Dataset Information

Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China.

ABSTRACT: Few study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods. We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed obvious global aggregation and local aggregation. In addition, the analysis at streets-level suggested population density and the number of hospitals were associated with COVID-19 cases number. The epidemic situation showed obvious global and local spatial aggregations. High population density with larger number of hospitals may account for the aggregations. The epidemic in Wuhan was under control in a short time after strong quarantine measures and restrictions on movement of residents were implanted.

SUBMITTER: Liu W

PROVIDER: S-EPMC8249501 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China.

Liu Wei W Wang Dongming D Hua Shuiqiong S Xie Cong C Wang Bin B Qiu Weihong W Xu Tao T Ye Zi Z Yu Linling L Yang Meng M Xiao Yang Y Feng Xiaobing X Shi Tingming T Li Mingyan M Chen Weihong W

Scientific reports 20210701 1

Few study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods. We found decrease in effective reproduction n ...[more]

PMID: 34211038

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Project description:The aim of this study was to gain insights into the epidemiology, spatial trends, spatial structure evolution, and spatiotemporal aggregation characteristics of influenza epidemics during seasonal influenza and COVID-19 pandemic in Fuzhou from 2013 to 2022. Utilizing influenza case report data from Fuzhou spanning 2013 to 2022, we applied descriptive epidemiological methods to analyze the epidemiological characteristics and distribution patterns of reported influenza cases across various time periods, populations, and regions. Furthermore, we employed trend-surface analysis, kernel density estimation, and space-time scanning statistics to investigate the evolution of spatial trends, changes in spatial structure, and the spatiotemporal aggregation characteristics of the reported influenza incidence rate at the county level. A total of 19,135 influenza cases were reported in Fuzhou during the period of 2013-2022. The male-to-female ratio of cases was 1.31:1. The age group most affected by influenza was 0-19 years, accounting for 13,600 cases (71.07%), and the occupations mostly affected were children in the diaspora (6,570 cases, 34.33%), students (4,402 cases, 23.00%), and preschool children (2,595 cases, 13.56%). Areas with a high number of reported influenza cases were mainly located in the central part of Fuzhou City. On the overall trend of Fuzhou, the reported incidence rate of influenza exhibits a spatial trend characterized by a "high in the middle" pattern. Its spatial structure has evolved from a "triple nucleus - double nucleus" configuration and demonstrates the contraction trend of "clustering in the central urban area". Simultaneously, the spatial structure has transitioned from a "triple nucleus" to a "double nucleus" pattern, reflecting a trend of contraction towards "central city clustering." The results of space-time scanning identified Class I clusters of influenza cases in the Gulou and Jinan districts (RR = 47.99, LLR = 6917.94, P < 0.01), predominantly occurring during June and July 2022. The average annual reported incidence of influenza in Fuzhou was notably higher during the COVID-19 pandemic than the levels recorded during seasonal influenza outbreaks. Additionally, the concentration of influenza cases in central Fuzhou reflects a significant degree of spatiotemporal clustering of the epidemic.

Dataset Information

Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China.

Publications

Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China.

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