IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0160759.html
   My bibliography  Save this article

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

Author

Listed:
  • Doyo G Enki
  • Paul H Garthwaite
  • C Paddy Farrington
  • Angela Noufaily
  • Nick J Andrews
  • Andre Charlett

Abstract

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace.

Suggested Citation

  • Doyo G Enki & Paul H Garthwaite & C Paddy Farrington & Angela Noufaily & Nick J Andrews & Andre Charlett, 2016. "Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.
  • Handle: RePEc:plo:pone00:0160759
    DOI: 10.1371/journal.pone.0160759
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0160759
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0160759&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0160759?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Christian Sonesson & David Bock, 2003. "A review and discussion of prospective statistical surveillance in public health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 5-21, February.
    2. C. P. Farrington & N. J. Andrews & A. D. Beale & M. A. Catchpole, 1996. "A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 547-563, May.
    3. Steffen Unkel & C. Paddy Farrington & Paul H. Garthwaite & Chris Robertson & Nick Andrews, 2012. "Statistical methods for the prospective detection of infectious disease outbreaks: a review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 49-82, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salmon, Maëlle & Schumacher, Dirk & Höhle, Michael, 2016. "Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i10).
    2. Christin Schröder & Luis Alberto Peña Diaz & Anna Maria Rohde & Brar Piening & Seven Johannes Sam Aghdassi & Georg Pilarski & Norbert Thoma & Petra Gastmeier & Rasmus Leistner & Michael Behnke, 2020. "Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
    3. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    4. Thais Paiva & Renato Assunção & Taynãna Simões, 2015. "Prospective space–time surveillance with cumulative surfaces for geographical identification of the emerging cluster," Computational Statistics, Springer, vol. 30(2), pages 419-440, June.
    5. Marianne Frisén, 2014. "Spatial outbreak detection based on inference principles for multivariate surveillance," IISE Transactions, Taylor & Francis Journals, vol. 46(8), pages 759-769, August.
    6. Bianca Cox & Françoise Wuillaume & Herman Oyen & Sophie Maes, 2010. "Monitoring of all-cause mortality in Belgium (Be-MOMO): a new and automated system for the early detection and quantification of the mortality impact of public health events," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 55(4), pages 251-259, August.
    7. Michael Höhle, 2007. "$${\tt surveillance}$$ : An R package for the monitoring of infectious diseases," Computational Statistics, Springer, vol. 22(4), pages 571-582, December.
    8. Chih-Chieh Wu & Chien-Hsiun Chen & Sanjay Shete, 2017. "Assessing current temporal and space-time anomalies of disease incidence," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-10, November.
    9. Angela Noufaily & Paddy Farrington & Paul Garthwaite & Doyo Gragn Enki & Nick Andrews & Andre Charlett, 2016. "Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 488-499, April.
    10. Bock, David & Pettersson, Kjell, 2007. "Explorative analysis of spatial aspects on the Swedish influenza data," Research Reports 2007:10, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    11. Bock, David & Andersson, Eva & Frisén, Marianne, 2007. "Similarities and differences between statistical surveillance and certain decision rules in finance," Research Reports 2007:8, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    12. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    13. Zhang, Ping & Wang, Jianwen & Atkinson, Peter M., 2019. "Identifying the spatio-temporal risk variability of avian influenza A H7N9 in China," Ecological Modelling, Elsevier, vol. 414(C).
    14. Max Petzold & Christian Sonesson & Eva Bergman & Helle Kieler, 2004. "Surveillance in Longitudinal Models: Detection of Intrauterine Growth Restriction," Biometrics, The International Biometric Society, vol. 60(4), pages 1025-1033, December.
    15. Chengcheng Bei & Shiping Liu & Yin Liao & Gaoliang Tian & Zichen Tian, 2021. "Predicting new cases of COVID‐19 and the application to population sustainability analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4859-4884, September.
    16. Rostyslav Bodnar & Taras Bodnar & Wolfgang Schmid, 2023. "Sequential monitoring of high‐dimensional time series," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 962-992, September.
    17. Wei Wei & Leonhard Held, 2014. "Calibration tests for count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 787-805, December.
    18. Santitissadeekorn, Naratip & Lloyd, David J.B. & Short, Martin B. & Delahaies, Sylvain, 2020. "Approximate filtering of conditional intensity process for Poisson count data: Application to urban crime," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    19. Christian Sonesson, 2003. "Evaluations of some Exponentially Weighted Moving Average methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1115-1133.
    20. Pettersson, Kjell, 2008. "On curve estimation under order restrictions," Research Reports 2007:15, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0160759. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.