Cargando…
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 constructe...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480131/ http://dx.doi.org/10.1007/s40314-021-01553-z |
_version_ | 1784576410878214144 |
---|---|
author | Ribeiro, Tatiane Fontana Cordeiro, Gauss M. Peña-Ramírez, Fernando A. Guerra, Renata Rojas |
author_facet | Ribeiro, Tatiane Fontana Cordeiro, Gauss M. Peña-Ramírez, Fernando A. Guerra, Renata Rojas |
author_sort | Ribeiro, Tatiane Fontana |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8480131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-84801312021-09-30 A new quantile regression for the COVID-19 mortality rates in the United States Ribeiro, Tatiane Fontana Cordeiro, Gauss M. Peña-Ramírez, Fernando A. Guerra, Renata Rojas Comp. Appl. Math. Article 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. Springer International Publishing 2021-09-29 2021 /pmc/articles/PMC8480131/ http://dx.doi.org/10.1007/s40314-021-01553-z Text en © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Ribeiro, Tatiane Fontana Cordeiro, Gauss M. Peña-Ramírez, Fernando A. Guerra, Renata Rojas A new quantile regression for the COVID-19 mortality rates in the United States |
title | A new quantile regression for the COVID-19 mortality rates in the United States |
title_full | A new quantile regression for the COVID-19 mortality rates in the United States |
title_fullStr | A new quantile regression for the COVID-19 mortality rates in the United States |
title_full_unstemmed | A new quantile regression for the COVID-19 mortality rates in the United States |
title_short | A new quantile regression for the COVID-19 mortality rates in the United States |
title_sort | new quantile regression for the covid-19 mortality rates in the united states |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480131/ http://dx.doi.org/10.1007/s40314-021-01553-z |
work_keys_str_mv | AT ribeirotatianefontana anewquantileregressionforthecovid19mortalityratesintheunitedstates AT cordeirogaussm anewquantileregressionforthecovid19mortalityratesintheunitedstates AT penaramirezfernandoa anewquantileregressionforthecovid19mortalityratesintheunitedstates AT guerrarenatarojas anewquantileregressionforthecovid19mortalityratesintheunitedstates AT ribeirotatianefontana newquantileregressionforthecovid19mortalityratesintheunitedstates AT cordeirogaussm newquantileregressionforthecovid19mortalityratesintheunitedstates AT penaramirezfernandoa newquantileregressionforthecovid19mortalityratesintheunitedstates AT guerrarenatarojas newquantileregressionforthecovid19mortalityratesintheunitedstates |