course ID: 2021_cSA

Harnessing systems modelling and simulation to guide strategic health investments and actions

This course is supported by:

  • Brain and Mind Centre, Faculty of Medicine & Health, The University of Sydney, Australia
  • Computer Simulation & Advanced Research Technologies (CSART), Australia



The last half century has witnessed remarkable advances in computing power, machine intelligence, and emergence of novel means of measurement that have led to new and cheaper technologies, advances in science and industry, and more powerful tools to support the efficient functioning of societies. Private industry and public sectors alike have harnessed these advances in numerous ways, not least of which is to better understand complex systems, solve challenging problems, optimize the allocation of resources, and improve system efficiency, performance, and public safety.

Unfortunately, the health and social sectors have lagged behind.  For the most part, these sectors rely on comparatively rudimentary tools and approaches when seeking to understand complex problems and to inform policy and planning decisions. Complex problems that are characterised by interaction of risk factors, feedback loops, thresholds (or breaking points), inertia, delays and changing behaviour over time, all violate the assumptions of traditional analytic methods. Traditional decision analytic tools which seek to prioritise interventions on the basis of their comparative costs, benefits, or return on investment, do not adequately account for population dynamics, behavioural dynamics, service or workforce dynamics, the variation in the intervention impacts over time or the non-additive effects of combining interventions. These limitations make them ill-suited for informing decision making to address complex public health problems. As a result, the application of traditional methods can lead to unrealistic expectations of the potential impact of evidence-based interventions in real-world settings and delay progress in improving population health and wellbeing.

In contrast, systems modelling and simulation are able to capture the complexity of global health issues and quantify uncertainty to better inform policy and planning decisions. Methods including system dynamics modelling, agent-based modelling and discrete event simulation are better suited to synthesising and operationalising disparate sources of research evidence and data to answer vital questions. Typically, we search for the ideal selection, design, targeting, timing, intensity, consistency, and coordination of integrated programs and services in a given context that will deliver the greatest impacts on health outcomes.

This 3-day course will expose students to:

  • The ‘what and why’ of systems modelling and simulation
  • Real-world applications that informed policy and planning through the participatory development of decision support tools
  • Exposure to systems modelling methods including their practical application
  • A hands-on approach to building simple- and intermediate-level prototype models with interactive user interfaces.


  • Day 1: Systems modelling to address population health challenges: The What and Why. Lectures will include insights from real-world applications of systems modelling to chronic disease prevention, tobacco control, suicide prevention, and the social and economic impacts of COVID-19 on mental health. Lectures will also provide an introduction to the architecture of system dynamics modelling through application to population dynamics and simple infectious disease models. Day 1 will be orientated to researchers, public health practitioners, and decision makers who may be interested in the application of systems modelling to support policy and planning decisions. Attendees may not necessarily wish to build technical competency in the method, but rather understand it conceptually so as to be able contribute meaningfully to participatory modelling projects.
  • Day 2: A deeper foray into system dynamics modelling applied to policy and planning – using mental health as an example a series of lectures provide a step-by-step guide to conceptualizing, parameterizing, and calibrating a system dynamics model using research evidence and data and includes user interface design and scenario testing.
  • Day 3: An introduction to discrete event simulation (DES) and agent-based modelling – a series of lectures that provide a step-by-step guide to developing DES and agent-based models to support service planning (COVID-19 vaccination logistics)


By the end of the course participants will:

  • Be familiar with the concepts, methods and applications of systems modelling and simulation in the context of population health;
  • Be able to define and describe the modelling methods, including their appropriate use for answering different questions;
  • Understand the value of systems modelling and simulation in the global health toolkit;
  • Be able to build, calibrate, and run simple and intermediate level prototype models using off the shelf software and conduct scenario testing;
  • Understand the value of participatory approaches to model development.


  • A laptop with 4 core CPU, 8 GB RAM and Windows 10 operating system or Mac OS.
  • Competency with computers and regular use of numerically oriented software programs such as Microsoft Excel.
  • Familiarity with descriptive statistics and in particular the concept of probability distribution.
  • Preparatory work before the course including recommended readings and downloading of free software. (~1-5 hours)

Pedagogical method:

The course will consist of lectures, interactive lessons, and experiential learning activities.

Assessment procedure:

Submission of a running prototype model – several will be developed during the course, however, only one will be required to be submitted at the discretion of the student.

Jo-An Occhipinti

A/Professor and Head, Systems Modelling, Simulation & Data Science, Brain and Mind Centre, University of Sydney

Managing Director, Computer Simulation & Advanced Research Technologies (CSART)

Ante Prodan

Senior Lecturer, School of Computing, Engineering and Mathematics, Western Sydney University, Australia

Director, Computer Simulation & Advanced Research Technologies (CSART)

Affiliate Senior Research Fellow, Systems Modelling, Simulation & Data Science, Brain and Mind Centre, University of Sydney

Mark Heffernan

Adjunct Professor, Western School of Computing, Engineering & Mathematics;  Sydney University

CEO, Dynamic Operations

 Executive Consultant, Proton Dynamics

Adam Skinner

Research Fellow & Senior System Dynamics Modeller, Brain and Mind Centre, University of Sydney