Examining the Effects of Voluntary Avoidance Behaviour and Policy-Mediated Behaviour Change on the Dynamics of Sars-Cov-2: A Mathematical Model

Abstract

Background: Throughout the SARS-CoV-2 pandemic, policymakers have had to navigate between recommending voluntary behaviour change and policy-driven behaviour change to mitigate the impact of the virus. While individuals will voluntarily engage in self-protective behaviour when there is an increasing infectious disease risk, the extent to which this occurs and its impact on an epidemic is not known.

Methods: This paper describes a deterministic disease transmission model exploring the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave (September 1, 2020 to February 28, 2021) of SARS-CoV-2 infections in Ontario, Canada. The model incorporates an information feedback function based on empirically derived behaviour data describing the degree to which avoidance behaviour changed in response to the number of new daily cases COVID-19. 

Results: Voluntary avoidance behaviour alone was estimated to reduce the final attack rate by 30.4%, the total number of hospitalizations by 32.5%, and cumulative deaths by 33.8% over 6 months compared to the counterfactual scenario in which there were no interventions or avoidance behaviour. A provincial shutdown order issued on December 26, 2020 was estimated to reduce the final attack rate by 67.5%, the total number of hospitalizations by 68.1%, and the total number of deaths by 69.7% compared to the counterfactual scenario. 

Conclusion: Given the dynamics of SARS-CoV-2 in a pre-vaccine era, individual avoidance behaviour in the absence of government action would have resulted in a moderate reduction in disease however, it would not have been sufficient to entirely mitigate transmission and the associated risk to the population in Ontario. Government action during the second wave of the COVID-19 pandemic in Ontario reduced infections, protected hospital capacity, and saved lives.

Note:
Funding Information: GB and AG are supported by the Canada Research Chairs program. DN and AT are supported by the Canadian Institutes for Health Research (CIHR). ZP is supported by the Natural Sciences and Engineering Research Council (NSERC). Funding to support data collection was provided by the Public Health Agency of Canada (PHAC), The National Collaborating Centre for Infectious Diseases (NCCID), and the University of Guelph. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Declaration of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics Approval Statement: The study protocol covering the collection of the data used to parameterize the model was obtained from the University of Guelph Research Ethics Board (protocol #20-04-011) and the University of Toronto Research Ethics Board (protocol #38251). The remainder of the data used were publicly available.

Keywords: Mathematical modelling, COVID-19, SARS-CoV-2, Human behaviour