Methodology of The Objective Regressive Regression In Function of The Prognosis For Deaths, Critical, Severe, Confirmed And New Cases of Covid-19 In Santa Clara Municipality And Cuba
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https://orcid.org/0000-0001-5237-0810
David del Valle Laveaga (MD)
Luis E. Jerez Puebla (PhD)
Claudia Osés Llanes (MD)
Frank M. Wilford González (Degree)
https://orcid.org/0000-0001-9554-0517
Abstract
The COVID-19 pandemic affecting planet Earth has had a peculiar development in our country. Objective: The objective of
the research was to model, using the Regressive Objective Regression (ROR) methodology, a set of parameters (deaths, critical,
severe, confirmed and new cases) inherent to the SARS CoV-2 COVID-19 pandemic, so far in 2020 in Cuba. The parameters
analyzed were: deaths, serious, critical, confirmed and new cases, in the municipality of Santa Clara, Villa Clara and Cuba. The
modeling used was Regressive Objective Regression (ROR) modeling, which is based on a combination of Dummy variables
with ARIMA modeling. In the ROR methodology, dichotomous variables DS, DI and NoC are created in a first step, and then
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. Mathematical models were obtained by means
of the ROR methodology that explain the behavior of the same, depending on the variable to study, 6, 4, 10 and 14 days in
advance, which made it possible to make long term prognoses, allowing to take measures in the clinical services, and thus to
avoid and to diminish the number of deaths and complications in patients with COVID-19. Despite being a new disease in the
world, COVID-19 can be followed by means of ROR mathematical modeling. This allows for a decrease in the number of dead,
serious and critical patients for a better management of the pandemic.
