Health economic evaluations of interventions in infectious disease are
commonly based on the predictions of ordinary differential equation (ODE) systems or
Markov models (MMs). Standard MMs are static, whereas ODE systems are usually
dynamic and account for herd immunity which is crucial to prevent overestimation of
infection prevalence. Complex ODE systems including distributions on model parameters
are computationally intensive. Thus, mainly ODE-based models including fixed parameter
values are presented in the literature. These do not account for parameter uncertainty.
As a consequence, probabilistic sensitivity analysis (PSA), a crucial component of health
economic evaluations, cannot be conducted straightforwardly. We present a dynamic
MM under a Bayesian framework. We extend a static MM by incorporating the force of
infection into the state allocation algorithm. The corresponding output is based on dynamic changes in prevalence
and thus accounts for herd immunity. In contrast to deterministic ODE-based models, PSA can be conducted
straightforwardly. We introduce a case study of a fictional sexually transmitted infection and compare our dynamic
Bayesian MM to a deterministic and a Bayesian ODE system. The models are calibrated to simulated time series data.
By means of the case study, we show that our methodology produces outcome which is comparable to the “gold
standard” of the Bayesian ODE system. In contrast to ODE systems in the literature, the dynamic MM includes
distributions on all model parameters at manageable computational effort (including calibration). The run time of the
Bayesian ODE system is 15 times longer.