Ensemble modelling in descriptive epidemiology: burden of disease estimation

Abstract

Abstract Ensemble modelling is a quantitative method that combines information from multiple individual models and has shown great promise in statistical machine learning. Ensemble models have a theoretical claim to being models that make the ‘best’ predictions possible. Applications of ensemble models to health research have included applying ensemble models like the super learner and random forests to epidemiological prediction tasks. Recently, ensemble methods have been applied successfully in burden of disease estimation. This article aims to provide epidemiologists with a practical understanding of the mechanisms of an ensemble model and insight into constructing ensemble models that are grounded in the epidemiological dynamics of the prediction problem of interest. We summarize the history of ensemble models, present a user-friendly framework for conceptualizing and constructing ensemble models, walk the reader through a tutorial of applying the framework to an application in burden of disease estimation, and discuss further applications.