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Stochastic Epidemic Models and Their Statistical Analysis

  • Håkan Andersson
  • Tom Britton

Part of the Lecture Notes in Statistics book series (LNS, volume 151)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Stochastic Modelling

    1. Front Matter
      Pages 1-2
    2. Håkan Andersson, Tom Britton
      Pages 3-9
    3. Håkan Andersson, Tom Britton
      Pages 11-18
    4. Håkan Andersson, Tom Britton
      Pages 19-26
    5. Håkan Andersson, Tom Britton
      Pages 27-37
    6. Håkan Andersson, Tom Britton
      Pages 39-49
    7. Håkan Andersson, Tom Britton
      Pages 51-61
    8. Håkan Andersson, Tom Britton
      Pages 63-72
    9. Håkan Andersson, Tom Britton
      Pages 73-83
  3. Estimation

    1. Front Matter
      Pages 85-86
    2. Håkan Andersson, Tom Britton
      Pages 87-97
    3. Håkan Andersson, Tom Britton
      Pages 99-106
    4. Håkan Andersson, Tom Britton
      Pages 107-115
    5. Håkan Andersson, Tom Britton
      Pages 117-125
  4. Back Matter
    Pages 127-140

About this book

Introduction

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.

Keywords

Analysis Markov Markov chain Markov process epidemics model modeling

Authors and affiliations

  • Håkan Andersson
    • 1
  • Tom Britton
    • 2
  1. 1.Group Financial Risk ControlSwedBankStockholmSweden
  2. 2.Department of MathematicsUppsala UniversityUppsalaSweden

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-1158-7
  • Copyright Information Springer-Verlag New York, Inc. 2000
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-95050-1
  • Online ISBN 978-1-4612-1158-7
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site
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