Network epidemiology

modeling spread of disease and risky behaviors through populations

This work focuses on modeling disease spread, contagion and diffusion processes across social and technical spaces in living systems. The goal is to develop mechanistically realistic predictive models of human disease and mobility at the meta-population level. Models are used to forecast spreading of infectious agents to determine risk threat such as thresholds of disease, containment strategies, and effectiveness of targeted interventions. Results from these projects are reported to the WHO and CDC who use our recommendations to set protocols and policies.

Featured publications

Nonlinear bias toward complex contagion in uncertain transmission settings

Guillaume St-Onge, Laurent Hébert-Dufresne, and Antoine Allard
November 24, 2023

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty

Emily Howerton, Lucie Contamin, Luke C. Mullany, Michelle Qin, Nicholas G. Reich, Samantha Bents, Rebecca K. Borchering, Sung-mok Jung, Sara L. Loo, Claire P. Smith, John Levander, Jessica Kerr, J. Espino, Willem G. van Panhuis, Harry Hochheiser, Marta Galanti, Teresa Yamana, Sen Pei, Jeffrey Shaman, Kaitlin Rainwater-Lovett, Matt Kinsey, Kate Tallaksen, Shelby Wilson, Lauren Shin, Joseph C. Lemaitre, Joshua Kaminsky, Juan Dent Hulse, Elizabeth C. Lee, Clifton D. McKee, Alison Hill, Dean Karlen, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Xinyue Xiong, Ana Pastore y Piontti, Alessandro Vespignani, Erik T. Rosenstrom, Julie S. Ivy, Maria E. Mayorga, Julie L. Swann, Guido España, Sean Cavany, Sean Moore, Alex Perkins, Thomas Hladish, Alexander Pillai, Kok Ben Toh, Ira Longini Jr., Shi Chen, Rajib Paul, Daniel Janies, Jean-Claude Thill, Anass Bouchnita, Kaiming Bi, Michael Lachmann, Spencer J. Fox, Lauren Ancel Meyers, Ajitesh Srivastava, Przemyslaw Porebski, Srini Venkatramanan, Aniruddha Adiga, Bryan Lewis, Brian Klahn, Joseph Outten, Benjamin Hurt, Jiangzhuo Chen, Henning Mortveit, Amanda Wilson, Madhav Marathe, Stefan Hoops, Parantapa Bhattacharya, Dustin Machi, Betsy L. Cadwell, Jessica M. Healy, Rachel B. Slayton, Michael A. Johansson, Matthew Biggerstaff, Shaun Truelove, Michael C. Runge, Katriona Shea, Cécile Viboud & Justin Lessler
Nature communications
November 20, 2023

The unequal effects of the health–economy trade-off during the COVID-19 pandemic

Marco Pangallo, Alberto Aleta, R. Maria del Rio-Chanona, Anton Pichler, David Martín-Corral, Matteo Chinazzi, François Lafond, Marco Ajelli, Esteban Moro, Yamir Moreno, Alessandro Vespignani & J. Doyne Farmer
Nature Human Behaviour
November 16, 2023

Recent publications

A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US

Matteo Chinazzi, Jessica T. Davis, Ana Pastore y Piontti, Kunpeng Mu, Nicolò Gozzi, Marco Ajelli, Nicola Perra, Alessandro Vespignani
March 5, 2024

Ensemble : Scenarios ensembling for communication and performance analysis

Clara Bay, Guillaume St-Onge, Jessica T. Davis, Matteo Chinazzi, Emily Howerton, Justin Lessler, Michael C. Runge, Katriona Shea, Shaun Truelove, Cecile Viboud, Alessandro Vespignani
February 8, 2024

Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic

Brennan Klein, Timothy LaRock, Stefan McCabe, Leo Torres, Lisa Friedland, Maciej Kos, Filippo Privitera, Brennan Lake, Moritz U.G. Kraemer, John S. Brownstein, Richard Gonzalez, David Lazer, Tina Eliassi-Rad, Samuel V. Scarpino, Alessandro Vespignani, Matteo Chinazzi
PLOS Digital Health
February 6, 2024

Towards Inferring Network Properties from Epidemic Data

Istvan Z. Kiss, Luc Berthouze & Wasiur R. KhudaBukhsh
Springer Link
December 8, 2023

Nonlinear bias toward complex contagion in uncertain transmission settings

Guillaume St-Onge, Laurent Hébert-Dufresne, and Antoine Allard
November 24, 2023
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Featured news coverage

Featured project

The Global Epidemic and mobility (GLEAM) project has developed new computational techniques and software for the simulation of emerging infectious diseases spreading across the world. The team uses a network approach to map complex human mobility patterns that account for social interaction behaviors, travel patterns (eg, commuting routes), and transportation infrastructures (eg, metro and airline connections). The simulation interface provides a dynamic visualization of spatial contagion across geographical census areas, and quantitatively evaluates the geo-temporal evolution of the disease.

Major funders

NIH/Fred Hutchinson Cancer Research, IARPA, Metabiota, WHO, CDC, ISI Foundation