Application of type-2 heptagonal fuzzy sets with multiple operators in multi-criteria decision-making for identifying risk factors of Zika virus.

Abstract

Purpose: 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.

Methods: 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.

Results: The proposed framework identified $Z_3$ (unprotected sexual activity) as the most critical risk factor with a score of 0.6717, followed by $Z_8$ (blood transfusions) at 0.5783, $Z_{10}$ (pregnancy) at 0.5753, $Z_9$ (mosquito bites) at 0.4917, and $Z_7$ (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.

Conclusion: 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.