VectorCam

Vector Control Innovations, Inc.

VectorCam

AI-powered mosquito detection tool for frontline health workers.

Baltimore, MD, USA

USA

Nonprofit

Today, malaria-endemic countries still rely on paper-based mosquito surveillance and a handful of overworked entomologists to interpret specimens shipped to distant laboratories. Weeks to months of reporting delays mean outbreaks spread unnoticed, interventions are mis-targeted, and scarce resources are wasted. In rural settings, microscopes, reagents, and internet connectivity are often unavailable, leaving community health workers unable to participate in real-time vector monitoring. Ministries of Health are forced to make critical decisions without reliable, geo-tagged data to guide spraying, bed-net distribution, or the rollout of emerging tools like gene-drive mosquitoes. This creates a dangerous information blind spot—one that not only costs lives but also leads to an estimated US $10–15 billion annually in misallocated malaria-control spending across Africa. The consequences are devastating: a child still dies of malaria every minute. The problem is not a lack of tools or effort. It is the absence of timely, high-quality entomological intelligence at the last mile. Without species-level data delivered quickly and consistently, early hotspots go undetected, and the impact of interventions remains unknown until it’s too late.

VectorCam is a palm-sized, 3D printed imaging box that clips onto any Android smartphone and runs a lightweight convolutional neural network entirely offline. A health worker simply places a mosquito inside, takes a photo, and receives species, sex, and feeding status all in under 18 seconds. This process is faster than a trained entomologist and delivers 94 percent accuracy, even in remote, low-resource settings. Each result is geo-tagged and, when connectivity allows, automatically synced to an open-source dashboard that integrates with national health information systems such as DHIS2. This enables ministries and NGOs to visualize mosquito hotspots, monitor insecticide resistance, and guide field teams in near real time. It functions like Google Maps, but for mosquitoes by providing timely, location-specific insights that improve targeting and response. The hardware is manufactured locally in Uganda using 3D printing for less than forty dollars, creating jobs, ensuring access to spare parts, and making national scale-up affordable. The platform is supported by cloud-based model retraining and a public API that allows partners to integrate VectorCam analytics into their own tools. In essence, VectorCam transforms ordinary smartphones into expert mosquito identification systems, unlocking the data needed for faster and smarter vector control.

Primary users are the more than one million community health workers, vector control officers, and entomology technicians who collect mosquitoes for malaria, dengue, and Zika programs but often lack laboratory support. VectorCam gives them the ability to identify species, sex, and physiological state directly in the field, providing instant feedback that validates their work and builds confidence. By shifting identification and reporting responsibilities to trained community-level workers, VectorCam expands the capacity of the health system and reduces the burden on central laboratories. Local entomologists are empowered to spend less time sorting samples and more time analyzing trends, guiding response strategies, and working closely with district leaders to take action. This consistent and high-quality mosquito data enables earlier detection of hotspots, more precise deployment of interventions, and real-time monitoring of effectiveness. Ministries of Health and national disease control programs receive timely, geo-tagged information that would otherwise take months to process, allowing for smarter and faster decision-making. By equipping more people across the health system to act on local data, VectorCam transforms mosquito surveillance from a slow, centralized task into a responsive, community-led engine for public health impact.