Predicting COVID-19 Mortality Rates: An Analysis of Case Incidence, Mask Usage, and Machine Learning Approaches in U.S. Counties

Jacob Pratt (), Serkan Varol () and Serkan Catma ()
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Jacob Pratt: University of Tennessee Chattanooga, USA
Serkan Varol: University of Tennessee Chattanooga, USA
Serkan Catma: University of Tennessee Chattanooga, USA

RAIS Conference Proceedings 2022-2024 from Research Association for Interdisciplinary Studies

Abstract: The COVID-19 pandemic has necessitated the use of multidisciplinary approach to assess public health interventions. Data science has been widely utilized to promote interdisciplinary collaboration especially during the post-COVID era. This study uses a comprehensive dataset, including mask usage and epidemiological metrics from U.S. counties, to explore the correlation between public compliance with mask-wearing guidelines and COVID-19 mortality rates. After employing machine learning approaches such as linear regression, decision tree regression, and random forest regression, our analysis identified the random forest model as the most accurate model in predicting mortality rates due to its efficacy with the lowest error metrics. The models' performances were rigorously evaluated through error metric comparisons, highlighting the random forest model's robustness in handling complex interactions between variables. These findings provide actionable insights for public health strategists and policy makers, suggesting that enhanced mask compliance could significantly mitigate mortality rates during the ongoing pandemic and future health crises.

Keywords: machine learning applications; predictive modeling for public health; COVID-19 analysis; pandemic; model comparison (search for similar items in EconPapers)
Pages: 11 pages
Date: 2024-08
New Economics Papers: this item is included in nep-big, nep-cmp and nep-hea
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Published in Proceedings of the 38th International RAIS Conference on Social Sciences and Humanities, November 21-22, 2024, pages 28-38

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