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IJE Advance Access published online on August 22, 2006
International Journal of Epidemiology, doi:10.1093/ije/dyl162
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2006; all rights reserved.
Accepted July 4, 2006

Original paper

Methods for monitoring influenza surveillance data

Benjamin J. Cowling 1 *, Irene O. L. Wong 1, Lai-Ming Ho 1, Steven Riley 1, and Gabriel M. Leung 1

1 Department of Community Medicine and School of Public Health, The University of Hong Kong, Pokfulam, Hong Kong

* To whom correspondence should be addressed.
Benjamin J. Cowling, E-mail: bcowling{at}hku.hk


  Abstract

Background A variety of Serfling-type statistical algorithms requiring long series of historical data, exclusively from temperate climate zones, have been proposed for automated monitoring of influenza sentinel surveillance data. We evaluated three alternative statistical approaches where alert thresholds are based on recent data in both temperate and subtropical regions.

Methods We compared time series, regression, and cumulative sum (CUSUM) models on empirical data from Hong Kong and the US using a composite index (range = 0-1) consisting of the key outcomes of sensitivity, specificity, and time to detection (lag). The index was calculated based on alarms generated within the first 2 or 4 weeks of the peak season.

Results We found that the time series model was optimal in the Hong Kong setting, while both the time series and CUSUM models worked equally well on US data. For alarms generated within the first 2 weeks (4 weeks) of the peak season in Hong Kong, the maximum values of the index were: time series 0.77 (0.86); regression 0.75 (0.82); CUSUM 0.56 (0.75). In the US data the maximum values of the index were: time series 0.81 (0.95); regression 0.81 (0.91); CUSUM 0.90 (0.94).

Conclusions Automated influenza surveillance methods based on short-term data, including time series and CUSUM models, can generate sensitive, specific, and timely alerts, and can offer a useful alternative to Serfling-like methods that rely on long-term, historically based thresholds.

Keywords: Influenza; public health; detection; population surveillance.
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