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Forecasting COVID-19: Vector Autoregression-Based Model
Forecasting the spread of COVID-19 infection is an important aspect of public health management. In this paper, we propose an approach to forecasting the spread of the pandemic based on the vector autoregressive model. Concretely, we combine the time series for the number of new cases and the number...
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/PMC8722659/ https://www.ncbi.nlm.nih.gov/pubmed/35004125 http://dx.doi.org/10.1007/s13369-021-06526-2 |
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author | Rajab, Khairan Kamalov, Firuz Cherukuri, Aswani Kumar |
author_facet | Rajab, Khairan Kamalov, Firuz Cherukuri, Aswani Kumar |
author_sort | Rajab, Khairan |
collection | PubMed |
description | Forecasting the spread of COVID-19 infection is an important aspect of public health management. In this paper, we propose an approach to forecasting the spread of the pandemic based on the vector autoregressive model. Concretely, we combine the time series for the number of new cases and the number of new deaths to obtain a joint forecasting model. We apply the proposed model to forecast the number of new cases and deaths in the UAE, Saudi Arabia, and Kuwait. Test results based on out-of-sample forecast show that the proposed model achieves a high level of accuracy that is superior to many existing methods. Concretely, our model achieves mean absolute percentage error (MAPE) of 0.35%, 2.03%, and 3.75% in predicting the number of daily new cases for the three countries, respectively. Furthermore, interpolating our predictions to forecast the cumulative number of cases, we obtain MAPE of 0.0017%, 0.002%, and 0.024%, respectively. The strong performance of the proposed approach indicates that it could be a valuable tool in managing the pandemic. |
format | Online Article Text |
id | pubmed-8722659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-87226592022-01-04 Forecasting COVID-19: Vector Autoregression-Based Model Rajab, Khairan Kamalov, Firuz Cherukuri, Aswani Kumar Arab J Sci Eng Research Article-Biological Sciences Forecasting the spread of COVID-19 infection is an important aspect of public health management. In this paper, we propose an approach to forecasting the spread of the pandemic based on the vector autoregressive model. Concretely, we combine the time series for the number of new cases and the number of new deaths to obtain a joint forecasting model. We apply the proposed model to forecast the number of new cases and deaths in the UAE, Saudi Arabia, and Kuwait. Test results based on out-of-sample forecast show that the proposed model achieves a high level of accuracy that is superior to many existing methods. Concretely, our model achieves mean absolute percentage error (MAPE) of 0.35%, 2.03%, and 3.75% in predicting the number of daily new cases for the three countries, respectively. Furthermore, interpolating our predictions to forecast the cumulative number of cases, we obtain MAPE of 0.0017%, 0.002%, and 0.024%, respectively. The strong performance of the proposed approach indicates that it could be a valuable tool in managing the pandemic. Springer Berlin Heidelberg 2022-01-03 2022 /pmc/articles/PMC8722659/ /pubmed/35004125 http://dx.doi.org/10.1007/s13369-021-06526-2 Text en © King Fahd University of Petroleum & Minerals 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 | Research Article-Biological Sciences Rajab, Khairan Kamalov, Firuz Cherukuri, Aswani Kumar Forecasting COVID-19: Vector Autoregression-Based Model |
title | Forecasting COVID-19: Vector Autoregression-Based Model |
title_full | Forecasting COVID-19: Vector Autoregression-Based Model |
title_fullStr | Forecasting COVID-19: Vector Autoregression-Based Model |
title_full_unstemmed | Forecasting COVID-19: Vector Autoregression-Based Model |
title_short | Forecasting COVID-19: Vector Autoregression-Based Model |
title_sort | forecasting covid-19: vector autoregression-based model |
topic | Research Article-Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8722659/ https://www.ncbi.nlm.nih.gov/pubmed/35004125 http://dx.doi.org/10.1007/s13369-021-06526-2 |
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