What is it about?

A new parametric regression model is developed based on the gamma-Maxwell distribution. We proved that the new model is more flexible than some others by analyzing COVID-19 mortality rates (with some covariables) in the fifty largest U.S. cities,

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Why is it important?

The analysis of the provided model yields several noteworthy conclusions. Firstly, the prevalence of diabetes (DB) is found to be extremely significant with a p-value of 0.00, and it is identified as the most influential predictor of mortality rate, in line with Corona et al. (2021). Counties with high diabetes rates also exhibit excess mortality not attributed to COVID-19, as per Stokes et al. (2020). Secondly, the unemployment rate (UN) has a significant impact, with a positive estimate and a p-value of 0.00, indicating that mortality rate increases in cities with higher unemployment rates, consistent with the findings of Paul et al. (2021) and Mirahmadizadeh et al. (2022). Thirdly, life expectancy (LE) is significant at the 1% level with a positive estimate, suggesting that mortality rates are slightly higher in cities with higher life expectancy, consistent with the observations of Notari and Torrieri (2022) and Wang et al. (2020), who demonstrated a positive correlation between life expectancy and the initial transmission growth rate of COVID-19.


This paper seeks to make a significant contribution to the existing literature by introducing a novel regression model while also examining the impact of COVID-19 in the United States. These innovative models not only enhance our understanding of pandemics but also offer valuable insights for mitigating and advancing pandemic-related research.

Nicollas Costa
Universidade Federal de Pernambuco

Read the Original

This page is a summary of: The gamma-Maxwell regression for COVID-19 mortality rates of the 50 U.S. largest cities, Model Assisted Statistics and Applications, September 2023, IOS Press,
DOI: 10.3233/mas-221419.
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