| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Stocks, Theresa, et al.
(författare)
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A stochastic model for the normal tissue complication probability (NTCP) and applications
- 2017
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Ingår i: Mathematical Medicine and Biology. - : Oxford University Press (OUP). - 1477-8599 .- 1477-8602. ; 34:4, s. 469-492
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Stocks, Theresa, et al.
(författare)
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Dynamic modeling of hepatitis C transmission among people who inject drugs
- 2020
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Ingår i: Epidemics. - : Elsevier BV. - 1755-4365 .- 1878-0067. ; 30
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Tidskriftsartikel (refereegranskat)abstract
- To reach the WHO goal of hepatitis C elimination, it is essential to identify the number of people unaware of their hepatitis C virus (HCV) infection and to investigate the effect of interventions on the disease transmission dynamics. In many high-income countries, one of the primary routes of HCV transmission is via contaminated needles shared by people who inject drugs (PWIDs). However, substantial underreporting combined with high uncertainty regarding the size of this difficult to reach population, makes it challenging to estimate the core indicators recommended by the WHO. To support progress toward the elimination goal, we present a novel multi-layered dynamic transmission model for HCV transmission within a PWID population. The model explicitly accounts for disease stage (acute and chronic), injection drug use status (active and former PWIDs), status of diagnosis (diagnosed and undiagnosed) and country of disease acquisition (domestic or abroad). First, based on this model, and using routine surveillance data, we estimate the number of undiagnosed PWIDs, the true incidence, the average time until diagnosis, the reproduction numbers and associated uncertainties. Second, we examine the impact of two interventions on disease dynamics: (1) direct-acting antiviral drug treatment, and (2) needle exchange programs. As a proof of concept, we illustrate our results for a specific data set. In addition, we develop a web application to allow our model to be explored interactively and with different parameter values.
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| 4. |
- Stocks, Theresa
(författare)
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Dynamic Modelling of Communicable and Non-Communicable Diseases
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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).
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| 5. |
- Stocks, Theresa, et al.
(författare)
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Dynamic modelling of hepatitis C transmission among people who inject drugs : A tool to support WHO elimination targets
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- To reach the WHO goal of hepatitis C elimination, it is essential to identify the number of people unaware of their hepatitis C virus (HCV) infection and to investigate the effect of interventions on the disease transmission dynamics. In developed countries, one of the primary routes of HCV transmission is via contaminated needles shared by people who inject drugs (PWIDs). However, substantial underreporting combined with high uncertainty regarding the size of this difficult to reach population, makes it challenging to estimate the core indicators recommended by the WHO. To help enable countries to monitor their progress towards the elimination goal, we present a novel multi-layered dynamic transmission model for HCV transmission within a PWIDs population. The model explicitly accounts for disease stage (acute and chronic), injection drug use status (active and former PWIDs), status of diagnosis (diagnosed and undiagnosed) and country of disease acquisition (domestic or abroad). First, based on this model, and using routine surveillance data, we estimate the number of undiagnosed PWIDs, the true incidence, the average time until diagnosis, the reproduction numbers and associated uncertainties. Second, we examine the impact of two interventions on disease dynamics: 1) direct-acting antiviral drug treatment, and 2) needle exchange programs. To make the model accessible to relevant users and to support communication of our results to public health decision makers, the model and its output are made available through a Shiny app. As a proof of concept, we illustrate our results for a specific data set; however, through the app our model can be easily adapted to other high-income countries with similar transmission patterns among PWIDs where the disease is endemic.
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| 6. |
- Stocks, Theresa
(författare)
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Iterated Filtering Methods for Markov Process Epidemic Models
- 2020
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Ingår i: Handbook of Infectious Disease Data Analysis. - Boca Raton, USA : CRC Press. - 9781138626713 ; , s. 199-220
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Bokkapitel (refereegranskat)abstract
- This chapter provides an entry point into developing models integrating serological data with transmission dynamics. The analysis of serological data within transmission models can be used as a tool to interpret the serological data of interest or as part of an evidence synthesis approach where serology provides part of the epidemiological evidence. We start by presenting an early study using a transmission model in order to interpret household data and to permit better quantification of secondary attack rates in households. We then review the role of sequential serological data in estimating existing population immunity. This is critical in order to decide for potential immunization catch-up campaigns or to calibrate models of recurring infections with long-lasting immunity. We also focus on the use of serology to track infectious during a pandemic. Finally, we present a model explicitly integrating both disease dynamics and stratified immunity. This model is developed as a worked example and some R code is provided.
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| 7. |
- Stocks, Theresa, et al.
(författare)
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Model selection and parameter estimation for dynamic epidemic models via iterated filtering : application to rotavirus in Germany
- 2020
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Ingår i: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 21:3, s. 400-416
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Tidskriftsartikel (refereegranskat)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 Software 69, 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 R0 using these data.
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| 8. |
- Stocks, Theresa, et al.
(författare)
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Pomp-astic inference methods for epidemic models illustrated on German rotavirus surveillance data
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 9. |
- Stocks, Theresa, 1990-
(författare)
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Stochastic dynamic modelling and statistical analysis of infectious disease spread and cancer treatment
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Mathematical models have proven valuable for public health decision makers as they can provide insights into the understanding, control and, ultimately, the prevention of diseases. This thesis contains four manuscripts dealing with stochastic dynamic modelling and statistical analysis of infectious disease spread and optimization of cancer treatment.Paper I is concerned with deriving a patient- and organ-specific measure for the estimated negative side effects of radiotherapy using a stochastic logistic birth-death process. Our analysis shows that the region of a maximum tolerable radiation dose can be related to the solution of a logistic differential equation; we illustrate our results for brachytherapy for prostate cancer.Paper II and III deal with inference for stochastic epidemic models. Parameter estimation for this model class can be challenging as disease spread is usually only partially observed, e.g. in the form of accumulated reported incidences within specified time periods. To perform inference for these types of models, a useful method for maximum likelihood estimation is iterated filtering which takes advantage of the fact that it is relatively easy to generate samples from the underlying transmission process while the likelihood function for the given data is intractable.Paper II is an application-oriented introduction to iterated filtering via the R package pomp (King et al., 2016) which contains a wide collection of simulation-based inference methods for partially observed Markov processes. We review the theoretical background of the method and discuss by two examples its performance and some associated practical difficulties.Paper III is concerned with model selection for partially observed epidemic models that differ with respect to the amount of variability they allow for and parameter estimation of those models from routinely collected surveillance data. We illustrate the model selection and inference framework via the R package pomp for rotavirus transmission in Germany, however, the method can be easily adapted to other diseases.In Paper IV we develop a transmission model for hepatitis C virus (HCV) infection among people who inject drugs (PWIDs) to enable countries to monitor their progress towards HCV elimination. In the scope of the WHO’s commitment to viral hepatitis elimination, this topic is highly relevant to public health since injection drug use is the main route of transmission in many countries. From the model and using surveillance data, we derive estimates of four key HCV-indicators. Furthermore, the model can be used to investigate the impact of two interventions, direct-acting antiviral drug treatment and needle exchange programs, on the disease dynamics. In order to make the model and its output accessible to relevant users, it is made available through a Shiny app.
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