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

Shock propagation from the Russia–Ukraine conflict on international multilayer food production network determines global food availability

Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang & Stefan Thurner
Nature food
June 15, 2023

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
Nature Communications Medicine
February 14, 2023

Recent publications

Shock propagation from the Russia–Ukraine conflict on international multilayer food production network determines global food availability

Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang & Stefan Thurner
Nature food
June 15, 2023

Identifying and Mitigating Instability in Embeddings of the Degenerate Core

David Liu, Tina Eliassi-Rad
SIAM Publications Library
May 21, 2023

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
Nature Communications Medicine
February 14, 2023

Memory failure predicts belief regression after the correction of misinformation

Briony Swire-Thompson, Mitch Dobbs, Ayanna Thomas, Joseph DeGutisde
Cognition
January 3, 2023

Estimating the impact of COVID-19 vaccine allocation inequities: a modeling study

Nicolò Gozzi, Matteo Chinazzi, Natalie E. Dean, Ira M. Longini Jr., M. Elizabeth Halloran, Nicola Perra, Alessandro Vespignani
medRxiv
November 18, 2022
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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