Data driven mathematical models for forecast of Covid-19 disease in Nigeria / Oludare Adedire ... [et al.]

• Main Authors: Adedire, Oludare, Sadiku, Yahaya, O Adedire, Olufemi., Collina, Kambai, O Oladejo, Afolabi., Sikiru, G.K
• Format: Article
• Language: English
• Published: UiTM Cawangan Perlis 2022
• Subjects: Mathematical statistics. Probabilities; Infectious and parasitic diseases
• Online Access: https://ir.uitm.edu.my/id/eprint/60635/1/60635.pdf; https://ir.uitm.edu.my/id/eprint/60635/; https://crinn.conferencehunter.com/

In this research, two mathematical models are proposed for investigation of laboratory confirmed daily COVID-19 disease incidence and total active daily infectious COVID-19 cases using data obtained from Nigeria Centre for Disease Control. Due to the observed patterns in the raw data, Autoregressive Integrated Moving Average(ARIMA) method is used on the data which covered a period of 521 days (27 February, 2020- 1st August 2021). While diagnostic check of ARIMA(11,1,0) indicate Ljung-Box Q(18) statistics value of 12.544 with p-value of 0.084, diagnostic check of ARIMA(1, 1, 1) indicate Ljung Box Q(18) statistics value of 22.420 with p-value of 0.130. Furthermore, stationary R- squared values are 0.803 and 0.858 at 95% confidence bound for ARIMA (11, 1, 0) and ARIMA (1, 1, 1) respectively which are indicative of good models. Results from ARIMA (11, 1, 0) forecast show a slightly moderate upward trend in confirmed daily COVID-19 incidence in Nigeria and results from ARIMA(1, 1, 1) indicate significant upward trend in total active daily infectious COVID-19 cases in Nigerian population. Therefore, the developed models can be adopted by presidential taskforce and other agencies in health sector regarding future vaccination towards prevention of the spread of COVID-19 disease in Nigeria provided that the present general prevailing conditions of disease spread remain fairly the same.