Discrete event simulation

Discrete event simulation (DES) is a computer-modelling technique used in the economic evaluation of health interventions. It works by simulating the experience of individual patients over time, tracking events and their consequences. Unlike cohort Markov models, which use fixed time cycles, DES allows events (e.g. treatment side effects, disease progression) to occur at variable times. Events and health states are constructed for each individual modelled patient; these can be aggregated to understand the overall experience of the patient cohort.
A key advantage of DES is its ability to model complex scenarios, when a patient’s history impacts future events, or when there are many possible events and health states to consider. For example, it’s particularly useful for conditions with numerous complications, like diabetes. The method’s ability to build detailed, individual patient histories often makes it appealing to clinical experts.
In its most sophisticated form, DES can also model interactions between individuals. This is particularly useful for simulating resource constraints and queues; for example, where capacity for diagnostic tests is limited, one patient’s diagnostic test can affect another’s waiting time. While powerful, DES can be more complex to build than other techniques and requires careful handling of time-to-event input values and distributions, which can be challenging to derive.