Performance assessment of Bayesian meta-analytic predictive model on kdr mutation in insecticide-resistant malarial...

Mosquito populations' selective pressure arising from the widespread and prolonged use of insecticides, especially pyrethroids, for both agricultural usages and public health outcomes, has immensely contributed to the emergence and heavily spread of insecticide resistance. In this study, a systematic review identified eight eligible case-control or cohort studies published between 2015 and 2025 across sub-Saharan Africa that reported both allele and/or genotype frequencies of L1014F and L1014S. The predictive performance and inferential robustness of a Bayesian meta-analytic model were applied and evaluated on two knockdown resistance (kdr) mutations, L1014F and L1014S, in the Anopheles mosquito populations. Using the Markov Chain Monte Carlo (MCMC) sampling to compute pooled concordance statistics, odds ratios, and perform funnel plot asymmetry tests (Egger, Macaskill, Debray). The results revealed that L1014F showed a stronger and more consistent association with phenotypic resistance compared to L1014S, with odds ratios (OR) as high as 4.44 (95% CI 3.40-5.80). However, concordance statistics for both mutations demonstrated wide confidence intervals (L1014F: 0.141; CI - 0.095 to 0.459; L1014S: 0.169; CI - 0.399 to 0.688), indicating moderate predictive reliability. The Bayesian framework effectively synthesized complex and heterogeneous resistance data, confirming the operational relevance of KDR mutations in resistance surveillance. The global significance of these results enhances the predictive analytics in resistance management, such that resistance evolution is temporally and spatially dynamic. The integration of Bayesian modelling into existing entomological surveillance systems shifts the paradigm towards more adaptive and anticipatory management. Although data sparsity and regional heterogeneity warrant cautious interpretation, integrating ecological and thermodynamic variables into predictive models is essential for enhancing future resistance forecasting.