Handbook of Infectious Disease Data Analysis
Chapman & Hall/CRC Handbooks of Modern Statistical Methods Series

Coordinators: Held Leonhard, Hens Niel, O'Neill Philip D, Wallinga Jacco

Language: Anglais
Cover of the book Handbook of Infectious Disease Data Analysis

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· 17.8x25.4 cm · Hardback

Recent years have seen an explosion in new kinds of data on infectious diseases, including data on social contacts, whole genome sequences of pathogens, biomarkers for susceptibility to infection, serological panel data, and surveillance data. The Handbook of Infectious Disease Data Analysis provides an overview of many key statistical methods that have been developed in response to such new data streams and the associated ability to address key scientific and epidemiological questions. A unique feature of the Handbook is the wide range of topics covered.

Key features

  • Contributors include many leading researchers in the field
  • Divided into four main sections: Basic concepts, Analysis of Outbreak Data, Analysis of Seroprevalence Data, Analysis of Surveillance Data
  • Numerous case studies and examples throughout
  • Provides both introductory material and key reference material

Leonhard Held is Professor of Biostatistics at the University of Zurich.

Niel Hens is Professor of Biostatistics at Hasselt University and the University of Antwerp.

Philip O?Neill is Professor of Applied Probability at the University of Nottingham.

Jacco Wallinga is Professor of Mathematical Modelling of Infectious Diseases at the Leiden University Medical Center.







I Introduction

1. Introduction (Held, Hens, O’Neill, Wallinga)

II Basic Concepts

1. Population dynamics of pathogens (Ottar Bjornstad)

2. Infectious disease data from surveillance, outbreak investigation and epidemiological studies

(Susan Hahné and Richard Pebody)

3. Key concepts in infectious disease epidemiology (Nick Jewell)

4. Key parameters in infectious disease epidemiology (Laura White)

5. Contact patterns for contagious diseases (Jacco Wallinga, Jan van de Kassteele, Niel Hens)

6. Basic stochastic transmission models and their inference (Tom Britton)

7. Analysis of vaccine studies and causal inference (Betz Halloran)

III Analysis of Outbreak Data

1. Markov chain Monte Carlo methods for outbreak data (Philip O’Neill, Theodore Kypraios)

2. Approximate Bayesian Computation methods for epidemic models (Peter Neal)

3. Iterated filtering methods for Markov process epidemic models (Theresa Stocks)

4. Pairwise survival analysis of infectious disease transmission data (Eben Kenah)

5. Methods for outbreaks using genomic data (Don Klinkenberg, Caroline Colijn, Xavier Didelot)

IV Analysis of Seroprevalence Data

1. Persistence of passive immunity, natural immunity (and vaccination) (Amy Winter, Jess Metcalf)

2. Inferring the time of infection from serological data (Maciej Boni, Kåre Mølbak, Karen Angeliki Krogfelt)

3. The use of seroprevalence data to estimate cumulative incidence of infection (Ben Cowling, Jes-sica Wong)

4. The analysis of serological data with transmission models (Marc Baguelin)

5. The analysis of multivariate serological data (Steven Abrams)

6. Mixture modelling (Emanuele Del Fava, Ziv Shkedy)

V Analysis of Surveillance Data

1. Modelling infectious diseases distributions: applications of point process methods (Peter J Diggle)

2. Prospective detection of outbreaks (Benjamin Allevius, Michael Höhle)

3. Underreporting and reporting delays (Angela Noufaily)

4. Spatio-temporal analysis of surveillance data (Jon Wakefield, Tracy Q Dong, Vladimir N Minin)

5. Analysing multiple epidemic data sources (Daniela de Angelis, Anne Presanis)

6. Forecasting based on surveillance data (Leonhard Held, Sebastian Meyer)

7. Spatial mapping of infectious disease risk (Ewan Cameron)