Forecasting COVID-19: Vector Autoregression-Based Model

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dc.contributor.authorRajab, Khairan
dc.contributor.authorKamalov, Firuz
dc.contributor.authorCherukuri, Aswani Kumar
dc.date.accessioned2022-01-19T12:22:24Z
dc.date.available2022-01-19T12:22:24Z
dc.date.copyright© 2022
dc.date.issued2022
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article paper is published in SArabian Journal for Science and Engineering (2022), available online at: https://doi.org/10.1007/s13369-021-06526-2en_US
dc.description.abstractForecasting 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. © 2022, King Fahd University of Petroleum & Minerals.en_US
dc.description.sponsorshipNajran University - NU/-/SERC/10/597 This work was supported in part by the Najran University under Grant NU/-/SERC/10/597.
dc.identifier.citationRajab, K., Kamalov, F., & Cherukuri, A. K. (2022). Forecasting COVID-19: Vector autoregression-based model. Arabian Journal for Science and Engineering, 47(6), 6851-6860. doi:10.1007/s13369-021-06526-2en_US
dc.identifier.issn2193567X
dc.identifier.urihttps://doi.org/10.1007/s13369-021-06526-2
dc.identifier.urihttp://hdl.handle.net/20.500.12519/499
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relationRajab, K., Najran University, Najran, Saudi Arabia; Kamalov, F., Canadian University Dubai, Dubai, United Arab Emirates; Cherukuri, A.K., Vellore Institute of Technology, Vellore, India
dc.relation.ispartofseriesArabian Journal for Science and Engineering;
dc.rightsLicense to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © 2022, King Fahd University of Petroleum & Minerals.
dc.rights.licenseLicense Number: 5231850217188 License date: Jan 18, 2022
dc.rights.urihttps://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=3d8d3de3-1a72-4da0-b6a6-94a679574552
dc.subjectARIMAen_US
dc.subjectAutoregressionen_US
dc.subjectCOVID-19en_US
dc.subjectForecastingen_US
dc.subjectVARen_US
dc.titleForecasting COVID-19: Vector Autoregression-Based Modelen_US
dc.typeArticleen_US
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