20. Role of Uninsured in the Spread of COVID-19: A Bayesian Approach
Contributed abstract in session MC-4: Analytics for Mis/Disinformation in Healthcare, stream Regular talks.
Monday, 11:00-12:30Room: Room S3
Authors (first author is the speaker)
| 1. | Ozgur Araz
|
| Supply Chain Management and Analytics, University of Nebraska Lincoln | |
| 2. | Graham Liu
|
| University of Nebraska |
Abstract
We develop and use several geospatial models to estimate the effects of health insurance status and vaccination coverage in the spread of COVID-19. Specifically, we focus on COVID-19 related mortality, infection, and fatality rates as 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. 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.
Keywords
- Epidemiology and disease modelling
- Analytics
- Data analysis and risk management
Status: accepted
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