Understanding population health using agent-based simulation methods
PHASE is a UK Prevention Research Partnership funded research network that brings together population health and simulation experts to tackle the complex challenges underlying non‑communicable diseases
Up to £25,000 is available to multidisciplinary teams to support proof of concept and exploratory work focussed on applying agent-based models to non-communicable disease prevention.
PHASE aims to deliver translational research that addresses the complex challenges faced by decision makers in the prevention of non-communicable diseases
Non-communicable diseases, such as heart disease, cancer and diabetes, account for over 70% of deaths worldwide. Despite advances in our understanding of the causes, the prevalence and inequalities in non-communicable diseases remain problematic.
New approaches are needed that take into account the complex systems and behaviours underlying these population-level health challenges.
Agent-based models are uniquely placed to understand the complex systems underlying population health challenges
Risk factors for non-communicable diseases include unhealthy diets, tobacco and alcohol use, physical inactivity and air pollution. Individual health choices around these risks are often the result of complex and interacting social and environmental factors.
Agent-based models can help us understand the mechanisms behind health behaviours, by allowing us to model the characteristics of individuals or organisations in a population and their social interactions with one another and with the environment.
What is an agent-based model?
How can agent-based models be used in population health?
Agent-based models are already being used to understand population health issues such as physical activity, alcohol and tobacco use, and obesity.
Agent-based models also allow us to evaluate the potential impact of new policies before they are implemented in the real-world. Our Frequently Asked Questions includes examples of agent-based models for population health.
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