ArticleApplied geography (Sevenoaks, England)2022
Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA.
Article in Applied geography (Sevenoaks, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- California Healthy Places Index and COVID-19 hospitalization risk: a patient-level analysis.BMC public health · 2026Article
- Modeling the spatial spread of COVID-19 in Kenya.BMC public health · 2025Article
- Modelling the joint association of socio-economic disadvantage, diabetes, and obesity on COVID-19 mortality in Greater Mexico City.Scientific reports · 2025Article
- Article
- A Bayesian spatio-temporal model of COVID-19 spread in England.Scientific reports · 2024Article
- Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model.PLoS neglected tropical diseases · 2023Article
- Daily changes in spatial accessibility to ICU beds and their relationship with the case-fatality ratio of COVID-19 in the state of Texas, USA.Applied geography (Sevenoaks, England) · 2023Article
- Interactive impacts of walkability, social vulnerability, & travel behavior on COVID-19 mortality: A hierarchical Bayesian spatial random parameter approach.Sustainable cities and society · 2023Article
- The burden of big spaces: Russian regions and cities in the COVID-19 pandemic.Letters in spatial and resource sciences · 2023Article
- COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities.The Lancet. Public health · 2022Review
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Authors and funding
4 authors.
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Abstract
Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models to assess the influence of socio-economic and health conditions on COVID-19 mortality in contiguous USA. For a start, stepwise and exploratory regression models were employed to isolate the main explanatory variables for COVID-19 mortality from the ensemble 33 socio-economic and health parameters between January 1st and 16th of September 2020. Preliminary results showed that only five out of the examined variables (case fatality rate, vulnerable population, poverty, percentage of adults that report no leisure-time physical activity, and percentage of the population with access to places for physical activity) can explain the variability of COVID-19 mortality across the Counties of contiguous USA within the study period. Consequently, we employ three global and two local regression algorithms to model the relationship between COVID-19 and the isolated socio-economic and health variables. The outcomes of the regression analyses show that the adopted models can explain 61%-81% of COVID-19 mortality across the contiguous USA within the study period. However, MGWR yielded the highest R
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