2016/02/29 by Piotr Gwiazda, Gwiazda, Piotr, Błażej Miasojedow +3
Mathematics · Medicine · Veterinary · #Applications (stat.AP) #Brucella: diagnosis, epidemiology, treatment #COVID-19 epidemiological studies #FOS: Computer and information sciences #FOS: Mathematics #Mathematical and Theoretical Epidemiology and Ecology Models #Numerical Analysis (math.NA)
paper · pdf · doi:10.48550/arxiv.1602.08861
openalex publication_date 2016/02/29 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
Dynamics of the infectious disease transmission is often best understood\ntaking into account the structure of population with respect to specific\nfeatures, in example age or immunity level. Practical utility of such models\ndepends on the appropriate calibration with the observed data. Here, we discuss\nthe Bayesian approach to data assimilation in case of two-state age-structured\nmodel. This kind of models are frequently used to describe the disease dynamics\n(i.e. force of infection) basing on prevalence data collected at several time\npoints. We demonstrate that, in the case when the explicit solution to the\nmodel equation is known, accounting for the data collection process in the\nBayesian framework allows to obtain an unbiased posterior distribution for the\nparameters determining the force of infection. We further show analytically and\nthrough numerical tests that the posterior distribution of these parameters is\nstable with respect to cohort approximation (Escalator Boxcar Train) to the\nsolution. Finally, we apply the technique to calibrate the model based on\nobserved sero-prevalence of varicella in Poland.\n