ArticlePloS one2016
Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Web-Based Apps for Responding to Acute Infectious Disease Outbreaks in the Community: Systematic Review.JMIR public health and surveillance · 2021Pooled it
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- Bayesian Spatio-Temporal Outbreak Detection for COVID-19 Mortality in South Africa: A Comparative Study of MCMC and Dynamic HMC Methods.Research square · 2026Article
- Adapting the Flexible Farrington Algorithm for daily situational awareness and alert system to support public health decision-making during the SARS-CoV-2 epidemic in England.Epidemiology and infection · 2025Article
- Early warning system using primary health care data in the post-COVID-19 pandemic era: Brazil nationwide case-study.Cadernos de saude publica · 2024Article
- Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis.Journal of the Royal Society, Interface · 2023Article
- Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease.PLoS computational biology · 2022Article
- Supervised learning using routine surveillance data improves outbreak detection of Salmonella and Campylobacter infections in Germany.PloS one · 2022Article
- Comparison of statistical algorithms for daily syndromic surveillance aberration detection.Bioinformatics (Oxford, England) · 2019Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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