<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zou, Jian</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Banks, David</style></author><author><style face="normal" font="default" size="100%">Heaton, Matthew J.</style></author><author><style face="normal" font="default" size="100%">Datta, Gauri</style></author><author><style face="normal" font="default" size="100%">Lynch, James</style></author><author><style face="normal" font="default" size="100%">Vera, Francisco</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian methodology for the analysis of spatial temporal surveillance data</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Analysis and Data Mining</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">conditional autoregressive process</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov random field</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial statistics</style></keyword><keyword><style  face="normal" font="default" size="100%">spatio-temporal</style></keyword><keyword><style  face="normal" font="default" size="100%">Syndromic surveillance</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1002/sam.10142</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">Wiley Subscription Services, Inc., A Wiley Company</style></publisher><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">194–204</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Early and accurate detection of outbreaks is one of the most important objectives of syndromic surveillance systems. We propose a general Bayesian framework for syndromic surveillance systems. The methodology incorporates Gaussian Markov random field (GMRF) and spatio-temporal conditional autoregressive (CAR) modeling. By contrast, most previous approaches have been based on only spatial or time series models. The model has appealing probabilistic representations as well as attractive statistical properties. Based on extensive simulation studies, the model is capable of capturing outbreaks rapidly, while still limiting false positives. Â© 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 194â€“204, 2012&lt;/p&gt;
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