Cargando…

Predictive modeling of COVID-19 death cases in Pakistan

BACKGROUND: The world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life. The disease results in an acute respiratory infection that may result in pain and death. In Pakistan, the diseas...

Descripción completa

Detalles Bibliográficos
Autores principales: Daniyal, Muhammad, Ogundokun, Roseline Oluwaseun, Abid, Khadijah, Khan, Muhammad Danyal, Ogundokun, Opeyemi Eyitayo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647892/
https://www.ncbi.nlm.nih.gov/pubmed/33195884
http://dx.doi.org/10.1016/j.idm.2020.10.011
_version_ 1783607004768501760
author Daniyal, Muhammad
Ogundokun, Roseline Oluwaseun
Abid, Khadijah
Khan, Muhammad Danyal
Ogundokun, Opeyemi Eyitayo
author_facet Daniyal, Muhammad
Ogundokun, Roseline Oluwaseun
Abid, Khadijah
Khan, Muhammad Danyal
Ogundokun, Opeyemi Eyitayo
author_sort Daniyal, Muhammad
collection PubMed
description BACKGROUND: The world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life. The disease results in an acute respiratory infection that may result in pain and death. In Pakistan, the disease curve shows a vertical trend by almost 256K established cases of the diseases and 6035 documented death cases till August 5, 2020. OBJECTIVE: The primary purpose of this study is to provide the statistical model to predict the trend of COVID-19 death cases in Pakistan. The age and gender of COVID-19 victims were represented using a descriptive study. METHOD: ology: Three regression models, which include Linear, logarithmic, and quadratic, were employed in this study for the modelling of COVID-19 death cases in Pakistan. These three models were compared based on R(2), Adjusted R(2), AIC, and BIC criterions. The data utilized for the modelling was obtained from the National Institute of Health of Pakistan from February 26, 2020 to August 5, 2020. CONCLUSION: The finding deduced after the prediction modelling is that the rate of mortality would decrease by the end of October. The total number of deaths will reach its maximum point; then, it will gradually decrease. This indicates that the curve of total deaths will continue to be flat, i.e., it will shift to be constant, which is also the upper bound of the underlying function of absolute death.
format Online
Article
Text
id pubmed-7647892
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher KeAi Publishing
record_format MEDLINE/PubMed
spelling pubmed-76478922020-11-09 Predictive modeling of COVID-19 death cases in Pakistan Daniyal, Muhammad Ogundokun, Roseline Oluwaseun Abid, Khadijah Khan, Muhammad Danyal Ogundokun, Opeyemi Eyitayo Infect Dis Model Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu BACKGROUND: The world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life. The disease results in an acute respiratory infection that may result in pain and death. In Pakistan, the disease curve shows a vertical trend by almost 256K established cases of the diseases and 6035 documented death cases till August 5, 2020. OBJECTIVE: The primary purpose of this study is to provide the statistical model to predict the trend of COVID-19 death cases in Pakistan. The age and gender of COVID-19 victims were represented using a descriptive study. METHOD: ology: Three regression models, which include Linear, logarithmic, and quadratic, were employed in this study for the modelling of COVID-19 death cases in Pakistan. These three models were compared based on R(2), Adjusted R(2), AIC, and BIC criterions. The data utilized for the modelling was obtained from the National Institute of Health of Pakistan from February 26, 2020 to August 5, 2020. CONCLUSION: The finding deduced after the prediction modelling is that the rate of mortality would decrease by the end of October. The total number of deaths will reach its maximum point; then, it will gradually decrease. This indicates that the curve of total deaths will continue to be flat, i.e., it will shift to be constant, which is also the upper bound of the underlying function of absolute death. KeAi Publishing 2020-11-07 /pmc/articles/PMC7647892/ /pubmed/33195884 http://dx.doi.org/10.1016/j.idm.2020.10.011 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
Daniyal, Muhammad
Ogundokun, Roseline Oluwaseun
Abid, Khadijah
Khan, Muhammad Danyal
Ogundokun, Opeyemi Eyitayo
Predictive modeling of COVID-19 death cases in Pakistan
title Predictive modeling of COVID-19 death cases in Pakistan
title_full Predictive modeling of COVID-19 death cases in Pakistan
title_fullStr Predictive modeling of COVID-19 death cases in Pakistan
title_full_unstemmed Predictive modeling of COVID-19 death cases in Pakistan
title_short Predictive modeling of COVID-19 death cases in Pakistan
title_sort predictive modeling of covid-19 death cases in pakistan
topic Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647892/
https://www.ncbi.nlm.nih.gov/pubmed/33195884
http://dx.doi.org/10.1016/j.idm.2020.10.011
work_keys_str_mv AT daniyalmuhammad predictivemodelingofcovid19deathcasesinpakistan
AT ogundokunroselineoluwaseun predictivemodelingofcovid19deathcasesinpakistan
AT abidkhadijah predictivemodelingofcovid19deathcasesinpakistan
AT khanmuhammaddanyal predictivemodelingofcovid19deathcasesinpakistan
AT ogundokunopeyemieyitayo predictivemodelingofcovid19deathcasesinpakistan