Nigeria
AI-Powered Hotspot Prediction for TB, HIV, Leprosy, Lassa Fever and Severe Malaria
For over five years, EPCON has been a trusted partner in Nigeria, supporting national programs and international organizations to fight TB, HIV, Lassa Fever, Leprosy, and Malaria.
What began as targeted TB hotspot mapping has grown into a comprehensive portfolio tackling the nation’s most pressing health challenges. Our long-term presence reflects our partners' trust and the proven impact of our AI-driven Epi-control platform.
1. Tuberculosis – TBLON-3 Project (USAID, 2020 - 2024)
Tuberculosis (TB) remains a severe public health issue in Nigeria. Conventional hotspot identification methods relied heavily on aggregated notification data, often limiting precision.
Objective
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Improve case finding, especially in underserved communities.
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Help the National TB Program target diagnostic activities more effectively.
EPCON's Approach
EPCON’s Epi-control platform brings together active case-finding data with socio-demographic and contextual datasets. This enables a more detailed visualization of TB hotspots at a granular level and guides partners in prioritizing locations for active case finding (ACF).
Key Outcomes and Impact
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Results were statistically significant and published as evidence of AI-driven precision in TB control.
Partners
2. Leprosy – Johnson & Johnson Foundation (2022–2024)
Nigeria continues to carry one of the highest burdens of leprosy worldwide. Early detection and treatment remain critical to preventing disabilities and stigma.
Objective
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Support digital collection and integration of leprosy case data from community-based active case-finding interventions.
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Predict and map high-risk areas for leprosy at the neighborhood level, enabling geographically precise interventions.
EPCON's Approach
EPCON applied its AI models to routine leprosy program data, enriched with socio-economic and environmental indicators, to identify areas where undetected leprosy cases were most likely to occur.
Key Outcomes and Impact
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Predicted rate (proportion) of leprosy cases at the neighborhood level
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Spatial clustering maps showing high-risk areas for leprosy across Nigerian states
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Web-based visual interface to support data-driven planning by national and subnational leprosy control programs
Partners
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3. HIV – Precision Mapping for Maternal and Child HIV/TB (2023–2026)
Mother-to-child HIV transmission and pediatric TB/HIV remain major drivers of morbidity in Nigeria. Multiple NGOs are engaged in scaling prevention, testing, and treatment.
Objective
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Strengthen detection of HIV and TB among pregnant women and children.
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Reduce transmission rates and improve early treatment access.
EPCON's Approach
Our Epi-control platform combines routine program data with socio-demographic, behavioral, and contextual datasets to map high-risk areas and vulnerable groups. The platform supports targeted microplanning and real-time monitoring through dashboards and builds local capacity for data-driven decision-making.
Key Outcomes and Impact
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Targeting high-burden areas leads to significantly higher TB/HIV positivity yields, optimizing resources and improving access to care, especially in underserved communities.
Partners
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IHVN and 9 national NGOs
4. Malaria – Severe Malaria Risk in Children (2024–2025)
Malaria is one of the leading causes of childhood mortality in sub-Saharan Africa. In Nigeria, children under 18 are particularly vulnerable, but reliable disaggregated risk data is lacking.
Objective
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Generate new evidence on the burden of severe malaria in children.
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Provide high-resolution risk maps to support national malaria programs.
EPCON's Approach
EPCON applies Bayesian inference models and machine learning to combine:
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Climate and environmental indicators.
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Population, health system access, and socio-economic factors.
Malaria prevalence, resistance patterns, and vector distribution.
Key Outcomes and Impact
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Severe malaria risk rates disaggregated by age groups (2-year intervals).
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High-resolution hotspot maps of pediatric malaria transmission.
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Evidence-based guidance for resource allocation and intervention targeting.
Partners
5. Viral Hemorrhagic Fevers – DECIPHER Project (2024–2027)
Nigeria continues to face risks from Lassa Fever outbreaks, with fragile health systems often challenged to respond effectively. Predictive intelligence is key to pandemic preparedness.
Objective
Identifying high-risk population groups and enabling data-driven intervention planning for viral haemorrhagic fevers (VHF) like Lassa fever can support pandemic preparedness.
EPCON's Approach
We are developing an AI model that integrates high-resolution contextual, environmental, and case data to estimate disease burden, simulate progression, and identify at-risk groups.
Key Outcomes and Impact
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EPCON’s AI modeling will enhance risk forecasting and strategic deployment of diagnostics, enabling more effective and efficient outbreak response.
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Data-driven deployment of diagnostics and treatment resources.
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Stronger outbreak response capacity and resilience.
