A Systematic Review of Fuzzy Logic Applications for the COVID-19 Pandemic

Introduction

The severe respiratory illness coronavirus type 2 causes a new coronavirus disease 19, commonly known as COVID-19 (SARS-CoV-2) (Manigandan et al., 2020). Dry cough, loss of smell and taste, fever, exhaustion, and respiratory disease such as shortness of breath are the most typical symptoms of COVID-19 infection (Jalaber et al., 2020). For the identification of coronavirus disease, two types of standard testing are being used: diagnostic tests and antibody tests. These methods are expensive, time-consuming, need particular materials and tools, and are ineffective in providing real positive rates. As a result, established methods for diagnosing and tracking coronavirus disease are ineffective (Pham et al., 2020). Recent research has revealed that fuzzy logic is a viable technique that may be used in a variety of industries, including process industries, agriculture, finance, computing, and healthcare (Liu et al., 2020; Wirtz et al., 2019).

Many issues in real-world applications might be tackled theoretically rather than analytically. However, owing to the complexity, uncertainty, and vast time required for computing, it is not possible to answer some issues theoretically. Different types of uncertainty may be discovered depending on the variable nature of the uncertainty, such as randomness, fuzziness, indistinguishability, and incompleteness. The Covid-19 pandemic is progressing, but concerns among individuals and policymakers continue to grow. This is because Covid-19 is a challenging task in a complicated system. By definition, complex systems are made up of many interconnected parts (Thurner et al., 2018). Such systems are open, dynamically evolving, unpredictable and self-organising (Thurner et al., 2018). Only through understanding complex systems in their totality can they be adequately understood; isolating a component of the system to “solve” it does not result in a solution that works across the system in the long run. Uncertainty, tension, and conflict are inevitable and must be accepted rather than addressed (Greenhalgh and Papoutsi, 2018). In the field of soft computing, many techniques, such as fuzzy logic, have been developed to deal with this ambiguity.

Eddy (1984) suggested about the global Covid 19 pandemic, “Uncertainty creeps into medical practice through every pore. Whether a physician is defining a disease, making a diagnosis, selecting a procedure, observing outcomes, assessing probabilities, assigning preferences, or putting it all together, he (or she) is walking on very slippery terrain”. COVID-19 has quickly become a disease associated with unfettered ambiguity in its aetiology and management, for healthcare systems and health professionals who provide care, as well as for patients and their families, who are its ultimate victims. Under these circumstances, conventional approaches may not be able for modeling these relationships effectively and efficiently. A variety of fuzzy logic approaches have been employed in order to handle these circumstances by examining the capability of fuzzy logic techniques and improve effectiveness and efficiency in various aspects of Covid 19 pandemic. Recently, there have been successful applications of fuzzy logic techniques in the area of Covid 19 pandemic. These application areas can be classified as more specifically: disease detection, disease diagnosis and epidemic forecasting. In this framework, this paper aims to: i) to introduce briefly the fuzzy logic concepts, ii) to review the literature, iii) to classify the literature based on the applications of fuzzy logic to COVID-19 pandemic, iv) to emphasize future developments and trends.

The remainder of this article is divided into the following sections. Subsequent to the introduction in Section 1, the research approach and procedure of this study are described in Section 2. The third section gives a quick review of fuzzy logic. The outcomes of this review are presented in Section 4 based on the study objectives and questions. The results based on the study questions are discussed in Section 5. Section 6 concludes with a summary of the findings, limitations, and recommendations for further study.

Methodology

The research methodology involves reviewing papers for fuzzy logic approaches in the battle against the COVID- 19 outbreak.