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

Abstract

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.