| An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time NE Kogan, L Clemente, P Liautaud, J Kaashoek, NB Link, AT Nguyen, ... Science advances 7 (10), eabd6989, 2021 | 201 | 2021 |
| A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models D Liu, L Clemente, C Poirier, X Ding, M Chinazzi, JT Davis, A Vespignani, ... arXiv preprint arXiv:2004.04019, 2020 | 162 | 2020 |
| Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches FS Lu, MW Hattab, CL Clemente, M Biggerstaff, M Santillana Nature communications 10 (1), 147, 2019 | 138 | 2019 |
| Real-time forecasting of the COVID-19 outbreak in Chinese provinces: Machine learning approach using novel digital data and estimates from mechanistic models D Liu, L Clemente, C Poirier, X Ding, M Chinazzi, J Davis, A Vespignani, ... Journal of medical Internet research 22 (8), e20285, 2020 | 85 | 2020 |
| Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations SM Mathis, AE Webber, TM León, EL Murray, M Sun, LA White, LC Brooks, ... Nature communications 15 (1), 6289, 2024 | 61 | 2024 |
| Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States LM Stolerman, L Clemente, C Poirier, KV Parag, A Majumder, S Masyn, ... Science Advances 9 (3), eabq0199, 2023 | 53 | 2023 |
| A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles SF McGough, L Clemente, JN Kutz, M Santillana Journal of The Royal Society Interface 18 (179), 20201006, 2021 | 46 | 2021 |
| Improved real-time influenza surveillance: using internet search data in eight Latin American countries L Clemente, F Lu, M Santillana JMIR public health and surveillance 5 (2), e12214, 2019 | 42 | 2019 |
| Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil G Koplewitz, F Lu, L Clemente, C Buckee, M Santillana PLoS Neglected Tropical Diseases 16 (1), e0010071, 2022 | 16 | 2022 |
| Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations SM Mathis, AE Webber, TM Leon, EL Murray, M Sun, LA White, LC Brooks, ... Nature communications 15 (1), 2024 | 11 | 2024 |
| & Santillana, M.(2020) D Liu, L Clemente, C Poirier, X Ding, M Chinazzi, JT Davis A machine learning methodology for real-time forecasting of the 2019-2020 …, 2004 | 10 | 2004 |
| An early warning approach to monitor COVID-19 activity with multiple digital traces in near real-time. Sci Adv. 2021 NE Kogan, L Clemente, P Liautaud, J Kaashoek, NB Link, AT Nguyen, ... | 10 | |
| A machine learning methodology for real-time forecasting of the 2019–2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models. arXiv … D Liu, L Clemente, C Poirier, X Ding, M Chinazzi, JT Davis, M Santillana Available online at: á https://arxiv. org/abs/2004.04019 á (accessed May 6 …, 2020 | 9 | 2020 |
| Fine-grained forecasting of COVID-19 trends at the county level in the United States TH Song, L Clemente, X Pan, J Jang, M Santillana, K Lee NPJ Digital Medicine 8 (1), 204, 2025 | 8 | 2025 |
| A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data AG Meyer, F Lu, L Clemente, M Santillana Epidemics 50, 100816, 2025 | 8 | 2025 |
| Ensemble approaches for short-term dengue fever forecasts: A global evaluation study S Wu, AG Meyer, L Clemente, LM Stolerman, F Lu, A Majumder, ... Proceedings of the National Academy of Sciences 122 (33), e2422335122, 2025 | 2 | 2025 |
| Correction: real-time forecasting of the COVID-19 outbreak in chinese provinces: machine learning approach using novel digital data and estimates from mechanistic models D Liu, L Clemente, C Poirier, X Ding, M Chinazzi, J Davis, A Vespignani, ... J Med Internet Res 22 (9), e23996, 2020 | 2 | 2020 |
| Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches via ARGONet FS Lu, MW Hattab, L Clemente, M Santillana bioRxiv, 344580, 2018 | 2 | 2018 |
| A Prospective Real-time Early Warning System to Anticipate Onsets and Peaks of Respiratory Diseases Outbreaks at the State Level in the US A Transfer Learning Approach … R Garrido Garcia, L Clemente, A Meyer, G Dewey, S Yang, M Santillana medRxiv, 2025.10. 10.25337739, 2025 | 1 | 2025 |
| Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach SF McGough, CL Clemente, JN Kutz, M Santillana bioRxiv, 666628, 2019 | 1 | 2019 |
Mauricio SantillanaProfessor of Physics and Electr. and Comp. Eng., Northeastern University, and Harvard T. H. Chan SPHVerified email at northeastern.edu
