Zheng Qinyue, Wang Xinwei, Bao Chunbing, Ji Yunpeng, Liu Hua, Meng Qingchun, Pan Qiuwei
School of Management, Shandong Key Laboratory of Social Supernetwork Computation and Decision Simulation, Shandong University, Jinan, China.
Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, China.
Transbound Emerg Dis. 2022 Mar;69(2):549-558. doi: 10.1111/tbed.14019. Epub 2021 Feb 21.
Epicentres are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model. With this model, we revealed insight into how COVID-19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic.
疫情中心是新冠病毒研究的重点，而由于人口流动导致主要为输入性病例的新兴地区却常常被忽视。经典的 compartmental 模型虽有用，但在研究疫情时可能过度简化了其复杂性。本研究旨在开发一个多区域、分层级的数学模型，以更好地理解新冠病毒传播和防控的复杂性及异质性。通过纳入流行病学和人口流动数据，我们成功构建了一个多区域、分层级的 SLIHR 模型。利用该模型，基于大量人口流动网络数据，我们揭示了新冠病毒如何从疫情中心武汉传播至中国大陆其他地区。通过对不同防控措施效果的综合分析，我们确定一级应急响应、社区预防以及大数据工具的应用与疫情中心以外中国不同省份的本地疫情控制效果显著相关。总之，我们的多区域、分层级 SLIHR 模型揭示了新冠病毒如何从疫情中心武汉传播至中国其他地区，以及随后对本地疫情的控制情况。这些发现对许多其他国家和地区更好地理解和应对与当前新冠疫情相关的本地疫情具有重要意义。
