FUNAAB Researchers Develop AI Surveillance System To Detect Disease Outbreaks Early

...IDS-Master targets delays in reporting mpox, Lassa fever, measles, diphtheria

Daud Olatunji

Researchers at the Federal University of Agriculture, Abeokuta, Ogun State, have developed an artificial intelligence-powered 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 hub based at the university, IDS-Master is described by its developers as Africa’s first fully AI-driven event-based surveillance system.

The platform is designed to identify potential disease outbreaks before they escalate by collecting and analysing reports from multiple sources, including community leaders, health agents, residents and other individuals who may observe unusual health incidents.

According to the developers, the innovation is intended to bridge gaps in disease reporting, particularly in underserved rural communities where limited access to healthcare facilities and delayed communication with health authorities can undermine early outbreak detection.

IDS-Master is designed to support surveillance for infectious diseases such as mpox, Lassa fever, measles and diphtheria, which can pose significant public health risks when cases are not detected and contained promptly.

Unlike systems that rely heavily on formal health facilities and trained personnel, the platform allows non-health workers to report suspected incidents, potentially expanding 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 in different communities to communicate health-related concerns.

The developers said the system uses machine learning to analyse incoming information, classify reports, filter out irrelevant data and predict potential disease hotspots.

At the centre of its architecture is a Smart Surveillance Manager, which processes information gathered from different sources through a centralised system supported by a real-time database.

The system is expected to transform raw field reports into actionable intelligence that public health authorities can use to identify emerging threats, prioritise interventions and coordinate responses.

By generating early warning alerts and providing predictive insights, 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 provides opportunities for anonymous reporting, which could encourage individuals who are reluctant to disclose their identities to report suspected health incidents in their communities.

Its community-centred approach is intended to strengthen public participation in disease surveillance while enabling continuous engagement and public health education.

The developers said IDS-Master could contribute to reducing the health and economic consequences of epidemics by improving the speed at 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, the reliability of community reports, data protection safeguards and the capacity of health authorities to investigate alerts and respond promptly.

AIA4OneHealth comprises researchers, artificial intelligence engineers, epidemiologists and public health experts working to develop technology-driven solutions to complex health challenges.