Spatiotemporal pattern recognition and dynamical analysis of COVID-19 in Shanghai, China. (7th December 2022)

• Record Type: Journal Article
• Authors: Zhong, Haonan; Wang, Kaifa; Wang, Wendi
• Is Part Of: Journal of theoretical biology. Volume 554(2022)
• Publication Date: 2022-12-07
• Subjects: Spatial statistics -- Autocorrelation analysis -- Dynamic model -- Daily reproduction number; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2022.111279
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: Shanghai suffered a large outbreak of Omicron mutant of COVID-19 at the beginning of March 2022. To figure out the spatiotemporal patterns of the epidemic, a retrospective statistical investigation, coupled with a dynamic model, is implemented in this study. The hotspots of SARS-CoV-2 transmissions are identified, and strong aggregative effects in the decay stage are found. Besides, the visualization of disease diffusion is provided to show how COVID-19 disease invades all districts of Shanghai in the early stage. Furthermore, the calculations from the dynamic model manifest the effect of detections to suppress the epidemic dissemination. These results reveal the strategies to improve the spatial control of disease. Highlights: The invasion mechanisms of COVID-19 at spatial scale in Shanghai are analyzed. Aggregative effect and spatiotemporal patterns of different stages are identified. Spatial geographic statistics and dynamic model are coupled and compared. Suggestions are given for the control of COVID-19 in an international metropolis.