Abstract
Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R2 values (for cases, R2 = 0.961; for deaths, R2 = 0.962), compared to GWR’s Adj. R2 values (for cases, R2 = 0.954; for deaths, R2 = 0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since it is not the first time humans are facing public health emergency, the findings of the present research on COVID-19 therefore can be used as a reference for policy designing and effective decision making.
Highlights
The Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2-COVID-19), first emerged in December 2019 in Wuhan province in China, has soon become a new public health concern across the world
To inform the policy-makers at both national and state levels, understanding the explanatory forces and related confounding factors with spatial patterns is of paramount importance
Crime, income, and migration were found to be strongly associated with COVID-19 casualties, and explained the maximum model variances
Summary
The Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2-COVID-19), first emerged in December 2019 in Wuhan province in China, has soon become a new public health concern across the world. The spillover effects and spatial transmission of the disease have been proven critical factors of the overall health burden caused by COVID-19. Spatial regression models can be useful for quantifying the risk of disease progression in the communities and developing spatially explicit maps to visualise the distribution of explanatory factors (Desmet SMU, Klaus; Romain Wacziarg UCLA, 2010; Ehlert, 2020; Xiong et al, 2020; Zhang and Schwartz, 2020). Developing spatial models and understanding the confounding effects of the variables is critical to sense the spatial variation of virus transmission at the local scale (Mollalo et al, 2020; Ren et al, 2020; Zhang and Schwartz, 2020)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
