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Population flow drives spatio-temporal distribution of COVID-19 in China

机译：人口流量推动中国Covid-19的时空分布

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Sudden, large-scale and diffuse human migration can amplify localized outbreaks of disease into widespread epidemics(1-4). Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here we use 11,478,484 counts of mobile phone data from individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until 19 February 2020, across mainland China. Third, we develop a spatio-temporal 'risk source' model that leverages population flow data (which operationalize the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, we use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan; the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks.Modelling of population flows in China enables the forecasting of the distribution of confirmed cases of COVID-19 and the identification of areas at high risk of SARS-CoV-2 transmission at an early stage.
机译：突然，大规模和弥漫性人类迁移可以扩增局部爆发的疾病进入广泛的流行病（1-4）。因此，汇总群体流量的快速准确跟踪可能是流行流行的信息。在这里，我们使用11,478,484次从1月1日至2020年1月24日至1月24日之间的武汉县离开或过境的移动电话数据计数，因为他们迁至中国大陆的296名县。首先，我们记录检疫的疗效在停止运动中。其次，我们表明，武汉人口流出的分布准确地预测了跨中国大陆2020年2月19日的严重急性呼吸综合征冠状病毒2（SARS-COV-2）的感染的相对频率和地理分布。第三，我们开发了一种时空的“风险源”模型，利用人口流量数据（从流行病综合征发出的风险）不仅要预测确认案件的分布，还可以识别具有高风险的地区在早期传输。第四，我们使用这种风险源模型来统计地推导到Covid-19的地理传播和基于武汉的人口流出的增长模式;该模型产生基准趋势和评估Covid-19的社区传输的风险，随着时间的推移为不同的位置的群落传播。这种方法可以通过任何国家的政策制定者使用，可提供数据，以使风险评估快速准确，并计划在持续爆发之前提前的有限资源分配。中国人口流量的规模也能够预测确诊病例的分配。 Covid-19在早期SARS-COV-2传输的高风险鉴定。

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  • 来源
    《Nature》 |2020年第7812期|389-394|共6页
  • 作者

    Jia Jayson S.; Lu Xin; Yuan Yun; Xu Ge; Jia Jianmin; Christakis Nicholas A.;

  • 作者单位

    Univ Hong Kong Fac Business & Econ Hong Kong Peoples R China;

    Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China|Karolinska Inst Dept Global Publ Hlth Stockholm Sweden;

    Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China;

    Hunan Univ Technol & Business Sch Management Changsha Peoples R China;

    Chinese Univ Hong Kong Sch Management & Econ Shenzhen Finance Inst Shenzhen Peoples R China|Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China;

    Yale Univ Yale Inst Network Sci New Haven CT USA;

  • 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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