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

Forecasting Seasonal Influenza Fusing Digital Indicators and a Mechanistic Disease Model

Q, Zhang, N. Perra, D. Perrotta, D. Paolotti, M. Tizzoni, A. Vespignani.
WWW '17
April 3, 2017

Improving election prediction internationally

Ryan Kennedy, Stefan Wojcik, David Lazer
February 3, 2017

Predicting and Interpolating State-level Polls using Twitter Textual Data

Nick Beauchamp
American Journal of Political Science
September 13, 2016

Recent publications

Quantifying the risk of local Zika virus transmission in the contiguous US during the 2015–2016 ZIKV epidemic

Kaiyuan Sun, Qian Zhang, Ana Pastore-Piontti, Matteo Chinazzi, Dina Mistry, Natalie E Dean, Diana Patricia Rojas, Stefano Merler, Piero Poletti, Luca Rossi, M Elizabeth Halloran, Ira M Longini Jr and Alessandro Vespignani
BMC Medicine
October 18, 2018

Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong
Proceedings of the NAACL
June 1, 2018

Success in Books: A Big Data Approach to Bestsellers

Burcu Yucesoy, Xindi Wang, Junming Huang and Albert-László Barabási
EPJ Data Science
April 6, 2018

Simulations for designing and interpreting intervention trials in infectious diseases

M. Elizabeth Halloran, Kari Auranen, Sarah Baird, Nicole E. Basta, Steven E. Bellan, Ron Brookmeyer, Ben S. Cooper, Victor DeGruttola, James P. Hughes, Justin Lessler, Eric T. Lofgren, Ira M. Longini, Jukka-Pekka Onnela, Berk Özler, George R. Seage, Thomas A. Smith, Alessandro Vespignani, Emilia Vynnycky and Marc Lipsitch
BMC Medicine
December 5, 2017

The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation

Marco Ajelli, Qian Zhang, Kaiyuan Sun, Stefano Merler, Laura Fumanelli, Gerardo Chowell, Lone Simonsen, Cecile Viboud, Alessandro Vespignani
September 13, 2017

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