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Arxiv|June 19, 2020
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic modelsDianbo Liu, Leonardo Clemente, Canelle Poirier, et al.Science Advances|January 18, 2023
Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United StatesLucas M Stolerman, Leonardo Clemente, Canelle Poirier, et al.Proceedings of the National Academy of Sciences of the United States of America|August 13, 2025
Ensemble approaches for short-term dengue fever forecasts: A global evaluation studySkyler Wu, Austin G Meyer, Leonardo Clemente, et al.Science Advances|March 6, 2021
An early warning approach to monitor COVID-19 activity with multiple digital traces in near real timeNicole E Kogan, Leonardo Clemente, Parker Liautaud, et al.Arxiv|July 18, 2020
An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-TimeNicole E Kogan, Leonardo Clemente, Parker Liautaud, et al.Medrxiv : the Preprint Server for Health Sciences|January 3, 2024
Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizationsSarabeth M Mathis, Alexander E Webber, Tomás M León, et al.Nature Communications|July 26, 2024
Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizationsSarabeth M Mathis, Alexander E Webber, Tomás M León, et al.Pageof 2