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

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

The effect of human mobility and control measures on the COVID-19 epidemic in China

Moritz U. G. Kraemer, Chia-Hung Yang, Bernardo Gutierrez, Chieh-Hsi Wu, Brennan Klein, David M. Pigott, Open COVID-19 Data Working Group, Louis du Plessis, Nuno R. Faria, Ruoran Li, William P. Hanage, John S. Brownstein, Maylis Layan, Alessandro Vespignani, Huaiyu Tian, Christopher Dye, Oliver G. Pybus, Samuel V. Scarpino
Science
March 25, 2020

Phase transitions in information spreading on structured populations

Jessica T. Davis, Nicola Perra, Qian Zhang, Yamir Moreno & Alessandro Vespignani
Nature Physics
March 2, 2020

Recent publications

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

Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19

Matthew Biggerstaff, Benjamin J. Cowling, Zulma M. Cucunubá, Linh Dinh, Neil M. Ferguson, Huizhi Gao, Verity Hill, Natsuko Imai, Michael A. Johansson, Sarah Kada, Oliver Morgan, Ana Pastore y Piontti, Jonathan A. Polonsky, Pragati Venkata Prasad, Talia M. Quandelacy, Andrew Rambaut, Jordan W. Tappero, Katelijn A. VandemaeleComments to Author , Alessandro Vespignani, K. Lane Warmbrod, Jessica Y. Wong, and for the WHO COVID-19 Modelling Parameters Group
CDC EID
September 11, 2020

Searching for the backfire effect: Measurement and design considerations

Briony Swire-Thompson, Joseph DeGutis, David Lazer
Journal of Applied Memory and Cognition
May 14, 2020

The effect of human mobility and control measures on the COVID-19 epidemic in China

Moritz U. G. Kraemer, Chia-Hung Yang, Bernardo Gutierrez, Chieh-Hsi Wu, Brennan Klein, David M. Pigott, Open COVID-19 Data Working Group, Louis du Plessis, Nuno R. Faria, Ruoran Li, William P. Hanage, John S. Brownstein, Maylis Layan, Alessandro Vespignani, Huaiyu Tian, Christopher Dye, Oliver G. Pybus, Samuel V. Scarpino
Science
March 25, 2020

Phase transitions in information spreading on structured populations

Jessica T. Davis, Nicola Perra, Qian Zhang, Yamir Moreno & Alessandro Vespignani
Nature Physics
March 2, 2020
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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