Abstract / Summary
Background Malaria remains a major global health threat, and effective vector surveillance is essential for timely vector control. However, surveillance systems are challenged by behavioral shifts in Anopheles mosquitoes, labor-intensive procedures, and delays in real-time data transmission. Existing electronic monitoring tools often suffer from low sensitivity and specificity, creating an urgent need for automated, high-precision alternatives. Methods We developed the “Black Box,” a 3D-printed, internet-enabled device integrating multispectral light, thermal simulation, chemical attractants, and photocatalytic materials with automated counting and wireless data transmission. Laboratory trials demonstrated trapping rates of 94.00%–95.00% for Anopheles sinensis , An. stephensi , and An. anthropophagus , with data consistency exceeding 97.00%. Semi-field tests yielded trapping rates of 73.60%–90.80% and consistency above 92.00%. In field deployments, the device captured 5,109 mosquitoes with a 94.17% consistency rate and captured more Anopheles than the light trap during the field observation period. Real-time data revealed distinct bimodal activity peaks at dawn and dusk. Among the 112 field-captured female An. sinensis , 21 (18.75%; exact 95% CI, 12.60%-26.97%) were blood-fed, indicating that blood-fed mosquitoes may occur in outdoor collections. Conclusions The Black Box provides a highly efficient, automated solution for real-time malaria vector surveillance. By delivering accurate, timely ecological data, it supports dynamic risk assessment and enables targeted interventions, addressing key limitations of current control programs.