Mauricio Santillana

About

Mauricio Santillana, PhD, MSc, is a professor in the Physics and Electrical and Computer Engineering Departments at Northeastern University and the director of the Machine Intelligence Research Lab in the Network Science Institute. His research areas include modeling geographic patterns of population growth, modeling fluid flow to inform coastal floods simulations, atmospheric global pollution transport models, and the design and implementation of disease outbreaks prediction platforms. His work has demonstrated that machine learning techniques can effectively monitor and predict disease outbreak dynamics using novel data sources such as Internet search activity, social media posts, clinician searches, human mobility, and weather data. His research has been published in prominent journals including Nature, Science, Proceedings of the National Academy of Sciences, Science Advances, Nature Communications, and Nature Climate Change. His research has received funding from organizations such as the National Institute of General Medical Sciences (NIH), the U.S. Centers for Disease Control and Prevention, the Bill and Melinda Gates Foundation, and other foundations. Santillana's work involves developing machine intelligence analytics tools aimed at predicting unobserved events in epidemiology and healthcare, tracking disease outbreaks globally, and exploring the influence of climate change and socio-economic factors on health outcomes.

Research topics

• Medicine
• Sociology
• Political Science
• Virology
• Environmental health
• Internal medicine
• Demography
• Geography
• Computer Science
• Nursing

Selected publications

• Prevalence and Correlates of Long COVID Symptoms Among US Adults

  JAMA Network Open · 2022 · 402 citations

  Importance: Persistence of COVID-19 symptoms beyond 2 months, or long COVID, is increasingly recognized as a common sequela of acute infection. Objectives: To estimate the prevalence of and sociodemographic factors associated with long COVID and to identify whether the predominant variant at the time of infection and prior vaccination status are associated with differential risk. Design, Setting, and Participants: This cross-sectional study comprised 8 waves of a nonprobability internet survey c…

  DOI

• Ensemble approaches for short-term dengue fever forecasts: A global evaluation study

  Proceedings of the National Academy of Sciences · 2025-08-13 · 4 citations

  articleOpen accessSenior authorCorresponding

  Dengue fever, a tropical vector-borne disease, is a leading cause of hospitalization and death in many parts of the world, especially in Asia and Latin America. Where timely dengue surveillance exists, decision-makers can better implement public health measures and allocate resources. Reliable near-term forecasts may help anticipate healthcare demands and promote preparedness. We propose ensemble modeling approaches combining mechanistic, statistical, and machine learning models to forecast deng…

  PublisherDOI

• Generative AI Use and Depressive Symptoms Among US Adults

  JAMA Network Open · 2026-01-21 · 3 citations

  articleOpen access

  Importance: Generative artificial intelligence (AI) has rapidly entered mainstream use in the US, but its association with mental health has not been characterized. Objective: To examine the associations of the extent and type of generative AI use among US adults with negative affective symptoms in a large, nationally representative sample. Design, Setting, and Participants: This survey study used data from a 50-state US internet nonprobability survey conducted between April and May 2025. Survey…

  PublisherOA PDFDOI

• Derivation of a 3-Item Patient Health Questionnaire as a Shortened Survey to Capture Depressive Symptoms

  JAMA Network Open · 2025-07-21 · 3 citations

  articleOpen access

  Importance: Screening measures of depressive symptoms (eg, 9-item Patient Health Questionnaire [PHQ-9]) are increasingly used in surveys and remote applications, where shorter versions would be valuable. Objective: To derive shorter versions of the PHQ-9 that maximize the variability in total depressive symptom severity captured. Design, Setting, and Participants: This survey study used data from 4 waves of a 50-state nonprobability web-based survey conducted between November 2, 2023, and July 2…

  PublisherOA PDFDOI

• Characterizing population-level changes in human behavior during the COVID-19 pandemic in the United States

  Proceedings of the National Academy of Sciences · 2025-09-11 · 3 citations

  articleOpen accessSenior authorCorresponding

  The transmission of communicable diseases in human populations is known to be modulated by behavioral patterns. However, detailed characterizations of how population-level behaviors change over time during multiple disease outbreaks and spatial resolutions are still not widely available. We used data from 431,211 survey responses collected in the United States, between April 2020 and June 2022, to provide a description of how human behaviors fluctuated during the first 2 y of the COVID-19 pandem…

  PublisherDOI

Recent grants

• Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease Activity

  NIH · $1.9M · 2018–2024

Frequent coauthors

• Roy H. Perlis

  349 shared

• Matthew A. Baum

  319 shared

• Katherine Ognyanova

  Rutgers Sexual and Reproductive Health and Rights

  308 shared

• James N. Druckman

  University of Rochester

  306 shared

• David Lazer

  Northeastern University

  281 shared

• Alexi Quintana

  Northeastern University

  213 shared

• Jon Green

  Duke University

  187 shared

• Jennifer Lin

  Northwestern University

  183 shared

Awards & honors

• Stanford University Top 2% Most-Cited Scientists (2023, 2024…
• Atlas of Inspiring Hispanic/Latinx Scientists (2024)

Similar researchers at Northeastern University

• Xiaolin Xuh-84
• Ravinder Dahiyah-81
• Milica Stojanovich-63