Literature DB >> 14615227

Representative threats for research in public health surveillance.

Michael M Wagner1, Virginia Dato, John N Dowling, Michael Allswede.   

Abstract

A large number of biological agents can cause natural or bioterroristic disease outbreaks and each can present in a bewildering number of ways (e.g., a few cases versus many cases, confined to a building versus widely disseminated). This 'problem space' is a challenge for designers of early warning systems for disease outbreaks and the sheer size of this space is a barrier to progress. This paper addresses this problem by deriving nine categories of threats that represent a parsimonious characterization of the problem space. A literature search also identified one or more example outbreaks for each of the nine categories. These outbreaks have occurred in recent times and could be used by researchers in need of actual outbreak data for investigations of the role of different types of surveillance data and algorithms in outbreak detection. The methodological contribution of this research is a Criterion Set of threats for analysis and evaluation of detection systems. This set characterizes the problem space in a tractable manner with less loss of generality than analyses based on one or two selected diseases, which is representative of current analyses.

Mesh:

Year:  2003        PMID: 14615227     DOI: 10.1016/s1532-0464(03)00065-0

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  5 in total

Review 1.  How outbreaks of infectious disease are detected: a review of surveillance systems and outbreaks.

Authors:  Virginia Dato; Michael M Wagner; Abi Fapohunda
Journal:  Public Health Rep       Date:  2004 Sep-Oct       Impact factor: 2.792

2.  A Bayesian network model for analysis of detection performance in surveillance systems.

Authors:  Masoumeh Izadi; David Buckeridge; Anna Okhmatovskaia; Samson W Tu; Martin J O'Connor; Csongor Nyulas; Mark A Musen
Journal:  AMIA Annu Symp Proc       Date:  2009-11-14

3.  Can syndromic surveillance data detect local outbreaks of communicable disease? A model using a historical cryptosporidiosis outbreak.

Authors:  D L Cooper; N Q Verlander; G E Smith; A Charlett; E Gerard; L Willocks; S O'Brien
Journal:  Epidemiol Infect       Date:  2006-02       Impact factor: 2.451

4.  Detection of disease outbreaks by the use of oral manifestations.

Authors:  M H Torres-Urquidy; G Wallstrom; T K L Schleyer
Journal:  J Dent Res       Date:  2009-01       Impact factor: 6.116

5.  Generating a reliable reference standard set for syndromic case classification.

Authors:  Wendy W Chapman; John N Dowling; Michael M Wagner
Journal:  J Am Med Inform Assoc       Date:  2005-07-27       Impact factor: 4.497

  5 in total

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