Imperial College London
Alternate

DrJamesBennett

Faculty of Medicine, School of Public Health

Statistical Manager
 
 
 
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umahx99

 
 
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Location

 

1120Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Boulieri:2020:biostatistics/kxy038,
author = {Boulieri, A and Bennett, JE and Blangiardo, M},
doi = {biostatistics/kxy038},
journal = {Biostatistics},
pages = {369--383},
title = {A Bayesian mixture modelling approach for public health surveillance},
url = {http://dx.doi.org/10.1093/biostatistics/kxy038},
volume = {21},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Boulieri,A
AU  - Bennett,JE
AU  - Blangiardo,M
DO  - biostatistics/kxy038
EP  - 383
PY  - 2020///
SN  - 1465-4644
SP  - 369
TI  - A Bayesian mixture modelling approach for public health surveillance
T2  - Biostatistics
UR  - http://dx.doi.org/10.1093/biostatistics/kxy038
UR  - http://hdl.handle.net/10044/1/61598
VL  - 21
ER  -
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