ARIMA Modelling and Forecasting of COVID-19 Daily Confirmed/Death Cases: A Case Study of Nigeria
Asian Journal of Probability and Statistics,
Aims: The aim of this work is to develop suitable ARIMA models which can be sued to forecast daily confirmed/death cases of COVID-19 in Nigeria. This is subject to developing the model, checking them for suitability and carrying out eight months forecast, and making recommendations for the Nigerian Health sector.
Study Design: The study used daily confirmed and death cases of COVID-19 in Nigeria.
Methodology: This work covers times series data on the on the daily confirmed/death cases of COVID-19 in Nigeria, obtained from the Nigerian Centre for Disease Control (NDCD) from 21 March 2020 to 5 May 2020, covering a total of 51 data points. This work is geared towards developing a suitable Autoregressive Integrated Moving Average (ARIMA) models which can be used to forecast total daily confirmed/death cases of COVID-19 in Nigeria. Two adequate subset ARIMA (2, 2, 1) and AR (1) models for the confirmed/death cases, respectively, is fitted and discussed
Results: A forecast of 239 days – from 6th May 2020 to 31 December 2020 was conducted using the fitted models and we observed that the COVID19 data has an upward trend and is best forecasted within a short period.
Conclusion: Critical investigation into the rate of spread of COVID-19 pandemic has shown that, that the daily confirmed cases as well as death cases of the disease tends to follow an upward trend. This work aimed at developing a suitable ARIMA models which can be used to fit a most appropriate subsets to statistically forecast the actual number of confirmed cases as well as death cases of COVID-19 recorded in Nigeria for a period of 8 months.
- time series
How to Cite
Box G, Jenkins G. Time series analysis: Forecasting and control. San Francisco, CA: Holden-Day; 1970.
Jing C, Mengmeng R, Shuyang X, Ingyu W. Predicting seasonal influenza based on SARIMA model, in Mainland China from 2915 to 2018. International Journal of Environmental Research and Public Health. 2019;16(23):47-60.
Lipsitch M, Swerdlow DL, Finelli L. Defining the epidemiology of Covid-19—studies needed. New England Journal of Medicine. 2020;382(13):1194-1196.
Nwuju, K, Lekara-Bayo IB, Modelling. Forecasting daily confirmed cases of Covid-19 in Africa: A case study of ECOWAS Countries: International Journal of Applied Science and Mathematical Theory. 2020; 6(2):37-54.
Dong Y, Mo X, Hu Y, Qi X, Jiang F, Jiang Z, Tong S. Epidemiology of COVID-19 among children in China. Pediatrics. 2020;145(6).
Stübinger J, Schneider L. Epidemiology of coronavirus COVID-19: Forecasting the future incidence in different countries. Healthcare. 2020;8(2).
Sahai AK, Rath N, Sood V, Singh MP. ARIMA modelling & forecasting of COVID-19 in top five affected countries. Diabetes & Metabolic Syndrome. 2020;14(5):1419–1427. DOI: https://doi.org/10.1016/j.dsx.2020.07.042
Qiuying Yang, Jie Wang, Hongli Ma, Xihao Wang. Research on COVID-19 based on ARIMA model—Taking Hubei, China as an example to see the epidemic in Italy. Journal of Infection and Public Health. 2020;13(10):1415 – 1418.
Napoli PE, Nioi M. Global spread of coronavirus disease 2019 and malaria: An epidemiological paradox in the early stage of a pandemic. J Clin Med. 2020 Apr 16;9(4):1138. DOI: 10.3390/jcm9041138. PMID: 32316118; PMCID: PMC7230338.
Etuk EH, Attoe SA, Essi ID. A seasonal Box-Jenkins model for Nigerian inflation rate series. Journal of Mathematics Research. 2012;4(4):107–113.
Nwuju K, Lekara-Bayo IB, Etuk EH. Intervention analysis of daily South African rand/Nigerian naira exchange rates. International Journal of Management Studies, Business & Entrepreneurship Research. 2019;4:17-26.
Our World in Dat, daily confirmed/death cases of COVID-19 from 20 March 2020 to 5 May; 2020. Available:https://ourworldindata.org/coronavirus/country/Nigeria
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