Sheela Rani M, Dhanasekar S
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, 600127, Tamilnadu, India.
BMC Infect Dis. 2025 Apr 1;25(1):450. doi: 10.1186/s12879-025-10741-9.
This study aims to identify and rank the key risk factors associated with the Zika virus by leveraging a novel multi-criteria decision-making (MCDM) framework based on type-2 heptagonal fuzzy sets. By integrating advanced aggregation operators, the framework effectively addresses uncertainties in expert assessments and enhances decision-making reliability.
A robust MCDM approach was developed using type-2 heptagonal fuzzy sets, which provide a more nuanced representation of uncertainty compared to traditional fuzzy models. These sets were selected due to their superior ability to handle vague, imprecise, and subjective expert judgments, common challenges in epidemiological risk assessments. Arithmetic and geometric aggregation operators were employed to process fuzzy data effectively. To ensure comprehensive and reliable rankings, the framework incorporated both outranking methods and distance-based approaches, specifically TOPSIS and WASPAS. A sensitivity analysis was conducted to validate the stability of the rankings under varying conditions.
The proposed framework identified (unprotected sexual activity) as the most critical risk factor with a score of 0.6717, followed by (blood transfusions) at 0.5783, (pregnancy) at 0.5753, (mosquito bites) at 0.4917, and (travel to endemic areas) at 0.4726. The rankings remained consistent across different MCDM methods (TOPSIS and WASPAS), demonstrating the robustness of the proposed approach. Pearson correlation analysis confirmed a strong agreement between methods, with correlation coefficients, reinforcing the reliability of the model.
This study introduces an advanced decision-support system for healthcare professionals to systematically identify and prioritize Zika virus risk factors. By leveraging type-2 heptagonal fuzzy sets, the framework effectively captures and processes uncertainty stemming from incomplete epidemiological data, imprecise expert assessments, and subjective linguistic evaluations. The consistency of rankings across multiple MCDM methods, along with sensitivity analysis confirming their stability, demonstrates the model's reliability. These findings provide a scientifically grounded tool for improving risk analysis and strategic public health interventions.
本研究旨在通过利用基于二型七边形模糊集的新型多准则决策（MCDM）框架，识别与寨卡病毒相关的关键风险因素并对其进行排名。通过集成先进的聚合算子，该框架有效解决了专家评估中的不确定性，提高了决策的可靠性。
使用二型七边形模糊集开发了一种稳健的MCDM方法，与传统模糊模型相比，它能更细致地表示不确定性。选择这些集合是因为它们在处理模糊、不精确和主观的专家判断方面具有卓越能力，而这些是流行病学风险评估中的常见挑战。采用算术和几何聚合算子有效处理模糊数据。为确保全面可靠的排名，该框架纳入了优势排序方法和基于距离的方法，特别是TOPSIS和WASPAS。进行了敏感性分析以验证不同条件下排名的稳定性。
所提出的框架确定（无保护性行为）为最关键的风险因素，得分为0.6717，其次是（输血），得分为0.5783，（怀孕）得分为0.5753，（蚊虫叮咬）得分为0.4917，以及（前往流行地区）得分为0.4726。不同MCDM方法（TOPSIS和WASPAS）的排名保持一致，证明了所提方法的稳健性。Pearson相关性分析证实了各方法之间的高度一致性，相关系数增强了模型的可靠性。
本研究为医疗保健专业人员引入了一个先进的决策支持系统，以系统地识别寨卡病毒风险因素并确定其优先级。通过利用二型七边形模糊集，该框架有效地捕捉和处理了源于不完整的流行病学数据、不精确的专家评估和主观语言评价的不确定性。多种MCDM方法排名的一致性，以及敏感性分析证实其稳定性，证明了该模型的可靠性。这些发现为改进风险分析和战略公共卫生干预提供了一个有科学依据的工具。
