Curve Estimation Modeling for Predictions of the Novel Coronavirus (Covid-19) Epidemic in Nigeria

Abstract

Based on the official Nigeria Centre for Disease Control (NCDC) data, the current research paper modeled the confirmed cases of the novel coronavirus disease 2019 (COVID-19) in Nigeria. Ten different curve regression models including linear, logarithmic, inverse, quadratic, cubic, compound, power, S-curve, growth, and exponential were used to fit the obtained official data. The cubic (R2 = 0.999) model gave the best fit for the entire country. However, the growth and exponential had the lowest standard error of estimate (0.958) and thus may best be used. The equations for these models were e0.78897+0.0944x and 2.2011e0.0944x respectively. In terms of confirmed cases in individual State, quadratic, cubic, compound, growth, power and exponential models generally best describe the official data for many states except for the state of Kogi which is best fitted with S-curve and inverse models. The error between the model and the official data curve is quite small especially for compound, power, growth and exponential models. The computed models will help to realized forward prediction and backward inference of the epidemic situation in Nigeria, and the relevant analysis help Federal and State governments to make vital decisions on how to manage the lockdown in the country.

Optimization and effect of varying catalyst concentration and trans-esterification temperature on the yield of biodiesel production from palm kernel oil and groundnut oil

“…The F test was carried out by comparing the variances of each model with the experimental results, it shows that each of the models is actually adequate because their calculated F values are less than the F critical based on 14 degrees of freedom for both the numerator and denominator. However, regression coefficient of a model should be more than or approximately equal to 0.95 [38][39]. Hence, the 4 th order polynomial is obviously the most suitable for both oils.…”