Algorithms for rapid outbreak detection: a research synthesis
- PMID: 15797000
- DOI: 10.1016/j.jbi.2004.11.007
Algorithms for rapid outbreak detection: a research synthesis
Abstract
The threat of bioterrorism has stimulated interest in enhancing public health surveillance to detect disease outbreaks more rapidly than is currently possible. To advance research on improving the timeliness of outbreak detection, the Defense Advanced Research Project Agency sponsored the Bio-event Advanced Leading Indicator Recognition Technology (BioALIRT) project beginning in 2001. The purpose of this paper is to provide a synthesis of research on outbreak detection algorithms conducted by academic and industrial partners in the BioALIRT project. We first suggest a practical classification for outbreak detection algorithms that considers the types of information encountered in surveillance analysis. We then present a synthesis of our research according to this classification. The research conducted for this project has examined how to use spatial and other covariate information from disparate sources to improve the timeliness of outbreak detection. Our results suggest that use of spatial and other covariate information can improve outbreak detection performance. We also identified, however, methodological challenges that limited our ability to determine the benefit of using outbreak detection algorithms that operate on large volumes of data. Future research must address challenges such as forecasting expected values in high-dimensional data and generating spatial and multivariate test data sets.
Similar articles
-
Outbreak detection through automated surveillance: a review of the determinants of detection.J Biomed Inform. 2007 Aug;40(4):370-9. doi: 10.1016/j.jbi.2006.09.003. Epub 2006 Oct 5. J Biomed Inform. 2007. PMID: 17095301 Review.
-
Communicable-disease surveillance in New Jersey.N J Med. 2004 Sep;101(9 Suppl):45-50; quiz 50-2. N J Med. 2004. PMID: 15497734 Review.
-
Estimating the joint disease outbreak-detection time when an automated biosurveillance system is augmenting traditional clinical case finding.J Biomed Inform. 2008 Apr;41(2):224-31. doi: 10.1016/j.jbi.2007.11.002. Epub 2007 Nov 21. J Biomed Inform. 2008. PMID: 18194876
-
Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs.Stat Med. 2007 Apr 15;26(8):1834-56. doi: 10.1002/sim.2793. Stat Med. 2007. PMID: 17221940
-
Bio-ALIRT biosurveillance detection algorithm evaluation.MMWR Suppl. 2004 Sep 24;53:152-8. MMWR Suppl. 2004. PMID: 15714645
Cited by
-
Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis.J R Soc Interface. 2023 Nov;20(208):20230410. doi: 10.1098/rsif.2023.0410. Epub 2023 Nov 15. J R Soc Interface. 2023. PMID: 37963560 Free PMC article.
-
Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods.Vet Res. 2023 Sep 8;54(1):75. doi: 10.1186/s13567-023-01197-3. Vet Res. 2023. PMID: 37684632 Free PMC article.
-
A statistical algorithm for outbreak detection in multisite settings: an application to sick leave monitoring.Bioinform Adv. 2023 Jun 14;3(1):vbad079. doi: 10.1093/bioadv/vbad079. eCollection 2023. Bioinform Adv. 2023. PMID: 37521307 Free PMC article.
-
Evaluating multi-purpose syndromic surveillance systems - a complex problem.Online J Public Health Inform. 2021 Dec 24;13(3):E15. doi: 10.5210/ojphi.v13i3.10818. eCollection 2021. Online J Public Health Inform. 2021. PMID: 35082977 Free PMC article.
-
Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods.Stat Med. 2021 Dec 10;40(28):6277-6294. doi: 10.1002/sim.9182. Epub 2021 Sep 7. Stat Med. 2021. PMID: 34491590 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical
