Proceedings of ICLT 2025
OPTIMIZING FOGGING AREAS IN DENGUE VECTOR CONTROL STRATEGIES USING GENETIC ALGORITHMS
Wan Nur Afrina Wan Muhammad Azan; Siti Meriam Zahari; S.Sarifah Radiah Shariff; Nurakmal Ahmad Mustafa; Aishah Hani Azil; Ruth Banomyong
Malaysia Institute of Transport (MITRANS), Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia; Malaysia Institute of Transport (MITRANS), Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia; Malaysia Institute of Transport (MITRANS), Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia; Othman Yeop Abdullah Graduate School of Business, Universiti Utara Malaysia, Sintok, Kedah, Malaysia; Department of Parasitology & Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bangi, Malaysia; Center of Excellence in Connectivity, Thammasat Business School, Thammasat University, Bangkok, Thailand
International Conference on Logistics & Transport 2025, Tokyo, Japan, pp. 159-166
Abstract
Purpose:This study aims to address the persistent challenge of dengue fever in Malaysia, particularly in the context of rapid urbanization and its impact on the rise of vector-borne diseases. It evaluates the effectiveness of different resource allocation strategies in dengue vector control by applying genetic algorithm-based fitness functions to optimize decision-making. This study contributes to the field of public health logistics by demonstrating how algorithmic optimization can improve the strategic deployment of limited resources in urban vector control operations. Design/methodology/approach: A comparative analysis of four fitness functions was conducted using a genetic algorithm framework to simulate resource distribution for dengue control. Fitness Function 1 applies uniform allocation, Fitness Function 2 incorporates severity-based weighting, Fitness Function 3 ranks areas by case counts, and Fitness Function 4 integrates both rank and variability. The performance and impact of each approach were assessed based on allocation efficiency and ability to target high-risk zones. Findings: Results indicate a clear progression in allocation effectiveness from the basic Fitness Function 1 to the more complex Fitness Function 4. While Fitness Functions 2 and 3 show improvements by focusing on severity and case count, respectively, Fitness Function 4 provides the most balanced and strategic allocation. It enhances resource efficiency by accounting for both severity and variability in dengue incidence, leading to improved targeting and reduced disease burden. Research limitations/implications (if applicable): The study is based on simulated models and secondary data, which may not fully capture real-world complexities such as human behavior, environmental variability, and cross-agency coordination. Future research should incorporate real-time field data and stakeholder feedback to validate model outcomes. Practical implications (if applicable): The findings support the integration of advanced optimization techniques in public health planning. By adopting Fitness Function 4, health authorities can allocate resources more effectively, prioritize high-risk areas, and enhance the overall impact of dengue control strategies, especially in rapidly urbanizing regions. Originality/value: This research introduces a novel application of genetic algorithm-based fitness functions for optimizing vector control efforts. By comparing multiple prioritization strategies, it provides valuable insights into data-driven dengue management and highlights the importance of adaptive and targeted intervention planning.
Keywords
Resource allocation; Dengue vector control; Genetic algorithm; Optimization techniques
Citation
Wan Nur Afrina Wan Muhammad Azan; Siti Meriam Zahari; S.Sarifah Radiah Shariff; Nurakmal Ahmad Mustafa; Aishah Hani Azil; Ruth Banomyong (2025). OPTIMIZING FOGGING AREAS IN DENGUE VECTOR CONTROL STRATEGIES USING GENETIC ALGORITHMS. Proceedings of the International Conference on Logistics & Transport (ICLT 2025), Tokyo, Japan, pp. 159-166.