Forecasting COVID-19: Vector Autoregression-Based Model

dc.contributor.author Rajab, Khairan
dc.contributor.author Kamalov, Firuz
dc.contributor.author Cherukuri, Aswani Kumar
dc.date.accessioned 2022-01-19T12:22:24Z
dc.date.available 2022-01-19T12:22:24Z
dc.date.copyright © 2022
dc.date.issued 2022
dc.description This 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/s13369-021-06526-2 en_US
dc.description.abstract 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. © 2022, King Fahd University of Petroleum & Minerals. en_US
dc.description.sponsorship Najran University - NU/-/SERC/10/597 This work was supported in part by the Najran University under Grant NU/-/SERC/10/597.
dc.identifier.citation Rajab, K., Kamalov, F., & Cherukuri, A. K. (2022). Forecasting COVID-19: Vector autoregression-based model. Arabian Journal for Science and Engineering, https://doi.org/10.1007/s13369-021-06526-2 en_US
dc.identifier.issn 2193567X
dc.identifier.uri https://doi.org/10.1007/s13369-021-06526-2
dc.identifier.uri http://hdl.handle.net/20.500.12519/499
dc.language.iso en en_US
dc.publisher Springer Science and Business Media Deutschland GmbH en_US
dc.relation Rajab, 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.ispartofseries Arabian Journal for Science and Engineering;
dc.rights License to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holder Copyright : © 2022, King Fahd University of Petroleum & Minerals.
dc.rights.license License Number: 5231850217188 License date: Jan 18, 2022
dc.rights.uri https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=3d8d3de3-1a72-4da0-b6a6-94a679574552
dc.subject ARIMA en_US
dc.subject Autoregression en_US
dc.subject COVID-19 en_US
dc.subject Forecasting en_US
dc.subject VAR en_US
dc.title Forecasting COVID-19: Vector Autoregression-Based Model en_US
dc.type Article en_US
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