Comparison of Linear ROR vs Nonlinear Weibull Model for COVID-19 in Iraq

In different fields of study cumulative growth models over time have played and still play an important role. The objective of the research was to compare the Weibull model with the ROR methodology based on the cumulative number of new cases of COVID-19 that affected Iraq in the year 2020. The cumulative daily new cases of the COVID-19 pandemic were modeled, where a linear mathematical model was obtained through the methodology of Regressive Objective Regression (ROR), which explains the behavior of the same, depending on 15 days in advance and was compared with the non-linear Weibull model for the same data. With the linear ROR methodology, better results were obtained, since the variance explained was 100% and the F statistic was also higher and the calculation of the Root Mean Square Error (RMSE) was improved by 72.38%; in addition, in six parameters the linear model outperformed the non-linear model. It is concluded that the cumulative cases of COVID-19 can be modeled with both models and even the cumulative cases of this new disease in the world can be predicted 15 days in advance by means of the mathematical modeling ROR, which allows reducing the number of dead, severe and critical patients for a better management of the pandemic, in spite of being the first time that a ROR model is applied to the processes of growth in the data with respect to time.