Prediction of Cases of Infection and Deaths Caused by COVID-19 in Mexico through the Construction of Probabilistic Models under Health Conditions in 2020
Juan Bacilio Guerrero Escamilla *
Autonomous University of Hidalgo State In the Academic Area of Sociology and Demographics, Mexico.
Sócrates López Pérez
Autonomous University of Hidalgo State In the Academic Area of Sociology and Demographics, Mexico.
Yamile Rangel Martínez
Autonomous University of Hidalgo State In the Academic Area of Sociology and Demographics, Mexico.
*Author to whom correspondence should be addressed.
Abstract
In the present research work, two probabilistic models are constructed, which are exponential regression and negative binomial regression. The first one refers to the number of positive cases of being infected by COVID-19. The second one refers to deaths. It was possible to estimate the dynamics of the phenomenon with both instruments, resulting in the presence of more than 106 thousand positive cases of COVID - 19, with an approximation of more than 9 thousand deaths, all of this, in approximately 4 months. In the first case, these were the results, which when updated with data issued by the federal government's health sector in November, changed the contagion scenarios and the estimates of deaths from covid-19.
Keywords: COVID – 19, exponential regression, negative binomial regression.