Modelling the Occurrence of the Novel Pandemic COVID-19 Outbreak– A Box and Jenkins Approach

Abstract

The corona virus disease 2019 (COVID-19) is a novel pandemic disease that spreads very fast and causes severe respiratory problem to its carrier and thereby results to death in some cases. In this research, we studied the trend, model Nigeria daily COVID-19 cases and forecast for the future occurrences in the country at large. We adopt the Box and Jenkins approach. The time plot showed that the cases of COVID-19 rises rapidly in recent time. KPSS test confirms the non-stationarity of the process (p < 0.05) before differencing. The test also confirmed the stationarity of the process (p > 0.05) after differencing. Various ARIMA (p,d,q) were examined with their respective AICs and Log-likelihood. ARIMA (1, 2, 1) was selected as the best model due to its least AIC (559.74) and highest log likelihood (-276.87). Both Shapiro-Wilk test and Box test performed confirm the fitness of the model (p > 0.05) for the series. Forecast for 30 days was then made for COVID-19 cases in Nigeria. Conclusively, the model obtained in this research can be used to model, monitor and forecast the daily occurrence of COVID-19 cases in Nigeria.

Characterization of partially observed epidemics through Bayesian inference: application to COVID-19

“…Our ignorance of its behavior in the human population is evident in the choice of modeling techniques used for the purpose. Time-series methods such as ARIMA [9,39] and logistic regression for cumulative time-series [28] have been used extensively, as have machine-learning methods using Long Short-Term Memory models [16,17] and autoencoders [18]. These approaches do not require any disease models and focus solely on fitting the data, daily or cumulative, of new cases as reported.…”