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Items: 1 to 20 of 149

1.

Approaches to the evaluation of outbreak detection methods.

Watkins RE, Eagleson S, Hall RG, Dailey L, Plant AJ.

BMC Public Health. 2006 Oct 24;6:263.

2.

Outbreak detection through automated surveillance: a review of the determinants of detection.

Buckeridge DL.

J Biomed Inform. 2007 Aug;40(4):370-9. Epub 2006 Oct 5. Review.

3.

Performance indicators for response to selected infectious disease outbreaks: a review of the published record.

Potter MA, Sweeney P, Iuliano AD, Allswede MP.

J Public Health Manag Pract. 2007 Sep-Oct;13(5):510-8. Review.

PMID:
17762697
4.

Estimating the joint disease outbreak-detection time when an automated biosurveillance system is augmenting traditional clinical case finding.

Shen Y, Adamou C, Dowling JN, Cooper GF.

J Biomed Inform. 2008 Apr;41(2):224-31. doi: 10.1016/j.jbi.2007.11.002. Epub 2007 Nov 21.

5.

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia.

Watkins RE, Eagleson S, Veenendaal B, Wright G, Plant AJ.

BMC Med Inform Decis Mak. 2008 Aug 13;8:37. doi: 10.1186/1472-6947-8-37.

6.

Comparing early outbreak detection algorithms based on their optimized parameter values.

Wang X, Zeng D, Seale H, Li S, Cheng H, Luan R, He X, Pang X, Dou X, Wang Q.

J Biomed Inform. 2010 Feb;43(1):97-103. doi: 10.1016/j.jbi.2009.08.003. Epub 2009 Aug 13.

7.

A methodology to enhance spatial understanding of disease outbreak events reported in news articles.

Chanlekha H, Collier N.

Int J Med Inform. 2010 Apr;79(4):284-96. doi: 10.1016/j.ijmedinf.2010.01.014. Epub 2010 Feb 13.

PMID:
20153972
8.

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

Fricker RD Jr, Hegler BL, Dunfee DA.

Stat Med. 2008 Jul 30;27(17):3407-29. doi: 10.1002/sim.3197.

PMID:
18240128
9.

Evaluation of an operational malaria outbreak identification and response system in Mpumalanga Province, South Africa.

Coleman M, Coleman M, Mabuza AM, Kok G, Coetzee M, Durrheim DN.

Malar J. 2008 Apr 27;7:69. doi: 10.1186/1475-2875-7-69.

10.

Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs.

Rolka H, Burkom H, Cooper GF, Kulldorff M, Madigan D, Wong WK.

Stat Med. 2007 Apr 15;26(8):1834-56.

PMID:
17221940
11.
12.

Factors affecting automated syndromic surveillance.

Wang L, Ramoni MF, Mandl KD, Sebastiani P.

Artif Intell Med. 2005 Jul;34(3):269-78.

PMID:
16023563
13.
14.

[Comparison between early outbreak detection models and simulated outbreaks of measles in Beijing].

Wang XL, Wang QY, Liu DL, Zeng DJ, Cheng H, Li S, Duan W, Li XY, Luan RS, He X.

Zhonghua Liu Xing Bing Xue Za Zhi. 2009 Feb;30(2):159-62. Chinese.

PMID:
19565878
15.

Benchmark data and power calculations for evaluating disease outbreak detection methods.

Kulldorff M, Zhang Z, Hartman J, Heffernan R, Huang L, Mostashari F.

MMWR Suppl. 2004 Sep 24;53:144-51.

16.

A simulation study comparing aberration detection algorithms for syndromic surveillance.

Jackson ML, Baer A, Painter I, Duchin J.

BMC Med Inform Decis Mak. 2007 Mar 1;7:6.

17.

Draft framework for evaluating syndromic surveillance systems.

Sosin DM.

J Urban Health. 2003 Jun;80(2 Suppl 1):i8-13.

18.

[Disease outbreak monitoring system based on ambulance transport data].

Bork KH, Klein BM, Pedersen UB, Trautner S, Heegaard ED.

Ugeskr Laeger. 2005 Sep 5;167(36):3401-2. Danish.

PMID:
16159492
19.

Algorithms for rapid outbreak detection: a research synthesis.

Buckeridge DL, Burkom H, Campbell M, Hogan WR, Moore AW.

J Biomed Inform. 2005 Apr;38(2):99-113.

20.

Benchmarking national surveillance systems: a new tool for the comparison of communicable disease surveillance and control in Europe.

Reintjes R, Thelen M, Reiche R, Csohán A.

Eur J Public Health. 2007 Aug;17(4):375-80. Epub 2006 Dec 2.

PMID:
17142827

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