Agent Based Modelling

This self-paced course, designed for approximately 30 study hours, will equip you with the skills to develop, implement, and analyze Agent-Based Models for infectious disease transmission. Through a structured learning approach, you will explore the principles of ABM, simulate disease spread, assess public health interventions, and optimize model parameters using real-world epidemiological scenarios.

With a mix of theoretical foundations, hands-on practicals, and project-based learning, this course will help you gain practical experience with ABM tools such as Covasim and ANTSIM. By the end of the course, you will have built and validated your own ABM models, enabling you to apply simulation-based approaches to inform public health decision-making.

This course provides a comprehensive introduction to Agent-Based Modelling (ABM) for infectious disease transmission, equipping learners with the theoretical foundations and practical skills needed to develop, simulate, and analyze ABM models in public health. Through seven structured modules, participants will explore key concepts in disease modeling, software tools, agent interactions, and scenario analysis. Hands-on exercises using Python and R, as well as real-world applications with tools like Covasim and ANTSIM, will reinforce learning. The course also covers model validation, sensitivity analysis, and intervention design, culminating in a team project where students develop and refine their own ABM models. Finally, the course emphasizes effective communication of results, ensuring participants can interpret and present their findings to inform public health decision-making.(Estimated time: 45 mins)

Before you begin the course, please take this assessment here to gauge your readiness and current capabilities as you embark on the course. (Estimated time: 15 mins)

This module introduces Agent-Based Modelling (ABM) and its role in infectious disease transmission. You will explore different disease modeling approaches, including compartmental models and ABM, and examine real-world applications in epidemiology. The module also covers fundamental principles of ABM, followed by practical sessions on setting up Python & R environments and hands-on experience with ANTSIM. A quiz at the end will help reinforce your understanding.

💻 To run your code in Visual Studio Code:
👉 If installed: Open VS Code and drag the project folder into it.
👉 If not installed: .

🐍 To open the project in PyCharm:
👉 If installed: Open PyCharm and choose "Open Project".
👉 If not installed: .

📊 To use RStudio:
👉 If installed: Open RStudio and choose "Open File" or "Open Project".
👉 If not installed: .

This module covers the core technical components of ABMs, including agent attributes, behaviors, and decision-making processes. Learners will examine how agents interact within different environments and how computational tools are used to build ABMs. Practical exercises will help participants design and implement simple agent-based models.

This module focuses on defining and fine-tuning model parameters to ensure accurate simulations. Learners will explore how interaction rules, transmission probabilities, and agent behaviours shape disease dynamics. Different approaches for obtaining parameters from empirical data and literature will be covered.

To run your code in Visual Studio Code, if installed. If not, please install it first.

This module explores how data is integrated into ABMs and the importance of model calibration. Learners will examine data sources such as surveys, administrative records, and real-time data streams. Calibration techniques will be introduced to align models with observed real-world patterns.]

To run your code in Visual Studio Code, if installed. If not, please install it first.

This module explores how public health interventions can be designed and tested using ABMs. Learners will study different intervention strategies and their impact on disease transmission. The module also covers uncertainty analysis and sensitivity testing to assess intervention effectiveness.

To run your code in Visual Studio Code, if installed. If not, please install it first.

This module evaluates the impact of public health interventions using ABMs. Learners will analyze real-world case studies to assess the effectiveness of interventions in controlling disease spread. Comparative analysis of different intervention approaches will be explored.

This module introduces the concept of intervention scenarios and their role in evaluating public health strategies using ABMs. Learners will explore different types of scenarios, such as predictive, exploratory, and normative, and analyze how these scenarios can inform policy decisions. The module also covers best practices in scenario development and implementation within ABM frameworks.

To run your code in Visual Studio Code, if installed. If not, please install it first.

This module focuses on the effective communication of ABM results to policymakers, stakeholders, and the general public. Learners will explore methods for presenting model outcomes, including visualization techniques, policy briefs, and stakeholder engagement strategies. The module emphasizes the importance of clear and accurate communication to ensure ABM findings influence evidence-based decision-making.

It is our hope that you have enjoyed the course and you are now ready to xxxxx , please take this end of course survey belowand provide us with your feedback. 

(Estimated time: 15 mins)