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Modeling Infectious Disease Parameters Based on Serological and Social Contact Data

A Modern Statistical Perspective

  • Book
  • © 2012

Overview

Authors:
  1. Niel Hens

    1. Center for Statistics, Hasselt University, Diepenbeek, Belgium

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  2. Ziv Shkedy

    1. Center for Statistics, Hasselt University, Diepenbeek, Belgium

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  3. Marc Aerts

    1. Centrum voor Statistiek, Dept. Wiskunde, Natuurkunde, Informatica, Universiteit Hasselt, Diepenbeek, Belgium

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  4. Christel Faes

    1. Center for Statistics, Hasselt University, Diepenbeek, Belgium

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  5. Pierre Van Damme

    1. Centre for the Evaluation of Vaccination, Epidemiology & Social Medicine, University of Antwerp, Antwerpen, Belgium

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  6. Philippe Beutels

    1. Centre for the Evaluation of Vaccination, Epidemiology & Social Medicine, University of Antwerp, Antwerpen, Belgium

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  • Demonstrates the application of modern statistical methods and models to estimate infectious disease parameters
  • Provides the reader with software guidance and data
  • Uses valuable case studies
  • Includes supplementary material: sn.pub/extras

Part of the book series: Statistics for Biology and Health (SBH, volume 63)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Introducing the Concept of the Book

    1. Front Matter

      Pages 1-1
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    2. Why This Book? An Introduction

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 3-7

  3. Mathematical Models for Infectious Diseases: An Introduction

    1. Front Matter

      Pages 9-9
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    2. A Priori and A Posteriori Models for Infectious Diseases

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 11-23

    3. The SIR Model

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 25-58

  4. Data Sources

    1. Front Matter

      Pages 59-59
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    2. Data Sources for Modeling Infectious Diseases

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 61-76

  5. Estimating the Force of Infection

    1. Front Matter

      Pages 77-77
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    2. Estimating the Force of Infection from Incidence and Prevalence

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 79-88

    3. Parametric Approaches to Model the Prevalence and Force of Infection

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 89-110

    4. Nonparametric Approaches to Model the Prevalence and Force of Infection

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 111-120

    5. Semiparametric Approaches to Model the Prevalence and Force of Infection

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 121-139

    6. The Constraint of Monotonicity

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 141-148

    7. Hierarchical Bayesian Models for the Force of Infection

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 149-165

    8. Modeling the Prevalence and the Force of Infection Directly from Antibody Levels

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 167-183

    9. Modeling Multivariate Serological Data

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 185-199

    10. Estimating Age-Time Dependent Prevalence and Force of Infection from Serial Prevalence Data

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 201-216

  6. Estimating Mixing Patterns and the Basic Reproduction Number

    1. Front Matter

      Pages 217-217
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    2. Who Acquires Infection from Whom? The Traditional Approach

      • Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
      Pages 219-232
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Keywords

About this book

Mathematical epidemiology of infectious diseases usually involves describing the flow of individuals between mutually exclusive infection states. One of the key parameters describing the transition from the susceptible to the infected class is the hazard of infection, often referred to as the force of infection. The force of infection reflects the degree of contact with potential for transmission between infected and susceptible individuals. The mathematical relation between the force of infection and effective contact patterns is generally assumed to be subjected to the mass action principle, which yields the necessary information to estimate the basic reproduction number, another key parameter in infectious disease epidemiology.

 

It is within this context that the Center for Statistics (CenStat, I-Biostat, Hasselt University) and the Centre for the Evaluation of Vaccination and the Centre for Health Economic Research and Modelling Infectious Diseases (CEV, CHERMID, Vaccine and Infectious Disease Institute, University of Antwerp) have collaborated over the past 15 years. This book demonstrates the past and current research activities of these institutes and can be considered to be a milestone in this collaboration.

 

This book is focused on the application of modern statistical methods and models to estimate infectious disease parameters. We want to provide the readers with software guidance, such as R packages, and with data, as far as they can be made publicly available.

Authors and Affiliations

  • Center for Statistics, Hasselt University, Diepenbeek, Belgium

    Niel Hens, Ziv Shkedy, Christel Faes

  • Centrum voor Statistiek, Dept. Wiskunde, Natuurkunde, Informatica, Universiteit Hasselt, Diepenbeek, Belgium

    Marc Aerts

  • Centre for the Evaluation of Vaccination, Epidemiology & Social Medicine, University of Antwerp, Antwerpen, Belgium

    Pierre Van Damme, Philippe Beutels

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