Innovative Strategies for the Control of Leishmaniasis, Locust Infestations, and Dengue Virus in NEOM, Saudi Arabia

Background Vector-borne diseases, such as leishmaniasis and dengue fever, along with locust infestations, pose significant threats to public health and agriculture in NEOM, Saudi Arabia, an arid region facing rapid urbanization and climate change impacts. Methods A comprehensive integrated pest and vector management (IPVM) approach was implemented, including entomological surveys, molecular detection techniques (PCR and qPCR), and remote sensing technologies. Genetic and biological control methods, such as the Sterile Insect Technique (SIT) and Wolbachia -based control, were tested. Machine learning models were utilized to predict locust outbreaks. Results The application of Wolbachia -infected mosquitoes reduced dengue virus transmission by 67.3%, while SIT decreased mosquito fertility. Biocontrol agents significantly reduced sandfly and locust populations. Drone-based forecasting models achieved 92.4% accuracy in predicting locust outbreaks, leading to a 42.8% reduction in pesticide use. Conclusion The findings highlight the effectiveness of combining genetic, biological, and AI-driven strategies for sustainable vector and pest control in arid environments, offering a resilient framework for future smart cities like NEOM.