Modeling Coronavirus Pandemic Using Univariate and Multivariate Models: The Nigerian Perspective

Adamu, Ibrahim and Justin Ogbonna, Chukwudi and Adamu, Yunusa and Zakari, Yahaya (2021) Modeling Coronavirus Pandemic Using Univariate and Multivariate Models: The Nigerian Perspective. Asian Journal of Probability and Statistics, 15 (4). pp. 134-143. ISSN 2582-0230

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Abstract

Corona virus Disease, a disease which was discovered in December, 2019 has been spreading worldwide like wildfire. In view of this, there is need of continuous findings on the impact, consequence and possible medications of the pandemic in Nigeria and the world at large. Therefore, this research is aimed at Analyzing the spread of Coronavirus pandemic in Nigeria, using univariate and multivariate models namely;(ARIMA) and (ARIMAX). The daily data used in this research was obtained from the NCDC official website dated from 19th April, 2020 to 20th April, 2021 with total of 384 observations using R and Eview10 software for the analysis. Three different variables were examined. The variables are; total confirmed, discharged and death cases for the purpose of establishing reliable forecast, for better decision making and a helping technique for drastic action in reducing the day to day spread of the pandemic. Summary statistics and stationary test were checked with the data being stationary at the first difference and design technique was conducted as well. Also, best fitted model was selected using Akaike Information Criteria (AIC). The ARIMA (1,1,3) model with an exogenous variable was chosen from the ARIMA models with minimum AIC. From the model, a prediction of sixty-days forecast showed the upward trend of the total confirmed cases of the pandemic in the country. The government on its part via its task force can use the predicted line to take much necessary measures and emphases on taking COVID-19 vaccines so as to prevent further spread of the virus

Item Type: Article
Subjects: GO for STM > Mathematical Science
Depositing User: Unnamed user with email support@goforstm.com
Date Deposited: 25 Feb 2023 11:59
Last Modified: 01 Jan 2024 12:35
URI: http://archive.article4submit.com/id/eprint/138

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