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Background. Health economic evaluations of interventions against infectiousdiseases are commonly based on the predictions of ordinary differentialequation (ODE) systems or Markov models (MMs). Standard MMs are static, whereasODE systems are usually dynamic and account for herd immunity which is crucialto prevent overestimation of infection prevalence. Complex ODE systemsincluding probabilistic model parameters are computationally intensive. Thus,mainly ODE-based models including deterministic parameters are presented in theliterature. These do not account for parameter uncertainty. As a consequence,probabilistic sensitivity analysis (PSA), a crucial component of healtheconomic evaluations, cannot be conducted straightforwardly.  Methods. We present a dynamic MM under a Bayesian framework. We extend astatic MM by incorporating the force of infection into the state allocationalgorithm. The corresponding output is based on dynamic changes in prevalenceand thus accounts for herd immunity. In contrast to deterministic ODE-basedmodels, PSA can be conducted straightforwardly. We introduce a case study of afictional sexually transmitted infection and compare our dynamic Bayesian MM toa deterministic and a Bayesian ODE system. The models are calibrated to timeseries data.  Results. By means of the case study, we show that our methodology producesoutcome which is comparable to the "gold standard" of the Bayesian ODE system.  Conclusions. In contrast to ODE systems in the literature, the setting of thedynamic MM is probabilistic at manageable computational effort (includingcalibration). The run time of the Bayesian ODE system is 44 times longer.

A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease