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Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non–extensive statistics

Non–extenstive statistics play a significant role in studying the dynamic behaviour of COVID-19 to assist epidemiological scientists to take appropriate decisions about pandemic planning. Generic non–extensive and modified–Tsallis statistics are used to analyze and predict the morbidity and mortalit...

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Autores principales: Yassin, Hayam, Abo Elyazeed, Eman R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10284937/
https://www.ncbi.nlm.nih.gov/pubmed/37344515
http://dx.doi.org/10.1038/s41598-023-36959-8
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author Yassin, Hayam
Abo Elyazeed, Eman R.
author_facet Yassin, Hayam
Abo Elyazeed, Eman R.
author_sort Yassin, Hayam
collection PubMed
description Non–extenstive statistics play a significant role in studying the dynamic behaviour of COVID-19 to assist epidemiological scientists to take appropriate decisions about pandemic planning. Generic non–extensive and modified–Tsallis statistics are used to analyze and predict the morbidity and mortality rates in future. The cumulative number of confirmed infection and death in Egypt at interval from 4 March 2020 till 12 April 2022 are analyzed using both non–extensive statistics. Also, the cumulative confirmed data of infection by gender, death by gender, and death by age in Egypt at interval from 4 March 2020 till 29 June 2021 are fitted using both statistics. The best fit parameters are estimated. Also, we study the dependence of the estimated fit parameters on the people gender and age. Using modified–Tsallis statistic, the predictions of the morbidity rate in female is more than the one in male while the mortality rate in male is greater than the one in female. But, within generic non-extensive statistic we notice that the gender has no effect on the rate of infections and deaths in Egypt. Then, we propose expressions for the dependence of the fitted parameters on the age. We conclude that the obtained fit parameters depend mostly on the age and on the type of the statistical approach applied and the mortality risk increased with people aged above 45 years. We predict - using modified–Tsallis - that the rate of infection and death in Egypt will begin to decrease till stopping during the first quarter of 2025.
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spelling pubmed-102849372023-06-23 Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non–extensive statistics Yassin, Hayam Abo Elyazeed, Eman R. Sci Rep Article Non–extenstive statistics play a significant role in studying the dynamic behaviour of COVID-19 to assist epidemiological scientists to take appropriate decisions about pandemic planning. Generic non–extensive and modified–Tsallis statistics are used to analyze and predict the morbidity and mortality rates in future. The cumulative number of confirmed infection and death in Egypt at interval from 4 March 2020 till 12 April 2022 are analyzed using both non–extensive statistics. Also, the cumulative confirmed data of infection by gender, death by gender, and death by age in Egypt at interval from 4 March 2020 till 29 June 2021 are fitted using both statistics. The best fit parameters are estimated. Also, we study the dependence of the estimated fit parameters on the people gender and age. Using modified–Tsallis statistic, the predictions of the morbidity rate in female is more than the one in male while the mortality rate in male is greater than the one in female. But, within generic non-extensive statistic we notice that the gender has no effect on the rate of infections and deaths in Egypt. Then, we propose expressions for the dependence of the fitted parameters on the age. We conclude that the obtained fit parameters depend mostly on the age and on the type of the statistical approach applied and the mortality risk increased with people aged above 45 years. We predict - using modified–Tsallis - that the rate of infection and death in Egypt will begin to decrease till stopping during the first quarter of 2025. Nature Publishing Group UK 2023-06-21 /pmc/articles/PMC10284937/ /pubmed/37344515 http://dx.doi.org/10.1038/s41598-023-36959-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Yassin, Hayam
Abo Elyazeed, Eman R.
Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non–extensive statistics
title Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non–extensive statistics
title_full Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non–extensive statistics
title_fullStr Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non–extensive statistics
title_full_unstemmed Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non–extensive statistics
title_short Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non–extensive statistics
title_sort prediction of the morbidity and mortality rates of covid-19 in egypt using non–extensive statistics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10284937/
https://www.ncbi.nlm.nih.gov/pubmed/37344515
http://dx.doi.org/10.1038/s41598-023-36959-8
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