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A Comparative Chaotic Analysis of COVID-19 Infection in Some States of Nigeria

Emmanuel J Ekpenyong, Chisimkwuo John, Charles Chinedu Nworu

SARS-CoV-2 epidemic broke out in Wuhan, China in early December 2019. It spread across almost the entire continents of the world. Consequently, the World Health Organization (WHO) declared it a global pandemic on March 11, 2020. In Nigeria, sub-Saharan Africa, the first case of Coronavirus disease was confirmed by the federal ministry of health in Lagos state, the commercial nerve centre of the country. To assist government to make efficient decision making, we developed a forecasting model that will assist in predicting likely deaths. To achieve this, we used daily confirmed cases of COVID-19 from the Nigeria Centre for Disease Control (NCDC) database from 29th February 2020 to 16th August 2020 (comprising 167 observations). The dataset is of two categories; total (cumulative) daily confirmed cases by states and daily confirmed cases by states. The data for each selected state is modelled by four different methods, namely ARIMA, ARIMA with intervention, INAR and GARCH for the daily confirmed cases while the polynomial regression with AR, ARIMA, ridge regression and INAR were used to model the cumulative daily cases. For both cases, their performances were evaluated using five different measures MAE, MSE, RMSE, MAPE and sMAPE. Based on the computed figures, we can deduce that the ARIMA model is the most suitable for modelling the daily cumulative cases of COVID-19 in Lagos, Kano, Bauchi, Gombe and Edo states. The polynomial regression offers the best fitting model for predicting the daily cumulative cases in FCT and Rivers state while for Kaduna, Oyo and Ebonyi states, the most appropriate model is the ridge regression. Therefore, it is recommended that these models will guide the government in the decision making process.