A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicentre to adjacent regions. Issue 2 (21st February 2021)

• Record Type: Journal Article
• Authors: Zheng, Qinyue; Wang, Xinwei; Bao, Chunbing; Ji, Yunpeng; Liu, Hua; Meng, Qingchun; Pan, Qiuwei
• Is Part Of: Transboundary and emerging diseases. Volume 69:Issue 2(2022)
• Page Start: 549
• Page End: 558
• Publication Date: 2021-02-21
• Subjects: COVID‐19 -- epicentre -- local epidemic control -- mathematical modelling -- population movement; Veterinary medicine -- Periodicals; 636.089
• DOI: 10.1111/tbed.14019
• Languages: English
• ISSNs: 1865-1674

Abstract

Abstract: 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.