Liu Yang, Gu Zhonglei, Xia Shang, Shi Benyun, Zhou Xiao-Nong, Shi Yong, Liu Jiming
Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
HKBU-CSD & NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control, Hong Kong, China.
EClinicalMedicine. 2020 Apr 18;22:100354. doi: 10.1016/j.eclinm.2020.100354. eCollection 2020 May.
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.
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.
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.
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.
General Research Fund of the Hong Kong Research Grants Council; Key Project Grants of the National Natural Science Foundation of China.
新型冠状病毒肺炎（COVID-19）已蔓延至六大洲。当下是深入了解可能发生情况的契机。这些发现有助于为疫情受影响国家的缓解策略提供参考。
在本研究中，我们考察了表征疾病传播模式的一个关键因素：人与人之间的相互作用。我们开发了一个计算模型，以不同年龄组人群的社会接触模式来揭示这种相互作用。我们将城市人口划分为七个年龄组：0 - 6岁（儿童）；7 - 14岁（中小学生）；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岁及以上（老年人）。我们考虑了可能导致疾病传播的四种具有代表性的社会接触场景：（1）个体家庭；（2）学校，包括中小学以及高校；（3）各类工作场所；（4）人们可以聚集的公共场所和社区，如体育场、市场、广场及有组织的旅游活动场所。计算得出一个接触矩阵，以描述这四种场景中不同年龄组之间的接触强度。通过将这四个接触矩阵与下一代矩阵相结合，我们定量地刻画了COVID-19在不同人群中的潜在传播模式。
我们的研究聚焦于中国6个具有代表性的城市：COVID-19在中国的疫情中心武汉，以及来自三个关键经济区的五个主要城市北京、天津、杭州、苏州和深圳。结果表明，基于社会接触的分析能够很好地解释潜在的疾病传播模式以及相关风险（包括确诊和未确诊病例）。在武汉，家庭及公共/社区中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在武汉早期主要在公共场所和家庭中传播。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）相符。此外，我们的模型能够很好地捕捉到武汉2月12日和13日报告的新增病例激增情况，显示出其对潜在/未确诊病例的预测能力。我们进一步估计了疫情爆发后这些城市不同复工计划所带来的疾病传播风险。估计结果与政策相对宽松的城市（如北京）和政策严格的城市（如深圳）的实际情况相符。
通过深入刻画基于特定年龄社会接触的传播情况，对疾病爆发的回顾性和前瞻性情况进行了计算分析和合理解释，包括过去和未来的传播风险、不同干预措施的效果以及恢复正常社会活动的疾病传播风险。该研究得出的结论不仅全面解释了中国COVID-19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，可直接应用于指导其他国家的干预规划和应对行动，从而从战略上减轻COVID-19大流行的影响。
香港研究资助局一般研究基金；中国国家自然科学基金重点项目资助。
