Eight interdisciplinary research projects – including one led by Duke University researchers – are part of a new $7.5+ million initiative by the U.S. National Science Foundation (NSF) focused on empowering more reliable prediction of the spread of infectious diseases, the effects of mitigation measures and other critical aspects of national health crises.
Duke assistant professors of Statistical Science Jason Xu and Alexander Volfovsky, along with David McAdams, professor of Business Administration and Economics, are looking to bridge the gap between statistical modeling of epidemic processes and economic models of human behavior. Their project, “Equilibrium, network formation and infectious-disease spread: bridging the divide between mathematical biology and economics,” aims to break the siloed progress made separately in the fields of mathematical biology and in economics – which they say is important in understanding and predicting epidemics.
Research by Xu and collaborators has overcome long-standing challenges in fitting stochastic epidemic models to noisy, partially informative data. However, the team explained that existing models still fall short until they are examined in the context of rich contact patterns, individual-level decision making based on social-economic costs, and changes in behavior as policies and other factors develop through the course of an epidemic.
“Several disciplines are realizing that incorporating human behavior is a big next step in the rich history of epidemic modeling,” Xu said. “We are excited to take a convergent approach modeling how individuals respond and make decisions as an epidemic unfolds, which will be critical to enable reliable forecasting and effective, real-time policy.”
The NSF award for the project began Sept. 1 and is estimated to be funded through Fall 2025.
This Duke team is also involved in a larger Duke initiative on Predictive Intelligence for Pandemic Prevention (PIPP). This interdisciplinary project, which received a $1 million grant from NSF, is tasked with pinpointing factors that are likely to turn a local outbreak into a global pandemic.
