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Comparative Study
. 2008 Aug 13:8:37.
doi: 10.1186/1472-6947-8-37.

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Rochelle E Watkins  1 , Serryn Eagleson, Bert Veenendaal, Graeme Wright, Aileen J Plant
Affiliations
Comparative Study

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Rochelle E Watkins et al. BMC Med Inform Decis Mak. .

Abstract

Background: The 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.

Methods: RRv 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.

Results: We 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.

Conclusion: Our 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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Figures

Figure 1
Figure 1
Ross River virus case notifications, expert-defined outbreak period (shaded) and cusum scores by day for the first outbreak dataset (days 1–235).
Figure 2
Figure 2
Sensitivity of Early Aberration Reporting System (EARS) and negative binomial cusum (NBC) algorithms according to false alarm rate.
See this image and copyright information in PMC

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