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Spatial Modeling of Sociodemographic Risk for COVID-19 Mortality.
Erich Seamon1, Benjamin J Ridenhour1,2, Craig R Miller1,3
1University of Idaho, Institute for Modeling, Collaboration, and Innovation, Moscow, 83843, USA.
Medrxiv : the Preprint Server for Health Sciences
|August 7, 2023
Summary
Social vulnerability and obesity predict COVID-19 mortality across US regions. Geographically weighted regression models reveal localized risk factors, improving understanding of spatiotemporal variation in pandemic deaths.
Area of Science:
- Epidemiology
- Public Health
- Geospatial Analysis
Background:
- COVID-19 spread rapidly across the US with significant geographic variation.
- Few studies have examined spatiotemporal variation in COVID-19 deaths at refined geographic scales.
- Understanding the association between socioeconomic, health, demographic, and political factors and COVID-19 mortality is crucial.
Approach:
- Utilized multivariate regression on Health and Human Services (HHS) regions and nationwide county-level geographically weighted random forest (GWRF) models.
- Analyzed data across three distinct time frames corresponding to different viral variants (pre-May 2021, May-Nov 2021, Dec 2021-Apr 2022).
- Compared the predictive power of different modeling strategies for COVID-19 mortality.
Key Points:
- Multivariate regression indicated that Social Vulnerability Index (SVI) measures predicted higher COVID-19 mortality across all regions and time windows.
- Geographically Weighted Random Forest (GWRF) models demonstrated superior evaluation of feature importance and prediction accuracy.
- GWRF models highlighted the predictive value of localized factors like obesity, often masked by coarser analyses.
Conclusions:
- GWRF models offer a more robust and nuanced approach to understanding spatial variations in COVID-19 mortality.
- Localized sociodemographic risk factors significantly influence COVID-19 mortality, necessitating geographically specific public health strategies.
- This refined modeling strategy is valuable for identifying and addressing spatial disparities in pandemic outcomes.
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