From AI-powered identification to actionable analytics — we're building the tools that make vector surveillance accessible, accurate, and impactful.
VectorCam combines deep learning, intuitive design, and field-tested hardware to create a complete toolkit for malaria and vector-borne disease surveillance. Our platform is built from the ground up for the realities of community health work in low-resource settings.
Our cutting-edge convolutional neural networks accurately identify 7 key mosquito vectors, including primary malaria vectors, with over 94% accuracy in resource-constrained settings. This breakthrough technology empowers effective malaria control and surveillance programs.
VectorCam's intuitive mobile app and simple hardware streamline data collection, making surveillance accessible to everyone from medical entomologists to community health workers. Built on deep user insights, our platform ensures accurate and efficient data capture even in remote areas.
Data-driven insights for impact transform raw data into actionable intelligence. VectorCam's interactive dashboards and maps provide clear visualizations, empowering stakeholders to make informed decisions for targeted malaria prevention and control.
We believe in open science and rigorous validation. Our research is published in leading journals.
VectorCam — a handheld tool for rapid morphological identification of mosquito species for community-based malaria vector surveillance: A summative usability assessment
JMIR Human Factors, 2024
doi.org/10.2196/56605 →Towards Transforming Malaria Vector Surveillance Using VectorBrain: A Novel Convolutional Neural Network for Mosquito Species, Sex, and Abdomen Status Identifications
Research Square, 2024
doi.org/10.21203/rs.3.rs-4462833/v1 →We're always looking for partners to help scale our impact. Reach out to start a conversation.
Contact Us