A0391
Title: Multivariable behavioral change epidemic models
Authors: Rob Deardon - University of Calgary (Canada) [presenting]
Abstract: Epidemic models are essential tools for understanding the spread of infectious diseases and developing strategies for their control, as well as for informing public health policies and resource allocation. While the COVID-19 pandemic highlighted the value of these models, it also exposed several limitations in existing modeling approaches. One significant shortcoming is the failure to account for human behavioral change, a key driver of infectious disease transmission. Recently, Bayesian behavioral change epidemic models were introduced, which allow the outbreak-related behavioral change exhibited by a population to be captured by a so-called alarm function. The purpose is to extend these models to enable population behavioral change to be dynamically influenced by multiple data sources, including case numbers and deaths, in the context of a COVID-19 model that allows for the effect of asymptomatic cases and hospitalizations. A thorough investigation of the proposed model's properties is conducted through simulation, and its effectiveness is demonstrated using COVID-19 data from Montreal and Miami.
