Predicting the global risk of chikungunya virus under climate change using ensemble species distribution models.

Introduction: Climate change is expanding vector-borne disease ranges, yet Chikungunya virus (CHIKV) risk projections remain limited by single-model uncertainty and lack of vector integration. CHIKV, transmitted by Methods: Using hierarchical ensemble modeling in Biomod2, we first projected vector distributions based on 19 bioclimatic variables and elevation, then integrated vector suitability as biological predictors for CHIKV under 16 CMIP6 scenarios (4 SSPs × 4 GCMs, 2021-2100). Eleven algorithms were evaluated and ensembled to minimize uncertainty. Results: Ensemble models achieved excellent performance ( Discussion: Multi-model projections identify region-specific invasion risks, with previously unaffected temperate areas emerging as high-priority surveillance zones by 2100. These findings provide actionable risk maps for targeted vector control and preparedness strategies in 139 at-risk countries, particularly those lacking population immunity. Model heterogeneity underscores the necessity of ensemble approaches for climate-health policy planning.