Abstract
Today’s most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method’s conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the ‘wicked’ problems in population health, and could make significant contributions to theory and intervention development in these areas.
Highlights
Health policy-making is, at the root, an attempt to undertake principled decisions in an environment of high uncertainty and high risk, in which competing pressures and interests from stakeholders have a significant effect on the actions taken [1]
We propose that the addition of Agent-Based Modelling (ABM) and related complex-systems-based approaches to the population health toolkit will enable better-informed and more robust decision-making in population health
While population health research has contributed to numerous high-profile health successes in modern times, there remain some highly complex, ‘wicked’ problems which defy traditional methods of epidemiological analyses, and have resisted our attempts to develop effective interventions at the population level
Summary
Health policy-making is, at the root, an attempt to undertake principled decisions in an environment of high uncertainty and high risk, in which competing pressures and interests from stakeholders have a significant effect on the actions taken [1]. These modelling techniques allow us to represent individual behaviours and their interactions, study the tangled web of causal relationships among environmental, physical and social factors affecting health-related behaviours, and simulate the effect of these relationships on the dynamics of public health problems at the population level.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call