Affiliations: [a] Department of Statistics, Faculty of Basic and Applied Science, Osun State University, Osogbo, Nigeria | [b] Creative Advanced Technologies, Dubai, UAE | [c] Mathematics and Statistics Department, The Ibarapa Polytechnic, Eruwa, Oyo State, Nigeria
Correspondence:
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Corresponding author: Nureni O. Adeboye, Department of Statistics, Faculty of Basic and Applied Science, Osun State University, P.M.B 4494 Osogbo, Nigeria. Tel.: +234 8033348141; E-mail: nureni.adeboye@uniosun.edu.ng.
Abstract: Nigeria was the first to announce the discovery of COVID-19 cases in Sub-Saharan Africa on the 27th of February 2020 and ever since then, the rate of spread has been rapid. The effects of Socio-Economic Indicators such as the percentage of the population below and above 65 years, Unemployment rate, HIV/AIDS prevalence rate, Population density, Literacy rate and Mortality rate on the incidence rate of COVID-19 in Nigeria cannot be overemphasized. The research thus used the Spatial Modelling techniques to examine the variations in COVID-19 incidence rate in the affected states in Nigeria occasioned by the above-listed socioeconomic factors, by applying four (4) different models vis-à-vis Ordinary Least Squared (OLS), Spatial Lag Model (SLM), Spatial Error Model (SEM) and Geographically weighted regression (GWR) to cover its scope and also performed spatial diagnostic analysis to ascertain the model goodness of fit. Based on the findings, GWR outperformed other models as it explained about 97.6% of the variability among the variables in the model, has the highest Log likelihood (16.873) which shows the goodness of fit and the lowest AIC (-4.192). More so, the results were able to estimate a consistent prediction and spatial variability of the incidence rate of COVID-19 in Nigerian states which could help drive policymaking.
