First-passage times and normal tissue complication probabilities in the limit of large populations

From Springer Nature via Jisc Publications RouterHistory: received 2019-07-07, accepted 2020-04-06, registration 2020-04-20, pub-electronic 2020-05-29, online 2020-05-29, collection 2020-12Publication status: PublishedAbstract: The time of a stochastic process first passing through a boundary is important to many diverse applications. However, we can rarely compute the analytical distribution of these first-passage times. We develop an approximation to the first and second moments of a general first-passage time problem in the limit of large, but finite, populations using Kramers–Moyal expansion techniques. We demonstrate these results by application to a stochastic birth-death model for a population of cells in order to develop several approximations to the normal tissue complication probability (NTCP): a problem arising in the radiation treatment of cancers. We specifically allow for interaction between cells, via a nonlinear logistic growth model, and our approximations capture the effects of intrinsic noise on NTCP. We consider examples of NTCP in both a simple model of normal cells and in a model of normal and damaged cells. Our analytical approximation of NTCP could help optimise radiotherapy planning, for example by estimating the probability of complication-free tumour under different treatment protocols

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Mathematical and statistical challenges for the surveillance of gastroenteritis

Gastroenteritis, causing vomiting and diarrhoea, is very common all over the world. Viral causes, such as norovirus and rotavirus, are the most frequent, although some bacteria, parasites and fungi can also lead to gastroenteritis. Many countries operate surveillance systems of diseases, including gastroenteritis or specific gastroenteritis causing pathogens. Typically, statistical methods are used to analyse surveillance data and alert public health authorities of unexpectedly high levels of illness. These methods use historical data to predict the expected value of current data. In this thesis, we address some of the challenges that remain when analysing gastroenteritis surveillance data, with a particular focus on syndromic surveillance data. We work with both mechanistic and statistical modelling approaches in an attempt to bridge the gap between the statistical methods that are used in practice for syndromic surveillance and mechanistic models that are used to model infectious diseases. In particular, we address three challenges. In chapter 2 we present a flexible framework for deriving approximations of stochastic mechanistic models of epidemics for fast inference. In chapter 3 we investigate day of the week and public holiday effects in syndromic indicators of gastroenteritis from syndromic surveillance systems operated by Public Health England in order to improve existing surveillance methodologies. In chapter 4 we identify and analyse additional online datasets for gastroenteritis, and in particular norovirus, surveillance

Gaussian process approximations for fast inference from infectious disease data

We present a flexible framework for deriving and quantifying the accuracy of Gaussian process approximations to non-linear stochastic individual-based models of epidemics. We develop this for the SIR and SEIR models, and show how it can be used to perform quick maximum likelihood inference for the underlying parameters given population estimates of the number of infecteds or cases at given time points. We also show how the unobserved processes can be inferred at the same time as the underlying parameters. [Abstract copyright: Copyright © 2018. Published by Elsevier Inc.

Calculating normal tissue complication probabilities and probabilities of complication-free tumour control from stochastic models of population dynamics

We use a stochastic birth-death model for a population of cells to estimate the normal tissue complication probability (NTCP) under a particular radiotherapy protocol. We specifically allow for interaction between cells, via a nonlinear logistic growth model. To capture some of the effects of intrinsic noise in the population we develop several approximations of NTCP, using Kramers-Moyal expansion techniques. These approaches provide an approximation to the first and second moments of a general first-passage time problem in the limit of large, but finite populations. We use this method to study NTCP in a simple model of normal cells and in a model of normal and damaged cells. We also study a combined model of normal tissue cells and tumour cells. Based on existing methods to calculate tumour control probabilities, and our procedure to approximate NTCP, we estimate the probability of complication free tumour control.Comment: 27 pages, 5 figures, 4 table

Spatio-temporal modelling of Leishmania infantum infection among domestic dogs : a simulation study and sensitivity analysis applied to rural Brazil

The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood-feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models. We have developed a stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fitted distributions for these entities to existing survey data. To identify the model parameters of highest importance, we performed a stochastic parameter sensitivity analysis of the prevalence of infection among dogs to the model parameters. We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The stochastic parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated parameters and the proportion of immigrant dogs already infected with L. infantum parasites. Establishing the model parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions

Ensuring the Week Goes Smoothly - Improving Daily Surveillance Visualization

Improving the visualization of daily data used in surveillance by improved modelling of day of the week and public holiday effects

Ensuring the Week Goes Smoothly - Improving Daily Surveillance Visualization

Improving the visualization of daily data used in surveillance by improved modelling of day of the week and public holiday effects