Beginning my journey in systems modelling more than a decade ago, I could hardly have imagined where it would lead. Starting with a simple question about how we might better understand the complex drivers of health and social problems, I have now overseen more than 40 systems models spanning everything from youth suicide prevention to alcohol policy, health system reform to homelessness strategy. Looking back, it has been a journey marked by both remarkable progress and persistent challenges that continue to test the field today.

Systems science emerged as a distinct field from mid-twentieth century developments in operations research, cybernetics and industrial engineering, where complex military and manufacturing problems required analytical tools that could account for interdependencies, feedback loops and nonlinear relationships. Since the mid-1950s, these methods have been successfully applied to urban planning, economic forecasting and disaster response planning.

The translation to health and social policy is more recent, building on the success of infectious disease modelling. Systems modelling addresses a limitation of traditional approaches: while conventional epidemiological methods excel at using data to understand what has already happened, systems modelling uses that same data to look forward. Combined with our best understanding of complex systems, it explores what is likely to happen under different scenarios. Systems modelling allows policymakers to test interventions virtually before committing to particular strategies.

The early years of working in systems modelling for health policy in Australia were defined by more than just convincing sceptics that complex problems required more sophisticated analytic tools than those being used. The institutional barriers were formidable. Systems modelling research struggled to find a home in mainstream medical and public health journals, with less than a third of published applications appearing in top-tier publications despite their methodological rigour and policy relevance.

Funding agencies were similarly cautious, viewing this interdisciplinary systems science approach as too removed from traditional biomedical research paradigms. I became acutely aware that choosing this path might limit my academic advancement, yet the potential to address real-world policy challenges in ways that traditional approaches simply could not match compelled me to persist.

The challenge was not just technical – it was fundamentally about communication and translation. How do you convey the value of dynamic, systems-based insights to audiences trained in linear thinking and individual-level interventions? I spent considerable time and effort learning to articulate complex methodologies in accessible terms, finding ways to demonstrate tangible policy value rather than just methodological sophistication.

Traditional approaches to health policy often relied on single interventions tested in isolation, missing the dynamic interrelationships that determine real-world outcomes. As epidemiologists, we were essentially trying to solve jigsaw puzzles by examining individual pieces rather than understanding how they fit together.

Demonstrating the value of systems modelling

One of the most rewarding aspects of this work has been witnessing the gradual shift from scepticism to genuine curiosity about what these tools might offer. Early conversations with policymakers often began with doubt about whether complex health challenges could meaningfully be represented by mathematical models and whether such models could even be trusted. Today, there is greater recognition that these models are an important part of the analytic toolkit, particularly given the growing realisation that the usual investment approaches are not yielding the anticipated impacts.

The COVID-19 pandemic served as an unexpected catalyst in this transformation, demonstrating to the world how rapidly deployed models could guide life-saving policy responses despite significant uncertainty. The public acceptance of “flattening the curve”, based on dynamic models, created a new understanding of how simulation can inform crucial decisions.

However, a critical development for systems modelling in health policy has occurred more recently, with the field gaining mainstream scientific credibility, especially through its integration with rigorous economic analyses. This convergence has unlocked the ability to conduct cost-effectiveness analyses that reflect the complex, interconnected nature of health systems interventions.

When policymakers can see not just what is projected to work, but what will deliver the best value for money while accounting for system interactions and unintended consequences, the conversation changes entirely.

Prioritising collaboration

The participatory approach that my research teams have refined over more than a decade – most recently at the University of Sydney’s Brain and Mind Centre – has also been central to this acceptance. Rather than working in academic isolation, we learned to bring stakeholders directly into the model development process. This meant sitting in rooms with clinicians, policymakers, service planners, community organisations and consumers, collectively mapping out the service system and the pathways through which a given health or social problem develops and can be addressed.

These workshops often revealed an understanding of a health policy or system that no single expert could have provided, while simultaneously building the trust and understanding necessary for models to influence real decisions. Some of the most important systems insights have emerged this way, including that sometimes even evidence-based interventions can produce unintended consequences.

For instance, our work in the Australian Capital Territory found that the modelled school-based suicide prevention – while effective at reducing suicidal behaviour – led to 45 per cent more adolescents accessing mental health services due to its screening and referral components, creating capacity constraints that increased waiting times. This resulted in people spending more time at higher levels of distress, ultimately producing negative quality-adjusted life years despite the program’s effectiveness.

A regional suicide prevention model developed for the North Coast of NSW in 2018 demonstrated that “more is not necessarily better” – namely that introducing additional interventions beyond the best-performing combination produced only marginal improvement in reducing self-harm hospitalisations and other population-level mental health outcomes.

Perhaps most importantly, our youth mental health modelling in Bogotá, Colombia revealed synergistic effects, where implementing selected programs concurrently yielded greater benefit than the sum of their individual effects. Such findings are particularly crucial in resource-constrained settings, where the ability to maximise population health outcomes through strategic combinations of interventions can dramatically enhance the efficiency of limited investments.

Yet for all the technical progress, we have learned that building these complex models is only half the challenge. Much more ongoing support than initially anticipated has been needed to build capacity and confidence in using these sophisticated tools for making decisions.

However, significant progress is being made through sustained partnerships between research teams and end users, demonstrating that effective collaboration must extend well beyond the model development process to ensure these decision support assets deliver their full potential.

Building capacity for systems modelling

Looking forward, the field stands at an inflection point. The demand for evidence-informed policy has never been higher, yet the increasing complexity of global challenges continues to outpace traditional analytical capabilities. The next decade will likely see systems modelling become even more central to policymaking, but success will depend on addressing a critical barrier: building sustained capacity in systems science and its analytical tools.

This capacity-building challenge operates at multiple levels. First, there is a fundamental shortage of skilled systems modellers with experience in health and social sectors, constraining the field’s impact.  More than just educational programs, we need secure employment opportunities for people with these specialised capabilities.

Second, institutional knowledge is needed to effectively translate model insights into policy action. This requires concrete action from policymakers: establishing dedicated systems modelling roles within government agencies, creating career progression pathways for public servants with these skills and fostering partnerships between universities and government departments.

While external expertise will always have a role, government agencies need sufficient internal capability to effectively commission, exercise, interpret and apply insights from systems models. This includes understanding what questions models can answer, what data they require, how to validate results, and how to translate findings into actionable policy. Without this internal expertise, even the most sophisticated models risk abandonment after initial use rather than functioning as living decision-support assets.

The most impactful applications of systems modelling emerge from sustained collaboration between modellers and decision-makers, but this requires stable funding and employment structures that enable long-term partnerships, rather than project-by-project engagements. Universities and research institutions alone cannot meet this demand. Government agencies, consulting firms and non-government entities (e.g., foundations, NGOs, think tanks) must invest in developing and retaining systems modelling expertise as a core institutional capacity rather than relying solely on external consultants and short-term projects.

A notable example is the $12.8 million investment by the BHP Foundation (2021-2026) to create the Right Care, First Time, Where you live Program. This program, led by the Brain and Mind Centre in partnership with Primary Health Networks, is delivering decision support infrastructure, based on systems modelling, to guide investments and actions that foster the mental health and wellbeing of young people in eight diverse communities across Australia. It is also attracting interest from research groups and philanthropic organisations in the UK, Canada, Switzerland, Colombia and other low- and middle-income countries seeking to replicate this approach.

A bright future

Reflecting on the last decade, I am struck by both how far the health and social sector systems modelling community in Australia have come and how far we still have to go. The journey has reinforced my conviction that in an increasingly complex world, we cannot afford to make policy decisions based on intuition, ideology or incomplete information alone.

Systems modelling offers a path toward more impactful decision-making, but realising its potential requires continued investment in both technical capabilities and the sustained human relationships that make the interpretation and implementation of modelling insights possible.

The next decade will undoubtedly bring new challenges and opportunities. Emerging generative AI capability also shows promise for evidence synthesis to support model parameterisation and faster global sensitivity analysis, yet the fundamental human elements – problem framing, stakeholder engagement, trust building and policy translation – remain critical. Regardless of possible technological advances, the fundamental question remains unchanged: how do we leverage these powerful tools to build a healthier, more equitable world?

Associate Professor Jo-An Occhipinti is an NHMRC Principal Research Fellow, Head of Systems Modelling & Simulation and Founding Co-Director of the Mental Wealth Initiative at the University of Sydney's Brain and Mind Centre. She also holds a non-salaried position as Managing Director of Computer Simulation & Advanced Research Technologies (CSART), an international alliance of centres of excellence in systems modelling for health and social policy.

This article draws on a decade of collaborative work with researchers, policymakers and communities across Australia and internationally. The insights shared represent lessons learned through both successes and failures in the ongoing effort to make systems modelling more useful for addressing our most pressing health and social challenges.

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