FUNAAB Launches Africa’s First AI Disease Surveillance System

Researchers at the Federal University of Agriculture, Abeokuta (FUNAAB), 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, positioning the institution at the forefront of a technological revolution in epidemic preparedness.

The system, known as IDS-Master, was developed by AIA4OneHealth, a multidisciplinary research hub based at the university, and is described by its developers as Africa's first fully AI-driven event-based surveillance system. According to the researchers, IDS-Master was designed to address delays in conventional disease surveillance platforms by enabling real-time reporting, automated data analysis, predictive alerts and coordinated responses to emerging health threats. The platform is intended 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.

The innovation represents a fundamental shift in how disease surveillance is conducted, moving beyond systems that rely heavily on formal health facilities and trained personnel. Unlike traditional platforms, IDS-Master allows non-health workers to report suspected incidents, potentially expanding disease surveillance beyond hospitals and clinics and into the communities where outbreaks often begin. This community-centred approach is particularly significant in underserved rural areas, where limited access to healthcare facilities and delayed communication with health authorities can undermine early outbreak detection.

IDS-Master was 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. The system 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. This flexibility is crucial in a continent where language diversity, limited internet connectivity and varying levels of digital literacy can create barriers to effective reporting.

At the centre of the platform's architecture is a Smart Surveillance Manager that processes information from different sources through a centralised system supported by a real-time database. The developers said the system uses machine learning to analyse incoming information, classify reports, filter out irrelevant data and predict potential disease hotspots. 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 helps health officials make evidence-based decisions, deploy resources more efficiently and respond to suspected outbreaks before they spread widely.

The innovation also offers anonymous reporting, which could encourage individuals reluctant to disclose their identities to report suspected health incidents in their communities. This feature addresses a significant barrier to disease surveillance in many African communities, where stigma, fear of ostracisation and distrust of authorities can deter people from reporting symptoms or seeking medical attention. By removing the fear of identification, IDS-Master aims 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 speeding up how quickly unusual disease events are reported, assessed and addressed. Across Africa, where public health systems have varying surveillance capacity and resource access, the platform is designed to support more responsive, data-driven national health systems and improve epidemic preparedness. However, its impact will depend on effective implementation, integration with existing public health structures, the reliability of community reports, data protection safeguards, and health authorities' capacity 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. The research hub said IDS-Master was among its flagship research-based innovations, describing it as a Community-Sensitive 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 outbreak preparedness and improve response coordination.

For Nigeria, which has experienced recurring outbreaks of Lassa fever, diphtheria, cholera and other infectious diseases in recent years, the development of a home-grown AI surveillance system represents a significant step towards health sovereignty. The platform's ability to leverage community knowledge and artificial intelligence simultaneously offers a model that could be replicated across the continent, transforming how Africa detects and responds to the health threats that have historically claimed millions of lives. As the continent continues to grapple with the dual challenge of infectious disease outbreaks and limited healthcare infrastructure, innovations like IDS-Master offer a powerful reminder that African solutions, developed by African researchers for African communities, can be the key to a healthier future.