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Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches.
Prieto, Kernel.
  • Prieto K; Instituto de Matemáticas, Universidad Nacional Autónoma de México, Mexico City, México.
PLoS One ; 17(1): e0259958, 2022.
Artigo em Inglês | MEDLINE | ID: covidwho-1643239
ABSTRACT
The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with comorbilities and COVID-19. This manuscript aims to present models to predict, firstly, the number of coronavirus cases and secondly, the hospital care demand and mortality based on COVID-19 patients who have been diagnosed with other diseases. For the first part, I present a projection of the spread of coronavirus in Mexico, which is based on a contact tracing model using Bayesian inference. I investigate the health profile of individuals diagnosed with coronavirus to predict their type of patient care (inpatient or outpatient) and survival. Specifically, I analyze the comorbidity associated with coronavirus using Machine Learning. I have implemented two classifiers I use the first classifier to predict the type of care procedure that a person diagnosed with coronavirus presenting chronic diseases will obtain (i.e. outpatient or hospitalised), in this way I estimate the hospital care demand; I use the second classifier to predict the survival or mortality of the patient (i.e. survived or deceased). I present two techniques to deal with these kinds of unbalanced datasets related to outpatient/hospitalised and survived/deceased cases (which occur in general for these types of coronavirus datasets) to obtain a better performance for the classification.
Assuntos

Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Diabetes Mellitus / Aprendizado de Máquina / COVID-19 / Hipertensão / Obesidade Tipo de estudo: Estudo observacional / Estudo prognóstico Limite: Humanos País/Região como assunto: México Idioma: Inglês Revista: PLoS One Assunto da revista: Ciência / Medicina Ano de publicação: 2022 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Diabetes Mellitus / Aprendizado de Máquina / COVID-19 / Hipertensão / Obesidade Tipo de estudo: Estudo observacional / Estudo prognóstico Limite: Humanos País/Região como assunto: México Idioma: Inglês Revista: PLoS One Assunto da revista: Ciência / Medicina Ano de publicação: 2022 Tipo de documento: Artigo