Mobility network models of COVID-19 explain inequities and inform reopening
Mobility network models of COVID-19 explain inequities and inform reopening
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DOI：
10.1038/s41586-020-2923-3
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发表时间：
2020-11-10
期刊：
影响因子：
64.8
通讯作者：
Leskovec, Jure
中科院分区：
文献类型：
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作者：
Chang, Serina;Pierson, Emma;Leskovec, Jure
2019年冠状病毒病(新冠肺炎)大流行显著改变了人类的流动模式，需要能够捕捉这些流动性变化对严重急性呼吸综合征冠状病毒2(SARS-CoV-2)传播影响的流行病学模型(1)。在这里，我们介绍了一个集合种群易感-暴露-感染-移除(SEIR)模型，该模型集成了细粒度的动态移动网络来模拟SARS-CoV-2在美国十个最大的大都市区的传播。我们的移动网络来自移动电话数据，绘制了9800万人从居民区(或人口普查区组)到餐馆和宗教机构等兴趣点的每小时流动地图，将56,945个人口普查区组与552,758个兴趣点连接起来，每小时有54亿条边。我们表明，通过整合这些网络，一个相对简单的SEIR模型可以准确地拟合真实情况的轨迹，尽管随着时间的推移，人口的行为发生了实质性的变化。我们的模型预测，一小部分超级传播者的兴趣点占感染的绝大多数，限制每个兴趣点的最大占有率比统一减少流动性更有效。我们的模型还正确地预测了弱势种族和社会经济群体(2-8)中更高的感染率，这完全是流动性差异的结果：我们发现，弱势群体未能大幅降低他们的流动性，他们访问的兴趣点更加拥挤，因此与更高的风险相关。通过捕捉谁在哪些地点感染，我们的模型支持详细的分析，这些分析可以为更有效和公平地应对COVID-19提供信息。一个整合了细粒度流动网络的流行病学模型阐明了与流动相关的机制，这些机制导致了弱势社会经济和种族群体中更高的感染率，并发现限制地点的最大占有率对于遏制感染特别有效。
The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)(1). Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups(2-8) solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.An epidemiological model that integrates fine-grained mobility networks illuminates mobility-related mechanisms that contribute to higher infection rates among disadvantaged socioeconomic and racial groups, and finds that restricting maximum occupancy at locations is especially effective for curbing infections.