Stochastic Epidemic Models With Inference
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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 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.
Stochastic Epidemic Models With Inference
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Author : Tom Britton
language : en
Publisher: Springer
Release Date : 2019-12-01
Stochastic Epidemic Models With Inference written by Tom Britton and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-01 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.
Bayesian Inference For Stochastic Epidemic Models
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Author : Philip Robert Giles
language : en
Publisher:
Release Date : 2005
Bayesian Inference For Stochastic Epidemic Models written by Philip Robert Giles and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Stochastic Modeling And Control
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Author : Ivan Ivanov
language : en
Publisher: IntechOpen
Release Date : 2012-11-28
Stochastic Modeling And Control written by Ivan Ivanov and has been published by IntechOpen this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-28 with Mathematics categories.
Stochastic control plays an important role in many scientific and applied disciplines including communications, engineering, medicine, finance and many others. It is one of the effective methods being used to find optimal decision-making strategies in applications. The book provides a collection of outstanding investigations in various aspects of stochastic systems and their behavior. The book provides a self-contained treatment on practical aspects of stochastic modeling and calculus including applications drawn from engineering, statistics, and computer science. Readers should be familiar with basic probability theory and have a working knowledge of stochastic calculus. PhD students and researchers in stochastic control will find this book useful.
Bayesian Inference For Stochastic Epidemic Models Using Markov Chain Monte Carlo Methods
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Author : Nikolaos Demiris
language : en
Publisher:
Release Date : 2004
Bayesian Inference For Stochastic Epidemic Models Using Markov Chain Monte Carlo Methods written by Nikolaos Demiris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Bayesian Nonparametric Inference For Stochastic Epidemic Models
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Author : Xiaoguang Xu
language : en
Publisher:
Release Date : 2015
Bayesian Nonparametric Inference For Stochastic Epidemic Models written by Xiaoguang Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Bayesian statistical decision theory categories.
Topics In Bayesian Inference And Model Assessment For Partially Observed Stochastic Epidemic Models
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Author : Georgios Aristotelous
language : en
Publisher:
Release Date : 2020
Topics In Bayesian Inference And Model Assessment For Partially Observed Stochastic Epidemic Models written by Georgios Aristotelous and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
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.
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.