Abstract
The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.
Highlights
The COVID-19 pandemic, initially reported in Wuhan, China in December 2019, is incredibly infectious and has had a large impact the world [1]
The eigenvector spatial filtering (ESF)-SVC model results were compared with those of the ordinary least square (OLS), ESF and geographically weighted regression (GWR) models, and the results showed that the proposed eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was a promising method in the context of COVID-19 health risk modeling and the discovery of spatial varying characteristics
The findings suggest that when temperature reached a certain point, the increase of temperature might have resulted in a decrease in the COVID-19 infection rate
Summary
The COVID-19 pandemic, initially reported in Wuhan, China in December 2019, is incredibly infectious and has had a large impact the world [1]. Organization (WHO) designated COVID-19 as a global pandemic on 11 March 2020 [2]. By November 2021, global cumulative cases were above 256 million and deaths were above 5.1 million, and the number keeps rising [3]. Given this background, researchers have conducted a large number and variety of studies (e.g., clinical research, statistical modeling and behavior analysis). Many have focused on trend analysis and time-series prediction [4,5,6,7,8], which could effectively estimate both the outbreak point and turning point of the COVID-19 pandemic as well as help to evaluate the effectiveness of measures and whether strategies should be strengthened [9,10,11]
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