Modeling and Forecasting the Third wave of Covid-19 Incidence Rate in Nigeria Using Vector Autoregressive Model Approach

Authors

  • Gabriel O. Odekina
    Department of Mathematics, Covenant University Ota, Ogun, Nigeria
  • Adedayo F. Adedotun
    Department of Mathematics, Covenant University Ota, Ogun, Nigeria
  • Ogbu F. Imaga
    Department of Mathematics, Covenant University Ota, Ogun, Nigeria

Keywords:

Covid-19, Vector Autoregressive model, Akaike Information Criterion, Final Prediction Error, Hannan Quinn Information Criterion

Abstract

Modeling the onset of a pandemic is important for forming inferences and putting measures in place. In this study, we used the Vector autoregressive model to model and forecast the number of confirmed covid-19 cases and deaths in Nigeria, taking into account the relationship that exists between both multivariate variables. Before using the Vector Autoregressive model, a co-integration test was performed. An autocorrelation test and a heteroscedasticity test were also performed, and it was discovered that there is no autocorrelation at lags 3 and 4, as well as no heteroscedasticity. According to the findings of the study, the number of covid-19 cases and deaths is on the rise. To forecast the number of cases and deaths, a Vector Autoregressive model with lag 4 was used. The projection likewise shows a steady increase in the number of deaths over time, but a minor drop in the number of confirmed Covid-19 cases.

Dimensions

”World Health Organization.”

”Nigeria Center for Disease Control,” (2020).

K. Temitope, et al., ”Gut microbial Diversity Is Reduced by the Probiotic VSL#3 and Correlates with Decreased TNBSInduced Colitis. Inflammatory Bowel Diseases”, 17 (2011) 289. http://dx.doi.org/10.1002/ibd.21366.

Z. Abdelhafid, H. Fouzi, D. Abdelkader, & S. Ying, ”Deep learning methods for forecasting covid-19 time series data”, (2020):www.elsevier.com/locate/chaos.

F. Petropoulos, S. Makridakis & N. Stylianou, ”COVID-19: Forecasting confirmed cases and deaths with a simple time series model”, International Journal of Forecasting (2020), https://doi.org/10.1016/j.ijforecast.2020.11.010.

M. F. Alexandre, D. C. Antonio, Daniela Cristina Moreira Marculino Figueiredo, Marc Saez, & Andr´es Cabrera Le´on, ”Impact of lockdown on covid-19 incidence and mortality in China: An interrupted time series study”, (2020). http://dx.doi.org/10.2471/BLT.20.256701.

M. A. Mohsen, R. M. B Mohammad, H. H. D Mohammad, & P. E KimHung, ”Time series modelling to forecast the confirmed and recovered cases of COVID-19”, (2020):www.elsevier.com/locate/chaos.

D. A. Hoseinpour, M. Alizadeh, P. Derakhshan, P. Babazadeh, & A. Jahandideh, ”Understanding epidemic data and statistics: A case study of COVID-19,” J MedVirol. 2020;1–15. https://doi.org/10.1002/jmv.25885.

J. Chu, ”A statistical analysis of the novel coronavirus (COVID19) in Italy and Spain”, PLoS ONE 16: e0249037 (2021). https://doi.org/10.1371/journal.pone.0249037.

O. O. Olusola-Makinde, & O. S Makinde, ”COVID-19 incidence and mortality in Nigeria: gender based analysis”, PeerJ 9:e10613 DOI 10.7717/peerj. (2021) 10613.

T. Hiteshi, R. Prabhat, C. Tanmoy, & S. Vandana, ”Coronavirus (covid19): ARIMA based time series analysis to forecast near future”, (2020). https://arxiv.org/abs/2004.07859.

H. G Dikko , G.O Odekina & T. Dahiru, ”Wind speed Modeling with some selected meteorological variables”, Journal of Nigerian Association of Mathematical Physics. 43(2017) 189.

A. F. Adedotun, O. G.Obadina, O. S, Adesina, K.O. Omosaanya,& R.J. Dare, ” Statistical analysis of the effect of environmental degradation on mortality rate: A Vector Autoregressive Model Approach”, International journal of Science and Engineering Invention, (2018) 2455.

W. Qinan, Z. Yaomu & C. Xiaofei , ”A Vector Autoregression Prediction Model for COVID-19 Outbreak”, [Stat. AP](2021) arXiv: 2102.04843v1.

A. Meimela, S. S Lestaris, F. Mahdy, T. Toharudin & B. N. Ruchjana, ”Modeling ofcovid-19 in Indonesia using vector autoregressive integrated moving average”, Journal of Physics: Conference Series, 1722.

R. S. Tsay, ”Analysis of financial time series”, John Wiley and Sons (2005).

S. Johansen, ”Statistical analysis of cointegration vectors”, Journal of Economic Dynamics and Control, 12(1988) 231.

G. E. P. Box & D. A. Pierce, ”Distribution of residual correlations in autoregressive-integrated moving average time series models”, Journal of the American Statistical Association, 65(1970) 1509.

A. Ghasemi & S. Zahediasl, ”Normality tests for statistical analysis: a guide for non-statisticians”, International journal of endocrinology and metabolism 10 (2012) 486.

H. Zhao & X. Rong, ”Portfolio selection problem with multiple risky assets under the constant elasticity of variance model”, Mathematics and Economics 50 (2012) 179.

X. Xiao, K, Yonggui & Kao, ”The optimal investment strategy of a DC pension plan under deposit loan spread and the O-U process”, (2020). Preprint submitted to Elsevier.

M. Jonsson & R. Sircar, ”Optimal investment problems and volatility homogenization approximations”, Modern Methods in Scientific Computing and Applications 75 (2002) 255.

Published

2022-02-27

How to Cite

Modeling and Forecasting the Third wave of Covid-19 Incidence Rate in Nigeria Using Vector Autoregressive Model Approach. (2022). Journal of the Nigerian Society of Physical Sciences, 4(1), 117-122. https://doi.org/10.46481/jnsps.2022.431

Issue

Volume 4, Issue 1, February 2022

Section

Original Research
Copyright & Licensing

Copyright (c) 2022 Journal of the Nigerian Society of Physical Sciences

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Modeling and Forecasting the Third wave of Covid-19 Incidence Rate in Nigeria Using Vector Autoregressive Model Approach. (2022). Journal of the Nigerian Society of Physical Sciences, 4(1), 117-122. https://doi.org/10.46481/jnsps.2022.431

Similar Articles

91-100 of 233

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)