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...IDS-Master targets delays in reporting measles, Lassa fever, measles and diphtheria
Daud Olatunji
Researchers at the Federal University of Agriculture, Abeokuta, Ogun State, have developed an AI-based disease surveillance system designed to improve early detection of infectious disease outbreaks and strengthen public health responses across Africa.
The system, known as IDS-Master, is designed to address delays associated with conventional disease surveillance platforms by enabling real-time reporting, automated data analysis, predictive alerts and coordinated responses to emerging health threats.
Developed by AIA4OneHealth, a multidisciplinary research center based at the university, IDS-Master is described by its developers as Africa’s first fully AI-based event-based surveillance system.
The platform is designed to identify potential disease outbreaks before they escalate by collecting and analyzing reports from multiple sources, including community leaders, health officials, residents and other individuals who may observe unusual health incidents.
According to the developers, the innovation is intended to fill gaps in disease reporting, particularly in disadvantaged rural communities where limited access to healthcare facilities and delayed communication with health authorities can compromise early detection of outbreaks.
IDS-Master is designed to support the surveillance of infectious diseases such as measles, Lassa fever, measles and diphtheria, which can pose significant public health risks when cases are not detected and contained in a timely manner.
Unlike systems that rely heavily on formal healthcare structures and trained staff, the platform allows non-healthcare workers to report suspicious incidents, potentially broadening the reach of disease surveillance beyond hospitals and clinics.
It also supports online and offline reporting, multiple languages and multimedia submissions, features intended to make it easier for people from different communities to communicate health-related concerns.
The developers said the system uses machine learning to analyze incoming information, classify reports, filter out irrelevant data and predict potential disease outbreaks.
At the heart of its architecture is a Smart Surveillance Manager, which processes information collected from different sources through a centralized system supported by a real-time database.
The system is expected to transform raw field reports into actionable information that public health authorities can use to identify emerging threats, prioritize interventions and coordinate responses.
By generating early warning alerts and providing predictive information, IDS-Master is designed to help health officials make evidence-based decisions, deploy resources more efficiently, and respond to suspected outbreaks before they spread widely.
The innovation also offers opportunities for anonymous reporting, which could encourage people reluctant to reveal their identities to report suspected health incidents in their communities.
Its community-focused approach is intended to strengthen public participation in disease surveillance while enabling ongoing engagement and public health education.
The developers said IDS-Master could help reduce the health and economic consequences of epidemics by improving the speed with which unusual disease events are reported, assessed and addressed.
Across Africa, where public health systems face varying levels of surveillance capacity and access to resources, the platform is designed to support more responsive, data-driven national health systems and improve epidemic preparedness.
However, the extent of its impact will depend on effective implementation, integration with existing public health structures, reliable community relationships, data protection safeguards and the ability of health authorities to investigate alerts and respond promptly.
AIA4OneHealth includes researchers, AI engineers, epidemiologists and public health experts working to develop technology-based solutions to complex health challenges.
The research center said IDS-Master is one of its key research-based innovations, describing it as a community-aware intelligent integrated surveillance system.
The initiative reflects growing efforts to apply artificial intelligence and digital technologies to public health challenges, particularly the need to detect infectious diseases early, strengthen epidemic preparedness, and improve coordination between communities and health authorities.
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