CEpiNet Background
Central Africa Epidemic Modelling Network (CEpiNet)

Evidence, Models, and Risk Mapping for Faster Response

CEpiNet transforms epidemiological, genomic, and geospatial data into actionable products—forecasts, scenarios, and risk maps—to support timely public health decisions in Central Africa.

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What We Deliver

Transforming data into decision-ready products for outbreak response

Rapid Analytics

Rapid Outbreak Analytics (48-72h)

Nowcasting, Rt estimation, short-term projections, and operational maps during alerts to support immediate response decisions.

Scenario Planning

Scenario Briefs for Decision-Making

Comparative impact of intervention options including targeting, logistics, coverage, and WASH strategies with uncertainty analysis.

Geospatial Intelligence

Geospatial Intelligence

Hotspot detection, accessibility analysis, micro-targeting, and atlas products for strategic resource allocation.

Genomic Data

Epidemiological & Genomic Integration

Link surveillance and lab/genomics data to inform transmission patterns, clusters, and outbreak interpretation.

Training

Capacity Strengthening

Courses, modelling clinics, fellowships, and hands-on internships to build regional analytical expertise.

Standards

Standards & Reproducibility

Model registry, data dictionaries, SOPs, and transparent assumptions for reliable decision support.

How CEpiNet Works

A structured approach to epidemic analytics and decision support

CEpiNet Process
01

Co-design Questions

Collaborate with public health stakeholders to define decision-relevant questions and analytical priorities for outbreak response.

02

Build Pipelines

Establish data quality, harmonization, and security protocols to ensure reliable and reproducible analytics workflows.

03

Run Reproducible Analyses

Execute documented methods with explicit uncertainty quantification and transparent assumptions for decision support.

04

Deliver Decision Products

Produce actionable outputs and iterate based on stakeholder feedback for continuous improvement.

Institutional Pillars

Three complementary institutions driving CEpiNet's mission

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INRB

Fiduciary & Data Leadership

Administrative and financial management, epidemiological and genomic data pipelines, quality assurance and interpretation for outbreak response.

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INOHA (UNIKIN)

Public Health & Infectious Disease Courses

Structured training modules, modelling clinics, and a community of practice for epidemic analytics capacity building.

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Université du Burundi

Modelling Practicums & Internships

Hands-on modelling placements, production-focused mentoring, and model development at Niyukuri Lab.

Leadership Team

Multidisciplinary expertise in epidemic modelling and analytics

David Niyukuri

David Niyukuri

Lead, Epidemic Modelling

Université du Burundi / Niyukuri Lab

David leads CEpiNet's applied epidemic modelling work, including the design and implementation of mathematical and statistical models for nowcasting, short-term forecasting, and intervention scenario analysis.

  • Transmission dynamics modeling
  • Forecasting and scenario analysis
  • Uncertainty quantification
  • Reproducible pipelines
Dev Ebango

Dev Ebango

Lead, Geospatial Analytics

INOHA / UNIKIN

Dev leads CEpiNet's geospatial intelligence, translating surveillance and contextual data into operational risk maps and decision dashboards for resource allocation during outbreaks.

  • Geocoding and spatial harmonization
  • Risk mapping and hotspot detection
  • Accessibility and service coverage
  • Spatial decision support tools
Placide Mbala

Placide Mbala

Lead, Epidemiological & Genomic Data

INRB

Placide leads CEpiNet's epidemiological and genomic data integration, supporting governance and quality of surveillance datasets and genomic information for outbreak interpretation.

  • Data governance and quality
  • Surveillance and line-list analytics
  • Laboratory metadata integration
  • Genomic epidemiology

Catalytic Projects

CEpiNet is supported by a portfolio of collaborative initiatives

MODE-MPOX DRC

Supported by Gates Foundation - Mpox modelling and decision briefs for outbreak control in DRC.

EDCTP: Strengthening Capacity

Supported by EDCTP - Standards, training, and network strengthening in epidemic analytics and modelling in Central Africa.

Advancing Mpox Outbreak Control

Supported by MRC/Imperial College London - Control strategy analytics translation for Central Africa.

AI-Powered Surveillance Network

Supported by IDRC/University of Toronto - Validated AI-enhanced analytics and response network.

Ready to Collaborate?

Contact CEpiNet to co-develop analytics, modelling products, training, or data pipelines for epidemic preparedness and response

Get in Touch

Whether you need outbreak analytics, training programs, or data support, we're here to help strengthen epidemic response in Central Africa.

Contact Us