Speakers

Title: Incorporating human behavior into infectious disease models

Abstract: The importance of understanding, predicting, and controlling infectious disease became increasingly evident during the COVID-19 pandemic. In particular, the pandemic highlighted the need for interpretable, quantitative models that link mechanism with data while accounting for variability. A unique feature of infectious disease spread is the necessary interaction between a susceptible individual and an infectious agent, often an infectious individual. Thus, the ability for a disease to transmit depends not only on the level of disease in a population but also on the behavior of both the susceptible and infectious individuals. Without incorporating behavioral feedback, mathematical models fail to capture salient features, such as whether and to what extent a disease spreads in a population. In this talk, we will examine an ordinary differential equation framework which includes behavioral response to risk perception. We overlay this endogenously incorporated behavioral response with heterogeneity in risk and the presence of multiple co-circulating diseases. The course and magnitude of epidemics are significantly altered when accounting for behavioral feedback, demonstrating how considering endogenous behavioral is key for accurately characterizing disease dynamics.  Thus, more realistic epidemic models enhance our ability to understand and predict infectious disease spread through a population.