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

Authors
  • Stocks, Theresa1
  • Britton, Tom1
  • Höhle, Michael1
  • 1 Department of Mathematics, Stockholm University, 10691 Stockholm, Sweden. , (Sweden)
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Jul 01, 2020
Volume
21
Issue
3
Pages
400–416
Identifiers
DOI: 10.1093/biostatistics/kxy057
PMID: 30265310
Source
Medline
Keywords
Language
English
License
Unknown

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 inference framework for the stochastic transmission models is provided by iterated filtering methods, which are readily implemented in the R package pomp by King and others (2016, Statistical inference for partially observed Markov processes via the R package pomp. Journal of Statistical Software69, 1-43). We illustrate our approach on German rotavirus surveillance data from 2001 to 2008, discuss practical difficulties of the methods used and calculate a model based estimate for the basic reproduction number $R_0$ using these data. © The Author 2018. Published by Oxford University Press.

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