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A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease

Author

Listed:
  • C. P. Farrington
  • N. J. Andrews
  • A. D. Beale
  • M. A. Catchpole

Abstract

Outbreaks of infectious diseases must be detected early for effective control measures to be introduced. When dealing with large amounts of data, automated procedures can usefully supplement traditional surveillance methods, provided that the wide variety of patterns and frequencies of infections are taken into account. This paper describes a robust system developed to process weekly reports of infections received at the Communicable Disease Surveillance Centre. A simple regression algorithm is used to calculate suitable thresholds. Organisms exceeding their threshold are then flagged for further investigation.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:159:y:1996:i:3:p:547-563
    DOI: 10.2307/2983331
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    Cited by:

    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. Johan Verbeeck & Christel Faes & Thomas Neyens & Niel Hens & Geert Verbeke & Patrick Deboosere & Geert Molenberghs, 2023. "A linear mixed model to estimate COVID‐19‐induced excess mortality," Biometrics, The International Biometric Society, vol. 79(1), pages 417-425, March.
    3. Michael Höhle, 2007. "$${\tt surveillance}$$ : An R package for the monitoring of infectious diseases," Computational Statistics, Springer, vol. 22(4), pages 571-582, December.
    4. Veli B. Shakhmurov & Muhammet Kurulay & Aida Sahmurova & Mustafa Can Gursesli & Antonio Lanata, 2023. "A Novel Nonlinear Dynamic Model Describing the Spread of Virus," Mathematics, MDPI, vol. 11(20), pages 1-15, October.
    5. 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.
    6. Janine Aron & John Muellbauer, 2022. "Excess Mortality Versus COVID‐19 Death Rates: A Spatial Analysis of Socioeconomic Disparities and Political Allegiance Across U.S. States," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(2), pages 348-392, June.
    7. Young‐Geun Choi & Lawrence P. Hanrahan & Derek Norton & Ying‐Qi Zhao, 2022. "Simultaneous spatial smoothing and outlier detection using penalized regression, with application to childhood obesity surveillance from electronic health records," Biometrics, The International Biometric Society, vol. 78(1), pages 324-336, March.
    8. Margaret E. Slade, 2022. "Many losers and a few winners: The impact of COVID‐19 on Canadian industries and regions," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 282-307, February.
    9. R. S. Sparks & T. Keighley & D. Muscatello, 2011. "Optimal exponentially weighted moving average (EWMA) plans for detecting seasonal epidemics when faced with non-homogeneous negative binomial counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2165-2181.
    10. 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.
    11. 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.
    12. Höhle, Michael & Paul, Michaela, 2008. "Count data regression charts for the monitoring of surveillance time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4357-4368, May.
    13. Sergio Di Martino & Sara Romano & Michela Bertolotto & Nattiya Kanhabua & Antonino Mazzeo & Wolfgang Nejdl, 2017. "Towards Exploiting Social Networks for Detecting Epidemic Outbreaks," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(1), pages 61-71, March.
    14. Rolf J F Ypma & Tjibbe Donker & W Marijn van Ballegooijen & Jacco Wallinga, 2013. "Finding Evidence for Local Transmission of Contagious Disease in Molecular Epidemiological Datasets," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-8, July.
    15. 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.
    16. 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.
    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. Byeong Choi & Ho Kim & Un Go & Jong-Hyeon Jeong & Jae Lee, 2010. "Comparison of various statistical methods for detecting disease outbreaks," Computational Statistics, Springer, vol. 25(4), pages 603-617, December.
    19. Yuta Tanoue & Cyrus Ghaznavi & Takayuki Kawashima & Akifumi Eguchi & Daisuke Yoneoka & Shuhei Nomura, 2022. "Changes in Health Care Access during the COVID-19 Pandemic: Estimates of National Japanese Data, June 2020–October 2021," IJERPH, MDPI, vol. 19(14), pages 1-12, July.

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