A Bayesian mixture modeling approach for public health surveillance.
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ABSTRACT:
SUBMITTER: Boulieri A
PROVIDER: S-EPMC7307974 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
A Bayesian mixture modeling approach for public health surveillance.
Boulieri Areti A Bennett James E JE Blangiardo Marta M
Biostatistics (Oxford, England) 20200701 3
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 ...[more]
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