Zuo Qi, Du Jiaman, Di Baofeng, Zhou Junrong, Zhang Lixia, Liu Hongxia, Hou Xiaoyu
Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, China.
The School of International Studies, Sichuan University, Chengdu, China.
Front Big Data. 2022 Apr 27;5:705698. doi: 10.3389/fdata.2022.705698. eCollection 2022.
The COVID-19 epidemic poses a significant challenge to the operation of society and the resumption of work and production. How to quickly track the resident location and activity trajectory of the population, and identify the spread risk of the COVID-19 in geospatial space has important theoretical and practical significance for controlling the spread of the virus on a large scale. In this study, we take the geographical community as the research object, and use the mobile phone trajectory data to construct the spatiotemporal profile of the potential high-risk population. First, by using the spatiotemporal data collision method, identify, and recover the trajectories of the people who were in the same area with the confirmed patients during the same time. Then, based on the range of activities of both cohorts (the confirmed cases and the potentially infected groups), we analyze the risk level of the relevant places and evaluate the scale of potential spread. Finally, we calculate the probability of infection for different communities and construct the spatiotemporal profile for the transmission to help guide the distribution of preventive materials and human resources. The proposed method is verified using survey data of 10 confirmed cases and statistical data of 96 high-risk neighborhoods in Chengdu, China, between 15 January 2020 and 15 February 2020. The analysis finds that the method accurately simulates the spatiotemporal spread of the epidemic in Chengdu and measures the risk level in specific areas, which provides an objective basis for the government and relevant parties to plan and manage the prevention and control of the epidemic.
新冠疫情给社会运行以及复工复产带来了重大挑战。如何快速追踪人群的居住位置和活动轨迹，并识别新冠病毒在地理空间中的传播风险，对于大规模控制病毒传播具有重要的理论和实践意义。在本研究中，我们以地理社区为研究对象，利用手机轨迹数据构建潜在高风险人群的时空概况。首先，通过时空数据碰撞方法，识别并恢复在同一时间与确诊患者处于同一区域的人员轨迹。然后，基于两个群体（确诊病例和潜在感染群体）的活动范围，我们分析相关场所的风险等级并评估潜在传播规模。最后，我们计算不同社区的感染概率并构建传播的时空概况，以帮助指导预防物资和人力资源的分配。使用2020年1月15日至2020年2月15日期间中国成都10例确诊病例的调查数据和96个高风险社区的统计数据对所提出的方法进行了验证。分析发现，该方法准确模拟了成都疫情的时空传播，并测量了特定区域的风险等级，为政府及相关方规划和管理疫情防控提供了客观依据。
