An approach to identifying public health threats by characterizing syndromic surveillance data in terms of its surprisability is discussed. Surprisability in our model is measured by assigning a probability distribution to a time series, and then calculating its entropy, leading to a straightforward designation of an alert. Initial application of our method is to investigate the applicability of using suitably-normalized syndromic counts (i.e., proportions) to improve early event detection.
|Original language||English (US)|
|Number of pages||1|
|Journal||AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium|
|Publication status||Published - Dec 1 2007|
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