Individual-based Modeling in Epidemiology: A Practical Introduction

About

Individual-based models (IBMs), also frequently referred to as agent-based models, are a relatively new class of models that can be used to gain insight into the population dynamics of complex systems that emerge from the characteristics and interactions of individuals in the population.

This course aims to give participants the skills to design, implement, and analyse IBMs with practical applications in epidemiology. The emphasis of the course will be on the process of designing, testing and analysing IBMs to address complex questions in epidemiology and not on the technical aspects of writing computer programs.

Course leader

Dr. Lander Willem (Infectious Disease Modeler & Scientific Computing) Prof. Dr. Wim Delva (Medical Doctor & Mathematical Epidemiologist)

Target group

Phd-students, postdocs and health science professionals whose work potentially involves the design and/or use of individual-based models (IBMs) in epidemiology.

Ideally, participants should have used R previously for data analysis and simple programming (e.g. writing your own function). However, this is not an absolute prerequisite. Prior experience with NetLogo is not required.

Course aim

To give participants the skills to design, implement, and analyse IBMs using R and NetLogo, a popular, open-access platform for IBMs:

- Model structure and complexity
- Individual action rules
- Emerging model dynamics
- Fitting to empirical data
- Model validation

Fee info