Infectious Disease Modelling for Public Health Decision-Making
Overview
NiyukuriLab (University of Burundi), in collaboration with Imperial College London, will host a five-day, hands-on training workshop on Infectious Disease Modelling for Public Health Decision-Making, supported by FCDO/MRC, from 2 to 6 February 2026 at the Doctoral School of the University of Burundi (Bujumbura).
This workshop is designed to strengthen national and provincial capacity to use modelling and analytics to support routine surveillance and outbreak response, including interpretation of epidemic trends, estimation of Rt, nowcasting and forecasting, model calibration, and intervention scenario analysis.
Why This Workshop Matters
Public health decision-makers are increasingly required to make rapid, evidence-based decisions during outbreaks and in high-burden disease programmes. Infectious disease modelling helps translate surveillance and programme data into actionable insights, including:
- Early detection of changes in transmission and emerging hotspots
- Short-term forecasts to support operational planning
- Scenario analysis to compare intervention strategies (e.g., vaccination and vector control)
- Clear communication of uncertainty to improve decision quality
Note: A dedicated module on data quality and reporting delays will support reliable Rt and forecast interpretation—an essential foundation for actionable modelling outputs.
Target Audience
This workshop is intended for:
- Public health officers and surveillance focal points in the five new provinces
- Data managers (HMIS), M&E/M&N staff, and biostatisticians/analysts
- Ministry of Health programme staff (outbreak preparedness/response, EPI, malaria, HMIS, etc.)
- Master's and PhD students in epidemiology, biostatistics, and mathematical modelling
What Participants Will Learn
Participants will gain practical skills to:
- Prepare surveillance datasets for modelling (definitions, cleaning, data quality checks)
- Interpret R0/Rt, growth rates, and epidemic curves
- Conduct Rt estimation, nowcasting, and short-term forecasting (with reporting delay adjustments)
- Build transmission models (SIR/SEIR) from "boxes and arrows" to equations and code
- Estimate model parameters from data and interpret uncertainty and sensitivity
- Model vaccination strategies and compare policy options
- Apply modelling to mpox and vector-borne diseases (e.g., malaria)
- Produce decision-ready outputs (one-page briefs, stakeholder slides, and clear visuals)
Training Approach
The workshop is applied and product-oriented, combining short lectures with guided hands-on practical sessions. Participants will use WODIN and supporting workflows in R and/or Python (guided). Training datasets will be anonymized and/or aggregated; no identifiable individual data will be used.
Draft Programme Highlights
| Day | Topics |
|---|---|
| Day 1 | Foundations—epidemiology and modelling basics; designing a transmission model |
| Day 2 | WODIN practicals + Data quality & reporting delays for Rt/forecast interpretation |
| Day 3 | Parameter estimation and calibration + vaccination modelling |
| Day 4 | Disease applications—mpox modelling and vector-borne disease modelling |
| Day 5 | Translating modelling outputs to decisions; panel discussion on operationalizing modelling in Burundi; certificates |
How to Apply
Application Information
Application Deadline: 18 January 2026
Notification of Selected Participants: 20 January 2026
Required Information
Send the following information by email:
- Full name, institution, position/title, province/programme
- Email and phone number
- Motivation statement (150–250 words): how you will use the skills in your work/studies
- Current level of experience (Excel / R / Python; beginner/intermediate/advanced)
- Confirmation of full availability for 2–6 February 2026
- For MoH/provincial staff: supervisor endorsement (name and contact)
Contact Information:
University of Burundi (Doctoral school)
Name: Dr. David Niyukuri
Email: david.niyukuri@ub.edu.bi
Imperial College London
Name: Dr. Lilith Whittes
Email: l.whittles@imperial.ac.uk
Name: Dr. Ruth McCabe
Email: ruth.mccabe17@imperial.ac.uk