ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION

Authors

  • Claris SHOKO
  • Peter NJUHO

DOI:

https://doi.org/10.21010/Ajidv17i1.1

Keywords:

ARIMA, Box-Jenkins, Forecast, SADC.

Abstract

Background: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence.

Methods: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models.

Results: The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box–Ljung test. The ARIMA(11,1,9) was the best candidate for the training set. A 15-day forecast was also made from the model, which shows a perfect fit with the testing set.  

Conclusion: The number of new COVID-19 cases per million for the SADC shows a downward trend, but the trend is characterized by peaks from time to time. Tightening up of the preventive measures continuously needs to be adapted in order to eradicate the coronavirus epidemic from the population

References

Akaike, H. (1974) A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, AC- 19, 716-723. http://dx.doi.org/10.1109/TAC.1974.1100705

Dickey, D.A. and Fuller, W.A. (1979) Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 47, 427-431.

Katoch, R. and Sidhu, A. (2021). An application of ARIMA model to forecast the dynamics of COVID-19 epidemic in India. Glob. Bus. Rev. https://doi.org/10.1177/0972150920988653

Kundu, L. R., Ferdous, M. Z., Islam, U. S., and Sultana, M. (2021). Forecasting the spread of COVID-19 pandemic in Bangladesh using ARIMA model. Asian Journal of Medical and Biological Research, 7(1), 21–32. https://doi.org/10.3329/ajmbr.v7i1.53305

LJUNG G. M. and BOX G. E. (1978). On a measure of lack of fit in time series models, Biometrika, 65(2):297–303. https://doi.org/10.1093/biomet/65.2.297

Massinga Loembé, M., Tshangela, A., Salyer, S.J., Varma, J.K., Ogwell Ouma, A.E., Nkengasong, J.N. (2020). COVID-19 in Africa: the spread and response. Natural Medicine, 26: 999–1003. https://doi.org/10.1038/s41591-020-0961-x

Singh S, Sundram B M, Rajendran K, Law K.B., Aris T., Abrahim H., Dass S.C., and Gill B S (2020). Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models. The Journal of Infection in Developing Countries 14(9):971-976. DOI: 10.3855/jidc.13116.

United Nations, Economic Commission for Africa (2020). Socio-Economic Impact of COVID-19 in Southern Africa. COVID-19 Response, 2020. https://www.uneca.org/sites/default/files/COVID-19/Presentations/socio-economic_impact_of_COVID-19_in_southern_africa_-_may_2020.pdf

WHO (2020). COVID-19. WHO African Region. External Situation Report 8.. https://apps.who.int/iris/bitstream/handle/10665/331840/SITREP_COVID_19_WHOAFRO_20200422-eng.pdf

WHO (2020). SADC REGIONAL RESPONSE TO COVID-19 PANDEMIC. https://www.sadc.int/files/9115/8697/9635/SADC_regional_response_to_COVID-19.pdf

Downloads

Published

2022-12-22

How to Cite

SHOKO, C., & NJUHO, P. (2022). ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION. African Journal of Infectious Diseases (AJID), 17(1), 1–9. https://doi.org/10.21010/Ajidv17i1.1

Issue

Section

Articles