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Mobility network models of COVID-19 explain inequities and inform reopening

机译：Covid-19的移动网络模型解释了不公平，并告知重新开放

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摘要

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.
机译：2019年冠状病毒疾病（Covid-19）大流行显着改变了人类流动模式，需要流行病学模型，可以捕获这些变化的流动性模型对严重急性呼吸综合征冠状病毒2（SARS-COV-2）〜（1）的蔓延的影响。 。在这里，我们介绍了一种易感性易感暴露的暴露（SEIR）模型，其集成了细粒度，动态移动网络，以模拟美国最大的十个大都市区的SARS-COV-2的扩散。我们的移动网络来自移动电话数据，从社区（或人口普查集团）的每小时移动到9800万人的移动到诸如餐馆和宗教场所等兴趣点，将56,945个人口普查集团与54亿的兴趣点连接到552,758点每小时边缘。我们表明，通过集成这些网络，尽管人口行为随着时间的推移，相对简单的SEIR模型可以准确地适合真实的案例轨迹。我们的模型预测，少数“超级普鲁斯普勒”的兴趣点占大多数感染，并且限制每个兴趣点的最大占用比均匀减少流动性更有效。我们的模型还正确预测了不利的种族和社会经济群体中的更高的感染率〜（2-8），仅作为流动性差异的结果：我们发现弱势群体并未能够尽可能地降低流动性，而且他们访问的兴趣更拥挤，因此风险更高。通过捕获谁被感染到哪个地方，我们的模型支持详细的分析，可以向Covid-19提供更有效和公平的政策响应。

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  • 来源
    《Nature》 |2021年第7840期|82-87|共6页
  • 作者

    Serina Chang; Emma Pierson; Pang Wei Koh; Jaline Gerardin; Beth Redbird; David Grusky; Jure Leskovec;

  • 作者单位

    Department of Computer Science Stanford University;

    Department of Computer Science Stanford University|Microsoft Research;

    Department of Computer Science Stanford University;

    Department of Preventive Medicine Northwestern University;

    Department of Sociology Northwestern University|Institute for Policy Research Northwestern University;

    Department of Sociology Stanford University|Center on Poverty and Inequality Stanford University;

    Department of Computer Science Stanford University|Chan Zuckerberg Biohub;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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