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Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA
Several researchers have used standard time series models to analyze future patterns of COVID-19 and the Causal impact of vaccinations in various countries. Bayesian structural time series (BSTS) and ARIMA (Autoregressive Integrated Moving Average) models are used to forecast time series. The goal o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9191752/ http://dx.doi.org/10.1007/s40745-022-00418-4 |
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author | Navas Thorakkattle, Muhammed Farhin, Shazia khan, Athar Ali |
author_facet | Navas Thorakkattle, Muhammed Farhin, Shazia khan, Athar Ali |
author_sort | Navas Thorakkattle, Muhammed |
collection | PubMed |
description | Several researchers have used standard time series models to analyze future patterns of COVID-19 and the Causal impact of vaccinations in various countries. Bayesian structural time series (BSTS) and ARIMA (Autoregressive Integrated Moving Average) models are used to forecast time series. The goal of this study is to look at a much more adaptable effective methodology for dissecting the major components of a time series that breaks down the main parts of a time series. Within the period of March 1, 2020, to June 30, 2021, we used these state space model to explore the forecast patterns of COVID-19 in five afflicted nations.In addition, we used intervention analysis under BSTS models to examine the casual effect of vaccines in these countries, and we reached higher levels of accuracy than ARIMA models. According to forecasts, the number of confirmed cases in the United States, the United Kingdom, the United Arab Emirates, Bahrain, and India will climb by 1.17%, 19.4%, 15.5%, 13.8% , and 8%, respectively, during the next 60 days. On the other side, death rates in the United States, the United Kingdom, the United Arab Emirates, Bahrain, and India are expected to rise by 2.7%, 3.5%, 15.8%, 9.4%, and 14.8%, respectively. In addition, By using effective and quick vaccination, the United States, United Kingdom, and UAE have been able to reduce the number of mortality. On the other hand, vaccination is currently unable to decrease the rate of cases and deaths in India. Overall, the Indian healthcare system is likely to be seriously over-burdened in the next month. Though the USA and UK have managed to cut down the rates of COVID-19 deaths,but in UK and UAE number of confirmed cases are high as compared to other nations,so serious efforts will be required to keep these controllable. On the other hand,To keep things under control, Bahrain and four other countries has to speed up vaccinations. |
format | Online Article Text |
id | pubmed-9191752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-91917522022-06-17 Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA Navas Thorakkattle, Muhammed Farhin, Shazia khan, Athar Ali Ann. Data. Sci. Article Several researchers have used standard time series models to analyze future patterns of COVID-19 and the Causal impact of vaccinations in various countries. Bayesian structural time series (BSTS) and ARIMA (Autoregressive Integrated Moving Average) models are used to forecast time series. The goal of this study is to look at a much more adaptable effective methodology for dissecting the major components of a time series that breaks down the main parts of a time series. Within the period of March 1, 2020, to June 30, 2021, we used these state space model to explore the forecast patterns of COVID-19 in five afflicted nations.In addition, we used intervention analysis under BSTS models to examine the casual effect of vaccines in these countries, and we reached higher levels of accuracy than ARIMA models. According to forecasts, the number of confirmed cases in the United States, the United Kingdom, the United Arab Emirates, Bahrain, and India will climb by 1.17%, 19.4%, 15.5%, 13.8% , and 8%, respectively, during the next 60 days. On the other side, death rates in the United States, the United Kingdom, the United Arab Emirates, Bahrain, and India are expected to rise by 2.7%, 3.5%, 15.8%, 9.4%, and 14.8%, respectively. In addition, By using effective and quick vaccination, the United States, United Kingdom, and UAE have been able to reduce the number of mortality. On the other hand, vaccination is currently unable to decrease the rate of cases and deaths in India. Overall, the Indian healthcare system is likely to be seriously over-burdened in the next month. Though the USA and UK have managed to cut down the rates of COVID-19 deaths,but in UK and UAE number of confirmed cases are high as compared to other nations,so serious efforts will be required to keep these controllable. On the other hand,To keep things under control, Bahrain and four other countries has to speed up vaccinations. Springer Berlin Heidelberg 2022-06-13 2022 /pmc/articles/PMC9191752/ http://dx.doi.org/10.1007/s40745-022-00418-4 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Navas Thorakkattle, Muhammed Farhin, Shazia khan, Athar Ali Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA |
title | Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA |
title_full | Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA |
title_fullStr | Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA |
title_full_unstemmed | Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA |
title_short | Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA |
title_sort | forecasting the trends of covid-19 and causal impact of vaccines using bayesian structural time series and arima |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9191752/ http://dx.doi.org/10.1007/s40745-022-00418-4 |
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