关键词: COVID-19疫情, 政府严控期, 疫情热点, 迁徙规模指数, 时空演变, 时空自相关, 空间马尔科夫链模型

Abstract:

The spatio-temporal evolution of major public infectious epidemics during government's strict control period in prefecture-level city can effectively reflect china's comprehensive emergency prevention and control capabilities. Based on statistical data including number of active cases, total confirmed, deaths of COVID-19 in 312 cities in China from January 24 to March 5, 2020, this paper uses methods including exploratory spatial data analysis, optimized hot spot analysis, spatial Markov chain, spatial panel data model to analyze spatio-temporal evolution characteristics of COVID-19 epidemic in China under government's strict control.The study found that: (1) The number of active cases of COVID-19 in China experienced characteristics of "rapid growth and diffusion, basic control, gradual decline, and complete control in some areas" and reached its peak on February 17, with an average daily growth rate of 17.5% during rising period and an average daily decline rate of 5.1% during falling period, and the epidemic change characteristics of most cities are similar to Nationwide's situation;(2) The high population mobility during Spring Festival transportation period is main reason for rapid expansion of epidemic. The Baidu's migration scale index for the 14 days prior to Wuhan closure was significantly correlated with total confirmed cases of COVID-19 in some cities; (3) The method called optimized hot spot analysis has identified that spatial distribution of hot spots of epidemic is stable and mainly distributed in 36 cities with Wuhan as the center and a radius of about 350 kilometers, while no statistically significant cold spot cities were identified; (4) The results of Markov chain transfer probability matrix analysis of active cased of COVID-19 in 312 cities show that various types are more stable and the probability of maintaining original type is greater than 0.85. The average probability of downward transfer is significantly higher than the probability of upward transfer. The probability of each type of transition changes significantly under the influence of different spatial lag types; (5) The estimation results of the spatial panel data model show that the number of active cases of COVID-19 in cites has spatial-temporal autocorrelation. This paper analyzed spatio-temporal evolution characteristics of COVID-19 epidemic during government's strict control period at prefecture-level city level from multiple perspectives, the focus of COVID-19 prevention and control is to reduce its spatio-temporal autocorrelation effects, this study provides a decision-making reference for government's current and future response to major public infectious epidemics.

Key words: COVID-19 epidemic, government's strict control period, epidemic's hot spot, migration scale index, spatio-temporal evolution, spatio-temporal autocorrelation, spatial markov chain model