School of Geography and Environment, Jiangxi Normal University, No. 99, Ziyang Rd., Nanchang 330022, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, No. 11A Datun Rd., Chaoyang District, Beijing 100101, China.
Wayz AI Technology Company Limited, No. 58, Xiangke Rd., Pudong District, Shanghai 201210, China.
Int J Infect Dis. 2020 Jul;96:489-495. doi: 10.1016/j.ijid.2020.05.048. Epub 2020 May 17.
The outbreak of atypical pneumonia caused by the novel coronavirus (COVID-19) has currently become a global concern. The generations of the epidemic spread are not well known, yet these are critical parameters to facilitate an understanding of the epidemic. A seafood wholesale market and Wuhan city, China, were recognized as the primary and secondary epidemic sources. Human movements nationwide from the two epidemic sources revealed the characteristics of the first-generation and second-generation spreads of the COVID-19 epidemic, as well as the potential third-generation spread.
We used spatiotemporal data of COVID-19 cases in mainland China and two categories of location-based service (LBS) data of mobile devices from the primary and secondary epidemic sources to calculate Pearson correlation coefficient,r, and spatial stratified heterogeneity, q, statistics.
Two categories of device trajectories had generally significant correlations and determinant powers of the epidemic spread. Bothr and q statistics decreased with distance from the epidemic sources and their associations changed with time. At the beginning of the epidemic, the mixed first-generation and second-generation spreads appeared in most cities with confirmed cases. They strongly interacted to enhance the epidemic in Hubei province and the trend was also significant in the provinces adjacent to Hubei. The third-generation spread started in Wuhan from January 17-20, 2020, and in Hubei from January 23-24. No obvious third-generation spread was detected outside Hubei.
The findings provide important foundations to quantify the effect of human movement on epidemic spread and inform ongoing control strategies. The spatiotemporal association between the epidemic spread and human movements from the primary and secondary epidemic sources indicates a transfer from second to third generations of the infection. Urgent control measures include preventing the potential third-generation spread in mainland China, eliminating it in Hubei, and reducing the interaction influence of first-generation and second-generation spreads.
新型冠状病毒（COVID-19）引起的非典型肺炎爆发目前已成为全球关注的焦点。疫情的传播代际尚不清楚，但这些都是帮助我们了解疫情的关键参数。中国的一个海鲜批发市场和武汉市被认为是原发性和次发性疫情源地。从这两个疫情源地向全国各地的人群流动揭示了 COVID-19 疫情第一代和第二代传播的特征，以及第三代传播的潜在风险。
我们使用中国大陆 COVID-19 病例的时空数据以及原发性和次发性疫情源地的两类基于位置服务（LBS）的移动设备轨迹数据，计算了 Pearson 相关系数 r 和空间分层异质性 q 统计量。
两类设备轨迹与疫情传播具有显著的相关性和决定力。r 和 q 统计量均随与疫情源地的距离增加而减小，且它们的相关性随时间而变化。疫情初期，混合的第一代和第二代传播出现在大多数有确诊病例的城市，它们相互作用强烈，加剧了湖北省的疫情，这种趋势在毗邻湖北省的省份也很明显。第三代传播始于 2020 年 1 月 17-20 日的武汉市，以及 1 月 23-24 日的湖北省。在湖北省以外地区，未检测到明显的第三代传播。
这些发现为量化人类活动对疫情传播的影响提供了重要基础，并为正在进行的控制策略提供了信息。疫情传播与原发性和次发性疫情源地人群流动之间的时空关联表明，感染已从第二代向第三代转移。紧急控制措施包括防止中国大陆第三代传播的潜在风险，在湖北省消除第二代传播的风险，以及降低第一代和第二代传播的相互影响。
