Bayesian modeling of dynamic behavioral change during an epidemic.

ABSTRACT: For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian proce

SUBMITTER: Ward C 

PROVIDER: S-EPMC10440573 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature