Comparing the impact of COVID-19 on three states: a data-driven approach

K. Shao and Q. Shao

International Journal of Data Science, 2024, vol. 9, issue 2, 162-172

Abstract: The states of Florida, Michigan, and Ohio implemented rather different public health emergency policies to flatten the curve and save lives after the COVID-19 outbreak. This study aims to provide insight into one of the most important and fundamental topics for making public health policy: how to effectively handle life-threatening infectious diseases while minimising overall disruption of society. To compare these three states objectively, three severity risk metrics are proposed, and their log odds data are analysed. Both linear and multivariate models are applied to the log odds of the three severity rates. Contrary to visual inspection of the count data, only the result of one hypothesis test is statistically significant from the linear model, and none are significant from the multivariate model, at the significance level of 0.05. For a significant result, the estimates of the model parameters are in favor of Florida and Ohio.

Keywords: COVID-19; population infection rate; case fatality rate; senior fatality rate; log odds; statistical models; statistical hypothesis testing; state of Florida; State of Michigan; State of Ohio. (search for similar items in EconPapers)
Date: 2024
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