COVID-19: Fitting a ROR Prediction Model for Cuba as Vaccination Advance

Background: We are still in the midst of a deadly pandemic caused by the SARS-CoV-2 virus causing COVID-19, and at the moment there is a shortage of licensed vaccines. Several models and methodologies have been applied in the study, analysis, prediction and modeling of COVID-19 in the world, so it is important to estimate the trend in the behavior of the epidemiological curve of the COVID-19 pandemic worldwide. Methods: In the realization of the study were used the data of the pandemic of new cases to COVID-19 in Cuba, which were taken from the press conference offered by the Ministry of Public Health (MINSAP), by Dr. Francisco Duran Garcia (National Director of Hygiene and Epidemiology of MINSAP), from Monday to Sunday, at 9.00am on national television and Cuban radio, since the pandemic began in March 2020 until July 15, 2021. The forecast was made using the methodology of Regressive Objective Regression (ROR), which has been implemented for different variables (viruses, parasites and bacteria that have circulated in Villa Clara province, Cuba) and even the SARS-CoV-2 virus causing COVID-19. A very long term forecast was made, up to November 1, 2021 with data taken up to July 15, 2021. The objective regressive modeling (ROR) is based on a combination of dummy variables with ARIMA modeling. In this methodology it is necessary to first create the dichotomous variables DS, DI and NoC (NoC: Number of cases of the base, DS represents a sawtooth function and DI this same function, but in inverted form), in such a way that the variable to be modeled is trapped between these parameters and a large amount of variance can be explained. Subsequently, the module corresponding to the Regression analysis of the statistical package SPSS version 19.0 is executed, specifically the ENTER method where the predicted variable and the ERROR are obtained. Then, the autocorrelograms of the ERROR variable will be obtained, paying attention to the maximums of the significant partial autocorrelations PACF. Finally, these regressed variables are included in the new regression in a process of successive approximations until a white noise in the regression errors is obtained. Objective: This research was undertaken to predict new COVID-19 cases, as well as the impact of vaccination in Cuba using the Regressive Objective Regression (ROR) methodology. Findings: The intensification and sustainability of hygienic and sanitary measures, as well as social distancing, and the massive, constant and sustained use of nasobuco, must be taken into account in the prognosis of new cases, since the number of new cases will be higher and may reach values higher than or close to 10,000 cases. As for the impact of vaccination in Cuba with the "Abdala" vaccine, progress is not yet as desired, since it will not be until October 2 of the current year that approximately 6.5 to 7 million people in Cuba will be vaccinated with the three doses, so that it will not be until October 22 that zero new cases could be reached, so that, from the first half of December of the current year, herd immunity could begin to appear in Cuba. Interpretation: Despite being a new disease in the world, COVID-19 can also be followed, modeled and predicted by means of ROR modeling, which allows reducing the number of deceased, severe and critical patients for a better management of the pandemic. On the other hand, the use of vaccines has shown that they can protect against COVID-19. Funding Information: None to declare. Declaration of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.