For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
9
views
3
references
 Top references
cited by
1
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      155
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: not found

      Algorithm Combination for Improved Performance in Biosurveillance : Univariate Monitoring

      chapter-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Chapter Overview

          This chapter proposes an enhancement to currently used algorithms for monitoring daily counts of pre-diagnostic data. Rather than use a single algorithm or apply multiple algorithms simultaneously, our approach is based on ensembles of algorithms. The ensembles lead to better performance in terms of higher true alert rates for a given false alert rate. Combinations can be employed at the data preprocessing step and/or at the monitoring step. We discuss the advantages of such an approach and illustrate its usefulness using authentic modern biosurveillance data.

          Related collections

          Most cited references3

          • Record: found
          • Abstract: found
          • Article: not found

          Automated time series forecasting for biosurveillance.

          Howard Burkom, Galit Shmueli, Kerry P S J Murphy (2007)
          For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predictive accuracy on each of 16 authentic syndromic data streams. The methods are (1) a non-adaptive regression model using a long historical baseline, (2) an adaptive regression model with a shorter, sliding baseline, and (3) the Holt-Winters method for generalized exponential smoothing. Criteria for comparing the forecasts were the root-mean-square error, the median absolute per cent error (MedAPE), and the median absolute deviation. The median-based criteria showed best overall performance for the Holt-Winters method. The MedAPE measures over the 16 test series averaged 16.5, 11.6, and 9.7 for the non-adaptive regression, adaptive regression, and Holt-Winters methods, respectively. The non-adaptive regression forecasts were degraded by changes in the data behaviour in the fixed baseline period used to compute model coefficients. The mean-based criterion was less conclusive because of the effects of poor forecasts on a small number of calendar holidays. The Holt-Winters method was also most effective at removing serial autocorrelation, with most 1-day-lag autocorrelation coefficients below 0.15. The forecast methods were compared without tuning them to the behaviour of individual series. We achieved improved predictions with such tuning of the Holt-Winters method, but practical use of such improvements for routine surveillance will require reliable data classification methods.
          Article 0 comments Cited 36 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Effect of new susceptibility breakpoints on reporting of resistance in Streptococcus pneumoniae--United States, 2003.

            (2004)
            In January 2003, the National Committee for Clinical Laboratory Standards (NCCLS) finalized new breakpoints for defining the susceptibility of Streptococcus pneumoniae isolates to cefotaxime and ceftriaxone. The former breakpoints were based on attainable concentrations of these antibiotics in cerebrospinal fluid (CSF) and the level at which it was thought that meningitis treatment failed because of elevated minimum inhibitory concentrations (MICs). The new breakpoints differ for S. pneumoniae isolates causing meningitis and those causing nonmeningeal clinical syndromes. To assess the effect of these new criteria on reporting of nonsusceptible S. pneumoniae isolates, CDC analyzed cefotaxime MIC data from the Active Bacterial Core Surveillance (ABCs) of the Emerging Infections Program (EIP) Network during 1998-2001. This report summarizes the results of that analysis, which indicated that after the new criteria were applied, the number of isolates defined as nonsusceptible to cefotaxime decreased 52.1%-61.2% for each year. Laboratory reports for clinicians should include interpretations using the new breakpoints for meningitis and nonmeningeal syndromes for all non-CSF isolates.
            Article 0 comments Cited 5 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              “Development, adaptation and assessment of alerting algorithms for biosurveillance

              H Burkom (2003)
              Article 0 comments Cited 1 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Carlos Castillo-Chavez: 480965-2115 , 480727-7343 , chavez@math.asu.edu
                Hsinchun Chen: 520621-2748 , 520621-2433 , hchen@eller.arizona.edu
                William B. Lober: 206616-6885 , lober@u.washington.edu
                Mark Thurmond: 530752-5635 , 530752-0414 , mcthurmond@ucdavis.edu
                Daniel Zeng: zengdaniel@gmail.com
                Journal
                Journal ID (publisher-id): 978-1-4419-6892-0
                Journal ID (doi): 10.1007/978-1-4419-6892-0
                Journal ID (nlm-ta): Infectious Disease Informatics and Biosurveillance
                Title: Infectious Disease Informatics and Biosurveillance
                Subtitle: Research, Systems and Case Studies
                ISBN (Print): 978-1-4419-6891-3
                ISBN (Electronic): 978-1-4419-6892-0
                Publication date (Electronic): 27 July 2010
                Publication date Collection: 2011
                Volume: 27
                Pages: 173-189
                Affiliations
                [ID1 ]GRID grid.215654.1, ISNI 0000000121512636, Dept. Mathematics & Statistics, , Arizona State University, ; Tempe, Arizona USA
                [ID2 ]GRID grid.134563.6, ISNI 000000012168186X, Eller College of Management, , University of Arizona, ; E. Helen St. 1130, Tucson, 85721 Arizona USA
                [ID3 ]GRID grid.34477.33, ISNI 0000000122986657, Health Sciences Center, , University of Washington, ; NE. Pacific St. 1959, Seattle, 98195 Washington USA
                [ID4 ]GRID grid.27860.3b, ISNI 0000000419369684, School of Veterinary Medicine, Dept. Medicine & Epidemiology, , University of California, Davis, ; Shields Avenue 1, Davis, 95616 California USA
                [ID5 ]GRID grid.134563.6, ISNI 000000012168186X, Eller College of Management, Dept. Management Information Systems, , University of Arizona, ; Tucson, 85721 Arizona USA
                [1_8 ]GRID grid.164295.d, ISNI 0000000109417177, Department of Decision, Operations & Information Technologies, , Robert H Smith School of Business, University of Maryland, ; College Park, MD 20742 USA
                [2_8 ]GRID grid.164295.d, ISNI 0000000109417177, Applied Mathematics & Scientific Computation Program, , University of Maryland, ; College Park, MD 20742 USA
                Article
                Publisher ID: 8
                DOI: 10.1007/978-1-4419-6892-0_8
                PMC ID: 7121873
                SO-VID: 9da08ffc-98e7-48fd-afe5-9d3ea660c72d
                Copyright © © Springer Science+Business Media, LLC 2011
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © Springer Science+Business Media, LLC 2011

                Keywords: control charts,monitoring,ensemble methods,optimization,pre-diagnostic data
                Data availability:
                Keywords: control charts, monitoring, ensemble methods, optimization, pre-diagnostic data

                Comments

                Comment on this article