Risk assessment of COVID-19 based on a new structure of neural fuzzy probabilistic model

Abstract

The risk assessment of the COVID-19 infection can save so many lives, reduce treatment costs, and increase public health. The unknown nature of the COVID-19 infection, the high impreciseness of available information, and not simply recognizing the relevant factors and their effectiveness may cause overestimating and underestimating of factors. This paper puts forward a development of a model with fewer limitations that are more consistent with progressive knowledge about COVID-19. Dealing with the situation of updating the statistical dataset daily, the proposed approach can effectively use the subjectivity inherent in the fuzzy probability interpretation of risk factors using expert knowledge in addition to the statistical dataset. Second, to this uncertainty handling improvement, a specificity-based parameter learning based on the learning network is also added to deal with the complexity aspect of the COVID-19 infection. The learning process helps the proposed structure better adjust the effectiveness of factors. From the achieved results, it is verified that people with advanced age, those with chronic obstructive pulmonary disease, lung cancer, and those having cancer treatments are at higher risk of death if they are infected by COVID-19. Undoubtedly, for vaccination, these three groups should be considered in order to prevent death situations.

A model to analyze human and organizational factors contributing to pandemic risk assessment in manufacturing industries: FBN-HFACS Modelling

“…Using linguistic terms and quantifying them in fuzzy logic helps deal with subjective uncertainties. Because in the case of many BEs, there is no failure rate, and if there is, we are constantly faced with uncertainty due to numerous cultural and social differences(61,62). Fuzzy theory is a suitable tool for ambiguous and uncertain conditions that can turn qualitative expressions into numerical probabilities(63).…”