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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.


Gianluca graduated in Statistics and Economics from the University of Florence (Italy). He then completed
a PhD programme in Applied Statistics again at the University of Florence, after a period at the Program on the
Pharmaceutical Industry at the MIT Sloan School of Management, Cambridge (USA); he then worked as a Research Fellow and then Temporary Lecturer in the Department of Statistical Sciences at University College London (UK). His main research interests are in Bayesian statistical modelling for cost effectiveness analysis and decision-making problems in the health systems, hierarchical/multilevel models and causal inference using the decision-theoretic approach. He leads the Statistics for Health Economic Evaluation research group within the department of Statistical Science.