ArticleBiostatistics (Oxford, England)2020
Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Accelerated inference for stochastic compartmental models with over-dispersed partial observations.Statistics and computing · 2026Article
- A filtering approach for statistical inference in a stochastic SIR model with an application to Covid-19 data.Biostatistics (Oxford, England) · 2025Article
- Inference on spatiotemporal dynamics for coupled biological populations.Journal of the Royal Society, Interface · 2024Article
- Informing policy via dynamic models: Cholera in Haiti.PLoS computational biology · 2024Article
- ePyDGGA: automatic configuration for fitting epidemic curves.Scientific reports · 2024Article
- Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series.BMC medical research methodology · 2023Article
- Using a latent Hawkes process for epidemiological modelling.PloS one · 2023Article
- Quantifying the scientific revolution.Evolutionary human sciences · 2023Article
- Disentangling snakebite dynamics in Colombia: How does rainfall and temperature drive snakebite temporal patterns?PLoS neglected tropical diseases · 2022Article
- Underdetection in a stochastic SIR model for the analysis of the COVID-19 Italian epidemic.Stochastic environmental research and risk assessment : research journal · 2022Article
- Containing the spread of mumps on college campuses.Royal Society open science · 2022Article
- New statistical model for misreported data with application to current public health challenges.Scientific reports · 2021Article
- Article
- Accounting for indirect protection in the benefit-risk ratio estimation of rotavirus vaccination in children under the age of 5 years, France, 2018.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2020Article
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3 authors.
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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.
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