A new quantile regression for the COVID-19 mortality rates in the United States

An outbreak of coronavirus disease 2019 (COVID-19) has quickly spread worldwide from December 2019, thus characterizing a pandemic. Until August 2020, the United States of America (U.S.) accounted for almost one-fourth of the total deaths by coronavirus. In this paper, a new regression is constructed to identify the variables that affected the first-wave COVID-19 mortality rates in the U.S. states. The mortality rates in these states are computed by considering the total of deaths recorded on 30, 90, and 180 days from the 10th recorded case. The proposed regression is compared to the Kumaraswamy and unit-Weibull regressions, which are useful in modeling proportional data. It provides the best goodness-of-fit measures for the mortality rates and explains [Formula: see text] of its variability. The population density, Gini coefficient, hospital beds, and smoking rate explain the median of the COVID-19 mortality rates in these states. We believe that this article’s results reveal important points to face pandemic threats by the State Health Departments in the U.S.