A Novel Chronic Disease Policy Model

We develop a simulation tool to support policy-decisions about healthcare for chronic diseases in defined populations. Incident disease-cases are generated in-silico from an age-sex characterised general population using standard epidemiological approaches. A novel disease-treatment model then simulates continuous life courses for each patient using discrete event simulation. Ideally, the discrete event simulation model would be inferred from complete longitudinal healthcare data via a likelihood or Bayesian approach. Such data is seldom available for relevant populations, therefore an innovative approach to evidence synthesis is required. We propose a novel entropy-based approach to fit survival densities. This method provides a fully flexible way to incorporate the available information, which can be derived from arbitrary sources. Discrete event simulation then takes place on the fitted model using a competing hazards framework. The output is then used to help evaluate the potential impacts of policy options for a given population.

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