Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective (Statistics for Biology and Health Book 63)

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. 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. ℗¡ Front Matter....Pages i-xvi Front Matter....Pages 1-1 Why This Book? An Introduction....Pages 3-7 Front Matter....Pages 9-9 A Priori and A Posteriori Models for Infectious Diseases....Pages 11-23 The SIR Model....Pages 25-58 Front Matter....Pages 59-59 Data Sources for Modeling Infectious Diseases....Pages 61-76 Front Matter....Pages 77-77 Estimating the Force of Infection from Incidence and Prevalence....Pages 79-88 Parametric Approaches to Model the Prevalence and Force of Infection....Pages 89-110 Nonparametric Approaches to Model the Prevalence and Force of Infection....Pages 111-120 Semiparametric Approaches to Model the Prevalence and Force of Infection....Pages 121-139 The Constraint of Monotonicity....Pages 141-148 Hierarchical Bayesian Models for the Force of Infection....Pages 149-165 Modeling the Prevalence and the Force of Infection Directly from Antibody Levels....Pages 167-183 Modeling Multivariate Serological Data....Pages 185-199 Estimating Age-Time Dependent Prevalence and Force of Infection from Serial Prevalence Data....Pages 201-216 Front Matter....Pages 217-217 Who Acquires Infection from Whom? The Traditional Approach....Pages 219-232 Informing WAIFW with Data on Social Contacts....Pages 233-243 Front Matter....Pages 245-245 Integrating Estimated Parameters in a Basic SIR Model....Pages 247-255 Back Matter....Pages 257-298 Résumé : 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 guide to the latest statistical techniques for estimating the parameters of infectious diseases arises from a groundbreaking collaboration between Hasselt and Antwerp universities in Belgium, and features valuable case studies and software advice.