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 | 151 | 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 | 149 | 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 | 109 | 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 | 57 | 2020 |
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 | 38 | 2019 |
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 | 29 | 2021 |
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 | 14 | 2023 |
& 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 | 7 | 2004 |
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 | 6 | 2022 |
An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time (preprint) NE Kogan, L Clemente, P Liautaud, J Kaashoek, NB Link, AT Nguyen, ... | 5 | 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, ... medRxiv, 2023 | 3 | 2023 |
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 |
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 |
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 medRxiv, 2024 | | 2024 |
School of Engineering and Sciences CLC Lopez Google, 2019 | | 2019 |
Predicting Influenza in Latin America: Using Voting Ensembles to Combine Google Search Activity and Geo-spatial Synchronicities from Historical Flu Activity CL Clemente Lopez Instituto Tecnológico y de Estudios Superiores de Monterrey, 2019 | | 2019 |