Examining the effects of voluntary avoidance behaviour and policy-mediated behaviour change on the dynamics of SARS-CoV-2: A mathematical model

IF 8.8 3区 医学 Q1 Medicine
Gabrielle Brankston , David N. Fisman , Zvonimir Poljak , Ashleigh R. Tuite , Amy L. Greer
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引用次数: 0

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 of SARS-CoV-2 infections in Ontario, Canada (September 1, 2020 to February 28, 2021). 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 23.1%, the total number of hospitalizations by 26.2%, and cumulative deaths by 27.5% over 6 months compared to a 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 66.7%, the total number of hospitalizations by 66.8%, and the total number of deaths by 67.2% 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.

研究自愿回避行为和政策中介行为变化对 SARS-CoV-2 动态的影响:一个数学模型
背景在 SARS-CoV-2 大流行期间,政策制定者不得不在建议自愿行为改变和政策驱动行为改变之间徘徊,以减轻病毒的影响。本文描述了一个确定性疾病传播模型,探讨了加拿大安大略省第二波 SARS-CoV-2 感染期间(2020 年 9 月 1 日至 2021 年 2 月 28 日)个人回避行为和政策介导的回避行为对流行病结果的影响。结果与没有干预措施或回避行为的反事实情景相比,仅靠自愿回避行为估计可在 6 个月内将最终发病率降低 23.1%,住院总人数降低 26.2%,累计死亡人数降低 27.5%。据估计,2020 年 12 月 26 日发布的省级关闭令将使最终发病率降低 66.7%,住院总人数降低 66.8%,死亡总人数降低 67.2%。结论鉴于 SARS-CoV-2 在未接种疫苗时代的动态变化,在政府未采取行动的情况下,个人的回避行为将导致疾病的适度减少,但这并不足以完全缓解传播和对安大略省人口的相关风险。在 COVID-19 大流行第二波期间,安大略省政府采取的行动减少了感染,保护了医院的能力,并挽救了生命。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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