An emergency response of dyadic intelligent fuzzy decision process to diagnose of Omicron

Abstract

The Severe Acute Respiratory Syndrome Coronavirus -2 (SARS-CoV-2) has created a challenging and threatening situation worldwide. The SARS-CoV-2 embodies diverse epidemiological trends, alongside emerging and reemerging pathogenic characteristics, which have raised great public health concerns. This study aims to investigate using this Dyadic Intelligent Fuzzy Decision Process to diagnose the global prevalence, biological and clinical characteristics of younger and middle-aged people more than previous variants. Worldwide health establishment should take immediate preventive measures to stop outbreaks of this emerging and reemerging  pathogenic variant across the globe to minimize  the disease burden on humanity. The control of spreading of Omicron in emergency situation the entire world is a challenge, and therefore, the aim of this study was to propose a Dyadic intelligent fuzzy decision model for control and diagnosis of Omicron. The emergency event is known to have aspects of short time and data, harmfulness, and ambiguity, and policy makers are often rationally bounded under uncertainty and threat. There are some classic approaches for representing and explaining the complexity and vagueness of the information. The effective tool to describe and reduce the uncertainty in data information is fuzzy set and their extension. Therefore, we used fuzzy logic to develop fuzzy mathematical model for control of transmission and spreading of Omicron. The fuzzy control of early transmission and spreading of coronavirus by fuzzy mathematical model will be very effective. The proposed research work is on fuzzy mathematical model of intelligent decision systems under the Dyadic fuzzy information. In the proposed work, we will develop a newly and generalized technique for Omicron based on the technique for order of preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) methods under extension of Dyadic fuzzy environment. Finally, an illustrative the emergency situation of OMAIGRAN is given for demonstrating the effectiveness of the suggested method, along with a sensitivity analysis and comparative analysis, showing the feasibility and reliability of its results.