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Pomp-astic inference methods for epidemic models illustrated on German rotavirus surveillance data
Stockholm University, Faculty of Science, Department of Mathematics.
Stockholm University, Faculty of Science, Department of Mathematics.
Stockholm University, Faculty of Science, Department of Mathematics.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Infectious disease surveillance data often provides only partial information about the progression of the disease in the individual while disease transmission is often modelled using complex mathematical models for large scale data, where variability only enters through a stochastic observation process. In this work it is shown that a rather simplistic, but truly stochastic transmission model, is competitive with respect to model fit when compared with more detailed deterministic transmission models and even preferable because the role of each parameter and its identifiability is clearly understood in the simpler model. The inference framework for the stochastic model is provided by iterated filtering methods which are readily implemented in the R package pomp available from the comprehensive R archive network (CRAN). We illustrate our findings on German rotavirus surveillance data from 2001 to 2008 and calculate a model based estimate for the reproduction number R0 using these data.

National Category
Mathematics
Identifiers
URN: urn:nbn:se:su:diva-137855OAI: oai:DiVA.org:su-137855DiVA: diva2:1064626
Available from: 2017-01-12 Created: 2017-01-12 Last updated: 2017-03-03Bibliographically approved
In thesis
1. Dynamic Modelling of Communicable and Non-Communicable Diseases
Open this publication in new window or tab >>Dynamic Modelling of Communicable and Non-Communicable Diseases
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis consists of two papers dealing with the stochastic dynamic modelling of one communicable and one non-communicable disease respectively. In the first paper we derive a patient- and organ-specific measure for the estimated negative side effects of radiotherapy using a stochastic logistic birth-death process. We find that the region of a maximum tolerable radiation dose can be approximated by an asymptotic simplification  and illustrate our findings on brachytherapy for prostate cancer. The second paper is concerned with the stochastic dynamic modelling of infectious disease spread in a large population to explain routine rotavirus surveillance data.  More specifically, we show that a partially observed dynamical system which includes structural variability in the transmission rates but which is simple with respect to disease progression is able to explain the available incidence data. A careful mathematical analysis addresses parameter identifiability and a model-based estimate for the basic reproduction number $R_0$ is given. As inference method we use iterated filtering which is implemented in the \texttt{R} package \texttt{pomp}, available from the comprehensive R archive network (CRAN).

Place, publisher, year, edition, pages
Department of Mathematics, Stockholm University, 2017
National Category
Mathematics
Identifiers
urn:nbn:se:su:diva-137860 (URN)
Presentation
2017-02-02, 22, House 5, Department of Mathematics, Stockholm, 13:15 (English)
Opponent
Supervisors
Available from: 2017-01-12 Created: 2017-01-12 Last updated: 2017-03-03Bibliographically approved

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