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VectorCAM: A Digital Innovation to Advance Entomological Surveillance
Sponsor: Johns Hopkins University
Summary
Mosquitoes spread diseases such as malaria, but tracking mosquito populations is often slow and depends on a small number of highly trained specialists. VectorCam is a smartphone-based tool that uses artificial intelligence (AI) to help identify mosquitoes from photographs and automatically report surveillance information. In this study, community health workers in Uganda used VectorCam during routine mosquito collection activities, while a comparison group followed standard methods that required mosquito identification by trained specialists using microscopes. The study evaluated how accurately mosquitoes could be identified, how quickly information could be reported, how complete the collected data were, and how easy the system was to use. The goal was to determine whether AI-enabled technology could help communities collect and share mosquito surveillance information more quickly and efficiently, while maintaining data quality under real-world conditions.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
48
Start Date
2023-09-01
Completion Date
2025-09-30
Last Updated
2026-11-02
Healthy Volunteers
Yes
Interventions
VectorCam
VectorCam is an artificial intelligence (AI)-enabled mosquito surveillance platform designed to support community-based entomological surveillance through task shifting. The system enables trained Village Health Teams (VHTs) to image mosquito specimens using a smartphone-based device and receive automated classifications without internet connectivity. During this study, the AI model classified specimens into seven categories: Anopheles gambiae s.l., Anopheles funestus s.l., Anopheles Other, Culex spp., Mansonia spp., Aedes spp., and Non-Mosquito specimens. The platform incorporates digital reporting workflows aligned with Uganda Ministry of Health surveillance requirements, allowing mosquito surveillance data to be collected and electronically reported. The intended use of VectorCam is to expand access to routine mosquito surveillance by enabling community health workers to perform species identification and reporting activities traditionally conducted by trained entomologists.
Routine Entomological Surveillance
The control arm followed routine Ministry of Health mosquito surveillance procedures. Village Health Teams (VHTs) collected mosquito specimens using standard collection methods, including pyrethrum spray catches and Centers for Disease Control (CDC) light traps, and recorded household-level surveillance data on paper forms. Specimens were packaged, labeled, and transported to district laboratories, where trained Vector Control Officers (VCOs) conducted morphological identification under light microscopy according to national standard operating procedures. Mosquitoes were classified by species, sex, and abdominal status, and results were manually recorded on surveillance forms. Surveillance data were subsequently entered into DHIS2 platform by district biostatisticians, following existing reporting workflows used within Uganda's national malaria surveillance program.
Locations (1)
District health office in Adjumani and in Mayuge
Kampala, Uganda