Ezhilarasan, E.Augustin, F.Kalaiselvan, S.Narayanamoorthy, S.Samy, C.A.Ranganathan, S.Dhivya, S.2025-11-152025-11-1520259780443338717978044333872410.1016/B978-0-443-33871-7.00015-52-s2.0-105019728046https://doi.org/10.1016/B978-0-443-33871-7.00015-5https://hdl.handle.net/20.500.14517/8542Tuberculosis (TB) remains a significant global health issue, with the challenges of diagnosing, treating, and preventing it becoming even more complex and uncertain in the aftermath of COVID-19. The pandemic strained healthcare systems worldwide, redirecting resources and focus, which has not only delayed TB diagnosis and treatment but also present big uncertainty in managing the disease. Fuzzy logic, which manages uncertainty and imprecision, is especially useful for analyzing complex issues where precise data is hard to obtain. This study focuses on healthcare accessibility and infrastructure in TB prevention using the bipolar fuzzy ARAS method, which emphasize the utility degree of the ideal solution in positive and negative perspectives. Findings reveal that primary healthcare centers are most crucial as core facilities for initial TB diagnosis, treatment, and continuity of care. © 2025 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessARAS MethodBipolar Fuzzy NumberBipolar Fuzzy SetCOVID-19Data ScienceHealth SystemHealthcareInfectious DiseaseInformation SystemsTuberculosis (TB)Book PartN/AN/A311327