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

Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability

Flávio C. Coelho, Raquel M. Lana, Oswaldo G. Cruz,Daniel A. M. Villela, Leonardo S. Bastos, Ana Pastore y Piontti, Jessica T. Davis, Alessandro Vespignani, Claudia T. Codeço, Marcelo F. C. Gomes
PLOS ONE
September 18, 2020

Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19

Alberto Aleta, David Martín-Corral, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova, Matteo Chinazzi, Natalie E. Dean, M. Elizabeth Halloran, Ira M. Longini Jr, Stefano Merler, Alex Pentland, Alessandro Vespignani, Esteban Moro & Yamir Moreno
Nature Human Behaviour
August 5, 2020

Socioeconomic bias in influenza surveillance

Samuel V. Scarpino, James G. Scott, Rosalind M. Eggo, Bruce Clements, Nedialko B. Dimitrov, Lauren Ancel Meyers
Plos Computational Biology
July 9, 2020

Recent publications

Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability

Flávio C. Coelho, Raquel M. Lana, Oswaldo G. Cruz,Daniel A. M. Villela, Leonardo S. Bastos, Ana Pastore y Piontti, Jessica T. Davis, Alessandro Vespignani, Claudia T. Codeço, Marcelo F. C. Gomes
PLOS ONE
September 18, 2020

Estimating the effect of cooperative versus uncooperative strategies of COVID-19 vaccine allocation: a modeling study

Matteo Chinazzi, Jessica T. Davis, Natalie E. Dean, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, M. Elizabeth Halloran, Ira M. Longini Jr, Alessandro Vespignani
September 14, 2020

The unintended consequences of inconsistent pandemic control policies

Benjamin M. Althouse, Brendan Wallace, Brendan Case, Samuel V. Scarpino, Andrew M. Berdahl, Easton R. White, and Laurent Hebert-Dufresne
medRxiv
August 24, 2020

Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19

Alberto Aleta, David Martín-Corral, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova, Matteo Chinazzi, Natalie E. Dean, M. Elizabeth Halloran, Ira M. Longini Jr, Stefano Merler, Alex Pentland, Alessandro Vespignani, Esteban Moro & Yamir Moreno
Nature Human Behaviour
August 5, 2020

Socioeconomic bias in influenza surveillance

Samuel V. Scarpino, James G. Scott, Rosalind M. Eggo, Bruce Clements, Nedialko B. Dimitrov, Lauren Ancel Meyers
Plos Computational Biology
July 9, 2020
View more

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