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Prediction of the COVID-19 transmission: a case study of Pakistan
The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochast...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244643/ https://www.ncbi.nlm.nih.gov/pubmed/37203211 http://dx.doi.org/10.1017/S0950268823000730 |
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author | Sabir, Qurat Ul An Shafqat, Ambreen Aslam, Muhammad |
author_facet | Sabir, Qurat Ul An Shafqat, Ambreen Aslam, Muhammad |
author_sort | Sabir, Qurat Ul An |
collection | PubMed |
description | The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We’ve used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum log L and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model. |
format | Online Article Text |
id | pubmed-10244643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102446432023-06-08 Prediction of the COVID-19 transmission: a case study of Pakistan Sabir, Qurat Ul An Shafqat, Ambreen Aslam, Muhammad Epidemiol Infect Original Paper The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We’ve used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum log L and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model. Cambridge University Press 2023-05-19 /pmc/articles/PMC10244643/ /pubmed/37203211 http://dx.doi.org/10.1017/S0950268823000730 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
spellingShingle | Original Paper Sabir, Qurat Ul An Shafqat, Ambreen Aslam, Muhammad Prediction of the COVID-19 transmission: a case study of Pakistan |
title | Prediction of the COVID-19 transmission: a case study of Pakistan |
title_full | Prediction of the COVID-19 transmission: a case study of Pakistan |
title_fullStr | Prediction of the COVID-19 transmission: a case study of Pakistan |
title_full_unstemmed | Prediction of the COVID-19 transmission: a case study of Pakistan |
title_short | Prediction of the COVID-19 transmission: a case study of Pakistan |
title_sort | prediction of the covid-19 transmission: a case study of pakistan |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244643/ https://www.ncbi.nlm.nih.gov/pubmed/37203211 http://dx.doi.org/10.1017/S0950268823000730 |
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