Reliability analysis using Bayesian network for medical test of Covid-19

Abstract

Covid-19 is a corona virus pandemic disease affected by a new corona virus. Maximum people infected by covid-19 will experience symptoms namely mild to moderate respiratory illness and recover without requiring any special treatment. However elderly people and those having underlying medical diseases such as diabetes, cardiovascular diseases, cancer and chronic respiratory disease are more prone to develop serious illness. Reliability analysis for medical test for covid-19 is performed using a Bayesian network. A Bayesian network (BN) is a probabilistic graphical model that represents knowledge about an uncertain domain where each node corresponds to a random variable and each edge represents the corresponding conditional probability. The BN is used to prioritize the factors that influence virus symptoms of covid-19. The BN model is constructed based on a list of general symptoms of covid-19. The marginal probabilities for all states are computed. The comparison of prior and conditional probabilities is determined. Using BN the reliability of medical test for covid-19 is obtained. © 2022 American Institute of Physics Inc.. All rights reserved.