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Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan
COVID-19 emerged in Wuhan, China in December 2019 has now spread around the world causes damage to human life and economy. Pakistan is also severely effected by COVID-19 with 202,955 confirmed cases and total deaths of 4,118. Vector Autoregressive time series models was used to forecast new daily co...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7405884/ https://www.ncbi.nlm.nih.gov/pubmed/32834659 http://dx.doi.org/10.1016/j.chaos.2020.110189 |
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author | Khan, Firdos Saeed, Alia Ali, Shaukat |
author_facet | Khan, Firdos Saeed, Alia Ali, Shaukat |
author_sort | Khan, Firdos |
collection | PubMed |
description | COVID-19 emerged in Wuhan, China in December 2019 has now spread around the world causes damage to human life and economy. Pakistan is also severely effected by COVID-19 with 202,955 confirmed cases and total deaths of 4,118. Vector Autoregressive time series models was used to forecast new daily confirmed cases, deaths and recover cases for ten days. Our forecasted model results show maximum of 5,363/day new cases with 95% confidence interval of 3,013–8,385 on 3rd of July, 167/day deaths with 95% confidence interval of 112–233 and maximum recoveries 4,016/day with 95% confidence interval of 2,182–6,405 in the next 10 days. The findings of this research may help government and other agencies to reshape their strategies according to the forecasted situation. As the data generating process is identified in terms of time series models, then it can be updated with the arrival of new data and provide forecasted scenario in future. |
format | Online Article Text |
id | pubmed-7405884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74058842020-08-05 Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan Khan, Firdos Saeed, Alia Ali, Shaukat Chaos Solitons Fractals Article COVID-19 emerged in Wuhan, China in December 2019 has now spread around the world causes damage to human life and economy. Pakistan is also severely effected by COVID-19 with 202,955 confirmed cases and total deaths of 4,118. Vector Autoregressive time series models was used to forecast new daily confirmed cases, deaths and recover cases for ten days. Our forecasted model results show maximum of 5,363/day new cases with 95% confidence interval of 3,013–8,385 on 3rd of July, 167/day deaths with 95% confidence interval of 112–233 and maximum recoveries 4,016/day with 95% confidence interval of 2,182–6,405 in the next 10 days. The findings of this research may help government and other agencies to reshape their strategies according to the forecasted situation. As the data generating process is identified in terms of time series models, then it can be updated with the arrival of new data and provide forecasted scenario in future. Elsevier Ltd. 2020-11 2020-08-05 /pmc/articles/PMC7405884/ /pubmed/32834659 http://dx.doi.org/10.1016/j.chaos.2020.110189 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Khan, Firdos Saeed, Alia Ali, Shaukat Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan |
title | Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan |
title_full | Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan |
title_fullStr | Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan |
title_full_unstemmed | Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan |
title_short | Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan |
title_sort | modelling and forecasting of new cases, deaths and recover cases of covid-19 by using vector autoregressive model in pakistan |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7405884/ https://www.ncbi.nlm.nih.gov/pubmed/32834659 http://dx.doi.org/10.1016/j.chaos.2020.110189 |
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