人类活动驱动的传染病模型

Ismael Villanueva-Miranda, M. Hossain, Monika Akbar
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摘要

在传统的疾病模型中,疾病特性是主要参数(例如,感染率、潜伏期)。从最近关于传染病的文献中可以看出,人类行为——尤其是流动性——在疾病传播中起着至关重要的作用。本文提出了一个流行病学模型SEIRD+m,该模型考虑了人类的流动性,而不是单独建模疾病特性。SEIRD+m依赖于核心确定性流行病模型SEIR(易感、暴露、感染和恢复),增加了一个新的隔间D -死亡,并通过从SafeGraph收集的手机数据中检索到的人类移动信息(如时间、位置和运动)增强了每个SEIRD组件。我们展示了一种通过限制被检测为COVID-19热点的特定人口普查街区群体(cbg)的流动性来减少COVID-19感染和死亡人数的方法。本文中的一个案例研究表明,流动性减少50%有助于根据种族、收入和年龄减少不同人口群体的感染和死亡人数。
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Human Mobility Driven Modeling of an Infectious Disease
In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age.
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