Abstract
Escalating insecticide resistance in mosquito vectors threatens the durability of vector-borne disease control and increasingly constrains the effectiveness of core interventions. This resistance is a multilayered adaptive phenotype arising from the combined action of target-site substitutions that reduce insecticide sensitivity, transcriptional and enzymatic upregulation of detoxification systems that enhance xenobiotic metabolism, cuticular and behavioral changes that limit exposure and penetration, and transporter-mediated efflux, with additional modulation by microbiota and local environmental conditions that shape phenotypic expression in the field. Current integrated vector management (IVM) strategies aim to mitigate resistance through operationally guided deployment of dual-active-ingredient or synergist-treated nets, indoor residual spraying with rotations or mixtures, integration of larval source management and habitat modification, and incorporation of nonchemical tools such as Wolbachia releases and genetic control, supported by routine resistance surveillance. However, much of the existing evidence remains fragmented, with an overreliance on a narrow set of insecticide classes and a limited number of genetic markers, variable phenotyping and performance metrics across settings, and insufficient prospective linkage between molecular signals and intervention impact under real transmission ecologies. Multi-omics frameworks provide a route to move beyond single-locus screening toward network-level reconstruction of resistance biology, enabling discovery of predictive biomarkers, pathway signatures, and metabolic readouts that can be translated into actionable diagnostics and locally optimized decision rules. Looking forward, omics-enabled precision surveillance integrated with field-deployable assays, standardized benchmarks, and model-informed adaptive management could support closed-loop resistance mitigation in which operational choices are continuously refined to preserve long-term intervention efficacy within IVM programs.