The influencing factors of interprovincial transmission and development of COVID-19: Data analysis based on 30 provinces and cities in China
- Author:
Cuifang QI
1
Author Information
- Publication Type:Journal Article
- Keywords: Coronavirus disease 2019; Development; Epidemicoutbreak; Influencing factor; Transmission
- From: Journal of Xi'an Jiaotong University(Medical Sciences) 2020;41(5):757-763
- CountryChina
- Language:Chinese
- Abstract: Objective: To explore the factors affecting the interprovincial transmission and development of coronavirus disease 2019 (COVID-19) in China, with a view to providing recommendations for the formulation of preventive and control measures according to the actual conditions in different regions during the outbreak of the severe infectious disease. Methods: We collected the total number of confirmed cases of COVID-19 in 30 provinces and cities in China by the end of 24:00 February 25, 2020. Then we also collected the distance from each region to Hubei province, the proportion of population moving out from Wuhan city from January 1 to January 23, population density, urban population, traffic passenger volume, passenger turnover volume and other relevant data of each region.The cumulative confirmed cases including the most of imported cases by the end of 24:00 January 29, 2020 were taken as the first-stage cases cluster, and the cumulative newly confirmed cases including the most of secondary cases from 0:00 January 30 to 24:00 February 25, 2020 were taken as the second-stage cases cluster. Pearson bivariate correlation and linear fitting regression method were adopted to analyze the effects of population migration, transportation, economy and other factors on the transmission and development of COVID-19 in different regions. In the linear fitting regression, the multi-factor optimal subset model was used to screen the factors most closely related to COVID-19. Results: The distance from each region to Hubei province was negatively correlated with the first-stage cases cluster with the most of imported cases and the second-stage cases cluster with the most of secondary cases(t=-3.654, t=-3.679, both P<0.05). The proportion of population moving out from Wuhan, GDP, urban population, traffic passenger volume, and passenger turnover volume were positively correlated with the first-stage and second-stage cases cluster (all t>2.760, all P<0.05). GDP and the proportion of population moving out from Wuhan were most closely related to the first-stage cases cluster with the most of imported cases (t=4.173, t=7.851, all P<0.05). The first-stage cases cluster, the proportion of population moving out from Wuhan, and urban population were most closely related to the second-stage cases cluster with the most of secondary cases (t=4.734, t=3.491, t=2.855, all P<0.05). Results: GDP and the proportion of population moving out from Wuhan city had the greatest impact on the stage with the most of imported cases. The imported cases, the proportion of population moving out from Wuhan and the urban population had the greatest impact on the stage with the most of secondary cases. In the early stage of epidemic outbreak with the most of imported cases,we should consider strengthening the prevention and control of the epidemic in areas with high level of GDP and high proportion of population moving out from the epidemic area.The flow of population should be restricted more strictly as soon as possible in order to effectively curb the outbreak of the epidemic.In the later-stage of epidemic with the most of secondary cases, regionalized control policies should be formulated mainly according to the indicators of imported cases, the population proportion fromtheepidemic area, and the urban population. Finally, the contact of population should be restricted reasonably to prevent further development of the epidemic.
