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

TL;DR

This paper introduces a dynamic Bayesian Markov model for infectious disease health economic evaluations, capturing herd immunity effects and parameter uncertainty more efficiently than traditional ODE models.

Contribution

It extends static Markov models by incorporating herd immunity and Bayesian parameter uncertainty, enabling straightforward probabilistic sensitivity analysis.

Findings

Produces outcomes comparable to Bayesian ODE models

Significantly reduces computational time (44 times faster)

Allows for probabilistic calibration and uncertainty quantification

Abstract

Background. Health economic evaluations of interventions against infectious diseases 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 probabilistic model parameters are computationally intensive. Thus, mainly ODE-based models including deterministic parameters 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. Methods. 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 time series data. Results. 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. Conclusions. In contrast to ODE systems in the literature, the setting of the dynamic MM is probabilistic at manageable computational effort (including calibration). The run time of the Bayesian ODE system is 44 times longer.