Application of probabilistic models for extreme values to the COVID-2019 epidemic daily dataset

Autor: Daniel Canton Enriquez, Jose A. Niembro-Ceceña, Martin Muñoz Mandujano, Daniel Alarcon, Jorge Arcadia Guerrero, Ivan Gonzalez Garcia, Agueda Areli Montes Gutierrez, Alfonso Gutierrez-Lopez
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Data in Brief, Vol 40, Iss , Pp 107783- (2022)
Druh dokumentu: article
ISSN: 2352-3409
DOI: 10.1016/j.dib.2021.107783
Popis: Worldwide, COVID-19 coronavirus disease is spreading rapidly in a second and third wave of infections. In this context of increasing infections, it is critical to know the probability of a specific number of cases being reported. We collated data on new daily confirmed cases of COVID-19 breakouts in: Argentina, Brazil, China, Colombia, France, Germany, India, Indonesia, Iran, Italy, Mexico, Poland, Russia, Spain, U.K., and the United States, from the 20th of January, 2020 to 28th of August 2021. A selected sample of almost ten thousand data is used to validate the proposed models. Generalized Extreme-Value Distribution Type 1-Gumbel and Exponential (1, 2 parameters) models were introduced to analyze the probability of new daily confirmed cases. The data presented in this document for each country provide the daily probability of rate incidence. In addition, the frequencies of historical events expressed as a return period in days of the complete data set is provided.
Databáze: Directory of Open Access Journals