Fitting Stochastic Epidemic Models To Multiple Data Types
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Fitting Stochastic Epidemic Models To Multiple Data Types
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Author : Mingwei Tang
language : en
Publisher:
Release Date : 2019
Fitting Stochastic Epidemic Models To Multiple Data Types written by Mingwei Tang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Traditional infectious disease epidemiology focuses on fitting deterministic and stochastic epidemics models to surveillance case count data. Recently, researchers began to make use of infectious disease agent genetic data to complement statistical analyses of case count data. Such genetic analyses rely on the field of phylodynamics --- a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. In this thesis, we aim at designing a general framework that can fit stochastic epidemic models to surveillance count data and to genetic data separately, or to use both sources of information at the same time. Firstly, we propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models. We bypass the current computationally intensive particle Markov chain Monte Carlo (MCMC) methods and achieve computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern MCMC tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). Next, we propose a joint model that allows us to integrate incidence data and genetic data. Finally, we consider the dependency of genetic sequence sampling times on the latent prevalence of the infectious disease and propose a preferential sampling phylodynamics model that improves performance of phylodynamic inference. In a series of simulation studies, we show that all our proposed estimation methods can successfully recover parameters of stochastic epidemic models. Moreover, we demonstrate that combining multiple data types helps resolve identifiability issues and improves estimation precision. Throughout the dissertation, we use the incidence and genetic data from the 2014 Ebola epidemic in Sierra Leone and Liberia to illustrate our methodological developments.
Epidemic Models
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Author : Denis Mollison
language : en
Publisher: Cambridge University Press
Release Date : 1995-07-13
Epidemic Models written by Denis Mollison and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-07-13 with Mathematics categories.
Surveys the state of epidemic modelling, resulting from the NATO Advanced Workshop at the Newton Institute in 1993.
Stochastic Epidemic Models With Inference
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Author : Tom Britton
language : en
Publisher: Springer Nature
Release Date : 2019-11-30
Stochastic Epidemic Models With Inference written by Tom Britton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-30 with Mathematics categories.
Focussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on "Stochastic models of epidemics" which took place in Ziguinchor, Senegal, December 5–16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.
Stochastic Population And Epidemic Models
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Author : Linda J. S. Allen
language : en
Publisher: Springer
Release Date : 2015-08-20
Stochastic Population And Epidemic Models written by Linda J. S. Allen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-20 with Mathematics categories.
This monograph provides a summary of the basic theory of branching processes for single-type and multi-type processes. Classic examples of population and epidemic models illustrate the probability of population or epidemic extinction obtained from the theory of branching processes. The first chapter develops the branching process theory, while in the second chapter two applications to population and epidemic processes of single-type branching process theory are explored. The last two chapters present multi-type branching process applications to epidemic models, and then continuous-time and continuous-state branching processes with applications. In addition, several MATLAB programs for simulating stochastic sample paths are provided in an Appendix. These notes originated as part of a lecture series on Stochastics in Biological Systems at the Mathematical Biosciences Institute in Ohio, USA. Professor Linda Allen is a Paul Whitfield Horn Professor of Mathematics in the Department of Mathematics and Statistics at Texas Tech University, USA.
Stochastic Epidemic Models And Their Statistical Analysis
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Author : Hakan Andersson
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Stochastic Epidemic Models And Their Statistical Analysis written by Hakan Andersson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
The present lecture notes describe stochastic epidemic models and methods for their statistical analysis. Our aim is to present ideas for such models, and methods for their analysis; along the way we make practical use of several probabilistic and statistical techniques. This will be done without focusing on any specific disease, and instead rigorously analyzing rather simple models. The reader of these lecture notes could thus have a two-fold purpose in mind: to learn about epidemic models and their statistical analysis, and/or to learn and apply techniques in probability and statistics. The lecture notes require an early graduate level knowledge of probability and They introduce several techniques which might be new to students, but our statistics. intention is to present these keeping the technical level at a minlmum. Techniques that are explained and applied in the lecture notes are, for example: coupling, diffusion approximation, random graphs, likelihood theory for counting processes, martingales, the EM-algorithm and MCMC methods. The aim is to introduce and apply these techniques, thus hopefully motivating their further theoretical treatment. A few sections, mainly in Chapter 5, assume some knowledge of weak convergence; we hope that readers not familiar with this theory can understand the these parts at a heuristic level. The text is divided into two distinct but related parts: modelling and estimation.
Deterministic And Stochastic Epidemic Models With Multiple Pathogens
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Author : Nadarajah Kirupaharan
language : en
Publisher:
Release Date : 2003
Deterministic And Stochastic Epidemic Models With Multiple Pathogens written by Nadarajah Kirupaharan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Deterministic chaos categories.
Bayesian Modeling Of Partially Observed Epidemic Count Data
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Author : Jonathan Fintzi
language : en
Publisher:
Release Date : 2018
Bayesian Modeling Of Partially Observed Epidemic Count Data written by Jonathan Fintzi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Epidemic count data reported by public health surveillance systems reflect the incidence or prevalence of an infectious agent as it spreads through a population. They are a primary source of information for shaping response strategies and for predicting how an outbreak will evolve. Incidence and prevalence counts are often the only source of information about historical outbreaks, or outbreaks in resource limited settings, which are of interest for researchers seeking to develop an understanding of disease transmission during ``peace time", with an eye on preparing for future outbreaks. The absence of subject--level information and the systematic underreporting of cases complicate the task of disentangling whether the data arose from a severe outbreak, observed with low fidelity, or a mild outbreak were most cases were detected. The magnitude of the missing data and the high dimensional state space of the latent epidemic process present challenges for fitting epidemic models that appropriately quantify the stochastic aspects of the transmission dynamics. In this dissertation, we develop computational algorithms for fitting stochastic epidemic models to partially observed incidence and prevalence data. Our algorithms are not specific to particular model dynamics, but rather apply to a broad class of commonly used stochastic epidemic models, including models that allow for time--inhomogeneous transmission dynamics. We use our methods to analyze data from an outbreak of influenza in a British boarding school, the 2014--2015 outbreak of Ebola in West Africa, and the 2009--2011 A(H1N1) influenza pandemic in Finland.
Simulating Innovation
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Author : Christopher Watts
language : en
Publisher: Edward Elgar Publishing
Release Date : 2014-01-31
Simulating Innovation written by Christopher Watts and has been published by Edward Elgar Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-31 with Computers categories.
Christopher Watts and Nigel Gilbert explore the generation, diffusion and impact of innovations, which can now be studied using computer simulations. ø Agent-based simulation models can be used to explain the innovation that emerges from interact
Stochastic Epidemic Models
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Author : Mathias Lindholm
language : en
Publisher:
Release Date : 2008
Stochastic Epidemic Models written by Mathias Lindholm and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Stochastic Epidemic Models With Inference
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Author : Tom Britton
language : en
Publisher:
Release Date : 2019
Stochastic Epidemic Models With Inference written by Tom Britton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Biomathematics categories.
Focussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on "Stochastic models of epidemics" which took place in Ziguinchor, Senegal, December 5-16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.