Welcome to the Short Course
Welcome to our intensive 5-day workshop on modelling infectious disease dynamics using R! This course will provide hands-on experience with computational epidemiological models.
π’ Pre-Course Information
- Preparation: We will use Jupyter Notebooks with R scripts - no need to install R or RStudio! All hosted notebook links will be provided in the modules section.
- Location: Course will be held in Ankara, Turkey from September 15-19, 2025.
- Materials: All course materials and datasets will be provided. Please review the readings in the Resources section.
Course Overview
This course provides a comprehensive introduction to principles of mathematical epidemiology applied to infectious diseases. Students will learn fundamental concepts of modelling compartmental transmission models and practical applications in R programming language.
Learning Objectives
- Introduction to modelling infectious diseases and their role in public health
- Understand concepts of probability, proportions, hazard rates and competing hazards applied to ID models
- Review alternatives for capturing disease events, transmission and interventions
- Introduce to SIR models, basic reproduction numbers, herd immunity and explore simulations to review these concepts
- Understand the importance of data and its relevance in outbreak settings to simulate transmission dynamics
Course Agenda
5-day intensive workshop schedule covering fundamental concepts to advanced applications in infectious disease modelling.
Day 1: Introduction to modelling infectious disease
Lecture: Introduction to modelling infectious diseases
Introduce to core concepts of infectious diseases dynamics and the use of mathematical systems to simulate transmission
Break
Lecture: Basic concepts of compartmental models
In this session we will go step by step in the process of designing a compartmental model. We will apply concepts of probability, proportions, hazard rates and competing hazards. We will also discuss alternatives for capturing disease events and interventions.
Lunch Break
Practical: Demonstration of a simple compartmental model
A brief demonstration of a simple compartmental model.
Break
Practical: Demonstration of a simple compartmental model (continued)
Participants coding their own cohort model. Rate calculations and interpretation of model output will be put in practice.
Day 2: The SIR model
Lecture: The SIR model
Understand why the SIR model is at the core of infectious disease dynamics. In its simplicity, SIR models are the gate to introduce more complex concepts like the basic reproductive number (Rβ) and herd immunity.
Break
Lecture: Understanding epidemic phases with an SIR model
Cover the mathematical expressions governing the SIR model. In practice, we will use R to understand an SIR model, understanding model variables, and model parameters.
Lunch Break
Practical: Building blocks of an SIR
Participants will code an SIR model and will gather information to inform the main model parameters.
Break
Practical: Introducing transmission dynamics
Using the previously coded SIR model we will add complexity by examining the role of Rβ, R_eff and see practically how herd immunity can be established for a simple SIR model.
Day 3: Stochastic models and assessing uncertainty
Lecture: Introduction to stochastic models 1
Introduce concepts of stochasticity and its relevance for understanding epidemic surge and probability of extinction.
Break
Lecture: Introduction to stochastic models 2
Cover types of stochastic models and modelling procedures.
Lunch Break
Lecture: Assessing model uncertainty and calibration
Explore uncertainty and methods to assess it in modelling.
Break
Practical: Explore stochasticity in a simple SIR model
Using a simple SIR model coded before, a stochastic process will be introduced and its output explored.
Day 4: Outbreaks and data
Lecture: Introduction to outbreak analysis
Cover estimation of key delays (e.g. incubation period, serial interval), the estimation of growth rates, doubling times, of the basic reproduction number and simple short-term forecasting.
Break
Lecture: Using outbreak data
Understand principles of collecting and using outbreak data and linelist.
Lunch Break
Practical: Outbreak data
Load, clean and analyse synthetic outbreak data.
Break
Practical: Outbreak estimates
Introduce tools and methods for producing estimates of serial interval and Rβ.
Day 5: Final assignment and wrap-up
Practical: Final assignment Part 1
Introduce the final project and hands-on working time.
Break
Practical: Final assignment Part 2
Complete assignment and prepare for group report back.
Lunch Break
Group activity
Groups report back on final assignment.
Break
Final lecture: How did we model COVID-19?
Review the evidence of what we know about COVID-19 and how to translate this into a mathematical model.
Wrap-up
Course Lectures
Access lecture slides for each day of the workshop (Right-click Lecture links to open PDF in a separate window).
Day 1: Introduction to modelling infectious disease
Foundational concepts of infectious disease dynamics and compartmental models.
Day 2: The SIR model
Core SIR model concepts, mathematical expressions, and transmission dynamics.
Day 3: Stochastic models and assessing uncertainty
Introduction to stochasticity, model uncertainty, and calibration methods.
Day 4: Outbreaks and data
Outbreak analysis, key epidemiological parameters, and real-world data applications.
Day 5: Final assignment and wrap-up
COVID-19 modeling case study and course integration.
Interactive Practicals
Access the interactive Jupyter Notebooks for hands-on practical sessions. All notebooks will be uploaded daily and can be run directly in your browser without any software installation.
π Launch Interactive Notebooks
Click the badge below to access all practical sessions in an interactive Jupyter environment powered by Binder. The environment includes R and all required packages pre-installed.
π Practical Sessions Include:
- Day 1: Building your first compartmental model
- Day 2: Coding and exploring SIR models
- Day 3: Implementing stochastic processes
- Day 4: Analyzing real outbreak data
- Day 5: Final project and COVID-19 case study
Note: Notebooks are updated daily during the course. If you encounter any issues accessing the interactive environment, please contact the instructor.
Final Assignment
The final assignment is designed to compile and apply all the skills you have gathered throughout the course.
π Capstone Project: Infectious Disease Modeling
This comprehensive assignment will allow you to demonstrate your understanding of infectious disease modeling concepts, from basic compartmental models to advanced stochastic approaches and real-world data analysis.
π Assignment Details:
- Format: Group activity (3-4 participants per group)
- Duration: Day 4 (introduction) to Day 5 (work & presentation)
- Day 5 Activities: Hands-on work time followed by group presentations
- Tools: Interactive Jupyter Notebook environment
Day 4: Assignment introduction and group formation
Day 5: Group presentations and course wrap-up
π Assignment Materials:
A dedicated Jupyter notebook with the complete assignment instructions and datasets will be uploaded on Day 4 of the course.
Note: This assignment integrates all course components - compartmental modeling, SIR dynamics, stochastic processes, and outbreak data analysis. Groups will present their findings and modeling approaches to the class on the final day.
Course Resources
Essential reading materials, online resources, and tools to support your learning in infectious disease modeling.
Scientific Literature
- π Grassly NC et al. review on ID mathematical models
- π Garnett et al. Mathematical models in the evaluation of health programmes
- π White et al. Mathematical Models in Infectious Disease Epidemiology
- π Bjornstad et al. SEIRS for ID modelling
- π O'Driscoll et al 2021.CID. R estimation methods
- π Fraser et al 2004. PNAS. Outbreak control
Online Resources
- π³ Epirecipes: ID modelling examples in different programming languages
- π Keeling & Rohani Modelling ID book
Online Books
π Lecture Slides 2Additional lectures
Software & Tools
All practical sessions will use Jupyter Notebooks with R kernels, accessible through Binder. No software installation required - everything runs in your browser!
Contact Information
Instructor
Name: Juan Vesga
Institution: Lead Vaccine Modeller at UK Health Security Agency and visiting researcher at Imperial College London
Email: juan.vesga@ukhsa.gov.uk; j.vesga10@ic.ac.uk
Profile: Imperial College London Profile
Course Communication
Primary communication will be through email and course announcements.
Course Details
Dates: September 15-19, 2025
Location: Ministry of Health (GDPH-MoH), Ankara, Turkiye
Duration: 5-day intensive workshop