Frailty Models for Infectious Disease Epidemiology

The aim of this book is to familiarise epidemiologists with frailty concepts and to highlight the importance of quantifying individual heterogeneity when estimating key epidemiological parameters. Although individual heterogeneity in the acquisition of infectious diseases is acknowledged by many epidemiologists, one rarely accounts for it in the statistical analysis. Hence, our work aims at increasing awareness among epidemiologists in infectious disease modelling to accommodate both observed and unobserved sources of heterogeneity. In addition, the methods illustrated in this book enable the identification of transmission routes for infections for which the main routes are unknown by modelling multiple infections simultaneously. These approaches are closely related to random effects models for multivariate correlated data, albeit that we are faced with Type I interval-censored time-to-event data. The aim of this book is to familiarise epidemiologists with frailty concepts and to highlight the importance of quantifying individual heterogeneity when estimating key epidemiological parameters. Although individual heterogeneity in the acquisition of infectious diseases is acknowledged by many epidemiologists, one rarely accounts for it in the statistical analysis. Hence, this book focuses on increasing awareness among epidemiologists in infectious disease modelling in order to accommodate both observed and unobserved sources of heterogeneity. In addition, the methods illustrated in this book enable the identification of transmission routes for infections for which the main routes are unknown by modelling multiple infections simultaneously. These approaches are closely related to random effects models for multivariate correlated data.