Human behavior strongly shapes epidemic dynamics, yet responses to risk are typically based on spatially aggregated and temporally delayed information. While behavioral adaptation, spatial structure, and delays have been studied separately, their combined effects on networks remain poorly understood.
We investigate these effects using stochastic epidemic simulations on small-world contact...
Host genetic structure can significantly alter disease transmission dynamics and long-term disease outcomes. Past work by Beck, Keener, Hoppensteadt, Feng, and others has shown that when pathogen transmission interacts with evolving host traits—such as susceptibility, recovery, or disease-induced mortality—the resulting coupled system can exhibit novel dynamics. These models demonstrated that...
Behavioral responses during epidemics alter transmission, yet most epidemiological models assume constant contact rates. We assessed the resulting bias by fitting a baseline SEIR model with fixed transmission and three behavioral variants, in which transmission declines with increasing mortality, to COVID-19 mortality data from 30 U.S. states during the first wave (March-July...
The Mathematical Biology program and other programs in the Directorate of Mathematical and Physical Sciences at the US National Science Foundation have funded many high impact research projects on various topics including mathematical modeling of infectious diseases. Some highlights will be presented.
The COVID-19 pandemic highlighted the critical need for improved formulations that incorporate human behavior into epidemiological models. Individual actions, manifested through behaviors such as mask-wearing or changes in mobility patterns, are influenced by public risk perception and, in turn, alter infectious disease spread locally and across the population. Furthermore, individual actions...
