Application of fuzzy logic and systems in COVID-19 pandemics: a comprehensive review

Abstract Purpose The COVID-19 pandemic has created many new challenges characterized by significant uncertainty. Fuzzy logic systems have been employed to address these uncertainties, contributing to areas such as disease detection, pandemic prediction, behavioral evaluation, healthcare management, and medical devices. Methods A comprehensive literature review was conducted on studies published between December 2019 and April 2025. Articles were selected from databases including PubMed and Scopus, based on predefined inclusion criteria. Only English-language peer-reviewed articles were included, while review articles, conference abstracts, animal studies, and non-peer-reviewed manuscripts were excluded. Results A total of 102 studies were included, categorized into five domains: disease detection (17 studies), management strategies (30 studies), pandemic prediction (21 studies), behavioral evaluation (21 studies), and medical devices (13 studies). Fuzzy logic systems demonstrated high diagnostic accuracy, with 11 (65%) of studies in disease detection reporting accuracy rates above 95%. In pandemic prediction, accuracy rates ranged from 80% to 98.60%, while behavioral and management studies largely provided qualitative insights. Medical devices employing fuzzy logic also showed innovations. Conclusion Fuzzy logic has been widely used to address challenges posed by COVID-19. It has proven highly effective in disease detection and useful in pandemic prediction, behavioral analysis, and resource management by offering valuable insights under uncertain conditions. Notably, the diagnostic domain stands out with fewer qualitative outcomes and higher accuracy rates compared to other applications, highlighting fuzzy logic’s strong suitability for diagnostic purposes. Despite these promising results, further advancements in algorithm development and integration are necessary to unlock its full potential.