Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany.

ABSTRACT: Despite the wide application of dynamic models in infectious disease epidemiology, the particular modeling of variability in the different model components is often subjective rather than the result of a thorough model selection process. This is in part because inference for a stochastic transmission model can be difficult since the likelihood is often intractable due to partial observability. In this work, we address the question of adequate inclusion of variability by demonstrating a systematic approach for model selection and parameter inference for dynamic epidemic models. For this, we perform inference for six partially observed Markov process models, which assume the same underlying transmission dynamics, but differ with respect to the amount of variability they allow for. The infere

SUBMITTER: Stocks T 

PROVIDER: S-EPMC7307980 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature