Network epidemiology
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
Pandemic monitoring with global aircraft-based wastewater surveillance networks
Bias in mobility datasets drives divergence in modeled outbreak dynamics
Tracking COVID-19 Infections Using Survey Data on Rapid At-Home Tests
Recent publications
Pandemic monitoring with global aircraft-based wastewater surveillance networks
Evaluation of stochastic trajectory-based epidemic models using the energy score
Revisiting the Relationship between Weather and Interannual Variation in Human Plague Cases in the Southwestern United States
Bias in mobility datasets drives divergence in modeled outbreak dynamics
Featured news coverage
Using Air Traffic Data to Predict Ebola’s Spread
Wall Street Journal, October 2014
How Big, Really, Is The Zika Outbreak In Florida?
NPR, August 2016
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.
Associated faculty
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