Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Rajab, Khairan, Kamalov, Firuz, Cherukuri, Aswani Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
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
_version_ 1784625559853072384
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
work_keys_str_mv AT rajabkhairan forecastingcovid19vectorautoregressionbasedmodel
AT kamalovfiruz forecastingcovid19vectorautoregressionbasedmodel
AT cherukuriaswanikumar forecastingcovid19vectorautoregressionbasedmodel