A Dynamical Framework for Modeling Fear of Infection and Frustration with Social Distancing in COVID-19 Spread

TL;DR

This paper presents a new modeling framework that incorporates psychological factors like fear and frustration into COVID-19 spread dynamics, capturing different outbreak scenarios and secondary waves.

Contribution

It introduces a novel SEIR-based model integrating social perception factors and demonstrates its ability to reproduce complex outbreak patterns and fit real-world data.

Findings

Regions with significant initial decline can contain secondary waves

Moderate initial mitigation often leads to secondary outbreaks

Model fits COVID-19 data from multiple regions

Abstract

In this paper, we introduce a novel modeling framework for incorporating fear of infection and frustration with social distancing into disease dynamics. We show that the resulting SEIR behavior-perception model has three principal modes of qualitative behavior---no outbreak, controlled outbreak, and uncontrolled outbreak. We also demonstrate that the model can produce transient and sustained waves of infection consistent with secondary outbreaks. We fit the model to cumulative COVID-19 case and mortality data from several regions. Our analysis suggests that regions which experience a significant decline after the first wave of infection, such as Canada and Israel, are more likely to contain secondary waves of infection, whereas regions which only achieve moderate success in mitigating the disease's spread initially, such as the United States, are likely to experience substantial secondary waves or uncontrolled outbreaks.