Open AccessPrediction of Cases of Infection and Deaths Caused by COVID-19 in Mexico through the Construction of Probabilistic Models under Health Conditions in 2020
Author(s) -
Juan Bacilio Guerrero Escamilla,
Sócrates López Pérez,
Yamile Rangel Martínez
Publication year - 2021
Publication title -
asian journal of probability and statistics
Language(s) - English
Resource type - Journals
ISSN - 2582-0230
DOI - 10.9734/ajpas/2020/v10i430252
Subject(s) - covid-19 , negative binomial distribution , probabilistic logic , statistics , econometrics , regression analysis , regression , count data , poisson regression , statistical model , mathematics , demography , poisson distribution , actuarial science , outbreak , medicine , economics , population , virology , sociology , disease , pathology , infectious disease (medical specialty)
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.