Metapopulation Network Models for Understanding, Predicting, and Managing the Coronavirus Disease COVID-19
Metapopulation Network Models for Understanding, Predicting, and Managing the Coronavirus Disease COVID-19
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DOI：
10.3389/fphy.2020.00261
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发表时间：
2020-06-19
影响因子：
3.1
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
SARS-CoV-2（导致COVID-19的病毒）传播的数学模型用于指导缓解措施的设计，并帮助确定即将发生的医疗保健系统激增能力的缺口。人口的地理异质性加剧了关于每日新感染和死亡人数的信息不足的挑战。这使得预测变得复杂，特别是在使用假设混合良好的群体的模型时。为了解决这个问题，我们使用基于网络的分布式模型来解释农村和城市环境之间的差异，其中大流行的传播在不同的本地队列中使用嵌套SE（A）IR模型来描述，即，包括感染性无症状个体的改进的SEIR模型。模型参数解释了SARS-CoV-2主要通过人与人之间的接触传播，以及城市和农村地区之间个人之间的接触频率不同，并可能随着时间的推移而变化。病毒传播到未受感染社区的概率与来自已存在感染的社区的个体涌入有关，因此每个节点的特征在于其内部联系以及与其他节点的连接。普查数据用于建立网络的邻接矩阵，可以修改该矩阵以模拟缓解措施的变化。我们的网络SE（A）IR模型依赖于从可用的社区水平数据估计的易于解释的参数。用贝叶斯技术估计的参数包括传播率和无症状感染者与有症状感染者的比率。该方法预测，随着疫情达到平衡，后一个数量接近0.5，与2020年5月22日CDC建模完全一致。网络模型产生了一个空间分布的计算模型，解释了传染的地理动态，例如，在被郊区和农村包围的大城市里。由网络模型预测的不同县的感染队列的时间过程与报告的观察结果非常相似。此外，该模型表明，监测每个县的感染流行率，并在感染率超过一定阈值时采取当地缓解措施，几乎与全面措施一样有效，而且比减少县间流动性更有效。
Mathematical models of SARS-CoV-2 (the virus which causes COVID-19) spread are used for guiding the design of mitigation steps and helping identify impending breaches of health care system surge capacity. The challenges of having only lacunary information about daily new infections and mortality counts are compounded by geographic heterogeneity of the population. This complicates prediction, particularly when using models assuming well-mixed populations. To address this problem, we account for the differences between rural and urban settings using network-based, distributed models where the spread of the pandemic is described in distinct local cohorts with nested SE(A)IR models, i.e., modified SEIR models that include infectious asymptomatic individuals. The model parameters account for the SARS-CoV-2 transmission mostly via human-to-human contact, and the fact that contact frequency among individuals differs between urban and rural areas, and may change over time. The probability that the virus spreads into an uninfected community is associated with influx of individuals from communities where the infection is already present, thus each node is characterized by its internal contact and by its connectivity with other nodes. Census data are used to set up the adjacency matrix of the network, which can be modified to simulate changes in mitigation measures. Our network SE(A)IR model depends on easily interpretable parameters estimated from available community level data. The parameters estimated with Bayesian techniques include transmission rate and the ratio asymptomatic to symptomatic infectious individuals. The methodology predicts that the latter quantity approaches 0.5 as the epidemic reaches an equilibrium, in full agreement with the May 22, 2020 CDC modeling. The network model gives rise to a spatially distributed computational model that explains the geographic dynamics of the contagion, e.g., in larger cities surrounded by suburban and rural areas. The time courses of the infected cohorts in the different counties predicted by the network model are remarkably similar to the reported observations. Moreover, the model shows that monitoring the infection prevalence in each county, and adopting local mitigation measures as infections climb beyond a certain threshold, is almost as effective as blanket measures, and more effective than reducing inter-county mobility.