Spatiotemporal Distribution And Influence Factors Study Of COVID-19 In Jiangsu Province

During the Spring Festival,the new type of coronary pneumonia spread from the South China seafood market in Wuhan,Hubei and even the whole country.The characteristics of its susceptible population and long incubation period make it difficult to control the new infectious disease,which seriously threatens human health and social stability.The novel coronavirus pneumonia epidemic prevention and control situation is grim in Jiangsu Province,located in the Yangtze River Delta.The spatiotemporal of COVID-19 in Jiangsu province was analyzed based on case data.Based on socioeconomic data,the dominant factors of infection risk change were studied,and the network of migration flow of the sick population was constructed,in order to provide prevention and control suggestions for the transmission of similar new infectious diseases.Covid-19 in Jiangsu province was studied based on Bayes spatiotemporal hierarchical model.The temporal trend,spatial pattern and local trend of infection were analyzed.Then,the covid infection risk differences were studied,mainly from the perspective of crowd,social economy and so on.Finally,the input-output flow network is constructed,and its spatial and temporal distribution characteristics are analyzed.Main research results include:(1)The infection risk of covid-19 in Jiangsu province increased rapidly and then slowly declined.The infection risk increased much faster than its decline rate.The covid-19 infection risk is heterogeneous in spatial distribution,and there is a phenomenon of cold hot spot metastasis.The hot spots first shifted from northern Jiangsu to South of Jiangsu,and then dispersed in various cities.The number of cold spots,hot spots and temperature points showed that the spread of covid-19 started from local.In addition,the trend of infection risk in cold and hot spots was also different in different time periods.(2)The covid-19 infection risk factors are affected by multiple factors in the regional difference of infection risk.From January 22 to January 30,2020,the distance to Wuhan and per capita disposable income were the main factors of infection risk;From January 31 to February 6,the leading factors of infection risk change were per capita disposable income,distance to Wuhan,number of doctors per thousand people,and employees in the primary industry;From February 7 to February 18,the leading factors of infection risk change were per capita disposable income and GDP.(3)In the transmission network of input and output flows,Xuzhou is seriously affected by internal flow,while Suzhou and Nanjing are seriously affected by external flow;The first type of migration flows mainly to southern Jiangsu,while the second type flows more evenly to all regions of the province;In addition,the impact of Hubei Province on the cities in the first and second categories of migratory flows is on the decline,while the interaction within the province is strengthening;Suzhou,Nanjing and Lianyungang play an important role in the communication network.The paper has 29 figures,22 tables and 88 references.