Literature DB >> 18240128

Comparing syndromic surveillance detection methods: EARS' versus a CUSUM-based methodology.

Ronald D Fricker1, Benjamin L Hegler, David A Dunfee.   

Abstract

This paper compares the performance of three detection methods, entitled C1, C2, and C3, that are implemented in the early aberration reporting system (EARS) and other syndromic surveillance systems versus the CUSUM applied to model-based prediction errors. The cumulative sum (CUSUM) performed significantly better than the EARS' methods across all of the scenarios we evaluated. These scenarios consisted of various combinations of large and small background disease incidence rates, seasonal cycles from large to small (as well as no cycle), daily effects, and various types and levels of random daily variation. This leads us to recommend replacing the C1, C2, and C3 methods in existing syndromic surveillance systems with an appropriately implemented CUSUM method.

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Year:  2008        PMID: 18240128     DOI: 10.1002/sim.3197

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  30 in total

1.  Assessing the relative timeliness of Ontario's syndromic surveillance systems for early detection of the 2009 influenza H1N1 pandemic waves.

Authors:  Anna Chu; Rachel Savage; Michael Whelan; Laura C Rosella; Natasha S Crowcroft; Don Willison; Anne-Luise Winter; Richard Davies; Ian Gemmill; Pia K Mucchal; Ian Johnson
Journal:  Can J Public Health       Date:  2013-05-14

2.  Characterizing the Effects of Extreme Cold Using Real-time Syndromic Surveillance, Ontario, Canada, 2010-2016.

Authors:  Nancy VanStone; Adam van Dijk; Timothy Chisamore; Brian Mosley; Geoffrey Hall; Paul Belanger; Kieran Michael Moore
Journal:  Public Health Rep       Date:  2017 Jul/Aug       Impact factor: 2.792

3.  Epitweetr: Early warning of public health threats using Twitter data.

Authors:  Laura Espinosa; Ariana Wijermans; Francisco Orchard; Michael Höhle; Thomas Czernichow; Pietro Coletti; Lisa Hermans; Christel Faes; Esther Kissling; Thomas Mollet
Journal:  Euro Surveill       Date:  2022-09

4.  Acute diarrheal syndromic surveillance: effects of weather and holidays.

Authors:  H J Kam; S Choi; J P Cho; Y G Min; R W Park
Journal:  Appl Clin Inform       Date:  2010-04-14       Impact factor: 2.342

5.  Disease surveillance using a hidden Markov model.

Authors:  Rochelle E Watkins; Serryn Eagleson; Bert Veenendaal; Graeme Wright; Aileen J Plant
Journal:  BMC Med Inform Decis Mak       Date:  2009-08-10       Impact factor: 2.796

6.  How to use near real-time health indicators to support decision-making during a heat wave: the example of the French heat wave warning system.

Authors:  Mathilde Pascal; Karine Laaidi; Vérène Wagner; Aymeric Bun Ung; Sabira Smaili; Anne Fouillet; Céline Caserio-Schönemann; Pascal Beaudeau
Journal:  PLoS Curr       Date:  2012-07-16

7.  Outbreak detection algorithms for seasonal disease data: a case study using Ross River virus disease.

Authors:  Anita M Pelecanos; Peter A Ryan; Michelle L Gatton
Journal:  BMC Med Inform Decis Mak       Date:  2010-11-24       Impact factor: 2.796

8.  Innovation in observation: a vision for early outbreak detection.

Authors:  Nh Fefferman; En Naumova
Journal:  Emerg Health Threats J       Date:  2010-05-20

9.  FluBreaks: early epidemic detection from Google flu trends.

Authors:  Fahad Pervaiz; Mansoor Pervaiz; Nabeel Abdur Rehman; Umar Saif
Journal:  J Med Internet Res       Date:  2012-10-04       Impact factor: 5.428

10.  Value of syndromic surveillance within the Armed Forces for early warning during a dengue fever outbreak in French Guiana in 2006.

Authors:  Jean-Baptiste Meynard; Hervé Chaudet; Gaetan Texier; Vanessa Ardillon; Françoise Ravachol; Xavier Deparis; Henry Jefferson; Philippe Dussart; Jacques Morvan; Jean-Paul Boutin
Journal:  BMC Med Inform Decis Mak       Date:  2008-07-02       Impact factor: 2.796

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