Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Dynamic Modelling of Communicable and Non-Communicable Diseases
Stockholm University, Faculty of Science, Department of Mathematics.
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: urn:nbn:se:su:diva-137860OAI: oai:DiVA.org:su-137860DiVA: diva2:1064666
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
List of papers
1. Pomp-astic inference methods for epidemic models illustrated on German rotavirus surveillance data
Open this publication in new window or tab >>Pomp-astic inference methods for epidemic models illustrated on German rotavirus surveillance data
(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:nbn:se:su:diva-137855 (URN)
Available from: 2017-01-12 Created: 2017-01-12 Last updated: 2017-03-03Bibliographically approved
2. A stochastic model for the normal tissue complication probability (NTCP) and applications
Open this publication in new window or tab >>A stochastic model for the normal tissue complication probability (NTCP) and applications
2016 (English)In: Mathematical Medicine and Biology, ISSN 1477-8599, E-ISSN 1477-8602Article in journal (Refereed) Epub ahead of print
Abstract [en]

The normal tissue complication probability (NTCP) is a measure for the estimated side effects of a given radiation treatment schedule. Here we use a stochastic logistic birth–death process to define an organ-specific and patient-specific NTCP. We emphasize an asymptotic simplification which relates the NTCP to the solution of a logistic differential equation. This framework is based on simple modelling assumptions and it prepares a framework for the use of the NTCP model in clinical practice. As example, we consider side effects of prostate cancer brachytherapy such as increase in urinal frequency, urinal retention and acute rectal dysfunction.

Keyword
normal tissue complication, probability logistic birth death process, tumour control probability, radiation treatment, side effects, TCP, NTCP, brachytherapy, prostate cancer
National Category
Mathematics
Identifiers
urn:nbn:se:su:diva-137846 (URN)10.1093/imammb/dqw013 (DOI)
Available from: 2017-01-12 Created: 2017-01-12 Last updated: 2017-06-16

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Stocks, Theresa
By organisation
Department of Mathematics
Mathematics

Search outside of DiVA

GoogleGoogle Scholar

Total: 637 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf