Modelo predictivo para identificar índices por país relacionados con la tasa de mortalidad por COVID-19

En aras de comprender un poco más las consecuencias que trae consigo esta situación de alta incertidumbre, se presentan en este artículo los resultados de la construcción de un análisis predictivo basado en redes neuronales mediante el cual se analizaron 27653 datos de reporte diario de fechas entre...

Full description

Saved in:
Bibliographic Details
Published in:RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação 2021-07 (E43), p.351-364
Main Authors: Pereáñez, José Antonio García, Arango, David Alberto García, Camarillo, Cianelli Hernández, Fernández, Faviola Herrera
Format: Article
Language:Spanish
Subjects:
Coronaviruses
COVID-19
Globalization
Mortality
Neural networks
Pandemics
Prediction models
Viruses
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:En aras de comprender un poco más las consecuencias que trae consigo esta situación de alta incertidumbre, se presentan en este artículo los resultados de la construcción de un análisis predictivo basado en redes neuronales mediante el cual se analizaron 27653 datos de reporte diario de fechas entre el 22 de enero y el 10 de julio de 2020 y que fueron extraídos del proyecto coronanet en los cuales, mediante la interacción de índices por país, se identificó el orden de importancia de predictores de la tasa de mortalidad del virus. The economic reports that measure the consequences of the measures of the different governments to reduce the rate of infections in the context of the pandemic, show a significant reduction in income and employment that ultimately results in a decrease in the quality of life of the population. In order to understand a little more the consequences that this situation of high uncertainty brings with it, this article presents the results of the construction of a predictive analysis based on neural networks by means of which 27653 data from the daily report of dates between the January 22 and July 10, 2020 and which were extracted from the coronanet project in which, through the interaction of indices by country, the order of importance of predictors of the virus mortality rate was identified. Among the results obtained, in addition to obtaining a model with a precision of 77.5%, it is highlighted that although the type of measure adopted by governments is an incident factor in the increase in mortality per country, they are specific characteristics such as the transparency index and the globalization index which allow a predictive analysis of mortality.
ISSN:1646-9895