Publikationer
I urval från Stockholms universitets publikationsdatabas
Theresa Stocks, Tom Britton, Michael Höhle.
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.

Avhandling (Lic) Dynamic Modelling of Communicable and NonCommunicable Diseases2017. Theresa Stocks (et al.).
This thesis consists of two papers dealing with the stochastic dynamic modelling of one communicable and one noncommunicable disease respectively. In the first paper we derive a patient and organspecific measure for the estimated negative side effects of radiotherapy using a stochastic logistic birthdeath 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 modelbased 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).

2016. Theresa Stocks (et al.). Mathematical Medicine and Biology
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 organspecific and patientspecific 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.
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 CV Theresa Stocks (243 Kb)