The possibility of having a methodology that allows the modelling and prediction in the short, medium and long term of biological, social, natural disaster processes and/or phenomena, as well as different infectious entities, constitutes something great. The objective of the research consisted of demonstrating the potential and real capacity of application of the methodology of the Regressive Objective Regression (ROR) in the field of medical sciences. In the ROR methodology, dichotomous variables DS, DI and NoC are created in a first step. Then the module corresponding to the Regression analysis of the SPSS statistical package (ENTER method) is executed, where the predicted variable and the ERROR are obtained; subsequently, the autocorrelograms of the ERROR variable are obtained, paying attention to the maximums of the significant partial autocorrelations, and the new variables are calculated according to the significant Lag of the PACF. Finally, these regressed variables are included in the new regression in a process of successive approximations until white noise is obtained. Wide possibilities of modelling and forecasting in the short, medium and long term, which go beyond the modelling of infectious entities of parasitic, bacterial, fungal and viral etiology, Acute Respiratory Infections, Acute Bronchial Asthma crises, forecasting of extreme meteorological disturbances, among many others. It is concluded that the ROR methodology has demonstrated potential and real capabilities of application in different fields and branches of science, and therefore constitutes a novel contribution to the science of modelling and forecasting variables to know the future, as well as the impact that different variables contribute to an event or phenomenon, and as it is universal, it can be applied anywhere in the universe.