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面对 COVID-19 大流行时人类选择自我隔离:一种博弈动态建模方法。

Human choice to self-isolate in the face of the COVID-19 pandemic: A game dynamic modelling approach.

机构信息

Department of Mathematics, University of Florida, Gainesville, FL 32611, USA; Emerging pathogens Institute, University of Florida, Gainesviille, FL 32610, USA.

Disease Modelling Lab, Department of Mathematics, School of Natural Sciences, Shiv Nadar University, UP 201314, India.

出版信息

J Theor Biol. 2021 Jul 21;521:110692. doi: 10.1016/j.jtbi.2021.110692. Epub 2021 Mar 23.

Abstract

Non-pharmaceutical interventions (NPIs) involving social-isolation strategies such as self-quarantine (SQ) and social-distancing (SD) are useful in controlling the spread of infections that are transmitted through human-to-human contacts, e.g., respiratory diseases such as COVID-19. In the absence of a safe and effective cure or vaccine during the first ten months of the COVID-19 pandemic, countries around the world implemented these social-isolation strategies and other NPIs to reduce COVID-19 transmission. But, individual and public perception play a crucial role in the success of any social-isolation measure. Thus, in spite of governments' initiatives to use NPIs to combat COVID-19 in many countries around the world, individual choices rendered social-isolation unsuccessful in some of these countries. This resulted in huge outbreaks that imposed a substantial morbidity, mortality, hospitalization, economic, etc., toll on human lives. In particular, human choices pose serious challenges to public health strategic decision-making in controlling the COVID-19 pandemic. To unravel the impact of this behavioral response to social-isolation on the burden of the COVID-19 pandemic, we develop a model framework that integrates COVID-19 transmission dynamics with a multi-strategy evolutionary game approach of individual decision-making. We use this integrated framework to characterize the evolution of human choices in social-isolation as the disease progresses and public health control measures such as mandatory lockdowns are implemented. Analysis of the model illustrates that SD plays a major role in reducing the burden of the disease compared to SQ. Parameter estimation using COVID-19 incidence data, as well as different lockdown data sets from India, and scenario analysis involving a combination of Voluntary-Mandatory implementation of SQ and SD shows that the effectiveness of this approach depends on the type of isolation, and the time and period of implementation of the selected isolation measure during the outbreak.

摘要

非药物干预(NPIs)措施包括自我隔离(SQ)和社会隔离(SD)等社交隔离策略,对于控制人际传播传染病的传播非常有效,例如 COVID-19 等呼吸道疾病。在 COVID-19 大流行的前十个月,由于缺乏安全有效的治疗方法或疫苗,世界各国实施了这些社交隔离策略和其他 NPIs 措施,以减少 COVID-19 的传播。但是,个人和公众的认知在任何社交隔离措施的成功中都起着至关重要的作用。因此,尽管各国政府采取了使用 NPIs 措施来对抗 COVID-19 的举措,但在世界上的一些国家,个人选择使社交隔离措施未能成功实施。这导致了一些国家爆发了大规模疫情,给人类生命带来了巨大的发病率、死亡率、住院治疗、经济等方面的负担。特别是,人类的选择对控制 COVID-19 大流行的公共卫生战略决策构成了严重挑战。为了阐明这种对社交隔离的行为反应对 COVID-19 大流行负担的影响,我们开发了一个模型框架,该框架将 COVID-19 传播动态与个体决策的多策略进化博弈方法相结合。我们使用这个综合框架来描述随着疾病的进展和公共卫生控制措施(例如强制性封锁)的实施,社交隔离中人类选择的演变。模型分析表明,与 SQ 相比,SD 在减轻疾病负担方面发挥了重要作用。使用 COVID-19 发病率数据以及来自印度的不同封锁数据集进行参数估计,以及涉及 SQ 和 SD 的自愿-强制实施组合的情景分析表明,这种方法的有效性取决于隔离的类型以及在疫情爆发期间实施选定隔离措施的时间和周期。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/627c/7986308/5bae1b7081b8/gr1_lrg.jpg

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