Researchers at Washington State University have created a mobile app aimed at identifying dogs that may carry rabies, with the goal of preventing exposure to humans. The study detailing this innovation was published in the journal Scientific Reports.
To evaluate the app’s performance, the team conducted tests at a veterinary clinic in Tanzania. There, dogs were vaccinated, microchipped, and registered, allowing researchers to compare the app’s detections with established vaccination and registration records.
Initial results show strong performance across key metrics. Operators using the application identified a high proportion of rabies-vaccinated dogs and a strikingly high rate of unvaccinated dogs within the tested population, indicating the tool’s potential to streamline field surveillance and vaccination campaigns.
Lead author Associate Professor Felix Lankester commented on the broader aim: since domestic dogs are the primary source of human rabies cases, controlling human rabies worldwide hinges on mass dog vaccination and effective surveillance strategies. The study suggests that digital tools can augment traditional approaches by rapidly flagging at-risk animals in real time, supporting vaccination outreach and bite-prevention efforts.
Rabies remains a significant global health challenge, causing tens of thousands of human deaths annually, with the vast majority occurring in Asia and Africa. In many affected regions, sustained vaccination coverage among dogs—often at or above a threshold of around 40 percent of the dog population—is considered essential to interrupt transmission chains and move toward disease elimination, according to public health researchers. The current work adds to the evidence that technology-enabled surveillance can complement vaccination programs and improve response times in resource-limited settings.
In related developments, researchers have demonstrated the ability of artificial intelligence to learn complex patterns in data that humans might overlook. Earlier efforts showed AI systems capable of interpreting doctor handwriting and validating signatures, highlighting the growing potential of AI to assist in medical and public health applications—while also underscoring the need for robust safeguards and quality assurance in real-world deployment. (Citations: field notes from the study and subsequent AI ethics discussions.)