How do we estimate the potential impact of evidence-based interventions in real-world settings? In this course you will learn methods of systems modelling and simulation to capture the complexity of quantifying uncertainty to better inform policy and planning decisions.
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 support strategic and effective decision making. Systems modelling is focused on understanding what is the ideal intervention combinations, and what targeting, timing, scale and duration of programs and services will deliver the greatest impacts on health outcomes.
This 3-day course will expose students to:
PLEASE NOTE THAT EACH DAY HAS A DIFFERENT TIME SCHEDULE.
Day 1 (10.30 - 17.30 CEST): Systems modelling to address public health challenges: The What and Why.
This short course 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. Lectures will include insights from real-world applications of systems modelling to chronic disease prevention, tobacco control, infectious disease transmission and the social and economic impacts of COVID-19 on mental health. Day 1 lectures will also provide an introduction to the architecture of system dynamics modelling, followed by a step-by-step guide to its application to population dynamics and simple infectious disease models.
Day 2 (9.00 - 16.30 CEST): A deeper foray into system dynamics modelling applied to policy and planning.
Using tobacco control policy 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 (9.00 - 16.45 CEST): 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 model to support service planning (COVID-19 vaccination logistics). Demonstration of a cardiovascular disease agent based model will also introduce participants to modelling at different scales to help understand the interaction between biological to behavioural processes.
Across the three days attendees may choose to engage with the step-by-step model building process (with in person support) to build technical competency in the methods, or may simply wish to listen and understand it conceptually so as to become more informed users of systems models. For those who wish to build technical competency in the methods, we highly recommend to attend onsite.
By the end of the course participants will:
At least one facilitator will be on-site in Lugano, and some will join online. Participants are welcomed to join either on-site in Lugano, or online. In case of a change of regional policies (e.g., Covid) or personal reasons, the course could change to online.