A dynamical framework for modeling fear of infection and frustration with social distancing in COVID-19 spread

Matthew D. Johnston; Bruce Pell; Matthew D. Johnston; Bruce Pell

doi:10.3934/mbe.2020401

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 7892-7915. doi: 10.3934/mbe.2020401

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A dynamical framework for modeling fear of infection and frustration with social distancing in COVID-19 spread

Matthew D. Johnston ^,,
Bruce Pell

Lawrence Technological University, 21000 W 10 Mile Rd., Southfield, MI 48075, USA

Received: 18 August 2020 Accepted: 25 October 2020 Published: 10 November 2020

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.
- COVID-19,
- pandemic,
- behavioral epidemiology,
- dynamical systems,
- SEIR
Citation: Matthew D. Johnston, Bruce Pell. A dynamical framework for modeling fear of infection and frustration with social distancing in COVID-19 spread[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7892-7915. doi: 10.3934/mbe.2020401

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Abstract

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.

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