Modeling Severe Acute Respiratory Syndrome Coronavirus 2019 (SARS-CoV-19) Incidence across Conterminous US Counties: A Spatial Perspective

Olawale Oluwafemi,Oluseyi Oladepo

doi:10.5194/ica-proc-4-79-2021

Abstract

Abstract. This study examines the spatial distribution of COVID-19 incidence and mortality rates across the counties in the conterminous US in the first 604 days of the pandemic. The dataset was acquired from Emory University, Atlanta, United States, which includes socio-economic variables and health outcomes variables (N = 3106). OLS estimates accounted for 31% of the regression plain (adjusted R2 = 0.31) with AIC value of 9263, and Breusch-Pagan test for heteroskedasticity indicated 472.4, and multicollinearity condition number of 74.25. This result necessitated spatial autoregressive models, which were performed on GeoDa 1.18 software. ArcGIS 10.7 was used to map the residuals and selected significant variables. Generally, the Spatial Lag Model (SLM) and Spatial Error Model (SEM) models accounted for substantial percentages of the regression plain. While the efficiency of the models is the order of SLM (AIC: 8264.4: BreucshPagan test: 584.4; Adj. R2 = 0.56) > SEM (AIC: 8282.0; Breucsh-Pagan test: 697.2; Adj. R2 = 0.56). In this case, the least predictive model is SEM. The significant contribution of male, black race, poverty and urban and rural dummies to the regression plain indicated that COVID-19 transmission is more of a function of socio-economic, and rural/urban conditions rather than health outcomes. Although, diabetes and obesity showed a positive relationship with COVID-19 incidence. However, the relationship was relatively low based on the dataset. This study further concludes that the policymakers and health practitioners should consider spatial peculiarities, rural-urban migration and access to resources in reducing the transmission of COVID-19 disease.

Highlights

SARS-CoV-19 otherwise known as Severe Acute Respiratory Syndrome Coronavirus 2019 was first reported on December 30, 2019, in Wuhan, China, as a pneumonia-related diagnosis (Xie et al, 2020).Several weeks later, The World Health Organization (WHO) officially tagged coronavirus "2019-ncov" as 2019 novel coronavirus and estimated its incubation period to be about 2 to 14 days
The study found Spatial Lag Model (SLM) to be a better and most preferred model above Spatial Error Model (SEM) due to low AIC value and Breusch-Pagan test when compared to SEM
The study revealed the footprints of COVID-19 incidences across counties in contiguous United States between January 21 and September 16 2020

Summary

Introduction

SARS-CoV-19 otherwise known as Severe Acute Respiratory Syndrome Coronavirus 2019 was first reported on December 30, 2019, in Wuhan, China, as a pneumonia-related diagnosis (Xie et al, 2020).Several weeks later, The World Health Organization (WHO) officially tagged coronavirus "2019-ncov" as 2019 novel coronavirus and estimated its incubation period to be about 2 to 14 days. The primary aim of the spatial analytical process is to measure geographic distributions, analyze patterns, map clusters, and model spatial relationships among observed variables. As of April, 7th 2021, in the US, the virus has infected 30,732,250 million people and resulted in 554,579 deaths (The New York Times, The COVID-19 Tracking Project, 2020). R0 will be greater than 1 during an outbreak and will drop to less than 1 as the outbreak subsides This statistic can be used to estimate the proportion to be vaccinated within a population in order to control the spread of the infection (Delamater, et al, 2019). Research suggested that the average number of days from transmission of COVID-19 to case confirmation was 18 (Backer, et al, 2020). The challenge of COVID19 has been of global concern because of it's "unknowns"

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