Spatiotemporal heterogeneity and its determinants of COVID-19 transmission in typical labor export provinces of China

Abstract

Background Previous studies have indicated that the risk of infectious disease spread is greatest in locations where a population has massive and convenient access to the epicenter of an outbreak. However, the spatiotemporal variations and risk determinants of COVID-19 in typical labor export regions of China remain unclear. Understanding the geographical distribution of the disease and the socio-economic factors affecting its transmission is critical for disease prevention and control. Methods A total of 2152 COVID-19 cases were reported from January 21 to February 24, 2020 across the 34 cities in Henan and Anhui. A Bayesian spatiotemporal hierarchy model was used to detect the spatiotemporal variations of the risk posed by COVID-19, and the GeoDetector q statistic was used to evaluate the determinant power of the potential influence factors. Results The risk posed by COVID-19 showed geographical spatiotemporal heterogeneity. Temporally, there was an outbreak period and control period. Spatially, there were high-risk regions and low-risk regions. The high-risk regions were mainly in the southwest areas adjacent to Hubei and cities that served as economic and traffic hubs, while the low-risk regions were mainly in western Henan and eastern Anhui, far away from the epicenter. The accessibility, local economic conditions, and medical infrastructure of Wuhan in Hubei province all played an important role in the spatiotemporal heterogeneity of COVID-19 transmission. The results indicated that the q statistics of the per capita GDP and the proportion of primary industry GDP were 0.47 and 0.47, respectively. The q statistic of the population flow from Wuhan was 0.33. In particular, the results showed that the q statistics for the interaction effects between population density and urbanization, population flow from Wuhan, per capita GDP, and the number of doctors were all greater than 0.8. Conclusions COVID-19 showed significant spatiotemporal heterogeneity in the labor export regions of China. The high-risk regions were mainly located in areas adjacent to the epicenter as well as in big cities that served as traffic hubs. Population access to the epicenter, as well as local economic and medical conditions, played an important role in the interactive effects of the disease transmission.

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

“…It was assumed that if a given factor (X) determined the response variable (Y), the response variable would present a similar spatial distribution to that of the factor [ 39 , 40 , 41 ]. It has been employed in the fields of geology, environment, health, disasters, and other many fields [ 42 , 43 , 44 ], as indicated in the following formula: where the q value ranged from 0 to 1, indicating the determinant power of the factor. The larger the value of q , the stronger the determinant power of the factor.…”

“…It was assumed that if a given factor (X) determined the response variable (Y), the response variable would present a similar spatial distribution to that of the factor [39][40][41]. It has been employed in the fields of geology, environment, health, disasters, and other many fields [42][43][44], as indicated in the following formula:…”

Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors

“…The basic idea of GeoDetector is that if a factor X affects a dependent variable Y to a certain extent, the dependent variable Y will exhibit a spatial distribution similar to that of factor X [ 24 , 27 , 28 ]. This idea is more comprehensive than traditional methods and truly reflects geographical phenomena, has been widely used in disaster and health fields [ 29 – 31 ]. In this study, GeoDetector was introduced to quantify the relationships between the dependent variable (population casualty rate) and the natural and socioeconomic factors, here we classified the values of each impact variable into 6 levels, and the definition of q as follows: where q represents the degree to which the influence factor X explains the spatial heterogeneity of the dependent variable Y (population casualty rate), and its value ranges from 0 to 1.…”

The Retreated Tendency of the COVID-19 Pandemic

“…Besides, Yechezkel et al also supported that human mobility and poverty are two key drivers of COVID-19 transmission and control, but those two drivers are contradictory to achieve because slowing human mobility during the pandemic is at cost of heavy restrictions (such as lock down), further leading economic crisis and exacerbating poverty [5]. Thus, Wang et al recommended a trade-off strategy between pandemic control and regular social activities through the spatiotemporal heterogeneity to replace the broad sweeping interventions [6,7]. On top of mentioned contradictory non-natural drivers, we cannot rule out the possibility that the observed COVID-19 dynamic trajectory is partially attribute to other unknown climatic factors [2,3].…”