A Bayesian mixture modelling approach for public health surveillance

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Title: A Bayesian mixture modelling approach for public health surveillance
Author(s): 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 aetiology of a public health issue, to assess the impact of an intervention, or to provide detection of unusual behaviour. In this paper, 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 behaviour. 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 to 2015.
Publication Date: 25-Sep-2018
Date of Acceptance: 19-Jun-2018
URI: http://hdl.handle.net/10044/1/61598
ISSN: 1465-4644
Publisher: Oxford University Press (OUP)
Journal / Book Title: Biostatistics
Copyright Statement: This paper is embargoed until publication. Once published it will be available fully open access.
Keywords: 0104 Statistics
0604 Genetics
Statistics & Probability
Publication Status: Accepted
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Faculty of Medicine
Epidemiology, Public Health and Primary Care



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