Bayesian Modeling of Dynamic Behavioral Change During an Epidemic

TL;DR

This paper introduces a Bayesian epidemic model that dynamically captures behavioral changes in populations during an outbreak, improving real-time transmission estimates by modeling population alarm as a function of epidemic history.

Contribution

It presents a novel data-driven model incorporating time-varying transmission driven by population alarm, estimated within a Bayesian framework using parametric and non-parametric functions.

Findings

Model accurately estimates behavioral change during epidemics.

Application to real epidemic data demonstrates practical utility.

Flexible modeling of alarm improves epidemic forecasting.

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 processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.