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Activity Number:
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6
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Type:
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Invited
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Date/Time:
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Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Environmental and Ecological Statistics
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| Abstract - #304929 |
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Title:
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Spatiotemporal Geoinformatic Disease Surveillance
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Author(s):
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Stephen L. Rathbun*+ and Ganapati P. Patil
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Companies:
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University of Georgia and The Pennsylvania State University
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Address:
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152 Environmental Health Science Building, Athens, GA, 30602,
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Keywords:
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upper level set ; geosurveillance ; generalized linear mixed model ; spatial probit model ; conditional autoregressive model
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Abstract:
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Disease surveillance for early warning requires quick and efficient methods for delineation of disease hotspots in space and time. The upper level set (ULS) scan statistic is a computationally efficient approach for delineating hotspots of arbitrary shape. However, the current version of the ULS scan statistic assumes the data are distributed independently, an assumption that may be untenable for georeferenced data. We shall investigate the statistical properties of the ULS scan statistic under a variety of models for spatial dependence, including spatial probit models for the spatial distribution of hotspots and generalized linear mixed models with conditional autoregressive random effects. Simple methods are sought for testing the significance of hotspots, adjusting for the effects of spatial dependence. Our approach is illustrated using zoonotic disease data.
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