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


CONCLUSIONS
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.
Watkins
Rochelle E
RE
Australian Biosecurity CRC, Faculty of Health Sciences, Curtin University of Technology, Perth, Australia. Rochelle.Watkins@curtin.edu.au
Eagleson
Serryn
S
Veenendaal
Bert
B
Wright
Graeme
G
Plant
Aileen J
AJ
D000465:Algorithms; D018354:Alphavirus Infections / Q000175:diagnosis / Q000453:epidemiology*; D019540:Area Under Curve; D016010:Binomial Distribution; D018563:Disease Notification; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D012189:Retrospective Studies; D012398:Ross River virus; D012680:Sensitivity and Specificity; D014904:Western Australia / Q000453:epidemiology