course ID: 2020_cSA

System analytics. Data-driven decision making made easy

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 SLHS PhD programme, Department of Health Sciences, University of Lucerne, Switzerland



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 is able to capture the  complexity of global health issues and quantify uncertainty to better inform policy and planning decisions. Methods include 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 of what are 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 applications
  • A hands-on-keyboards approach to interacting with existing models and building simple prototypes with user interfaces.

Examples used throughout the course will come from applications in mental health service planning and suicide prevention (including youth mental health), tobacco control, childhood overweight and obesity, cardiovascular disease, chemotherapy service planning, child protection, and homelessness.


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 public 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 competently interact with / run models using off the shelf software;
  • Be able to build a simple prototype model;
  • Understand the value of participatory approaches to model development.


Please note:

  • Before the course: Preparatory work before the course including recommended readings and downloading of free software. (~1-5 hours)
  • Participants must bring a laptop computer with the free software installed
  • The course will consist of lectures, interactive lessons, and experiential learning activities

Jo-An Atkinson

A/Professor and Head of Systems Modelling & Simulation, Brain and Mind Centre, Faculty of Medicine & Health, The University of Sydney.                                                                                    Managing Director, Computer Simulation & Advanced Research Technologies (CSART); Director, Decision Analytics.

Ante Prodan

Senior Lecturer, School of Computer, Data and Mathematical Sciences, Western Sydney University, Australia

Mark Heffernan

Professor,  School of Computer, Data and Mathematical Sciences, Western Sydney University, Australia

Adam Skinner

Mathematical Modeller and Biostatistician with Systems Modelling & Simulation, Brain and Mind Centre, Faculty of Medicine & Health, The University of Sydney