Modeling Severe Acute Respiratory Syndrome Coronavirus 2019 (SARS-CoV-19) Incidence across Conterminous US Counties: A Spatial Perspective
نویسندگان
چکیده
Abstract. This study examines the spatial distribution of COVID-19 incidence and mortality rates across counties in conterminous US first 604 days pandemic. The dataset was acquired from Emory University, Atlanta, United States, which includes socio-economic variables health outcomes (N = 3106). OLS estimates accounted for 31% regression plain (adjusted R2 0.31) with AIC value 9263, Breusch-Pagan test heteroskedasticity indicated 472.4, multicollinearity condition number 74.25. result necessitated autoregressive models, were performed on GeoDa 1.18 software. ArcGIS 10.7 used to map residuals selected significant variables. Generally, Spatial Lag Model (SLM) Error (SEM) models substantial percentages plain. While efficiency is order SLM (AIC: 8264.4: BreucshPagan test: 584.4; Adj. 0.56) > SEM 8282.0; Breucsh-Pagan 697.2; 0.56). In this case, least predictive model SEM. contribution male, black race, poverty urban rural dummies that transmission more a function socio-economic, rural/urban conditions rather than outcomes. Although, diabetes obesity showed positive relationship incidence. However, relatively low based dataset. further concludes policymakers practitioners should consider peculiarities, rural-urban migration access resources reducing disease.
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ژورنال
عنوان ژورنال: Proceedings of the ICA
سال: 2021
ISSN: ['2570-2092']
DOI: https://doi.org/10.5194/ica-proc-4-79-2021