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

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

Nicole E. Kogan, Leonardo Clemente, Parker Liautaud, Justin Kaashoek, Nicholas B. Link, Andre T. Nguyen, Fred S. Lu, Peter Huybers, Bernd Resch, Clemens Havas, Andreas Petutschnig, Jessica Davis, Matteo Chinazzi, Backtosch Mustafa, William P. Hanage, Alessandro Vespignani, Mauricio Santillana
Science Advances
March 5, 2021

Comparative cost-effectiveness of SARS-CoV-2 testing strategies in the USA: a modelling study

Zhanwei Du, Abhishek Pandey, Yuan Bai, Meagan C Fitzpatrick, Matteo Chinazzi, Ana Pastore y Piontti, Michael Lachmann, Alessandro Vespignani, Benjamin J Cowling, Alison P Galvani, Lauren Ancel Meyers
The Lancet
February 4, 2021

Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study

Benjamin Rader, Laura F White, Michael R Burns Jack Chen, Joseph Brilliant, Jon Cohen, Jeffrey Shaman, Larry Brilliant, Moritz; U G Kraemer, Jared B Hawkins, Samuel V Scarpino, Christina M Astley, John S Brownstein
The Lancet
January 19, 2021

Recent publications

The effect of eviction moratoria on the transmission of SARS-CoV-2

Anjalika Nande, Justin Sheen, Emma L Walters, Brennan Klein, Matteo Chinazzi, Andrei Gheorghe, Ben Adlam, Julianna Shinnick, Maria Florencia Tejeda, Samuel V Scarpino, Alessandro Vespignani, Andrew J Greenlee, Daniel Schneider, Michael Z Levy, Alison L Hill
medRxiv
March 19, 2021

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

Nicole E. Kogan, Leonardo Clemente, Parker Liautaud, Justin Kaashoek, Nicholas B. Link, Andre T. Nguyen, Fred S. Lu, Peter Huybers, Bernd Resch, Clemens Havas, Andreas Petutschnig, Jessica Davis, Matteo Chinazzi, Backtosch Mustafa, William P. Hanage, Alessandro Vespignani, Mauricio Santillana
Science Advances
March 5, 2021

Comparative cost-effectiveness of SARS-CoV-2 testing strategies in the USA: a modelling study

Zhanwei Du, Abhishek Pandey, Yuan Bai, Meagan C Fitzpatrick, Matteo Chinazzi, Ana Pastore y Piontti, Michael Lachmann, Alessandro Vespignani, Benjamin J Cowling, Alison P Galvani, Lauren Ancel Meyers
The Lancet
February 4, 2021

Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study

Benjamin Rader, Laura F White, Michael R Burns Jack Chen, Joseph Brilliant, Jon Cohen, Jeffrey Shaman, Larry Brilliant, Moritz; U G Kraemer, Jared B Hawkins, Samuel V Scarpino, Christina M Astley, John S Brownstein
The Lancet
January 19, 2021

Data curation during a pandemic and lessons learned from COVID-19

Moritz U. G. Kraemer, Samuel V. Scarpino, Vukosi Marivate, Bernardo Gutierrez, Bo Xu, Graham Lee, Jared B. Hawkins, Caitlin Rivers, David M. Pigott, Rebecca Katz & John S. Brownstein
Nature Computational Science
January 14, 2021
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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