An alternative approach to determination of Covid-19 personal risk index by using fuzzy logic

Abstract

COVID-19 disease is an outbreak that seriously affected the whole world, occurred in December 2019, and thus was declared a global epidemic by WHO (World Health Organization). To reduce the impact of the epidemic on humans, it is important to detect the symptoms of the disease in a timely and accurate manner. Recently, several new variants of COVID-19 have been identified in the United Kingdom (UK), South Africa, Brazil and India, and preliminary findings have been suggested that these mutations increase the transmissibility of the virus. Therefore, the aim of this study is to construct a support system based on fuzzy logic for experts to help detect of COVID-19 infection risk in a timely and accurate manner and to get a numerical output on symptoms of the virus from every person. The decision support system consists of three different sub and one main Mamdani type fuzzy inference systems (FIS). Subsystems are Common- Serious symptoms (First), Rare Symptoms (Second) and Personal Information (Third). The first FIS has five inputs, fever-time, cough-time, fatigue-time, shortness of breath and chest pain/dysfunction; the second FIS has four inputs, Loss of Taste/Smell, Body Aches, Conjuctivitis, and Nausea/Vomiting/Diarrhea; and the third FIS has three inputs, Age, Smoke, and Comorbidities. Then, we obtain personal risk index of individual by combining the outputs of these subsystems in a final FIS. The results can be used by health professionals and epidemiologists to make inferences about public health. Numerical output can also be useful for self-control of an individual.

Epidemic exposure risk assessment in digital contact tracing: A fuzzy logic approach

“…A fuzzy logic-based support system with three distinct subsystems is used in order to evaluate a personal risk index in. 25 The ANFIS system is suggested by the researchers in 26 for the classification and examination of coronavirus disease symptoms-based predictions. However, most of these studies did not take contact and the environmental factors into account, and risk assessments are only based on raw RSS BLE measurements.…”

Coronavirus risk factor by Sugeno fuzzy logic

“…Graphical user interface (GUI) was constructed. Şimşek and Yangın [16] proposed an alternative method in order to determine individual's risk of COVID-19. Fuzzy logic was employed in this study.…”

A Fuzzy Logic Inference Model for the Evaluation of the Effect of Extrinsic Factors on the Transmission of Infectious Diseases

“…For example, Dhiman & Sharma [21] introduced a fuzzy logic technique to assess the risk of COVID-19 based on six parameters such as breathing difficulty, atmospheric temperature, body temperature, etc. Şimşek & Yangın [22] created a fuzzy logic system to swiftly detect COVID-19 risks, using three subsystems for common and rare symptoms and personal information. Shatnawi et al [23] proposed a smart fuzzy inference system to diagnose COVID-19 based on the symptoms that appear in the patient.…”