may include suggesting that the user travel to a different destination.

In some embodiments, the database may further comprise indications of the at least one disease. As mentioned above, the disease database may comprise indications of said at least one disease. Non-limiting examples of such indications include identification of the disease (e.g., disease name), identification of at least one pathogen associated with the disease (e.g., a bacterial pathogen (including bacteria as described elsewhere herein), a viral pathogen (including viruses as described elsewhere herein)), identification of at least one symptom associated with the disease, and biochemical profiles associated with the disease (e.g., biochemical profiles of bodily fluids, biochemical profiles of tissue samples). In some embodiments, the indication of the at least one disease comprises identification information of at least one virus, at least one bacterium, and/or at least one protozoan.

In some embodiments, the at least one virus may be selected from human immunodeficiency virus i (hiv i), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, EB virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, zika virus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus, rift valley fever virus, herpes simplex virus, hepatitis e virus, epstein-virus, hepatitis e-virus, dengue virus, influenza virus, herpes virus, Chikungunya virus, enterovirus, coxsackievirus and norovirus.

In some embodiments, the at least one bacterium may be selected from the group consisting of bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, haemophilus influenzae, streptococcus pyogenes, streptococcus pneumoniae, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii, yersinia pestis, and salmonella.

In some embodiments, the at least one protozoan may be selected from the group consisting of plasmodium and leishmania donovani.

In some embodiments, the identity may include at least one of a name, age, and gender of the user. Further, the identity may include any other suitable authentication information that allows the user to be authenticated. Non-limiting authentication information may include biometric information such as fingerprints, palm veins, facial recognition, DNA, palm prints, palm shapes, iris recognition, retina and odor/fragrance.

In some embodiments, the physiological state may include at least one of heart rate, blood pressure, cough frequency, cough intensity, sneeze frequency, sneeze intensity, chest tightness level, nasal obstruction level, body temperature, sweat level, weight, height, respiratory frequency, blood pressure, nerve conduction velocity, lung volume, urine production rate, defecation frequency, presence of enlarged lymph nodes, and biochemical spectrum of body fluid of the user.

In some embodiments, the method may further comprise providing a total risk of contracting at least one disease via route points to a destination. The total risk may be obtained by statistical analysis of the risk of contracting at least one disease at various route points and destinations. For example, events that infect at least one disease at various route points and destinations can be considered independent of each other. Thus, the total risk may be calculated as a combined probability of contracting at least one disease at least one geographic location in the respective route points and destinations. Of course, if the base models of the combined probabilities are different (e.g., events infecting at least one disease at various route points and destinations are somewhat interdependent), the algorithm may be altered to account for them. In some embodiments, the risk of contracting at least one disease during a journey between the origin, route and destination may also be taken into account in the calculation of the total risk.

The risk of contracting the at least one disease during a journey between geographical locations may be assessed qualitatively or quantitatively. In the case where the quantitative measurements are used to provide an estimate, one or more calculation algorithms may be used to calculate the quantitative measurements. In some cases, disease progression information retrieved during a disease database search may be used in the calculation. In some cases, the mode of transportation for the journey may be taken into account in the assessment, as described elsewhere herein.

In some embodiments, the search query may further include information regarding travel to a destination via route points. The route may include a time to reach each route point or destination, a time to leave each route point or origin, and/or a time to stay at each route point. In some cases, the itinerary may further include transportation used in travel, such as transportation used from a starting point to a first route point, from one route point to a next route point, from a last route point to a destination, and so forth. If there is no route point, the trip may include a time to leave the departure point and a time to reach the destination. In some cases, the route may further include a mode of transportation used from the origin to the destination, between route points, from the origin to the route points, and/or from the route points to the destination.

The mode of transportation may be any suitable means for carrying passengers from one geographic location to another. Non-limiting examples of modes of transportation include driving, coaches, trains, planes, ferries, and the like.

In some embodiments, providing the user with an assessment of the risk of contracting the at least one disease may further include taking into account the journey. The journey may be processed, for example by a computer processor, to allow the future geographic location of the passenger to be determined. This may be advantageous because the itinerary may be determined based on disease progression information that the disease may progress or resolve at a future geographic location when the traveler plans to stop, arrive at, or leave the geographic location. By taking into account this type of information, the risk of contracting at least one disease at a future geographical location can be determined in a more accurate or precise manner.

For example, if the trip shows that the passenger will arrive at route point a three days later, and the disease progression information indicates that the disease will regress or disappear at route point a within two days, it may be determined that the risk of contracting the disease at route point a will be low.

Furthermore, the information about the means of transportation on the journey may also allow the risk of contracting at least one disease during a journey between geographical locations to be determined in a more accurate or precise manner. For example, it may be determined that during a journey, one mode of transportation results in a higher risk of contracting at least one disease than another mode of transportation. For some modes of transportation that require one or more stops to get on and off passengers, the disease progression information at the stop may be taken into account in determining the risk of contracting at least one disease during the journey.

In some aspects, the route points to the destination may not be input by a user, but rather determined by a computer processor. That is, a route from the departure point to the destination is determined. Thus, in another of the aspects, the present disclosure provides a method of providing a user with an assessment of risk of contracting at least one disease. The method may include receiving a search query of a user over a network, the search query including information related to a departure place and a destination selected by the user. A search query may be processed with the aid of a computer processor and a travel cost data structure to (i) identify a route from an origin to a destination within the travel cost data structure, and (ii) determine one or more route points along the route, wherein the one or more route points include at least the origin and the destination, and wherein the travel cost data structure includes travel costs between a geographic location and an adjacent geographic location. The method may further comprise searching a disease database containing disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations (including the destination) using the one or more route points to identify the at least one disease and disease progression information. Further, the method may comprise providing the user with an assessment of the risk of contracting the at least one disease at the destination or along the route based on the identified disease progression information.

The term "travel cost" as described herein refers to a quantification of desirability of travel between geographic locations. The higher the travel cost, the less desirable the travel between geographic locations. In some embodiments, the travel cost may include one or more items selected from the group consisting of travel time, travel fares, travel comfort, dwell time, predictability, safety, punctuality, robustness, and combinations thereof (hereinafter referred to as "travel cost components").

The term "travel time" as used herein refers to travel time from one geographic location to another. Travel time depends on various factors including, but not limited to, transportation, "reserved" time before taking a vehicle (e.g., many airports require passengers to check in for a certain amount of time before aircraft take off), weather, traffic conditions, time of year (e.g., some routes may require more time in one part of the year than another part), etc. Generally, the shorter the travel time, the lower the travel cost, and vice versa.

The term "dwell time" as used herein refers to the time it takes for a passenger to stay at a geographic location, rather than traveling. The dwell time may be affected by the smoothness of the connection between the travel legs. For example, if the passenger arrives at a route point without a means of transportation for the passenger to travel from the route point to the next route point or destination, the passenger may have to remain at the current route point for a dwell time before a means of transportation for the next trip is available. The presence of the dwell time may cause some seemingly faster transportation to take longer travel times (total travel time includes both travel time and dwell time) than a seemingly slower transportation. Generally, the shorter the dwell time, the lower the cost of travel and vice versa.

The term "travel fares" as used herein refers to fares borne by passengers for travel, accommodation, food, and other applicable fares. Generally, the less travel cost, the lower the travel cost, and vice versa.

The term "travel comfort level" as used herein refers to the comfort level enjoyed by a passenger during travel (including during vehicle and accommodation), as well as other factors that may affect the comfort level of a passenger during travel, such as scenic roads, transportation and/or accommodation services, the passenger's preference for certain transportation, and the like. Generally, the higher the travel comfort, the lower the travel cost, and vice versa.

The term "punctuality" as described herein refers to the probability of reaching a geographic location at a planned time. Certain modes of transportation may have higher punctuality than another mode of transportation. Other non-limiting factors that may affect the punctuality include the nature of the geographic location and the route between them, weather, geographic conditions, traffic infrastructure, and the like. Generally, the higher the punctuality, the lower the travel cost and vice versa.

The term "security" as described herein refers to the probability of a non-event and accident trip. In some cases, the severity of an event and/or accident may also be considered if it occurs. One mode of transportation may have greater safety than another mode of transportation. Non-limiting factors that may affect safety include the nature of the geographic locations and the routes between them, weather, geographic conditions, traffic infrastructure, and the like. Generally, the higher the security, the lower the travel cost and vice versa.

In some embodiments, the travel cost may include two or more travel cost components selected from the group, the two or more being a weighted combination. Each member from the aforementioned set of travel cost components may be assigned a coefficient for calculating a weighted travel cost. The coefficients for each member may be determined by one or more computational algorithms, or predetermined. The coefficients for each member may be adjusted according to the user's preferences. For example, a user may be more interested in short travel times relative to high travel comfort or low travel fares. Therefore, the coefficient assigned to the travel time of the user may have a relatively higher value than the coefficient assigned to the travel comfort or travel fare of the user. In some implementations, the user may be provided with different sets of predetermined coefficients to select from. Each of the different groups may represent a different priority or preference or may represent a balancing option. Non-limiting examples of such groups may include a preference to cut or avoid one or more travel cost components, a preference for one mode of transportation over another, or no preference.

The travel cost data structure may contain travel costs between the geographic location and neighboring geographic locations. The travel cost data may be organized in various ways to provide a travel cost data structure. Non-limiting data structures that may be suitable for use with the present disclosure include abstract data structures (such as lists, stacks, queues, collections, etc.), arrays, connected data structures, trees, graphs, and the like. The travel cost data structure as used herein is organized such that travel costs from one geographic location to another may be retrieved or calculated.

The travel cost data structure may be a graph, such as a weighted graph. In some embodiments, the travel cost data structure may be a weighted graph that includes geographic locations as vertices and travel costs between adjacent geographic locations as weighted boundaries. In some embodiments, there may be more than one weighted boundary between two adjacent geographic locations, which represents more than one mode of transportation. In some embodiments, the weighted boundaries may be directional, that is, the cost of travel from one geographic location to another may be different than the cost of travel of the return trip. In some cases, the weighted graph may be presented as an electronic map.

The travel cost data structure may be an array, such as a two-dimensional or three-dimensional table. In some embodiments, the travel cost data structure may be a table containing geographic locations in columns and rows and travel costs between adjacent geographic locations in cells. In some embodiments, the table may contain a third dimension, such as pages, where each page represents a mode of transportation.

The term "adjacent geographical locations" should not be construed as limited to physically adjacent or connected geographical locations, but rather should be understood in the context of transportation. Two geographical locations may be considered "adjacent" if they are directly connected to one another by some means of transportation without transit. For example, geographic location a may be considered adjacent to geographic location B if there is at least one direct flight or direct train service between them, even though geographic locations a and B may be thousands of miles apart or located in different continents (such as in an intercontinental flight).

In some embodiments, a route from an origin to a destination within a travel cost data structure may be generated by employing a routing algorithm on the travel cost data structure. The routing algorithm is capable of finding the shortest route from the departure point to the destination. The shortest path may be defined as having the lowest total value of travel costs along the entire route. Alternatively, the shortest path may be defined as having the lowest total value of one or more travel cost components along the entire route as described elsewhere herein. Non-limiting examples of routing algorithms may include A, Dijkstra, BFS, DFS, Greedy, and combinations thereof.

In some embodiments, the method may further include creating a trip based on the route. The stroke may be any stroke as described elsewhere herein. In the case where a itinerary is provided, providing the user with an assessment of the risk of contracting the at least one disease may further include considering the itinerary as described elsewhere herein.

In some aspects, a route from an origin to a destination may be generated by considering a risk of infection with at least one disease along the route. For example, the risk of contracting at least one disease at one or more geographical locations and/or the risk of contracting at least one disease on a journey from one geographical location to another geographical location may be taken into account when determining the route.

In some cases, the risk of contracting at least one disease at two geographic locations connected by a journey and/or the risk of contracting at least one disease on a journey from one geographic location to another may be considered a travel cost component from a first geographic location to a second geographic location (hereinafter "risk of illness"). The disease risk may be combined with one or more other travel cost components in a weighted combination to calculate an adjusted travel cost. For example, the risk of illness may be assigned a factor and included in the cost of travel. Where more than one disease is considered, the disease risk for each disease is considered a separate travel cost component for inclusion in the travel cost.

In some aspects, the travel cost data structure may be optimized by considering disease risk. Thus, in another of some aspects, the present disclosure provides a method of optimizing a travel cost data structure comprising a plurality of geographic locations and a travel cost data structure between adjacent geographic locations. The method may include searching a disease database using each of the plurality of geographic locations to identify at least one disease and disease progression information associated with geographic locations of the at least plurality of geographic locations, the disease database including disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations. The method may further include, based on the identified at least one disease and disease progression information, (i) determining a risk of contracting the at least one disease, and (ii) optimizing the travel cost data structure by adjusting travel costs between each and all of the plurality of geographic locations based on the risk. The method may further comprise repeating the above steps until all of the plurality of geographic locations are traversed, thereby optimizing the travel cost data structure. The optimized travel data structure may be used to generate a route from the origin to the destination, in some cases, by using a routing algorithm as described elsewhere herein.

In another of some aspects, the present disclosure is directed to a method of providing a user with a trip to a destination using an optimized travel cost data structure. The method may include receiving a search query of a user over a network, the search query including information related to a departure place and a destination selected by the user. The method may further include processing the search query with the aid of a computer processor and an optimized travel cost data structure to identify an optimal route from the origin to the destination within the travel cost data structure. The method may further include generating a trip for the user using the optimal route.

The optimal route and/or trip is used to assess the risk of contracting at least one disease along the route and/or at the destination. Thus, in some embodiments, the method further comprises searching a disease database containing disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations (including destinations) using each of the one or more route points to identify the at least one disease and the disease progression information. The method may further comprise providing the user with an assessment of the risk of contracting the at least one disease at the destination or along the route based on the identified disease progression information.

In some embodiments, providing the user with an assessment of the risk of contracting the at least one disease may further include taking into account a trip as described elsewhere herein.

In several important respects, the risk of illness is different from many other travel cost components. The risk of disease is temporary and its level can change rapidly over a period of weeks, even days or less. In addition, disease risk is more difficult to predict than many other travel cost components. For example, travel times may vary due to the completion of traffic infrastructure or future weather changes, but can often be predicted months or even years in advance. Conversely, even in the near future, it may be difficult to estimate disease progression information for any particular geographic location. Moreover, the travel cost data structure may contain millions of data regarding travel costs for a geographic location and between neighboring geographic locations. Updating such a travel cost data structure with up-to-date disease progression information may not be cost effective, depending on the frequency of search queries made by the user.

Accordingly, in some aspects, the present disclosure provides alternative methods for generating a route and/or trip from a starting location to a destination.

In some aspects, the travel cost data structure is not optimized prior to generating the route. Rather, routes are first generated without regard to risk of disease. After generating the route, it is determined whether the route passes through any route point where the risk of disease must be considered. If so, the travel cost data structure is optimized only at such routes. The optimized travel cost data structure may then be used to generate a new route. This process may be an iterative process if the new route thus generated passes through any new route point where the risk of disease must be considered. That is, the process is repeated iteratively as needed, for example, until no more route points need to be optimized or the process has been repeated a threshold number of times.

Accordingly, in another of some aspects, the present disclosure provides a method of providing a user with a trip to a destination. The method may include receiving a search query of a user over a network, the search query including information related to a departure place and a destination selected by the user. The search query may be processed with the aid of a computer processor and a travel cost data structure to (i) identify a route from an origin to a destination within the travel cost data structure, and (ii) determine a plurality of route points along the route, wherein the plurality of route points includes at least the origin and the destination, and wherein the travel cost data structure contains travel costs between a geographic location and adjacent geographic locations. The method may further include searching a disease database containing disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations using each of the plurality of route points to identify the at least one disease and disease progression information associated with a route point of the plurality of route points. The method may further include, based on the disease progression information identified in (c), (i) determining a risk of contracting the at least one disease, and (ii) optimizing the travel cost data structure by adjusting travel costs between the geographic location associated with the route point and an adjacent geographic location based on the risk. The method may further comprise repeating the above steps as necessary to generate an optimal route, wherein the optimal route reduces the risk of infection with the at least one disease. The method may further include generating a trip for the user using the optimal route.

In some embodiments, the itinerary may be provided to the user on a graphical user interface as described elsewhere herein. For example, the GUI may be a component of an electronic display of an electronic device as described elsewhere herein. In some embodiments, the electronic device may be a portable electronic device. In some embodiments, the graphical user interface may be provided by a mobile computer application.

In some embodiments, providing the user with the itinerary may further include providing the user with an assessment of the risk of contracting at least one disease as described elsewhere herein. For example, the evaluation may be provided via a notification or alert on a network as described elsewhere herein. Such notifications or alerts may be provided to the electronic devices described herein, including by text message, by email, by social media, and/or by an application available on the electronic device, for example. In some embodiments, providing an assessment to the user may include providing the user with one or more suggested preventative measures to reduce the rate of progression of the at least one disease at the destination and/or route point as described elsewhere herein.

In some embodiments, providing the user with an assessment may include suggesting that the user avoid proceeding to the destination. A threshold for the number of repetitions of the method may be predetermined. Further, a threshold value for travel cost may be predetermined. If the method repeats for a threshold number of times without the level of total travel costs falling below the threshold of travel costs, the method may be terminated and the user may be advised to avoid proceeding to the destination. Alternatively, a threshold for disease risk may be predetermined. If the method repeats for a threshold number without the disease risk level along the route falling below the threshold for travel costs, the method may be terminated and the user may be advised to avoid proceeding to the destination. In some embodiments, providing the user with an assessment may include suggesting that the user travel to a different destination.

In some aspects, rather than just selecting a route, multiple routes are first selected to determine whether they cross any route points where disease risk must be considered. By employing multiple routes, route points affected by at least one disease may be identified more quickly, which may allow for more quickly identifying an optimal route. Thus, multiple routes are first generated without regard to risk of disease. After the routes are generated, it is determined whether the routes cross any route points where the risk of disease must be considered. If so, the travel cost data structure is optimized only at such routes. The optimized travel cost data structure may then be used to generate one or more new routes. The process may be an iterative process if the new route thus generated passes through any new route points for which the risk of disease must be considered. That is, the process is repeated iteratively as needed, for example, until no more route points need to be optimized or the process has been repeated a threshold number of times.

The number of routes generated in each iteration of the method may be the same. Alternatively, the number of routes generated in each iteration of the method may be different. For example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, 300, 500, 1000 or more than 1000 routes, or any integer between the values listed above, may be generated in each iteration of the method.

In some embodiments, the plurality of routes may be selected randomly. Alternatively, the plurality of routes may be routes that are capped at a lowest travel cost among the available routes.

Accordingly, in another of some aspects, the present disclosure is directed to a method of providing a user with a trip to a destination. The method may include receiving a search query of a user over a network, the search query including information related to a departure place and a destination selected by the user. A search query may be processed with the aid of a computer processor and a travel cost data structure to (i) identify a plurality of routes leading from a start point to a destination within the travel cost data structure, and (ii) determine, for each of the plurality of routes, a plurality of route points along the route, wherein the plurality of route points includes at least the start point and the destination, and wherein the travel cost data structure contains travel costs between a geographic location and an adjacent geographic location. For each of the plurality of routes, a search may be conducted in a disease database containing disease progression information indicative of progression or regression of at least one disease at one or more geographic locations using each of the plurality of route points to identify at least one disease and disease progression information associated with a route point of the plurality of route points. The method may further include, for each of the plurality of routes, based on the identified disease progression information, (i) determining a risk of contracting at least one disease along the route, and (ii) optimizing the travel cost data structure by adjusting a travel cost between a geographic location associated with a route point and an adjacent geographic location based on the risk. The method may further include repeating the above steps as necessary to generate an optimal route, wherein the optimal route results in a lowest travel cost among the plurality of routes. The method may further include generating a trip for the user using the optimal route.

The method may further include providing a plurality of routes (e.g., optimal routes) and/or trips for selection therefrom by a user. The plurality of routes and/or itineraries may be the route and/or itinerary having the lowest total travel cost. Alternatively, the plurality of routes and/or itineraries may each be the route and/or itinerary with the lowest total travel cost, according to individual preference settings. Each preference setting may correspond to a different set of coefficients. For example, the user may be presented with a tour labeled "prefer short travel time," "prefer cheap travel fare," "prefer low risk of illness," "no preference," etc., from which the user may select.

The present disclosure provides a computer control system programmed to implement the methods of the present disclosure. FIG. 4 illustrates an exemplary computer system 401 that may be programmed or configured in a variety of ways, including being configured to process a user's search query; comprises a disease database; generating quantitative measurements of biomarkers from the nucleic acid amplification data; processing the quantitative measurements of the biomarkers to obtain disease information indicative of disease progression or regression; processing such disease information to obtain trends and/or correlations; assessing the risk of contracting a disease; and/or display information to a user. The computer system 401 may regulate aspects of biological sample processing via nucleic acid amplification, e.g., amplification protocols performed by a thermal cycler or other type of amplification device. Computer system 401 may be a user's electronic device or a computer system that is remotely located from the electronic device. The electronic device may be a mobile electronic device.

Computer system 401 includes a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 405, which may be a single or multi-core processor, or multiple processors for parallel processing. Computer system 401 also includes a memory or storage location 410 (e.g., random access memory, read only memory, flash memory), an electronic storage unit 415 (e.g., hard disk), a communication interface 420 (e.g., a network adapter) for communicating with one or more other systems, and peripherals 425 such as cache memory, other memory, data storage, and/or an electronic display adapter. Memory 410, storage unit 415, interface 420, and peripherals 425 communicate with CPU405 through a communication bus (solid lines) such as a motherboard. The storage unit 415 may be a data storage unit (or data repository) for storing data. Computer system 401 may be operatively coupled to a computer network ("network") 430 by way of a communication interface 420. The network 430 may be the internet, the internet and/or an extranet, or an intranet and/or extranet in communication with the internet. In some cases, network 430 is a telecommunications and/or data network. Network 430 may include one or more computer servers that may support distributed computing, such as cloud computing. In some cases, network 430 may implement a peer-to-peer network with computer system 401, which may enable devices coupled with computer system 401 to function as clients or servers.

The CPU405 may execute a series of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a storage location such as memory 410. The instructions may be directed to the CPU405, the CPU405 then programming or otherwise configuring the CPU405 to implement the methods of the present disclosure. Examples of operations performed by the CPU405 may include fetch, decode, execute, and write back.

The CPU405 may be part of a circuit such as an integrated circuit. One or more other components of system 401 may be included in a circuit. In some cases, the circuit is an Application Specific Integrated Circuit (ASIC).

The storage unit 415 may store files such as drivers, libraries, and saved programs. The storage unit 415 may store user data, such as user preferences and user programs. In some cases, computer system 401 may include one or more additional data storage units located external to computer system 401, such as on a remote server in communication with computer system 401 over an intranet or the internet.

Computer system 401 may communicate with one or more remote computer systems over a network 430. For example, computer system 401 may communicate with a remote computer system of a user. Examples of remote computer systems include a personal computer (e.g., a laptop PC), a tablet or tablet PC (e.g.,iPad、galaxy Tab), telephone, smartphone (e.g.,iPhone, Android-enabled device,) Or a personal digital assistant. A user may access computer system 401 via network 430.

The methods as described herein may be implemented by way of machine (e.g., computer processor) executable code that is stored on an electronic storage location of computer system 401, such as on memory 410 or electronic storage unit 415. The machine executable code or machine readable code may be provided in the form of software. In use, the code may be executed by the processor 405. In some cases, the code may be retrieved from storage unit 415 and stored on memory 410 for retrieval by processor 405. In some cases, electronic storage unit 415 may be eliminated, and machine-executable instructions stored on memory 410.

The code may be pre-compiled and configured for use with a machine having a processor adapted to execute the code, or may be compiled during runtime. The code may be provided in a programming language, which may be selected to enable the code to be executed in a pre-compiled or just-in-time (as-compiled) manner.

Aspects of the systems and methods provided herein, such as computer system 401, may be embodied in programming. Aspects of the technology may be considered an "article of manufacture" or "article of manufacture" typically in the form of machine (or processor) executable code and/or associated data carried or embodied on a type of machine-readable medium. The machine executable code may be stored on an electronic storage unit such as a memory (e.g., read only memory, random access memory, flash memory) or a hard disk. A "storage" type medium may include any or all of the tangible memory, processors, etc., or associated modules thereof, of a computer, such as the various semiconductor memories, tape drives, disk drives, etc., that may provide non-transitory storage for software programming at any time. All or part of the software may sometimes be in communication via the internet or various other telecommunications networks. Such communication, for example, may enable software to be loaded from one computer or processor into another computer or processor, for example, from a management server or host into the computer platform of an application server. Thus, another type of medium that can carry software elements includes optical, electrical, and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical land-line networks, and through various air links. The physical elements that carry such waves, such as wired or wireless links, optical links, etc., may also be considered as media carrying software. As used herein, unless limited to a non-transitory tangible "storage" medium, terms such as a computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

Thus, a machine-readable medium, such as computer executable code, may take many forms, including but not limited to: tangible storage media, carrier wave media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, any storage device in any computer, etc., such as may be used to implement a database or the like as shown in the figures. Volatile storage media includes dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise the buses within a computer system. Carrier-wave transmission media can take the form of electrical or electromagnetic signals, or acoustic or light waves, such as those generated during Radio Frequency (RF) and Infrared (IR) data communications. Thus, common forms of computer-readable media include, for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards, paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer can read programming code and/or data. Many of these computer-readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

The computer system 401 may include or may be in communication with an electronic display 435, the electronic display 435 including a User Interface (UI)440 for providing, for example, information (e.g., disease information, disease trends, recommendations for disease treatment, recommendations for disease prevention, questionnaires, reports as described elsewhere herein, warnings/notifications, or any other type of information described elsewhere herein). Electronic display 435 may be part of a user's mobile electronic device (e.g., a portable computer, a smart phone, or a tablet personal computer). Examples of UIs include, but are not limited to, Graphical User Interfaces (GUIs) and web-based user interfaces.

The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented in software when executed by the central processing unit 405. For example, the algorithm can determine quantitative measurements of biomarkers from nucleic acid amplification data; processing the quantitative measurements to obtain disease information indicative of progression or regression of the disease; processing the disease information to generate a disease trend; determining an update of the trend; providing a risk assessment of an infectious disease; determining an association between the questionnaire results and the disease; and processing the search query and searching in a disease database.

Examples

Example 1: disease risk assessment

A user in san francisco, california accesses a mobile application on his or her smartphone. The mobile application provides the user with a graphical user interface having a search bar in which the user can enter a string of keywords for use as a search query. The user enters the keywords "chest distress", "body temperature 39 ℃" and "san francisco, ca" and clicks the "search" button next to the search bar. The smartphone transmits the keyword to a remote computer system via a wireless network/internet connected thereto, and the remote computer system receives the keyword therefrom. The remote computer system processes these keywords by means of its computer processor and identifies the tags "closed", "39 ℃ and" san Francisco "as tags that can be used to search in a database of diseases stored in the remote computer system memory.

Using the above identified tags, the computer processor performs a search in the disease database using the tags and identifies "stuffy", "39 ℃ and" san francisco "as being associated with influenza b virus. The computer processor also identifies information indicating that the prevalence of influenza b virus is relatively high in a 25-35 year old population in san francisco. The prevalence information is provided to the database by disease monitoring data obtained from users 25-35 years old, san francisco. Based on the relatively high prevalence, the computer processor calculates a risk assessment that includes a quantitative score indicating a relatively high risk of the user being infected with influenza b/a relatively high likelihood that the user has influenza b virus. The risk assessment is transmitted back to the smartphone over a network, where the mobile application displays the risk assessment to a user. The mobile application will display to the user, together with the risk assessment, preventive measures that can be taken to avoid infection with influenza b (e.g., regular hand washing, use of hand washing solutions, wearing of masks that cover the nose and mouth of the user, vaccination against influenza b, etc.) and/or to treat influenza b and its symptoms (e.g., taking anti-inflammatory drugs that reduce fever/pain, drinking large amounts of liquid, taking one or more immune stimulants (e.g., vitamin C), getting sufficient rest, etc.).

Example 2: disease monitoring of a subject

Subjects provided multiple 0.1mL whole blood samples obtained at different time points, respectively, directly to the reaction vessels of a point of care (POC) device. The whole blood sample has not undergone purification to isolate nucleic acids from the whole blood sample. The POC apparatus further includes: a heater that cycles the temperature of the reaction mixture in the reaction vessel; an optical detector for detecting a reaction product generated in the reaction vessel; and on-board electronics that process the detection data into an amount of the biomarker in the reaction mixture based on the detected amplification product. The POC device also includes an electronic display containing a GUI that allows the subject or another user to control nucleic acid amplification and display various forms of information (e.g., disease information, etc.) and other items (e.g., questionnaires) to the subject or other users, such as healthcare professionals as described elsewhere herein.

H3N2 influenza virus was identified as a disease of interest by the subject's responses to the questionnaire provided to the subject by POC, such responses including the subject's age, gender, geographic location and symptoms. Thus, the reaction vessel contains a reaction mixture which, in addition to a given whole blood sample, also comprises the reagents necessary for the amplification of any nucleic acid biomarker indicative of the H3N2 influenza virus. These reagents include reverse transcriptase, DNA polymerase, nucleotides and one or more primers having sequence homology to the RNA specific sequence of the H3N2 influenza virus. The reaction mixture also contains TaqMan probes that target the amplification products, which can be used for optical detection of the amplification products as described elsewhere herein. Each whole blood sample obtained from a subject is processed separately in a POC device.

After the thermal cycling begins, the H3N2 influenza nucleic acid is reverse transcribed by the action of a reverse transcriptase and the resulting DNA transcript is subsequently amplified by the action of a DNA polymerase (e.g., an RT-PCR process) to form an amplification product indicative of an H3N2 influenza nucleic acid biomarker in the sample. Nucleic acid amplification is completed in less than 10 minutes, typically less than 5 minutes. During amplification, the signal of the released optical dye from the TaqMan probe is detected, and the amount of the amplified product is determined. POC's on-board computer processor determines the amount of H3N2 influenza nucleic acid in a given whole blood sample using the amount of amplification and the number of amplification cycles.

The on-board computer processor then processes the amount of the H3N2 nucleic acid biomarker by comparing the amounts of the H3N2 nucleic acid biomarkers obtained at the various time points to each other and to a baseline biomarker amount stored in POC memory. The baseline biomarker amount corresponds to the amount of nucleic acid biomarkers indicative of health status, which are not considered to be associated with H3N2 influenza virus. In this particular example, the amount of H3N2 in the subject's blood was elevated at multiple time points of the test and its value was statistically higher than healthy amounts at all time points of the test. Thus, the computer processor determines that the H3N2 influenza virus has progressed in the subject. The output of the disease information is provided to the subject or another user (e.g., a healthcare professional as described elsewhere herein) on an electronic display, such as a POC device. The output may also include a determined association between one or more responses to the questionnaire by the subject and the disease information, for example, a determined association between the progression of the H3N2 influenza virus in the subject and the geographic location of the subject. In some cases, the output is transmitted over a network to a remote computer storage system for subsequent retrieval and use.

Example 3: disease monitoring among multiple subjects

The prevalence of streptococcus pneumoniae infections is monitored in the gulf of san jose, california, san francisco, california, and auckland, california. Each of a plurality of subjects located in a particular geographic location of the gulf of san francisco was provided a plurality of 0.1mL saliva samples obtained at different time points, respectively, directly to the reaction vessels of the POC device. Samples from multiple subjects were processed using multiple POC devices. The saliva sample has not undergone purification to isolate nucleic acids from the saliva sample. Each POC device further includes: a heater that cycles the temperature of the reaction mixture in the reaction vessel; an optical detector for detecting a reaction product generated in the reaction vessel; and on-board electronics that process the detection data into an amount of the biomarker in the reaction mixture based on the detected amplification product. Each POC device also includes an electronic display containing a GUI that allows the subject or another user to control nucleic acid amplification and display various forms of information (e.g., disease information, etc.) and other items (e.g., questionnaires) to the subject or other users, such as healthcare professionals as described elsewhere herein. Further, each POC device is in electronic communication with a remote computer system that stores information obtained from the POC device.

In each POC device, the reaction vessel contains a reaction mixture that, in addition to a given saliva sample, also contains the reagents necessary to amplify any nucleic acid biomarkers indicative of streptococcus pneumoniae. These reagents include a DNA polymerase, nucleotides and one or more primers having sequence homology to a specific sequence of Streptococcus pneumoniae DNA. The reaction mixture also contains TaqMan probes that target the amplification products, which can be used for optical detection of the amplification products as described elsewhere herein. Each saliva sample obtained from a subject was processed separately in a POC device.

After the thermal cycling has begun, the streptococcus pneumoniae nucleic acids are amplified (e.g., by a PCR process) by the action of a DNA polymerase to form amplification products indicative of the streptococcus pneumoniae nucleic acid biomarkers in a given saliva sample. Nucleic acid amplification is completed in less than 10 minutes, typically less than 5 minutes. During amplification, the signal of the released optical dye from the TaqMan probe is detected, and the amount of the amplified product is determined. The POC's on-board computer processor determines the amount of streptococcus pneumoniae nucleic acid in a given saliva sample using the amount of amplification and the number of amplification cycles.

Then, for each subject, the on-board computer processor of the POC device processes the amount of the streptococcus pneumoniae nucleic acid biomarker by comparing the amounts of the streptococcus pneumoniae nucleic acid biomarker obtained at the respective time points with each other and with a baseline biomarker amount stored in the POC memory. The baseline biomarker amount corresponds to an amount of a nucleic acid biomarker indicative of a healthy state, which is not considered to be associated with streptococcus pneumoniae. For example, the amount of streptococcus pneumoniae in the blood of a subject can be elevated at multiple time points of the test, and its value can be statistically higher than a healthy amount at all time points of the test. Thus, the computer processor determines that streptococcus pneumoniae has progressed in the subject. Saliva samples from other subjects were processed in parallel or at different times and information on the progression/regression of streptococcus pneumoniae was determined for each of the other subjects.

The streptococcus pneumoniae progression/regression information obtained from multiple subjects is transmitted from the POC device over a network, such as a wireless network/internet, to a remote computer system that aggregates and stores the collected information in its computer memory. The computer processor of the remote computer system then processes the disease information to determine a trend of streptococcus pneumoniae in the san francisco bay area. In this particular example, the information from the majority of subjects analyzed shows the progression of streptococcus pneumoniae, where the amount of streptococcus pneumoniae biomarkers in the saliva sample increases over time and is statistically higher than the reference level. Thus, the computer processor generates a trend toward an increased prevalence of streptococcus pneumoniae in the gulf of san francisco.

The output of the trend is provided to a user (e.g., one or more subjects, such as a healthcare professional as described elsewhere herein) on an electronic display, such as a POC device or a mobile computing device. The output may be provided to the user as a notification or alert, such as a text message, email, or page, prompting the user to take appropriate medical action, if any. In some cases, the output of the trend is stored in a storage location for subsequent retrieval and use. The output may be stored on a remote computer system, transmitted back to one or more POC devices over a network, or transmitted back to one or more other remote computer systems over a network.

Then, repeating the analysis using a plurality of second subjects, which may be the same plurality of subjects as the first plurality of subjects analyzed; a group comprising at least a subset of the analyzed first plurality of subjects; or an entirely different group of subjects from the gulf of san francisco. Disease information is processed to derive trends that show even greater rates of disease progression, including an increase in the prevalence of streptococcus pneumoniae in the gulf of san francisco. This trend is output to one or more users as described above for further alerts and actions.

Example 4: generation of itineraries and risk assessment

A user in beijing, china accesses applications on his or her tablet computer. The application provides the user with a graphical user interface having a search bar in which the user can enter a string of keywords for use as a search query. The search bar is labeled "destination". The user enters the keyword "serenti" (Serengeti). The tablet computer automatically determines the location of the user using multilateration of wireless signals between a plurality of base stations of a data network to which the tablet computer is connected. The tablet computer then transmits the keyword to a remote computer system over a data network along with the geographic location of the user, whereby the remote computer system receives the keyword over the internet connected to the data network. The remote computer system processes the keyword and the user's geographic location with the aid of its computer processor and identifies the geographic location tags "beijing china" and "serendian National Park" (tanezania National Park) as tags that can be used to search for electronic maps stored in the remote computer system memory.

Using the aforementioned geo-location tag, the computer processor identifies a route from the origin "beijing of china" to the destination "tamangya serendi national park" that includes three route points, namely UAE (united cacique of arabia) abbabi (Abu Dhabi), tamangya darussa (Dar es Salaam, Tanzania) and tamangia serendi national park. The computer processor then searches the disease database using these three routes and identifies "tanzania darusssalam" as being related to disease progression information about the Zika virus epidemic. Based on the disease progression information, the computer processor determines that there is a high incidence of a Zika virus epidemic recently in Texas, darussalam, and that the probability of resolution of the Zika virus epidemic in the near future is low. The computer processor also calculates a risk assessment indicating a relatively high risk of infecting the Zika virus by the user. Based on the risk assessment, the computer processor recalculates travel costs associated with tamsang ni darussalam, thereby optimizing the electronic map.

The computer processor then identifies a second route from the origin "beijing china" to the destination "tamansania zerland national park" using the optimized electronic map. This second route contains four route points, namely hong kong, UAE debye (Dubai, UAE), kennevrio (Nairobi, Kenya) and tanzania serendi national park. The computer processor then searches the disease database using these four route points and identifies "kenneyrobi" as being relevant to disease progression information regarding AIDS. Based on the disease progression information, the computer processor calculates a risk assessment indicating a low risk of the user contracting AIDS. Based on the risk assessment, the computer processor recalculates travel costs associated with the kennevrio to optimize the electronic map.

The computer processor then attempts to identify a third route from the origin "beijing of china" to the destination "tamangonia celencuti national park" using the optimized electronic map. The third route is identical to the second route. The computer then determines that no further optimization is required and that this third route is the best route.

The computer processor then generates a trip based on the optimal route. The computer processor further calculates a risk assessment including a quantitative score indicating a low risk of the user contracting AIDS along the route. The itinerary and risk assessment are transmitted back to the tablet computer over a data network, where the application displays them to the user on the screen of the tablet computer.

Example 5: generation of itineraries and risk assessment

A group of visitors was trapped in resort a on island B during the prevalence of a certain disease C. Guests would like to arrive at a unique airport D on island B to exit the island. A guest accesses an application on his or her laptop. The application provides the user with a graphical user interface having at least two search bars in which the user can enter a string of keywords for use as a search query. One search bar is labeled as "origin" and the other search bar is labeled as "destination". The user enters the keyword "vacation village a" in the first search field and the keyword "airport D" in the second search field. The notebook computer transmits the keyword to a remote computer system through a wired network connected to the internet, whereby the remote computer system receives the keyword. The remote computer system processes the keyword with the aid of its computer processor and identifies geographic location tags associated with vacation village a and airport D that can be used to search an electronic map stored in the remote computer system memory.

Using the aforementioned geographic location tags, the computer processor identifies the five routes (E1-E5) from the origin "resort village A" to the destination "airport D" with the lowest total travel costs. E1 to E3 all relate to arriving first at long distance bus stop F and arriving at airport D on a different bus route. E4 relates to arrival at port G and boarding to airport D. E5 relates to walking to a train station H near the resort village a and riding on a train to a bus station I near the airport D and riding on a bus to the airport.

The computer processor then searches the disease database using route points F through I as previously described and identifies all as relevant to disease progression information for disease C. Based on the disease progression information, the computer processor calculates an assessment of risk of contracting disease C at each of the route points, taking into account the mode of transportation to and from the route points. Based on the risk assessment, the computer processor recalculates the travel costs associated with each route point, thereby optimizing the electronic map.

The computer processor then identifies five new routes (J1-J5) from the origin "resort village A" to the destination "airport D" with the lowest total travel costs using the optimized electronic map. All five new routes (J1 to J5) involve arriving at limousine company K, and renting limousines to airport D. The computer processor searches the disease database using route point K and identifies it as being related to disease progression information for disease C. Based on the disease progression information, the computer processor calculates an assessment of risk of contracting disease C at each of the route points, taking into account the mode of transportation to and from the route points. Based on the risk assessment, the computer processor recalculates the travel cost associated with route point K, thereby optimizing the electronic map.

The computer processor then repeats this process several times, resulting in five routes (L1-L5) from the origin "resort village A" to the destination "airport D" with the lowest total travel costs, while further repeating the process does not identify any new waypoints as being relevant to any disease progression information. However, the computer processor determines that none of the travel costs incurred by the five routes (L1-L5) are below the predetermined threshold for travel costs.

The computer processor then determines, based on search patterns from other users stored in the remote computer system memory, that there is a high probability that the user is aiming to find a way to leave the island. The computer processor then determines that the ferry M may also serve the user's purpose. The computer processor then uses the ferry M as the destination and repeats the foregoing process several times and identifies the five routes from the origin "resort village a" to the destination "ferry M" with the lowest total travel costs (N1-N5).

The computer processor then generates a trip based on each of the five routes. The computer processor further calculates a risk assessment including a quantitative score indicating the user's risk of contracting disease C along each route. The itinerary and risk assessment are transmitted back to the laptop over a wired network along with two notifications, one advising the user to avoid going to airport D, the other advising the user to go to ferry M, where the application displays it to the user on the screen of the laptop for the user to select between the five itineraries.

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited to the specific examples provided in the specification. While the invention has been described with reference to the foregoing specification, the descriptions and illustrations of the embodiments herein should not be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Further, it is to be understood that all aspects of the present invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the present invention shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims (228)

1. A method of providing a user with an assessment of risk of contracting at least one disease, comprising:

(a) receiving a search query of a user over a network, the search query including information related to at least any two of an identity, a geographic location, and a physiological state of the user;

(b) processing, with the aid of a computer processor, the search query to identify one or more tags that are useful for searching in a disease database, wherein the disease database comprises (i) an indication of the at least one disease, (ii) disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations, (iii) subject information selected from two or more of an identity, geographic location, and physiological state of each of a plurality of subjects, and (iv) one or more associations between the at least one disease, disease progression information, and subject information;

(c) searching the disease database using the one or more tags to identify the at least one disease and the disease progression information; and

(d) providing the user with the assessment of risk of contracting the at least one disease based on the disease progression information.

2. The method of claim 1, wherein the assessment of the risk of contracting the at least one disease is provided to the user on a graphical user interface on an electronic display of an electronic device.

3. The method of claim 2, wherein the electronic device is a portable electronic device.

4. The method of claim 2, wherein the graphical user interface is provided by a mobile computer application.

5. The method of claim 1, wherein the information relates to the identity, geographic location, and physiological state of the user.

6. The method of claim 1, wherein the evaluation is provided via a notification or alert on the network.

7. The method of claim 1, wherein providing the user with the assessment comprises providing the user with one or more suggested precautions to reduce the rate of progression of the at least one disease at the geographic location.

8. The method of claim 1, wherein the indication of the at least one disease comprises identification information of at least one virus, at least one bacterium, and/or at least one protozoan.

9. The method of claim 8, wherein the at least one virus is selected from human immunodeficiency virus I (HIV I), human immunodeficiency virus II (HIV II), orthomyxovirus, Ebola virus, dengue virus, influenza virus, hepatitis A virus, hepatitis B virus, hepatitis C virus, hepatitis D virus, hepatitis E virus, hepatitis G virus, EB virus, mononucleosis virus, cytomegalovirus, SARS virus, West Nile fever virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, Zika virus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus, Rift valley fever virus, chikungunya fever virus, enterovirus, coxsackievirus and norovirus.

10. The method of claim 8, wherein the at least one bacterium is selected from the species Bordetella pertussis, Chlamydia pneumoniae, Chlamydia trachomatis, Campylobacter jejuni, helicobacter pylori, Borrelia bacteria, Mycoplasma pneumoniae, Mycobacterium tuberculosis, Haemophilus influenzae, Streptococcus pyogenes, Streptococcus pneumoniae, Clostridium tetani, Treponema pallidum, Trypanosoma cruzi, Toxoplasma gondii, Yersinia pestis, and Salmonella.

11. The method of claim 8, wherein the at least one protozoan is selected from the group consisting of plasmodium and leishmania donovani.

12. The method of claim 1, wherein the identity comprises at least one of a name, an age, and a gender of the user.

13. The method of claim 1, wherein the physiological state comprises at least one of heart rate, blood pressure, cough frequency, cough intensity, sneeze frequency, sneeze intensity, chest tightness level, nasal congestion level, body temperature, sweat level, weight, height, respiratory rate, blood pressure, nerve conduction velocity, lung volume, urine production rate, defecation frequency, presence of swollen lymph nodes, and biochemical spectrum of bodily fluids of the user.

14. The method of claim 1, wherein the geographic location is a continent, an island, a archipelago, a city/town/village, a county/county, a municipality/county, a district/religion, a province, a state/nation, a region, an administrative district, a country, and/or a group of countries.

15. The method of claim 13, wherein the geographic location is an area within the continent, the island, the archipelago, the city/town/village, the county/county, the grade city/county, the district/religion, the province, the state/nation, the region, the administrative district, the country, and/or the set of countries.

16. A method for monitoring at least one disease in a subject, comprising:

(a) processing biological samples obtained directly from the subject at a plurality of time points to (i) identify one or more biomarkers in the biological samples, and (ii) obtain quantitative measurements of at least a subset of the one or more biomarkers at the plurality of time points, wherein each of the one or more biomarkers is indicative of the presence of the at least one disease in the subject, wherein the processing is performed with nucleic acid amplification of each of the biological samples having a sample volume of less than or equal to about 1 milliliter (mL) and a time period of less than or equal to about 10 minutes;

(b) processing the quantitative measurements with the aid of a computer processor to determine disease information indicative of progression or regression of the at least one disease in the subject; and

(c) generating an output of the disease information.

17. The method of claim 16, wherein each of the biological samples is obtained directly from the subject and processed without subjecting the biological samples to purification to isolate the one or more biomarkers.

18. The method of claim 16, wherein the at least one disease is monitored at a fixed geographic location.

19. The method of claim 16, wherein the biological sample comprises whole blood.

20. The method of claim 16, wherein the biological sample comprises saliva.

21. The method of claim 16, wherein the biological sample comprises urine.

22. The method of claim 16, wherein the biological sample comprises sweat.

23. The method of claim 16, wherein the biological sample is processed without extracting nucleic acids from the biological sample.

24. The method of claim 16, wherein the nucleic acid amplification comprises Polymerase Chain Reaction (PCR).

25. The method of claim 16, wherein the nucleic acid amplification comprises reverse transcription polymerase chain reaction (RT-PCR).

26. The method of claim 16, wherein said processing said biological sample comprises (i) providing a reaction vessel comprising a given biological sample of said biological sample and reagents necessary to perform nucleic acid amplification, and (ii) subjecting said given biological sample to nucleic acid amplification under conditions sufficient to produce an amplification product indicative of the presence of said one or more biomarkers.

27. The method of claim 26, wherein the reagent comprises a polymerase.

28. The method of claim 26, wherein the reagent comprises one or more primers having a sequence complementary to the one or more biomarkers.

29. The method of claim 26, wherein the nucleic acid amplification comprises reverse transcription performed in parallel with deoxyribonucleic acid (DNA) amplification, and wherein the reagents comprise (i) a reverse transcriptase, (ii) a DNA polymerase, and (iii) a primer set of ribonucleic acid (RNA) indicative of the at least one disease.

30. The method of claim 16, wherein processing the quantitative measurements comprises comparing the quantitative measurements for the plurality of time points to a reference to identify the progression or regression of the at least one disease in the subject.

31. The method of claim 16, wherein the one or more biomarkers comprise a nucleic acid.

32. The method of claim 31, wherein the nucleic acid is derived from a virus.

33. The method of claim 32, wherein the virus is selected from human immunodeficiency virus i (hiv i), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, EB virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, zikayavirus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus (huang fever virus), Rift valley fever virus, chikungunya fever virus, enterovirus, coxsackievirus and norovirus.

34. The method of claim 31, wherein the nucleic acid is derived from a bacterium.

35. The method of claim 34, wherein the bacteria is selected from the group consisting of bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, haemophilus influenzae, streptococcus pneumoniae, streptococcus pyogenes, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii, yersinia pestis, and salmonella species.

36. The method of claim 31, wherein the nucleic acid is derived from a protozoan.

37. The method of claim 36, wherein the protozoan is selected from the group consisting of plasmodium and leishmania donovani.

38. The method of claim 16, wherein each of the biological samples is processed in a time period of less than or equal to about 5 minutes.

39. The method of claim 38, wherein each of the biological samples is processed in a time period of less than or equal to about 2 minutes.

40. The method of claim 39, wherein each of the biological samples is processed in a time period of less than or equal to about 1 minute.

41. The method of claim 40, wherein each of the biological samples is processed over a time period of less than or equal to about 0.5 minutes.

42. The method of claim 16, wherein the sample volume is less than or equal to about 0.5 mL.

43. The method of claim 42, wherein the sample volume is less than or equal to about 0.1 mL.

44. The method of claim 43, wherein the sample volume is less than or equal to about 0.01 mL.

45. The method of claim 16, wherein generating the output in (c) comprises providing the disease information to a user on a graphical user interface of an electronic display.

46. The method of claim 45, wherein the graphical user interface is provided by a mobile computer application.

47. The method of claim 46, wherein the user is the subject.

48. The method of claim 46, wherein the user is a healthcare professional.

49. The method of claim 16, wherein generating the output in (c) comprises transmitting the disease information to a remote data storage unit.

50. The method of claim 16, further comprising providing a questionnaire to the subject to assess the geographic location and/or physiological state of the subject; and identifying the at least one disease from the results of the questionnaire.

51. The method of claim 50, wherein the questionnaire is provided to the subject on a user interface of an electronic device.

52. The method of claim 51, wherein the user interface is provided by a mobile computer application.

53. The method of claim 50, further comprising obtaining an association between the results of the questionnaire and the at least one disease.

54. A method for monitoring at least one disease, comprising:

(a) receiving disease information for each of a plurality of subjects over a network, wherein for a given subject in the plurality of subjects, the disease information is generated by:

i. processing biological samples obtained directly from the given subject at a plurality of time points to identify one or more biomarkers in the biological samples, wherein each of the one or more biomarkers is indicative of the presence of the at least one disease in the given subject, and wherein the processing is performed with nucleic acid amplification of each of the biological samples having a sample volume of less than or equal to about 1 milliliter (mL) and a time period of less than about 10 minutes;

obtaining quantitative measurements of at least a subset of the one or more biomarkers at the plurality of time points; and

processing the quantitative measurements with the aid of a computer processor to determine the disease information, wherein the disease information is indicative of the progression or regression of the at least one disease of the given subject;

(b) gathering the disease information in a storage location;

(c) processing the disease information aggregated in (b) to identify trends in the disease (i) at a given geographic location or (ii) across multiple geographic locations; and

(d) generating an output indicative of the trend.

55. The method of claim 54, wherein each of the biological samples is obtained directly from the subject and processed without subjecting the biological sample to purification to isolate the one or more biomarkers.

56. The method of claim 54, wherein the biological sample comprises whole blood.

57. The method of claim 54, wherein the biological sample comprises saliva.

58. The method of claim 54, wherein the biological sample comprises urine.

59. The method of claim 54, wherein the biological sample comprises sweat.

60. The method of claim 54, wherein the biological sample is processed without extracting nucleic acids from the biological sample.

61. The method of claim 54, wherein the nucleic acid amplification comprises Polymerase Chain Reaction (PCR).

62. The method of claim 54, wherein the nucleic acid amplification comprises reverse transcription polymerase chain reaction (RT-PCR).

63. The method of claim 54, wherein said processing said biological sample comprises (i) providing a reaction vessel comprising a given biological sample of said biological sample and reagents necessary to perform nucleic acid amplification, and (ii) subjecting said given biological sample to nucleic acid amplification under conditions sufficient to produce an amplification product indicative of the presence of said one or more biomarkers.

64. The method of claim 63, wherein the reagent comprises a polymerase.

65. The method of claim 63, wherein the reagents comprise one or more primers having sequences complementary to the one or more biomarkers.

66. The method of claim 63, wherein the nucleic acid amplification comprises reverse transcription performed in parallel with deoxyribonucleic acid (DNA) amplification, and wherein the reagents comprise (i) a reverse transcriptase, (ii) a DNA polymerase, and (iii) a primer set of ribonucleic acid (RNA) indicative of the at least one disease.

67. The method of claim 54, wherein processing the quantitative measurements comprises comparing the quantitative measurements for the plurality of time points to a reference to identify the progression or regression of the at least one disease in the subject.

68. The method of claim 54, wherein the one or more biomarkers comprise a nucleic acid.

69. The method of claim 68, wherein the nucleic acid is derived from a virus.

70. The method of claim 69, wherein the virus is selected from human immunodeficiency virus I (HIV I), human immunodeficiency virus II (HIV II), orthomyxovirus, Ebola virus, dengue virus, influenza virus, hepatitis A virus, hepatitis B virus, hepatitis C virus, hepatitis D virus, hepatitis E virus, hepatitis G virus, EB virus, mononucleosis virus, cytomegalovirus, SARS virus, West Nile fever virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, Zika virus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus, Rift valley fever virus, chikungunya fever virus, enterovirus, coxsackievirus and norovirus.

71. The method of claim 68, wherein the nucleic acid is derived from a bacterium.

72. The method of claim 71, wherein the bacteria is selected from the group consisting of Bordetella pertussis, Chlamydia pneumoniae, Chlamydia trachomatis, Campylobacter jejuni, helicobacter pylori, Haemophilus influenzae, Borrelia bacteria, Mycoplasma pneumoniae, Mycobacterium tuberculosis, Streptococcus pneumoniae, Streptococcus pyogenes, Clostridium tetani, Treponema pallidum, Trypanosoma cruzi, Toxoplasma gondii, Yersinia pestis, and Salmonella species.

73. The method of claim 68, wherein the nucleic acid is derived from a protozoan.

74. The method of claim 73, wherein said protozoa is selected from the group consisting of Plasmodium and Leishmania donovani.

75. The method of claim 54, wherein each of the biological samples is processed in a time period of less than or equal to about 5 minutes.

76. The method of claim 75, wherein each of the biological samples is processed in a time period of less than or equal to about 2 minutes.

77. The method of claim 76, wherein each of the biological samples is processed in a time period of less than or equal to about 1 minute.

78. The method of claim 77, wherein each of said biological samples is processed over a time period of less than or equal to about 0.5 minutes.

79. The method of claim 54, wherein the sample volume is less than or equal to about 0.5 mL.

80. The method of claim 79, wherein the sample volume is less than or equal to about 0.1 mL.

81. The method of claim 80, wherein the sample volume is less than or equal to about 0.01 mL.

82. The method of claim 54, wherein generating the output in (d) comprises providing the trend to a user on a graphical user interface of an electronic display.

83. The method of claim 82, wherein the graphical user interface is provided by a mobile computer application.

84. The method of claim 82, wherein the user is a given subject in the plurality of subjects.

85. The method of claim 82, wherein the user is a healthcare professional.

86. The method of claim 54, wherein generating the output in (d) comprises storing the trend in a storage location.

87. The method of claim 54, wherein generating the output in (d) comprises providing a notification or warning to a user about the trend.

88. The method of claim 54, wherein the biological sample is processed on a designated point of care device of a plurality of point of care devices.

89. The method of claim 54, wherein generating the output in (d) comprises providing an update regarding the trend.

90. The method of claim 89, wherein said updating indicates an increased prevalence of said at least one disease.

91. The method of claim 89, wherein said updating indicates a decreased prevalence of said at least one disease.

92. The method of claim 54, wherein the trend of the disease is in a given geographic location.

93. The method of claim 92, wherein each of the plurality of subjects is located at the given geographic location.

94. The method of claim 54, wherein the trend of the disease is across multiple geographic locations.

95. The method of claim 94, wherein each of said plurality of subjects is located at a given geographic location of said plurality of geographic locations.

96. A non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, implements a method for providing a user with an assessment of risk of contracting at least one disease, the method comprising:

(a) receiving a search query of a user over a network, the search query including information related to at least any two of an identity, a geographic location, and a physiological state of the user;

(b) processing, with the aid of a computer processor, the search query to identify one or more tags that are useful for searching in a disease database, wherein the disease database comprises (i) an indication of the at least one disease, (ii) disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations, (iii) subject information selected from two or more of an identity, geographic location, and physiological state of each of a plurality of subjects, and (iv) one or more associations between the at least one disease, disease progression information, and subject information;

(c) searching the disease database using the one or more tags to identify the at least one disease and the disease progression information; and

(d) providing the user with the assessment of the risk of contracting the at least one disease based on the disease progression information.

97. A non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, implements a method for providing a user with an assessment of risk of contracting at least one disease, the method comprising:

(a) processing biological samples obtained directly from the subject at a plurality of time points to (i) identify one or more biomarkers in the biological samples, and (ii) obtain quantitative measurements of at least a subset of the one or more biomarkers at the plurality of time points, wherein each of the one or more biomarkers is indicative of the presence of the at least one disease in the subject, wherein the processing is performed with nucleic acid amplification of each of the biological samples having a sample volume of less than or equal to about 1 milliliter (mL) and a time period of less than or equal to about 10 minutes;

(b) processing the quantitative measurements with the aid of a computer processor to determine disease information indicative of progression or regression of the at least one disease in the subject; and

(c) generating an output of the disease information.

98. A non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, implements a method for providing a user with an assessment of risk of contracting at least one disease, the method comprising:

(a) receiving disease information for each of a plurality of subjects over a network, wherein for a given subject in the plurality of subjects, the disease information is generated by:

i. processing biological samples obtained directly from the given subject at a plurality of time points to identify one or more biomarkers in the biological samples, wherein each of the one or more biomarkers is indicative of the presence of the at least one disease in the given subject, and wherein the processing is performed with nucleic acid amplification of each of the biological samples having a sample volume of less than or equal to about 1 milliliter (mL) and a time period of less than about 10 minutes;

obtaining quantitative measurements of at least a subset of the one or more biomarkers at the plurality of time points; and

processing the quantitative measurements with the aid of a computer processor to determine the disease information, wherein the disease information is indicative of the progression or regression of the at least one disease of the given subject;

(b) gathering the disease information in a storage location;

(c) processing the disease information aggregated in (b) to identify trends in the disease (i) at a given geographic location or (ii) across multiple geographic locations; and

(d) generating an output indicative of the trend.

99. A method of providing a user with an assessment of risk of contracting at least one disease, comprising:

(a) receiving a search query of a user over a network, the search query including information about a destination and optionally one or more route points;

(b) processing the search query with the aid of a computer processor to identify one or more geo-location tags associated with the destination and optionally one or more route points for searching in a disease database, wherein the disease database contains disease progression information indicating progression or regression of the at least one disease at one or more geo-locations including the destination;

(c) searching the disease database using the one or more geo-location tags to identify the at least one disease and the disease progression information; and

(d) providing the user with the assessment of the risk of contracting the at least one disease at the destination and optionally the one or more pathway points based on the disease progression information identified in (c).

100. The method of claim 99, wherein the assessment of the risk of contracting the at least one disease is provided to the user on a graphical user interface on an electronic display of an electronic device.

101. The method of claim 100, wherein the electronic device is a portable electronic device.

102. The method of claim 100, wherein the graphical user interface is provided by a mobile computer application.

103. The method of claim 99, wherein the search query further comprises an identity and/or a physiological state of the user.

104. The method of claim 99, wherein the search query includes a place of departure for the user.

105. The method of claim 99, wherein the evaluation is provided via a notification or alert on the network.

106. The method of claim 99, wherein providing the user with the assessment includes providing the user with one or more suggested precautions to reduce the rate of progression of the at least one disease at the destination and/or route point.

107. The method of claim 99, wherein providing the user with the assessment comprises suggesting that the user avoid proceeding to the destination.

108. The method of claim 99, wherein providing the user with the assessment includes suggesting that the user avoid passing at least one of the one or more route points.

109. The method of claim 99, wherein providing the user with the assessment comprises suggesting that the user travel to a different destination.

110. The method of claim 99, wherein the database further comprises indications of the at least one disease.

111. The method of claim 110, wherein the indication of the at least one disease comprises identification information of at least one virus, at least one bacterium, and/or at least one protozoan.

112. The method of claim 111, wherein the at least one virus is selected from human immunodeficiency virus i (hivi), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, EB virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, zika virus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus, Rift valley fever virus, chikungunya fever virus, enterovirus, coxsackievirus and norovirus.

113. The method of claim 111, wherein the at least one bacterium is selected from the species bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, haemophilus influenzae, streptococcus pyogenes, streptococcus pneumoniae, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii, yersinia pestis, and salmonella.

114. The method of claim 111, wherein the at least one protozoan is selected from the group consisting of plasmodium and leishmania donovani.

115. The method of claim 103, wherein the identity comprises at least one of a name, an age, and a gender of the user.

116. The method of claim 103, wherein the physiological state comprises at least one of heart rate, blood pressure, cough frequency, cough intensity, sneeze frequency, sneeze intensity, chest tightness level, nasal congestion level, body temperature, sweat level, weight, height, respiratory rate, blood pressure, nerve conduction velocity, lung volume, urine production rate, defecation frequency, presence of swollen lymph nodes, and biochemical spectrum of bodily fluids of the user.

117. The method of claim 99, further comprising providing a total risk of contracting the at least one disease via the route points to the destination.

118. The method of claim 99, wherein the search query further includes information about a trip to the destination via the route points.

119. A method according to claim 118 wherein the itinerary includes a time to reach, a time to leave and/or a time to stop at each route point or destination.

120. The method of claim 119, wherein providing the user with the assessment of the risk of contracting the at least one disease in (d) further comprises taking into account the itinerary.

121. A method of providing a user with an assessment of risk of contracting at least one disease, comprising:

(a) receiving a search query of a user through a network, the search query including information about a departure place and a destination selected by the user;

(b) processing the search query with the aid of a computer processor and a travel cost data structure to (i) identify a route from the origin to the destination within the travel cost data structure, and (ii) determine one or more route points along the route, wherein the one or more route points include at least the origin and the destination, and wherein the travel cost data structure contains travel costs between a geographic location and an adjacent geographic location;

(c) searching a disease database containing disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations including the destination and/or the one or more route points using the one or more route points to identify the at least one disease and the disease progression information; and

(d) providing the user with the assessment of the risk of infecting the at least one disease at the destination and/or along the route based on the disease progression information identified in (c).

122. The method of claim 121, wherein the travel cost comprises one or more selected from the group consisting of travel time, travel expense, travel comfort, dwell time, predictability, safety, punctuality, and combinations thereof.

123. The method of claim 122, wherein said travel cost includes two or more items selected from said group, the two or more items being a weighted combination.

124. The method of claim 121, wherein the assessment of the risk of contracting the at least one disease is provided to the user on a graphical user interface on an electronic display of an electronic device.

125. The method of claim 124, wherein the electronic device is a portable electronic device.

126. The method of claim 124, wherein the graphical user interface is provided by a mobile computer application.

127. The method of claim 121, wherein the search query further comprises an identity and/or a physiological state of the user.

128. The method of claim 121, wherein the evaluation is provided via a notification or an alert on the network.

129. The method of claim 121, wherein providing the user with the assessment includes providing the user with one or more suggested precautions to reduce the rate of progression of the at least one disease at the destination and/or route point.

130. The method of claim 121, wherein providing the user with the assessment includes suggesting that the user avoid proceeding to the destination.

131. The method of claim 121, wherein providing the user with the assessment includes suggesting that the user travel to a different destination.

132. The method of claim 121, wherein the travel cost data structure is a weighted graph that includes the geographic locations as vertices and the travel costs between neighboring geographic locations as weighted boundaries.

133. The method of claim 121, wherein the travel cost data structure is a table that includes geographic locations in columns and rows and the travel costs between adjacent geographic locations in a cell.

134. The method of claim 121, wherein said database further comprises indications of said at least one disease.

135. The method of claim 134, wherein said indication of said at least one disease comprises identification information of at least one virus, at least one bacterium, and/or at least one protozoan.

136. The method of claim 135, wherein the at least one virus is selected from human immunodeficiency virus i (hivi), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, EB virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, zika virus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus, Rift valley fever virus, chikungunya fever virus, enterovirus, coxsackievirus and norovirus.

137. The method of claim 135, wherein the at least one bacterium is selected from the group consisting of species of bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, haemophilus influenzae, streptococcus pyogenes, streptococcus pneumoniae, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii, yersinia pestis, and salmonella.

138. The method of claim 135, wherein the at least one protozoan is selected from the group consisting of plasmodium and leishmania donovani.

139. The method of claim 127, wherein the identity comprises at least one of a name, an age, and a gender of the user.

140. The method of claim 127, wherein the physiological state comprises at least one of heart rate, blood pressure, cough frequency, cough intensity, sneeze frequency, sneeze intensity, chest tightness level, nasal congestion level, body temperature, sweat level, weight, height, respiratory rate, blood pressure, nerve conduction velocity, lung volume, urine production rate, defecation frequency, presence of swollen lymph nodes, and biochemical spectrum of bodily fluid of the user.

141. The method of claim 121, further comprising providing a total risk of contracting the at least one disease via the route points to the destination.

142. The method of claim 121, further comprising creating a trip based on the route.

143. A method according to claim 142 wherein the itinerary includes a time to reach each route point or destination, a time to leave each route point or origin, and/or a time to stay at each route point.

144. The method of claim 143, wherein providing the user with the assessment of risk of contracting the at least one disease in (d) further comprises taking into account the itinerary.

145. The method of claim 121, wherein in (b) the route from the origin to the destination within the travel cost data structure is generated by employing a routing algorithm on the travel cost data structure.

146. The method of claim 145, wherein said routing algorithm is selected from the group consisting of a, Dijkstra, BFS, DFS, Greedy, and combinations thereof.

147. A method of providing a user with a trip to a destination, comprising:

(a) receiving a search query of a user through a network, the search query including information about a departure place and a destination selected by the user;

(b) processing the search query with the aid of a computer processor and a travel cost data structure to (i) identify a route from the origin to the destination within the travel cost data structure, and (ii) determine a plurality of route points along the route, wherein the plurality of route points includes at least the origin and the destination, and wherein the travel cost data structure contains travel costs between a geographic location and an adjacent geographic location;

(c) searching a disease database containing disease progression information indicating progression or regression of the at least one disease at one or more geographic locations using each of the plurality of route points to identify the at least one disease and the disease progression information associated with the route points in the plurality of route points;

(d) based on the disease progression information identified in (c), (i) determining a risk of contracting the at least one disease, and (ii) optimizing the travel cost data structure by adjusting travel costs between a geographic location associated with the route point and an adjacent geographic location based on the risk;

(e) repeating (b) through (d) as needed to generate an optimal route, wherein the optimal route reduces the risk of the infection with the at least one disease; and

(f) generating a trip for the user using the optimal route in (e).

148. A method according to claim 147 wherein the itinerary includes a time to reach each route point or the destination, a time to leave each route point or origin and/or a time to stay at each route point.

149. The method of claim 148, wherein determining the risk of contracting the at least one disease in (d) further comprises taking into account the journey.

150. The method of claim 147, wherein the travel cost comprises one or more selected from the group consisting of travel time, travel expense, travel comfort, dwell time, predictability, safety, punctuality, and combinations thereof.

151. The method of claim 150, wherein the travel cost includes two or more items selected from the group, the two or more items being a weighted combination.

152. The method of claim 147, wherein the itinerary is provided to the user on a graphical user interface on an electronic display of an electronic device.

153. The method of claim 152, wherein the electronic device is a portable electronic device.

154. The method of claim 152, wherein the graphical user interface is provided by a mobile computer application.

155. The method of claim 147, wherein the search query further comprises an identity and/or a physiological state of the user.

156. The method of claim 147, wherein the itinerary is provided via a notification or alert on the network.

157. The method of claim 147, wherein providing the user with the itinerary further comprises providing the user with an assessment of risk of contracting at least one disease.

158. The method of claim 157, wherein providing the user with the assessment includes providing the user with one or more suggested precautions to reduce the rate of progression of the at least one disease at the destination and/or route point.

159. The method of claim 157, wherein providing the user with the assessment comprises suggesting that the user avoid proceeding to the destination.

160. The method of claim 157, wherein providing the user with the assessment includes suggesting that the user travel to a different destination.

161. The method of claim 147, wherein the travel cost data structure is a weighted graph that includes the geographic locations as vertices and the travel costs between neighboring geographic locations as weighted boundaries.

162. The method of claim 147, wherein the travel cost data structure is a table that includes geographic locations in columns and rows and the travel costs between adjacent geographic locations in a cell.

163. The method of claim 147, wherein the database further comprises indications of the at least one disease.

164. The method of claim 163, wherein said indication of said at least one disease comprises identifying information for at least one virus, at least one bacterium, and/or at least one protozoan.

165. The method of claim 164, wherein the at least one virus is selected from human immunodeficiency virus i (hivi), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, EB virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, zika virus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus, Rift valley fever virus, chikungunya fever virus, enterovirus, coxsackievirus and norovirus.

166. The method according to claim 164, wherein the at least one bacterium is selected from the group consisting of species of bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, haemophilus influenzae, streptococcus pyogenes, streptococcus pneumoniae, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii, yersinia pestis and salmonella.

167. The method according to claim 164, wherein the at least one protozoan is selected from the group consisting of plasmodium and leishmania donovani.

168. The method of claim 155, wherein the identity comprises at least one of a name, an age, and a gender of the user.

169. The method of claim 155, wherein the physiological state comprises at least one of a heart rate, a blood pressure, a cough frequency, a cough intensity, a sneeze frequency, a sneeze intensity, a chest tightness level, a nasal obstruction level, a body temperature, a sweat level, a weight, a height, a breathing frequency, a blood pressure, a nerve conduction velocity, a lung volume, a urine production rate, a defecation frequency, a presence of swollen lymph nodes, and a biochemical spectrum of a bodily fluid of the user.

170. The method of claim 147, further comprising providing a total risk of contracting the at least one disease via the route points to the destination.

171. The method of claim 147 wherein in (b) the route from the origin to the destination within the travel cost data structure is generated by employing a routing algorithm on the travel cost data structure.

172. The method of claim 171, wherein said way-finding algorithm is selected from the group consisting of a, Dijkstra, BFS, DFS, Greedy, and combinations thereof.

173. A method of providing a user with a trip to a destination, comprising:

(a) receiving a search query of a user through a network, the search query including information about a departure place and a destination selected by the user;

(b) processing the search query with the aid of a computer processor and a travel cost data structure to (i) identify a plurality of routes leading from the origin to the destination within the travel cost data structure, and (ii) determine, for each of the plurality of routes, a plurality of via points along the route, wherein the plurality of via points includes at least the origin and the destination, and wherein the travel cost data structure contains travel costs between a geographic location and an adjacent geographic location;

(c) for each of the plurality of routes, conducting a search in a disease database containing disease progression information indicating progression or regression of the at least one disease at one or more geographic locations using each of the plurality of route points to identify the at least one disease and the disease progression information associated with the route point of the plurality of route points;

(d) based on the disease progression information identified in (c), for each of the plurality of routes, (i) determining a risk of contracting the at least one disease along the route, and (ii) optimizing the travel cost data structure by adjusting travel costs between a geographic location associated with the route point and a neighboring geographic location based on the risk;

(e) repeating (b) through (d) as needed to generate an optimal route, wherein the optimal route results in a lowest travel cost among the plurality of routes; and

(f) generating a trip for the user using the optimal route in (e).

174. A method according to claim 173 wherein the itinerary includes a time to reach, a time to leave and/or a time to stop at each route point or destination.

175. A method according to claim 174 wherein the itinerary includes a time to reach, a time to leave and/or a time to stop at each route point or destination.

176. The method of claim 175, wherein determining the risk of infecting the at least one disease in (d) further comprises taking into account the journey.

177. The method of claim 173, wherein the travel cost comprises one or more selected from the group consisting of travel time, travel expense, travel comfort, dwell time, predictability, safety, punctuality, and combinations thereof.

178. The method of claim 177, wherein the travel cost includes two or more items selected from the group, the two or more items being a weighted combination.

179. The method of claim 173, wherein the itinerary is provided to the user on a graphical user interface on an electronic display of an electronic device.

180. The method of claim 179, wherein the electronic device is a portable electronic device.

181. The method of claim 179, wherein the graphical user interface is provided by a mobile computer application.

182. The method of claim 173, wherein the search query further includes an identity and/or a physiological state of the user.

183. The method of claim 173, wherein the itinerary is provided via a notification or alert on the network.

184. The method of claim 173, wherein providing the user with the itinerary further comprises providing the user with an assessment of risk of contracting at least one disease.

185. The method of claim 184, wherein providing the user with the assessment comprises providing the user with one or more suggested precautions to reduce the rate of progression of the at least one disease at the destination and/or route point.

186. The method of claim 184, wherein providing the user with the assessment comprises suggesting that the user avoid proceeding to the destination.

187. The method of claim 184, wherein providing the user with the assessment includes suggesting that the user travel to a different destination.

188. The method of claim 173, wherein the travel cost data structure is a weighted graph that includes the geographic locations as vertices and the travel costs between neighboring geographic locations as weighted boundaries.

189. The method of claim 173, wherein the travel cost data structure is a table that includes geographic locations in columns and rows and the travel costs between adjacent geographic locations in a cell.

190. The method of claim 173, wherein the database further comprises indications of the at least one disease.

191. The method of claim 190, wherein the indication of the at least one disease comprises identification information of at least one virus, at least one bacterium, and/or at least one protozoan.

192. The method of claim 191, wherein the at least one virus is selected from human immunodeficiency virus i (hivi), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, EB virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, zika virus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus, Rift valley fever virus, chikungunya fever virus, enterovirus, coxsackievirus and norovirus.

193. The method of claim 191, wherein the at least one bacterium is selected from the group consisting of bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, haemophilus influenzae, streptococcus pyogenes, streptococcus pneumoniae, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii, yersinia pestis, and salmonella.

194. The method of claim 191, wherein the at least one protozoan is selected from the group consisting of plasmodium and leishmania donovani.

195. The method of claim 182, wherein the identity comprises at least one of a name, an age, and a gender of the user.

196. The method of claim 182, wherein the physiological state comprises at least one of heart rate, blood pressure, cough frequency, cough intensity, sneeze frequency, sneeze intensity, chest tightness level, nasal congestion level, body temperature, sweat level, weight, height, respiratory rate, blood pressure, nerve conduction velocity, lung volume, urine production rate, defecation frequency, presence of swollen lymph nodes, and biochemical spectrum of bodily fluid of the user.

197. The method of claim 173 further comprising providing a total risk of contracting the at least one disease via travel points to the destination.

198. The method of claim 173 wherein in (b) the route from the origin to the destination within the travel cost data structure is generated by employing a routing algorithm on the travel cost data structure.

199. The method of claim 198, wherein said routing algorithm is selected from the group consisting of a, Dijkstra, BFS, DFS, Greedy, and combinations thereof.

200. A method for optimizing a travel cost data structure including a plurality of geographic locations and travel cost data structures between adjacent geographic locations, comprising:

(a) using each of the plurality of geographic locations to search in a disease database to identify at least one disease and disease progression information associated with geographic locations of the at least plurality of geographic locations, the disease database comprising disease progression information indicative of progression or regression of the at least one disease at one or more geographic locations;

(b) based on the at least one disease and disease progression information identified in (a), (i) determining a risk of contracting the at least one disease, and (ii) optimizing the travel cost data structure by adjusting travel costs between the each and all of the plurality of geographic locations based on the risk; and

(c) repeating (a) through (b) until all of the plurality of geographic locations are traversed, thereby optimizing the travel cost data structure.

201. The method of claim 200, wherein the travel cost data structure is a weighted graph that includes the geographic locations as vertices and the travel costs between neighboring geographic locations as weighted boundaries.

202. The method of claim 200, wherein the travel cost data structure is a table that includes geographic locations in columns and rows and the travel costs between adjacent geographic locations in a cell.

203. The method of claim 200, wherein the travel cost comprises one or more selected from the group consisting of travel time, travel expense, travel comfort, dwell time, predictability, safety, punctuality, and combinations thereof.

204. The method of claim 200, wherein the travel cost includes two or more items selected from the group, the two or more items being a weighted combination.

205. A method of providing a user with a trip to a destination using a travel cost data structure optimized according to any one of claims 200-204, comprising:

i. receiving a search query of a user through a network, the search query including information about a departure place and a destination selected by the user;

processing, with the aid of a computer processor and the optimized travel cost data structure, the search query to identify an optimal route from the origin to the destination within the travel cost data structure; and

generating a trip for the user using the optimal route in ii.

206. The method of claim 205, wherein the itinerary includes a time to reach, a time to leave, and/or a time to stop at each route point or the destination.

207. The method of claim 205, wherein in (b), the route from the origin to the destination within the travel cost data structure is generated by employing a routing algorithm on the travel cost data structure.

208. The method of claim 207, wherein said routing algorithm is selected from the group consisting of a, Dijkstra, BFS, DFS, Greedy, and combinations thereof.

209. The method of claim 205, wherein the itinerary is provided to the user on a graphical user interface on an electronic display of an electronic device.

210. The method of claim 209, wherein the electronic device is a portable electronic device.

211. The method of claim 209, wherein the graphical user interface is provided by a mobile computer application.

212. The method of claim 205, wherein the search query further includes an identity and/or a physiological state of the user.

213. The method of claim 205, wherein the itinerary is provided via a notification or alert on the network.

214. The method of claim 205, wherein providing the user with the itinerary further comprises providing the user with an assessment of risk of contracting at least one disease.

215. The method of claim 214, further comprising determining one or more route points along the optimal route, wherein the one or more route points include at least the origin point and the destination point.

216. The method of claim 215, further comprising

(a) Searching a disease database containing disease progression information indicating progression or regression of the at least one disease at one or more geographic locations including the destination using each of the one or more route points to identify the at least one disease and the disease progression information; and

(b) providing the user with the assessment of the risk of infecting the at least one disease at the destination or along the route based on the disease progression information identified in (a).

217. The method of claim 216, wherein providing the user with the assessment of the risk of contracting the at least one disease in (b) further comprises taking into account the itinerary.

218. The method of claim 216, further comprising providing a total risk of contracting the at least one disease via the route points to the destination.

219. The method of claim 214, wherein providing the user with the assessment includes providing the user with one or more suggested precautions to reduce the rate of progression of the at least one disease at the destination and/or route point.

220. The method of claim 214, wherein providing the user with the assessment comprises suggesting that the user avoid proceeding to the destination.

221. The method of claim 214, wherein providing the user with the assessment includes suggesting that the user travel to a different destination.

222. The method of claim 216, wherein the database further comprises indications of the at least one disease.

223. The method of claim 222, wherein the indication of the at least one disease comprises identification information of at least one virus, at least one bacterium, and/or at least one protozoan.

224. The method of claim 223, wherein the at least one virus is selected from human immunodeficiency virus i (hivi), human immunodeficiency virus ii (hiv ii), orthomyxovirus, ebola virus, dengue virus, influenza virus, hepatitis a virus, hepatitis b virus, hepatitis c virus, hepatitis d virus, hepatitis e virus, hepatitis g virus, EB virus, mononucleosis virus, cytomegalovirus, SARS virus, west nile virus, poliovirus, measles virus, herpes simplex virus, smallpox virus, adenovirus, varicella zoster virus, Human Papilloma Virus (HPV), human T cell leukemia virus (HTLV), mumps virus, Respiratory Syncytial Virus (RSV), parainfluenza virus, rubella virus, zika virus, Middle East Respiratory Syndrome (MERS) virus, yellow fever virus, Rift valley fever virus, chikungunya fever virus, enterovirus, coxsackievirus and norovirus.

225. The method of claim 223, wherein the at least one bacterium is selected from the group consisting of bordetella pertussis, chlamydia pneumoniae, chlamydia trachomatis, campylobacter jejuni, helicobacter pylori, borrelia bacteria, mycoplasma pneumoniae, mycobacterium tuberculosis, haemophilus influenzae, streptococcus pyogenes, streptococcus pneumoniae, clostridium tetani, treponema pallidum, trypanosoma cruzi, toxoplasma gondii, yersinia pestis, and salmonella.

226. The method of claim 223, wherein the at least one protozoan is selected from the group consisting of plasmodium and leishmania donovani.

227. The method of claim 212, wherein the identity comprises at least one of a name, an age, and a gender of the user.

228. The method of claim 212, wherein the physiological state comprises at least one of heart rate, blood pressure, cough frequency, cough intensity, sneeze frequency, sneeze intensity, chest tightness level, nasal congestion level, body temperature, sweat level, weight, height, respiratory rate, blood pressure, nerve conduction velocity, lung volume, urine production rate, defecation frequency, presence of swollen lymph nodes, and biochemical spectrum of bodily fluids of the user.