SKEWED AUTO-REGRESSIVE PROCESS WITH EXOGENOUS INPUT VARIABLES: AN APPLICATION IN THE ADMINISTERED VACCINE DOSES ON COVID-19 SPREAD

Mohsen Maleki, Mohammad Reza Mahmoudi, Hamid Bidram and Amir Mosavi
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Mohsen Maleki: Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan 81746-73441, Iran
Mohammad Reza Mahmoudi: ��Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars, Iran
Hamid Bidram: Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan 81746-73441, Iran
Amir Mosavi: ��Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany§John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary¶Institute of Information Society, University of Public Service, 1083 Budapest, Hungary∥Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia

FRACTALS (fractals), 2022, vol. 30, issue 05, 1-10

Abstract: This study focuses on the prevalence of COVID-19 disease along with vaccination in the United States. We have considered the daily total infected cases of COVID-19 with total vaccinated cases as exogenous input and modeled them using light/heavy tailed auto-regressive with exogenous input model based on the innovations that belong to the flexible class of the two-piece scale mixtures of normal (TP–SMN) family. We have shown that the prediction of COVID-19 spread is affected by the rate of vaccine injection. In fact, the presence of exogenous input variables in time series models not only increases the accuracy of modeling, but also causes better and closer approximations in some issues including predictions. An Expectation-Maximization (EM) type algorithm has been considered for finding the maximum likelihood (ML) estimations of the model parameters, and modeling as well as predicting the infected numbers of COVID-19 in the presence of the vaccinated cases in the US.

Keywords: Auto-Regressive with Exogenous Inputs; COVID-19; Coronavirus; COVID-19 Vaccine; Time Series; Two-Piece Scale Mixtures (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X2240148X

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