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Modeling Infectious Disease Parameters Based on Serological and Social Contact Data

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Cover of 'Modeling Infectious Disease Parameters Based on Serological and Social Contact Data'

Table of Contents

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    Book Overview
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    Chapter 1 Why This Book? An Introduction
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    Chapter 2 A Priori and A Posteriori Models for Infectious Diseases
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    Chapter 3 The SIR Model
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    Chapter 4 Data Sources for Modeling Infectious Diseases
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    Chapter 5 Estimating the Force of Infection from Incidence and Prevalence
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    Chapter 6 Parametric Approaches to Model the Prevalence and Force of Infection
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    Chapter 7 Nonparametric Approaches to Model the Prevalence and Force of Infection
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    Chapter 8 Semiparametric Approaches to Model the Prevalence and Force of Infection
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    Chapter 9 The Constraint of Monotonicity
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    Chapter 10 Hierarchical Bayesian Models for the Force of Infection
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    Chapter 11 Modeling the Prevalence and the Force of Infection Directly from Antibody Levels
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    Chapter 12 Modeling Multivariate Serological Data
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    Chapter 13 Estimating Age-Time Dependent Prevalence and Force of Infection from Serial Prevalence Data
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    Chapter 14 Who Acquires Infection from Whom? The Traditional Approach
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    Chapter 15 Informing WAIFW with Data on Social Contacts
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    Chapter 16 Integrating Estimated Parameters in a Basic SIR Model
Attention for Chapter 12: Modeling Multivariate Serological Data
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Citations

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Chapter title
Modeling Multivariate Serological Data
Chapter number 12
Book title
Modeling Infectious Disease Parameters Based on Serological and Social Contact Data
Published by
Springer, New York, NY, January 2012
DOI 10.1007/978-1-4614-4072-7_12
Book ISBNs
978-1-4614-4071-0, 978-1-4614-4072-7
Authors

Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Professor > Associate Professor 1 50%
Readers by discipline Count As %
Mathematics 1 50%
Computer Science 1 50%

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