Format:
Online-Ressource (XVI, 298 p. 122 illus., 2 illus. in color, digital)
ISBN:
9781461440727
,
1283623714
,
9781283623711
Series Statement:
Statistics for Biology and Health 63
Content:
Mathematical models for infectious diesease -- The static model -- The dynamic model -- The stochastic model -- Implementation of models in MATLAB -- Data sources for modelling infectious diseases -- Estimation from serological data -- Parametric models for teh prevalence and the force of infection -- Non-parametric approaches to model the prevalence and force of infection -- Semi-parametric approaches to model the prevalence and force of infection -- A Bayesian approach -- Modelling the prevalence and the force of infection direction from antibody levels -- Modelling multivariate serological data -- Estimation from other data sources -- Estimating mixing patterns and Ro in a heterogenous population -- Modelling in a homogeneous population -- Modelling in a heterogeneous population -- Modelling AIDS outbreak data -- Modelling hepatitis C among injection drug users -- Modelling dengue -- Modelling bovine herpes virus in cattle.
Content:
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. .
Note:
Description based upon print version of record
,
Modeling Infectious Disease Parameters Based on Serological and SocialContact Data; Preface; Acknowledgements; Contents; Part I Introducing the Concept of the Book; Chapter 1 Why This Book? An Introduction; 1.1 Terms for Germs; 1.2 Models of Infectious Diseases; 1.3 Where Does This Book Fit in the Field?; 1.4 A Road Map for This Book; Part II Mathematical Models for Infectious Diseases:An Introduction; Chapter 2 A Priori and A Posteriori Models for Infectious Diseases; 2.1 The Theory of Happenings; 2.2 An Example: A Basic Model for HIV/AIDS; 2.2.1 A Mathematical Model for HIV/AIDS
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2.2.2 A Statistical Model for the Initial HIV/AIDS Outbreak2.3 The Mechanism that Generates the Data; 2.4 The Basic Epidemiological Parameters; 2.5 Discussion; Chapter 3 The SIR Model; 3.1 Introduction; 3.1.1 The SIR Model; 3.1.2 The Basic Model Dynamics; 3.1.3 The Basic Model in R; 3.1.4 Vaccination in the Basic Model; 3.1.5 The Basic SIR Model with Vaccination in R; 3.1.6 The Critical Vaccination Coverage; 3.2 The SIR Model in Endemic Equilibrium; 3.2.1 Compartments in the Time Homogeneous SIR Model; 3.2.2 The SIR Model with Constant Force of Infectionat Endemic State in R
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3.2.3 The Critical Proportion of Vaccination in the TimeHomogeneous SIR Model3.3 The Time Homogeneous SIR Model and Serological Data; 3.4 Models with Maternal Antibodies and Latent Periods; 3.5 Transmission Within Multiple Subpopulations; 3.5.1 An SIR Model with Interacting Subpopulations; 3.5.2 Transmission Over Age and Time; 3.5.3 Estimating the WAIFW Matrix; 3.6 Discussion; Part III Data Sources; Chapter 4 Data Sources for Modeling Infectious Diseases; 4.1 Serological Data; 4.1.1 Hepatitis A; 4.1.2 Hepatitis B; 4.1.3 Hepatitis C; 4.1.4 Mumps; 4.1.5 Parvovirus B19; 4.1.6 Rubella
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4.1.7 Tuberculosis4.1.8 Varicella; 4.1.9 Multisera Data; 4.2 Hepatitis B Incidence Data; 4.3 Belgian Contact Survey; Part IV Estimating the Force of Infection; Chapter 5 Estimating the Force of Infection from Incidence and Prevalence; 5.1 Serological Data; 5.2 Age-Dependent Force of Infection; 5.3 Modeling Issues; 5.4 Incidence Data; Chapter 6 Parametric Approaches to Model the Prevalenceand Force of Infection; 6.1 Modeling the Force of Infection: A Historical Perspective; 6.1.1 Polynomial Models; 6.1.2 Nonlinear Models; 6.1.3 Discussion; 6.2 Fractional Polynomial Models
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6.2.1 Motivating Example6.2.2 Model Selection; 6.2.3 Constrained Fractional Polynomials; 6.2.4 Selection of Powers and Back to Nonlinear Models; 6.2.5 Application to the Data; 6.2.6 Influence of the Link Function; 6.2.7 From Fractional Polynomials Back to NonlinearModels; 6.3 Discussion; Chapter 7 Nonparametric Approaches to Model the Prevalenceand Force of Infection; 7.1 Nonparametric Approaches; 7.1.1 The First Nonparametric Approaches; 7.1.2 Local Estimation by Polynomials; 7.2 Application to UK Mumps Data; 7.3 Concluding Remarks
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Chapter 8 Semiparametric Approaches to Model the Prevalenceand Force of Infection
Additional Edition:
ISBN 9781461440710
Additional Edition:
Buchausg. u.d.T. ISBN 978-1-461-44071-0
Language:
English
DOI:
10.1007/978-1-4614-4072-7
URL:
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