Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting.

ABSTRACT: Delayed reporting of health data may hamper the early detection of infectious diseases in surveillance systems. Furthermore, combining multiple data streams, e.g. aiming at improving a system's sensitivity, can be challenging. In this study, we used a Bayesian framework where the result is presented as the value of evidence, i.e. the likelihood ratio for the evidence under outbreak versus baseline conditions. Based on a historical data set of routinely collected cattle mortality events, we evaluated outbreak detection performance (sensitivity, time to detection, in-control run length) under the Bayesian approach among three scenarios: presence of delayed data reporting, but not accounting for it; presence of delayed data reporting accounted for; and absence of delayed data reporting (i.e.

SUBMITTER: Struchen R 

PROVIDER: S-EPMC5430846 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature