ArticleBMC medical informatics and decision making2008
Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia.
Article in BMC medical informatics and decision making, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Epidemiological models for predicting Ross River virus in Australia: A systematic review.PLoS neglected tropical diseases · 2020Pooled it
- Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development.JMIR public health and surveillance · 2020Pooled it
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- Using statistical methods and genotyping to detect tuberculosis outbreaks.International journal of health geographics · 2013Article
- Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence.PloS one · 2013Article
- Quantitative assessment of preventive behaviors in France during the Fukushima nuclear crisis.PloS one · 2013Article
- FluBreaks: early epidemic detection from Google flu trends.Journal of medical Internet research · 2012Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods.
methodsRRv disease case notification data between 1991 and 2004 were assessed retrospectively by two experienced epidemiologists, and the timing of identified outbreaks was compared with signals generated from two different types of cusum-based automated monitoring algorithms; the three Early Aberration Reporting System (EARS) cusum algorithms (C1, C2 and C3), and a negative binomial cusum.
resultsWe found the negative binomial cusum to have a significantly greater area under the receiver operator characteristic curve when compared with the EARS algorithms, suggesting that the negative binomial cusum has a greater level of agreement with epidemiological opinion than the EARS algorithms with respect to the existence of outbreaks of RRv disease, particularly at low false alarm rates. However, the performance of individual EARS and negative binomial cusum algorithms were not significantly different when timeliness was also incorporated into the area under the curve analyses.
conclusionOur retrospective analysis of historical data suggests that, compared with the EARS algorithms, the negative binomial cusum provides greater sensitivity for the detection of outbreaks of RRv disease at low false alarm levels, and decreased timeliness early in the outbreak period. Prospective studies are required to investigate the potential usefulness of these algorithms in practice.
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