Abstract
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) |
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Number of pages | 1 |
Journal | AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium |
State | Published - 2007 |
ASJC Scopus subject areas
- Medicine(all)