Modeling Epidemic Spread among a Commuting Population Using Transport Schemes
Modeling Epidemic Spread among a Commuting Population Using Transport Schemes
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DOI：
10.3390/math9161861
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发表时间：
2021-08-01
期刊：
影响因子：
2.4
通讯作者：
Somersalo, Erkki
中科院分区：
文献类型：
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作者：
Calvetti, Daniela;Hoover, Alexander P.;Somersalo, Erkki
了解COVID-19在相连社区之间传播的动态是规划适当缓解措施的基础。为此，我们提出并分析了一种新的集合种群网络模型，特别适合于通勤交通模式建模，该模型考虑到了一组异质社区之间的连通性，每个社区都有自己的感染动态。在我们提出的新集合种群模型中，最优运输理论中的运输方案提供了一种有效且易于实现的方法来描述由于交通而导致的临时人口再分布，例如工作和居住之间的日常通勤交通。在当地，个别社区的感染动态是根据可接受-暴露-感染-恢复（SEIR）隔室模型描述的，该模型经过修改以考虑COVID-19的具体特征，特别是无症状和症状前感染者的传播。我们的集合人口网络模型的数学基础类似于两个人口分布之间的运输方案，即住宅分布和工作场所分布，其接口可以从美国人口普查局提供的通勤流动性数据中推断出来。我们使用所提出的集合种群模型来测试COVID-19在两个网络上的传播动态，一个较小的网络包括俄亥俄州大克利夫兰地区的7个县，另一个较大的网络包括伊利湖美国海岸线后的匹兹堡-克利夫兰-底特律走廊的74个县。模型模拟表明，人口稠密地区有效地放大了周围人口不太稠密地区的感染，这与COVID-19大流行期间观察到的感染模式一致。计算的例子表明，该模型也可以用来测试不同的缓解策略，包括一个基于国家一级的旅行限制，另一个县一级触发的社交距离，以及两者的组合。
Understanding the dynamics of the spread of COVID-19 between connected communities is fundamental in planning appropriate mitigation measures. To that end, we propose and analyze a novel metapopulation network model, particularly suitable for modeling commuter traffic patterns, that takes into account the connectivity between a heterogeneous set of communities, each with its own infection dynamics. In the novel metapopulation model that we propose here, transport schemes developed in optimal transport theory provide an efficient and easily implementable way of describing the temporary population redistribution due to traffic, such as the daily commuter traffic between work and residence. Locally, infection dynamics in individual communities are described in terms of a susceptible-exposed-infected-recovered (SEIR) compartment model, modified to account for the specific features of COVID-19, most notably its spread by asymptomatic and presymptomatic infected individuals. The mathematical foundation of our metapopulation network model is akin to a transport scheme between two population distributions, namely the residential distribution and the workplace distribution, whose interface can be inferred from commuter mobility data made available by the US Census Bureau. We use the proposed metapopulation model to test the dynamics of the spread of COVID-19 on two networks, a smaller one comprising 7 counties in the Greater Cleveland area in Ohio, and a larger one consisting of 74 counties in the Pittsburgh-Cleveland-Detroit corridor following the Lake Erie's American coastline. The model simulations indicate that densely populated regions effectively act as amplifiers of the infection for the surrounding, less densely populated areas, in agreement with the pattern of infections observed in the course of the COVID-19 pandemic. Computed examples show that the model can be used also to test different mitigation strategies, including one based on state-level travel restrictions, another on county level triggered social distancing, as well as a combination of the two.