Modeling of a Positive Case of Covid-19 Through Regressive Objective Regression Without Doing PCR

Abstract

Currently, new technological advances in biomedicine make the creation of multidisciplinary teams of vital importance. These groups can be made up clinicians, epidemiologists, mathematicians, statisticians, computer scientists, biologists, among others, all together they can achieve an accurate prediction of infectious diseases and thus draw up the appropriate strategies by the competent authorities. The fundamental objective of this work is to obtain, through Regressive Objective Regession (ROR), the modeling of the next positive case that arrived with COVID-19 without performing PCR at the “Marta Abreu” Trashing Polyclinic in the city of Santa Clara. In this work, daily data were used from January to March corresponding to the year 2021 of the number of Covid-19 cases in the “Marta Abreu” Teaching Polyclinic in the city of Santa Clara, in the province of Villa Clara, Cuba, a total of 3294 cases of them 58 positive, of which they are assigned in the database an order number (No) according to how they were registered in the database. In the short-term modeling, the model was assigned to 19.7% with an error of0.12 the dichotomous variables, saw tooth and inverted saw tooth, and the risk returned in 1.3 and 12 cases, the trend is negative and not significant. The ROR modeling of predictions obtained give very significant results for the study of the COVID-19 pandemic at the Marta Abreu Teaching Polyclinic. With the results of the study, the authorities are provided, and in fact they are already doing so, with information on the short-and medium-term behavior of variables of great interest to understand the expansion of SARS-CoV2, which could be used for decision-marking.