Epistemological Value of Statistical Models Regarding the Fatality Rate Due to COVID-19

Pérez G.A.; Benavente J.E.M.; Becerra R.M.Á.; Galindo J.S.A.; Guillén N.E.A. · Revista Cubana de Salud Publica (2024)

Resumen

Introduction: Statistical methods allow predicting the prevalence of epidemics, although they are insufficient when pandemics are random and, therefore, it is difficult to generalize a result. Objective: To describe the epistemological value of statistical models regarding the fatality rate due to COVID-19. Methods: The Google Scholar database was selected where the information was managed in English and the filter precision with the symbology of quotes and the Boolean operators AND and OR. The search equation was “statistical modeling” and “prediction case fatality rate”, pandemic “COVID-19”, infection prevalence. Using non-probabilistic selection for convenience, nine scientific articles published in 2020 were analyzed. According to the inclusion criterion, 50 or more citations were discriminated. Conclusions: Given the description of the contagion cases and the fatality rate in 2021, the prediction of the mathematical models was imprecise for the control of COVID-19.

Instituciones: Universidad Nacional San Luis Gonzaga; Universidad Nacional Jorge Basadre Grohmann