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A Bayesian mixture modelling approach for public health surveillance

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Title: A Bayesian mixture modelling approach for public health surveillance
Authors: Boulieri, A
Bennett, JE
Blangiardo, M
Item Type: Journal Article
Abstract: Spatial monitoring of trends in health data plays an important part of public health surveillance. Most commonly, it is used to understand the etiology of a public health issue, to assess the impact of an intervention, or to provide detection of unusual behavior. In this article, we present a Bayesian mixture model for public health surveillance, which is able to provide estimates of the disease risk in space and time, and also to detect areas with unusual behavior. The model is designed to deal with a range of spatial and temporal patterns in the data, and with time series of different lengths. We carry out a simulation study to assess the performance of the model under different scenarios, and we compare it against a recently proposed Bayesian model for short time series. Finally, the proposed model is used for surveillance of road traffic accidents data in England over the years 2005–2015.
Issue Date: 1-Jul-2020
Date of Acceptance: 19-Jun-2018
URI: http://hdl.handle.net/10044/1/61598
DOI: 10.1093/biostatistics/kxy038
ISSN: 1465-4644
Publisher: Oxford University Press (OUP)
Start Page: 369
End Page: 383
Journal / Book Title: Biostatistics
Volume: 21
Issue: 3
Copyright Statement: © The Author 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution,and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Funder's Grant Number: MR/L01632X/1
Keywords: Bayesian hierarchical analysis
Mixture modeling
Public health surveillance
Road traffic accidents
Small-area detection
Spatio-temporal modeling
0104 Statistics
0604 Genetics
Statistics & Probability
Publication Status: Published
Online Publication Date: 2018-09-25
Appears in Collections:Faculty of Medicine
School of Public Health

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