Toward More Reliable Dengue Early Warning: Bridging the Gap Between Theory and Practice

Dengue continues to expand globally, increasing pressure on health systems across tropical and subtropical regions. Despite advances in surveillance, data availability, and predictive modeling, early warning often remains insufficiently linked to timely public health action. The challenge, therefore, is not prediction alone but translating heterogeneous surveillance information into actionable intelligence. Key determinants, including climate, human mobility, adult female mosquito abundance, vector infection, asymptomatic infection, and population immunity, vary in feasibility, spatial and temporal resolution, and early-warning value. Climate indicators provide important context but may lack local specificity, and symptomatic case surveillance is constrained by underascertainment and reporting delays. Human mobility can improve spatial risk assessment and, in selected settings, forecasting, whereas adult mosquito surveillance provides complementary information on local vector abundance. The authors of the present study propose an Operationally Prioritized Early Warning (OPEW) framework that integrates sentinel surveillance, scale-aware interpretation of complementary signals, feasibility-based prioritization, event-triggered intensification, and explicit links between surveillance thresholds and predefined actions. The OPEW framework is not a validated prediction model but a testable operational framework that prioritizes information according to epidemiological relevance, timeliness, feasibility, and decision value. Prospective evaluation should assess predictive performance, actionable lead time, resource requirements, and public health outcomes against existing approaches.