What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization
What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization
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DOI：
10.1016/j.eclinm.2020.100354
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发表时间：
2020-05-01
影响因子：
15.1
通讯作者：
Liu, Jiming
中科院分区：
文献类型：
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作者：
Liu, Yang;Gu, Zhonglei;Liu, Jiming
背景：COVID-19已蔓延至六大洲。现在是更深入地了解可能发生的事情的时机。研究结果可以帮助通知缓解战略在疾病影响的countries.Methods：在这项工作中，我们研究的一个重要因素，表征疾病的传播模式：人与人之间的相互作用。我们开发了一个计算模型，以揭示不同年龄组的人口之间的社会接触模式的相互作用。我们将一个城市的人口分为7个年龄段：0-6岁（儿童）; 7-14岁（小学生和初中生）; 15-17岁（高中生）; 18-22岁（大学生）; 23 - 44岁（青年/中年人）; 45-64岁（中年/老年人）; 65岁或以上（老年人）。我们考虑了可能导致疾病传播的四种有代表性的社会接触环境：（1）个人家庭;（2）学校，包括小学/高中以及学院和大学;（3）各种物理工作场所;以及（4）人们可以聚集的公共场所和社区，如体育场，市场，广场和有组织的图尔斯。计算接触矩阵来描述四种设置中的每一种设置中不同年龄组之间的接触强度。通过整合四个接触矩阵与下一代矩阵，我们定量描述了COVID-19在不同人群中的潜在传播模式。研究结果：我们的研究集中在中国的6个代表性城市，即中国COVID-19的震中武汉，以及北京，天津，杭州，苏州和深圳，这是三个重点经济区的五个主要城市。结果表明，基于社会接触的分析可以很容易地解释潜在的疾病传播模式以及相关的风险（包括确诊和未确诊病例）。武汉市家庭和公共/社区接触相对密集的年龄组分布较为分散。这可以解释为什么COVID-19早期的传播主要发生在武汉的公共场所和家庭。我们估计2020年2月11日是武汉传播风险最高的日期，这与报告病例数的实际高峰期（2月4日至14日）一致。此外，2月12日和13日武汉报告的新增病例数量激增可以很容易地使用我们的模型来捕捉，这表明它有能力预测潜在/未经证实的病例。我们进一步估计了疫情发生后这些城市不同复工计划相关的疾病传播风险。估算结果与北京等政策相对宽松的城市和深圳等政策相对严格的城市的实际情况一致。解读：通过对特定年龄段的社会接触传播的深入描述，疾病爆发的回顾性和前瞻性情况，包括过去和未来的传播风险，不同干预措施的有效性，以及恢复正常社会活动的疾病传播风险，进行了计算分析和合理解释。该研究得出的结论不仅全面解释了COVID-19在中国的潜在传播模式，更重要的是，提供了基于社会接触的风险分析方法，可随时用于指导其他国家的干预计划和操作响应，从而战略性地减轻COVID-19大流行的影响。(C)2020作者（S）爱思唯尔有限公司出版
Background: COVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries.Methods: In this work, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's population into seven age-groups: 0-6 years old (children); 7-14 (primary and junior high school students); 15-17 (high school students); 18-22 (university students); 23 -44 (young/middle-aged people); 45-64 years old (middle-aged/elderly people); and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households; (2) schools, including primary/high schools as well as colleges and universities; (3) various physical workplaces; and (4) public places and communities where people can gather, such as stadiums, markets, squares, and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups in each of the four settings. By integrating the four contact matrices with the next-generation matrix, we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations.Findings: We focus our study on 6 representative cities in China: Wuhan, the epicenter of COVID-19 in China, together with Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, which are five major cities from three key economic zones. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases). In Wuhan, the age-groups involving relatively intensive contacts in households and public/communities are dispersedly distributed. This can explain why the transmission of COVID-19 in the early stage mainly took place in public places and families in Wuhan. We estimate that Feb. 11, 2020 was the date with the highest transmission risk in Wuhan, which is consistent with the actual peak period of the reported case number (Feb. 4-14). Moreover, the surge in the number of new cases reported on Feb. 12 and 13 in Wuhan can readily be captured using our model, showing its ability in forecasting the potential/unconfirmed cases. We further estimate the disease transmission risks associated with different work resumption plans in these cities after the outbreak. The estimation results are consistent with the actual situations in the cities with relatively lenient policies, such as Beijing, and those with strict policies, such as Shenzhen.Interpretation: With an in-depth characterization of age-specific social contact-based transmission, the retrospective and prospective situations of the disease outbreak, including the past and future transmission risks, the effectiveness of different interventions, and the disease transmission risks of restoring normal social activities, are computationally analyzed and reasonably explained. The conclusions drawn from the study not only provide a comprehensive explanation of the underlying COVID-19 transmission patterns in China, but more importantly, offer the social contact-based risk analysis methods that can readily be applied to guide intervention planning and operational responses in other countries, so that the impact of COVID-19 pandemic can be strategically mitigated. (C) 2020 The Author(s). Published by Elsevier Ltd.