A tutorial on spatio-temporal disease risk modelling in R using Markov chain Monte Carlo simulation and the CARBayesST package

Lee, D. (2020) A tutorial on spatio-temporal disease risk modelling in R using Markov chain Monte Carlo simulation and the CARBayesST package. Spatial and Spatio-Temporal Epidemiology, 34, 100353. (doi: 10.1016/j.sste.2020.100353)

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Abstract

Population-level disease risk varies in space and time, and is typically estimated using aggregated disease count data relating to a set of non-overlapping areal units for multiple consecutive time periods. A large research base of statistical models and corresponding software has been developed for such data, with most analyses being undertaken in a Bayesian setting using either Markov chain Monte Carlo (MCMC) simulation or integrated nested Laplace approximations (INLA). This paper presents a tutorial for undertaking spatio-temporal disease modelling using MCMC simulation, utilising the CARBayesST package in the R software environment. The tutorial describes the complete modelling journey, starting with data input, wrangling and visualisation, before focusing on model fitting, model assessment and results presentation. It is illustrated by a new case study of pneumonia mortality at the local authority level in England, and answers important public health questions including the effect of covariate risk factors, spatio-temporal trends, and health inequalities.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lee, Professor Duncan
Authors: Lee, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Spatial and Spatio-Temporal Epidemiology
Publisher:Elsevier
ISSN:1877-5845
ISSN (Online):1877-5853
Published Online:16 May 2020
Copyright Holders:Copyright © 2020 Elsevier Ltd.
First Published:First published in Spatial and Spatio-Temporal Epidemiology 34: 100353
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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