Modelling resource allocation in dengue management as Negative Binomial distribution-based model

Abstract

Dengue fever remains one of the most pressing public health challenges in tropical regions, with its rapid spread placing significant strain on healthcare resources. Efficient resource allocation is critical to mitigating outbreaks, particularly in areas with varying disease severity. This study presents an optimization framework that integrates Genetic Algorithms (GA) with the Negative Binomial distribution to enhance resource allocation for dengue management in Kedah, Malaysia, from 2011 to 2023. The model incorporates constraints on manpower, insecticides, and budget, with the objective of maximizing fogging coverage while prioritizing high-burden areas as classified by the Dengue Monitoring and Surveillance System (DMOSS). Three GA configurations were tested, varying population size, mutation rate, and crossover probability, and results were compared to the baseline allocation. The findings reveal that GA-based optimization outperforms static allocation strategies by directing more resources to high-severity districts, thereby increasing severity-weighted effective coverage. Among the tested configurations, the model with a population size of 100 (Trial c) achieved the highest fitness value (4.7743), covering 5,572.71 km² with 3,486.54 km² severity-weighted coverage. The results demonstrate the potential of combining probabilistic severity modeling with metaheuristic optimization to improve the efficiency and equity of dengue control interventions, offering a replicable approach for other vector-borne disease management contexts.