Mauricio Santillana

About

Mauricio Santillana, PhD, MSc is the director of the Machine Intelligence Research Lab in the Network Science Institute. He is a Professor in the Physics and Electrical and Computer Engineering Departments at Northeastern University. Dr. Santillana’s research areas include the modeling of geographic patterns of population growth, modeling fluid flow to inform coastal floods simulations and atmospheric global pollution transport models, and most recently, the design and implementation of disease outbreaks prediction platforms and mathematical solutions to healthcare. His research has shown that machine learning techniques can be used to effectively monitor and predict the dynamics of disease outbreaks using novel data sources not designed for these purposes such as: Internet search activity, social media posts, clinician’s searches, human mobility, weather, etc.

His original research and perspectives have appeared in journals such as Nature, Science, Proceedings of the National Academy of Science, Science Advances, Nature Communications, and Nature Climate Change, among others. His work has been funded by the National Institute of General Medical Sciences (National Institutes of Health, NIH), the U.S. Centers for Disease Control and Prevention, the Bill and Melinda Gates Foundation, and multiple foundations such as: the Johnson and Johnson Foundation, Ending Pandemics Fund,

Selected Publications

• Stolerman, Lucas M., Clemente, Leonardo, Poirier, Canelle, Parag, Kris V., Majumder, Atreyee, Masyn, Serge, Resch, Bernd, Santillana, Mauricio (2023). Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States. Science Advances, 9(3). 10.1126/SCIADV.ABQ0199
• Druckman J, Ognyanova K, Baum M, Lazer D, Perlis R, Volpe JD, Santillana M, Chwe H, Quintana A, Simonson M. The role of race, religion, and partisanship in misinformation about COVID-19. Group Processes & Intergroup Relations. 2021;24 (4) :638–657.
• Perlis R, Santillana M, Ognyanova K, Green J, Druckman J, Lazer D, Baum M. Factors associated with self-reported symptoms of depression among adults with and without a previous COVID-19 diagnosis. JAMA Network Open. 2021;4 (6).
• Castro LA, Generous N, Luo W, y Piontti AP, Martinez K, Gomes MFC, Osthus D, Fairchild G, Ziemann A, Vespignani A, et al. Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. PLoS Neglected Tropical Diseases. 2021.Abstract
• de Salazar P, Link N, Lamarca K, Santillana M. High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in Long-Term Care Facilities. Nature Communications Medicine. 2021;1 (16).Abstract
• Perlis RH, Ognyanova K, Santillana M, Baum MA, Lazer D, Druckman J, Volpe JD. Association of Acute Symptoms of COVID-19 and Symptoms of Depression in Adults. JAMA Network Open. 2021;4 (3) :e213223.Abstract
• de Salazar PM, Lu F, Hay JA, Gomez-Barroso D, Fernandez-Navarro P, Martinez EV, Astray-Mochales J, Amillategui R, Garcia-Fulgueiras A, Chirlaque MD, et al. Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data. medRxiv. 2021.Abstract
• Mena G, Martinez PP, Mahmud AS, Marquet PA, Buckee CO, Santillana M. Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile. Science. 2021 :eabg5298.Abstract
• Kiang MV, Santillana M, Chen JT, Onnela J-P, Krieger N, Engø-Monsen K, Ekapirat N, Areechokchai D, Maude R, Buckee CO. Incorporating human mobility data improves forecasts of Dengue fever in Thailand. Scientific Reports. 2021;11 (923).Abstract
• Kogan NE, Clemente L, Liautaud P, Kaashoek J, Link NB, Nguyen AT, Lu FS, Huybers P, Resch B, Havas C, et al. An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time. Science Advances. 2021;7 (10).Abstract
• Poirier C, Hswen Y, Bouzille G, Cuggia M, Lavenu A, Brownstein JS, Brewer T, Santillana M. Influenza forecasting for the French regions by using EHR, web and climatic data sources with an ensemble approach. PLoS One. 2021.Abstract
• Lu FS, Nguyen AT, Link N, Molina M, Davis JT, Chinazzi M, Xiong X, Vespignani A, Lipsitch M, Santillana M. Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: four complementary approaches. PLoS Computational Biology . 2021.Abstract
• Aiken EL, Nguyen AT, Viboud C, Santillana M. Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions. Science Advances. 2021;7 (25).Abstract
• McGough S, Kutz NJ, Clemente LC, Santillana M. A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles. Journal of the Royal Society Interface. 2021;18 (179).Abstract

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