GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

Abstract

Journal Pre-proof J o u r n a l P r e -p r o o f 2 GIS-based Spatial Modeling of COVID-19 Incidence Rate in the Continental United States AbstractDuring the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R 2 : 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.

County-Level Longitudinal Clustering of COVID-19 Mortality to Incidence Ratio in the United States

“…Wu et al and Mollalo et al, in nationwide studies in the USA, showed that exposure to PM increased COVID-19 mortality and severity 42,50,51 . Setti et al reported a significant relationship between PM and experiencing COVID-19 in Italy (January 1, 2020) 52 .…”

Distribution of the environmental and socioeconomic risk factors on COVID-19 death rate across continental United States: A spatial nonlinear analysis

“…They suggested rigorous prevention measure should be followed by the high-risk susceptible people. Mollalo et al (2020) mapped the spatial variability of the relationships between COVID-19 incidence rate and income inequality, median household, the proportion of black females and proportion of nurse practitioners using multiscale geographically weighted regression (MGWR).…”

An Approach for Measuring Spatial Similarity Among COVID-19 Epicenters

“…Economic, cultural, social, and human activities have been disrupted. Therefore, it is very important to study the prevalence of this disease, the rate of spread and predictability, to identify high-risk areas and affective factors (Mollalo et al, 2020).…”

“…The GIS is an essential computer based tool for studying the spatial distribution, effective factors and spatio-temporal distribution of infectious diseases (Mollalo et al, 2020;Razavi Termeh and Sadeghi Niaraki., 2015). Despite the prevalence of COVID-19 in many countries around the world, there has been a notable increase in the number of cases and deaths in some countries such as the United States, Spain, and Italy (WHO, 2020a).…”

“…Recent worldwide studies have shown that the severity and speed of the COVID-19 spread can be affected by multiple factors such as air pollution (Wu et al, 2020), high temperature and humidity (Wang et al, 2020;Ma et al, 2020), smoking (Taghizadeh-Hesary and Akbari, 2020). Mollalo et al (2020) used geographically weighted regression (GWR) and multiscale GWR (MGWR) models to investigate spatial dependence for COVID-19 cases associated with 35 environmental, socioeconomic, topographic, and demographic factors. Zhao et al (2020) investigated the association between national train transportation and the COVID-19 outbreak in China.…”