Network forecasting

develop new algorithms and platforms to overcome limitations and biases of big data

This thrust focuses on developing new methods for the analysis of publicly available data in order to anticipate and/or predict significant societal events, such as political instability, humanitarian crises, disease outbreaks, economic instability, and devastating effects of natural disasters. We aim to develop data assimilation algorithms, forecasting algorithms, and data collection platforms for studies on human behavior, with deep exploration into foundational issues of measurement, construct validity and reliability, and dependencies within data.

Featured publications

Integrating explanation and prediction in computational social science

Jake M. Hofman, Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J. Salganik, Simine Vazire, Alessandro Vespignani, Tal Yarkoni
Nature
June 30, 2021

Survey data and human computation for improved flu tracking

Stefan Wojcik, Avleen S. Bijral, Richard Johnston, Juan M. Lavista Ferres, Gary King, Ryan Kennedy, Alessandro Vespignani & David Lazer
Nature Communications
January 8, 2021

Recent publications

Memory failure predicts belief regression after the correction of misinformation

BrionySwire-Thompson, MitchDobbs, Ayanna Thomas, Joseph DeGutisde
Science Direct
September 26, 2022

Forecasting hospital-level COVID-19 admissions using real-time mobility data

Brennan Klein, Ana C. Zenteno, Daisha Joseph, Mohammadmehdi Zahedi, Michael Hu, Martin Copenhaver, Moritz U.G. Kraemer, Matteo Chinazzi, Michael Klompas, Alessandro Vespignani, Samuel V. Scarpino, Hojjat Salmasian
medrxiv
June 8, 2022

Collaborative Hubs: Making the Most of Predictive Epidemic Modeling

Nicholas G. Reich, Justin Lessler, Sebastian Funk, Cecile Viboud, Alessandro Vespignani, Ryan J. Tibshirani, Katriona Shea, Melanie Schienle, Michael C. Runge, Roni Rosenfeld, Evan L. Ray, Rene Niehus, Helen C. Johnson, Michael A. Johansson, Harry Hochheiser, Lauren Gardner, Johannes Bracher, Rebecca K. Borchering, and Matthew Biggerstaff
American Journal of Public Health
April 14, 2022

Integrating explanation and prediction in computational social science

Jake M. Hofman, Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J. Salganik, Simine Vazire, Alessandro Vespignani, Tal Yarkoni
Nature
June 30, 2021
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Featured news coverage

Featured project

The Data Forecasting Project focuses on developing new methodologies for forecasting by curating massive data sets from social media and mobility patterns. Epidemiological models will be built from tracking Infuenza-Like Illness (ILI) to detect early cases of ILI in small geographic regions; and from voter registration data collected from 1.7M Twitter handles from 86 countries of more than 500 elections. We use these data to develop a forecasting approach that combines digital indicators and mechanistic models. General formalizations of these forecasting models are applied to a wide range of behaviors including social movements, media consumption, and epidemiological prediction.

Major funders

IARPA