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An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics
Chaos, Solitons & Fractals ( IF 7.8 ) Pub Date : 2021-01-07 , DOI: 10.1016/j.chaos.2020.110616
Marco A Amaral 1 , Marcelo M de Oliveira 2 , Marco A Javarone 3
Affiliation  

During pandemic events, strategies such as social distancing can be fundamental to reduce simultaneous infections and mitigate the disease spreading, which is very relevant to the risk of a healthcare system collapse. Although these strategies can be recommended, or even imposed, their actual implementation may depend on the population perception of the risks associated with a potential infection. The current COVID-19 crisis, for instance, is showing that some individuals are much more prone than others to remain isolated. To better understand these dynamics, we propose an epidemiological SIR model that uses evolutionary game theory for combining in a single process social strategies, individual risk perception, and viral spreading. In particular, we consider a disease spreading through a population, whose agents can choose between self-isolation and a lifestyle careless of any epidemic risk. The strategy adoption is individual and depends on the perceived disease risk compared to the quarantine cost. The game payoff governs the strategy adoption, while the epidemic process governs the agent’s health state. At the same time, the infection rate depends on the agent’s strategy while the perceived disease risk depends on the fraction of infected agents. Our results show recurrent infection waves, which are usually seen in previous historic epidemic scenarios with voluntary quarantine. In particular, such waves re-occur as the population reduces disease awareness. Notably, the risk perception is found to be fundamental for controlling the magnitude of the infection peak, while the final infection size is mainly dictated by the infection rates. Low awareness leads to a single and strong infection peak, while a greater disease risk leads to shorter, although more frequent, peaks. The proposed model spontaneously captures relevant aspects of a pandemic event, highlighting the fundamental role of social strategies.



中文翻译:

具有由进化博弈动力学控制的自愿隔离策略的流行病学模型

在大流行事件期间,社交距离等策略对于减少同时感染和减缓疾病传播至关重要,这与医疗系统崩溃的风险密切相关。尽管可以推荐甚至强加这些策略,但它们的实际实施可能取决于人们对与潜在感染相关的风险的认知。例如,当前的 COVID-19 危机表明,有些人比其他人更容易保持孤立。为了更好地理解这些动态,我们提出了一种流行病学 SIR 模型,该模型使用进化博弈论将社会策略、个人风险感知和病毒传播结合在一个过程中。特别是,我们考虑一种通过人群传播的疾病,他们的代理人可以在自我隔离和不顾任何流行病风险的生活方式之间做出选择。策略的采用因人而异,取决于与检疫成本相比的感知疾病风险。博弈收益决定了策略的采用,而流行过程决定了代理人的健康状态。同时,感染率取决于代理人的策略,而感知的疾病风险取决于受感染代理人的比例。我们的结果显示反复出现的感染浪潮,这通常出现在以前自愿隔离的历史性流行病场景中。特别是,随着人们对疾病的认识降低,这种浪潮会再次发生。值得注意的是,发现风险感知是控制感染高峰幅度的基础,而最终感染规模主要取决于感染率。低意识会导致单一且强烈的感染高峰,而较高的疾病风险会导致更短但更频繁的感染高峰。所提出的模型自发地捕捉了大流行事件的相关方面,突出了社会策略的基本作用。

更新日期:2021-01-07
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