Value of evidence from syndromic surveillance with delayed reporting

Description: 

Taking into account reporting delays in surveillance systems is not methodologically trivial. Consequently, most use the date of the reception of data, rather than the (often unknown) date of the health event itself. The main drawback of this approach is the resulting reduction in sensitivity and specificity1. Combining syndromic data from multiple data streams (most health events may leave a “signature” in multiple data sources) may be performed in a Bayesian framework where the result is presented in the form of a posterior probability for a disease2.

Objective

We apply an empirical Bayesian framework to perform change point analysis on multiple cattle mortality data streams, accounting for delayed reporting of syndromes.

Primary Topic Areas: 
Original Publication Year: 
2016
Event/Publication Date: 
December, 2016

August 26, 2017

Contact Us

NSSP Community of Practice

Email: syndromic@cste.org

 

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