Literature DB >> 16177698

High-fidelity injection detectability experiments: a tool for evaluating syndromic surveillance systems.

Garrick L Wallstrom1, M Wagner, W Hogan.   

Abstract

INTRODUCTION: When public health surveillance systems are evaluated, CDC recommends that the expected sensitivity, specificity, and timeliness of surveillance systems be characterized for outbreaks of different sizes, etiologies, and geographic or demographic scopes. High-Fidelity Injection Detectability Experiments (HiFIDE) is a tool that health departments can use to compute these metrics for detection algorithms and surveillance data that they are using in their surveillance system.
OBJECTIVE: The objective of this study is to develop a tool that allows health departments to estimate the expected sensitivity, specificity, and timeliness of outbreak detection.
METHODS: HiFIDE extends existing semisynthetic injection methods by replacing geometrically shaped injects with injects derived from surveillance data collected during real outbreaks. These injects maintain the known relation between outbreak size and effect on surveillance data, which allows inferences to be made regarding the smallest outbreak that can be expected to be detectable.
RESULTS: An example illustrates the use of HiFIDE to analyze detectability of a waterborne Cryptosporidium outbreak in Washington, DC.
CONCLUSION: HiFIDE enables public health departments to perform system validations recommended by CDC. HiFIDE can be obtained for no charge for noncommercial use (http://www.hifide.org).

Entities:  

Mesh:

Year:  2005        PMID: 16177698      PMCID: PMC3586808     

Source DB:  PubMed          Journal:  MMWR Suppl        ISSN: 2380-8942


  7 in total

1.  Waterborne cryptosporidiosis outbreak, North Battleford, Saskatchewan, Spring 2001.

Authors:  R Stirling; J Aramini; A Ellis; G Lim; R Meyers; M Fleury; D Werker
Journal:  Can Commun Dis Rep       Date:  2001-11-15

2.  Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales.

Authors:  Anna Goldenberg; Galit Shmueli; Richard A Caruana; Stephen E Fienberg
Journal:  Proc Natl Acad Sci U S A       Date:  2002-04-16       Impact factor: 11.205

3.  Using temporal context to improve biosurveillance.

Authors:  Ben Y Reis; Marcello Pagano; Kenneth D Mandl
Journal:  Proc Natl Acad Sci U S A       Date:  2003-02-06       Impact factor: 11.205

4.  Design of a national retail data monitor for public health surveillance.

Authors:  Michael M Wagner; J Michael Robinson; Fu-Chiang Tsui; Jeremy U Espino; William R Hogan
Journal:  J Am Med Inform Assoc       Date:  2003-06-04       Impact factor: 4.497

5.  Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group.

Authors:  James W Buehler; Richard S Hopkins; J Marc Overhage; Daniel M Sosin; Van Tong
Journal:  MMWR Recomm Rep       Date:  2004-05-07

6.  High-fidelity injection detectability experiments: a tool for evaluating syndromic surveillance systems.

Authors:  Garrick L Wallstrom; M Wagner; W Hogan
Journal:  MMWR Suppl       Date:  2005-08-26

7.  Time series modeling for syndromic surveillance.

Authors:  Ben Y Reis; Kenneth D Mandl
Journal:  BMC Med Inform Decis Mak       Date:  2003-01-23       Impact factor: 2.796

  7 in total
  10 in total

1.  High-fidelity injection detectability experiments: a tool for evaluating syndromic surveillance systems.

Authors:  Garrick L Wallstrom; M Wagner; W Hogan
Journal:  MMWR Suppl       Date:  2005-08-26

2.  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

3.  Template-driven spatial-temporal outbreak simulation for outbreak detection evaluation.

Authors:  Min Zhang; Garrick L Wallstrom
Journal:  AMIA Annu Symp Proc       Date:  2008-11-06

4.  Evaluating multi-purpose syndromic surveillance systems - a complex problem.

Authors:  Roger Morbey; Gillian Smith; Isabel Oliver; Obaghe Edeghere; Iain Lake; Richard Pebody; Dan Todkill; Noel McCarthy; Alex J Elliot
Journal:  Online J Public Health Inform       Date:  2021-12-24

5.  Validity of evaluation approaches for outbreak detection methods in syndromic surveillance systems.

Authors:  M Karami
Journal:  Iran J Public Health       Date:  2012-11-01       Impact factor: 1.429

6.  Using GIS to create synthetic disease outbreaks.

Authors:  Rochelle E Watkins; Serryn Eagleson; Sam Beckett; Graeme Garner; Bert Veenendaal; Graeme Wright; Aileen J Plant
Journal:  BMC Med Inform Decis Mak       Date:  2007-02-14       Impact factor: 2.796

7.  Approaches to the evaluation of outbreak detection methods.

Authors:  Rochelle E Watkins; Serryn Eagleson; Robert G Hall; Lynne Dailey; Aileen J Plant
Journal:  BMC Public Health       Date:  2006-10-24       Impact factor: 3.295

8.  An empirical comparison of spatial scan statistics for outbreak detection.

Authors:  Daniel B Neill
Journal:  Int J Health Geogr       Date:  2009-04-16       Impact factor: 3.918

9.  Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems.

Authors:  Doyo G Enki; Paul H Garthwaite; C Paddy Farrington; Angela Noufaily; Nick J Andrews; Andre Charlett
Journal:  PLoS One       Date:  2016-08-11       Impact factor: 3.240

10.  A test of syndromic surveillance using a severe acute respiratory syndrome model.

Authors:  David J Wallace; Bonnie Arquilla; Richard Heffernan; Martin Kramer; Todd Anderson; David Bernstein; Michael Augenbraun
Journal:  Am J Emerg Med       Date:  2009-05       Impact factor: 2.469

  10 in total

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