Fuzzy model for evaluation of predicting factors for Sars-Cov-2 infection severity

Autores

  • Cláudio Henrique dos Santos Grecco Instituto de Engenharia Nuclear
  • Patrícia Carvalho da Silva Balieiro Universidade do Estado do Amazonas
  • Maria Gabriela de Almeida Rodrigues Universidade do Estado do Amazonas
  • Rahyja Teixeira dos Santos Universidade do Estado do Amazonas
  • Vanderson de Souza Sampaio Universidade do Estado do Amazonas
  • Carlos Alberto Nunes Cosenza COPPE/UFRJ - Programa de Engenharia de Produção
  • Maria Emilia Barrios Rodrigues COPPE/UFRJ - Programa de Engenharia de Produção
  • Jaqueline Tavares Viana de Souza Instituto de Engenharia Nuclear
  • Samuel Benjamin Aguiar de Oliveira Universidade do Estado do Amazonas
  • Arlene dos Santos Pinto Universidade do Estado do Amazonas
  • Antonio José Leal Costa IESC/UFRJ

Resumo

The Sars-Cov-2 virus has been a challenge due to its pandemic potential, reflecting its transmissibility power that is perceptible through the high transmissibility and mortality rate. Operational limitations related to the displacement of serious cases to Manaus, where the high and medium complexity assistance is concentrated in the State of Amazonas, contribute to the increase in the hospital occupancy rate during the epidemic peaks of Covid-19.

In order to minimize this scenario, a tool that assists in the prediction of severity, through variables of the Health Information Systems (SIS), will contribute to the optimization of patient management. The study is a prospective cohort using secondary data. The SIS used are SIVEP-Gripe, which notifies serious cases, and e-SUS, which notifies mild cases. This work proposes to develop of a fuzzy model to assess and predict the severity in cases of Covid-19 in Amazonas, in the period from 2020 to 2022, based on the Hsu-Chen model. The indicators come from the linkage between the mentioned SIS, through the analysis of 23 predictive factors, which are characterized by comorbidities, signs, and symptoms.

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Publicado

2021-08-12

Edição

Seção

Complex Systems Engineering