A statistical algorithm for the early detection of outbreaks of infectious disease

C. P. Farrington; Nicholas Andrews; A. D. Beale; M. A. Catchpole

doi:10.2307/2983331

A statistical algorithm for the early detection of outbreaks of infectious disease

C. P. Farrington^*, Nicholas Andrews, A. D. Beale, M. A. Catchpole

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

288 Citations (Scopus)

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.

Original language	English
Pages (from-to)	547-563
Number of pages	17
Journal	Journal of the Royal Statistical Society. Series A: Statistics in Society
Volume	159
Issue number	3
DOIs	https://doi.org/10.2307/2983331
Publication status	Published - 1996

Keywords

Clustering
Dispersion
Epidemiology
Exceedance
Glim
Outbreak
Regression
Reweighting
Seasonality
Threshold
Trend

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2307/2983331

Cite this

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AU - Beale, A. D.

AU - Catchpole, M. A.

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AB - 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.

KW - Clustering

KW - Dispersion

KW - Epidemiology

KW - Exceedance

KW - Glim

KW - Outbreak

KW - Regression

KW - Reweighting

KW - Seasonality

KW - Threshold

KW - Trend

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JO - Journal of the Royal Statistical Society. Series A: Statistics in Society

JF - Journal of the Royal Statistical Society. Series A: Statistics in Society

IS - 3

ER -

A statistical algorithm for the early detection of outbreaks of infectious disease

Abstract

Keywords

UN SDGs

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