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Estimating the Role of Uninsured in the Spread of COVID-19 via Geospatial Bayesian Models

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  • Yanxin Liu
  • Özgür M. Araz

Abstract

In this article, we develop and use several geospatial models to estimate the effects of health insurance status and vaccination coverage in the spread of COVID-19. We focus on COVID-19 related mortality, infection, and fatality rates as health outcomes. Utilizing these existing methods an analysis is conducted at the national level using data for every county in the United States, then a case is formed with analyses where the counties are restricted to those within Texas and Mid USA, which includes Nebraska, Iowa, Missouri, and Kansas. The main goal of this study is to investigate how different factors, such as vaccination status, socioeconomic status including insurance, and geographic location, may have affected the geospatial spread of COVID-19 pandemic and related health outcomes. Results show that the percentage of the senior population, the vaccination rate and the uninsured percentage are the most important variables for predicting infection rates and the fatality rates, while the overall social vulnerability index has a huge impact on mortality rates and infection rates. Our analyses can shed light on the impact of health insurance status and inform policy makers regarding the importance of health insurance coverage.

Suggested Citation

  • Yanxin Liu & Özgür M. Araz, 2025. "Estimating the Role of Uninsured in the Spread of COVID-19 via Geospatial Bayesian Models," North American Actuarial Journal, Taylor & Francis Journals, vol. 29(1), pages 199-223, January.
    • Handle: RePEc:taf:uaajxx:v:29:y:2025:i:1:p:199-223
    DOI: 10.1080/10920277.2024.2336229
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