The behavioral spillover effect: Modeling behavioral interdependencies in multi-pathogen dynamics

TL;DR

This paper models how behavioral responses to one disease can influence the spread of another, explaining observed patterns during pandemics like COVID-19 and influenza.

Contribution

It introduces a model of behavioral spillover effects, capturing interdependencies between multiple pathogens and human responses affecting disease dynamics.

Findings

NPIs targeting one disease can reduce others' spread

Behavioral interdependencies can lead to co-existence of multiple diseases

Shifts in disease prevalence are driven by human response and perceived risk

Abstract

During the recent pandemic, a rise in COVID-19 cases was followed by a decline in influenza. In the absence of cross-immunity, a potential explanation for the observed pattern is behavioral: non-pharmaceutical interventions (NPIs) designed and promoted for one disease also reduce the spread of others. We study short-term and long-term dynamics of two pathogens where NPIs targeting one pathogen indirectly influence the spread of another - a phenomenon we term behavioral spillover. We examine how perceived risk of and response to one disease substantially alters the spread of other pathogens, revealing how waves of different pathogens emerge over time as a result of behavioral interdependencies and human response. Our analysis identifies the parameter space where two diseases simultaneously co-exist, and where shifts in prevalence occur. Our findings are consistent with observations from the COVID-19 pandemic, where NPIs contributed to significant declines in infections such as influenza, pneumonia, and Lyme disease.