Bayesian inference for age-structured population model of infectious disease with application to varicella in Poland
For epidemiologists and public health researchers, this provides a robust Bayesian calibration approach for age-structured models, though it is incremental as it extends existing methods to a specific model structure.
This paper presents a Bayesian inference method for calibrating age-structured infectious disease models using prevalence data, demonstrating unbiased posterior distributions and stability under cohort approximation, with application to varicella in Poland.
Dynamics of the infectious disease transmission is often best understood taking into account the structure of population with respect to specific features, in example age or immunity level. Practical utility of such models depends on the appropriate calibration with the observed data. Here, we discuss the Bayesian approach to data assimilation in case of two-state age-structured model. This kind of models are frequently used to describe the disease dynamics (i.e. force of infection) basing on prevalence data collected at several time points. We demonstrate that, in the case when the explicit solution to the model equation is known, accounting for the data collection process in the Bayesian framework allows to obtain an unbiased posterior distribution for the parameters determining the force of infection. We further show analytically and through numerical tests that the posterior distribution of these parameters is stable with respect to cohort approximation (Escalator Boxcar Train) to the solution. Finally, we apply the technique to calibrate the model based on observed sero-prevalence of varicella in Poland.