Participants pose for a group photo during the VectorCam Uganda Mid-Review Dissemination Meeting, held in a blended format with researchers, entomologists, public health practitioners, policymakers and partners participating both physically and virtually.

Mosquito surveillance is central to malaria control. But in many settings, identifying mosquito species still depends on specialised expertise, microscopy and time. A shortage of trained entomologists can limit the scale of surveillance, while delays in getting information from the field to decision-makers can make it harder to respond quickly.

Bridging this gap requires innovations that can make mosquito surveillance faster, more accessible and easier to conduct closer to where mosquitoes are collected. For malaria programmes, the value of such innovation is not simply in identifying mosquitoes more quickly, but in generating timely and reliable information that can help guide decisions on vector-control interventions.

This is where innovation can make a difference. The Makerere University School of Public Health (MakSPH), through the Centre of Excellence for Maternal, Newborn and Child Health (MNCH Centre), together with its research partners, is exploring how artificial intelligence can strengthen mosquito surveillance in Uganda. One such effort is VectorCam, a smartphone-based AI tool that uses computer vision to identify mosquitoes by species, sex and feeding status.

Bringing mosquito identification closer to the field

VectorCam is designed to address one of the practical challenges in mosquito surveillance: the need to collect mosquitoes in the field and then rely on specialised expertise and conventional methods to determine their characteristics.

At its core is a smartphone-based, AI-powered application that uses computer vision to quickly identify mosquitoes by species, sex, and feeding status whether a mosquito has fed recently or not. Traditionally, such analysis requires the expertise of entomologists and takes considerable time.

The approach is already being tested in real-world settings. In two districts, 24 Village Health Team members have used VectorCam to identify more than 73,000 mosquitoes. This experience demonstrates the potential of AI-supported identification to expand surveillance capacity and enable frontline teams to contribute to mosquito surveillance beyond the traditional pool of specialised entomological personnel.

The field experience is also helping researchers understand what it takes to move an innovation from a promising idea into routine practice.

Reviewing progress and learning from implementation

On 27 August 2026, the MNCH Centre joined researchers, Ministry of Health officials and partners at the VectorCam Uganda Mid-Review Dissemination Meeting to review progress, emerging quantitative and qualitative findings and lessons from implementation as the work moves into its next phase.

The dissemination held under the theme, “Transforming Mosquito Surveillance through AI-Powered Identification,”  brought together researchers, entomologists, public health practitioners, vector-control stakeholders, policymakers and other partners working to strengthen malaria surveillance in Uganda.

From concept to field application

In a prerecorded overview of the project, Dr. Soumyadipta Acharya, Co-Founder of VectorCam at Johns Hopkins University, reflected on the journey behind the innovation, noting that its development began about four to five years ago.

He explained that the development process involved close collaboration with entomologists and Ugandan stakeholders, with the aim of creating a tool capable of turning a smartphone into a field-based entomology tool.

The experience of the 24 Village Health Team members, who identified more than 73,000 mosquitoes across two districts, provides an important demonstration of what this approach could make possible. Rather than limiting mosquito identification to specialised laboratory or entomological settings, VectorCam is being explored as a way of bringing elements of that capability closer to communities where surveillance activities take place.

Dr. Acharya also emphasised the importance of collaboration and co-creation with Ugandan stakeholders, highlighting the role of local expertise and field experience in shaping the innovation.

Potential for Uganda’s malaria response

The need for stronger mosquito surveillance is particularly important in Uganda, where malaria continues to place a significant burden on the health system.

Speaking during the meeting, Prof. Peter Waiswa, Professor at MakSPH, highlighted the potential of VectorCam to contribute to Uganda’s malaria control efforts. He noted that the innovation could support Uganda’s malaria control strategy, with potential for wider scale-up across the country and, ultimately, in other malaria-affected settings.

Dr. Christine Maiteki Ssebuguzi, Deputy Programme Manager at the National Malaria Elimination Division, Ministry of Health, noted that malaria accounts for more than 40% of outpatient visits and about 20% of hospital admissions nationally.

She stressed the importance of understanding mosquito species and their characteristics to inform appropriate vector-control interventions. The ability to generate more timely and accessible surveillance information could therefore help strengthen the evidence available to guide malaria-control decisions.

What the field experience is teaching researchers

As with any emerging innovation, implementation is generating lessons about what works and what needs to be improved.

Earlier usability work among Village Health Team members in Mayuge and Adjumani showed promising usability of the VectorCam system. At the same time, the assessment identified practical challenges that need to be addressed as the innovation develops. These included difficulties related to imaging mosquitoes, loading specimens, data entry and the amount of training required before users could operate the system independently.

Such lessons are central to the next phase of the work. The question is not simply whether AI can identify mosquitoes accurately. Researchers are also examining whether the approach is practical for frontline users, how much training and support is required, how it can fit within routine surveillance workflows and how the information generated can be used by malaria programmes.

Harnessing innovation through partnership

The VectorCam initiative brings together the Makerere University School of Public Health, through the MNCH Centre, the Johns Hopkins Center for Bioengineering Innovation & Design (CBID), Johns Hopkins University, the Ministry of Health Uganda and the University of Notre Dame, with funding from the Gates Foundation.

For the MNCH Centre, VectorCam is one example of how research partnerships are being used to explore new approaches to complex health challenges. The Centre is not only generating and translating evidence; it is also creating space for researchers and partners to test how emerging innovations can work in real-world health systems.