Optimizing Use of Multistream Influenza Sentinel Surveillance Data

Abstract

We applied time-series methods to multivariate sentinel surveillance data recorded in Hong Kong during 1998-2007. Our study demonstrates that simultaneous monitoring of multiple streams of infl uenza surveillance data can improve the accuracy and timeliness of alerts compared with monitoring of aggregate data or of any single stream alone.T he use of separate data streams based on sentinel surveillance has long been an accepted approach to monitor community incidence and to enable timely detection of infectious disease outbreaks (1,2). Recently, more attention has been given to the combined analysis of multivariate sentinel data (3)(4)(5).In this study we explored the possibility of improving the ability to more quickly detect peak periods of infl uenza activity in Hong Kong through simultaneous monitoring of multiple streams of sentinel surveillance data. Our fi ndings have general implications in the choice of surveillance algorithms where multistream data are available. The StudyThe local Department of Health publishes weekly reports (6) from a network of 50 private-sector sentinel general practitioners (GP) and 62 public-sector sentinel general outpatient clinics (GOPC) on the proportion of patients seeking treatment for infl uenza-like illness (ILI), defi ned as fever plus cough or sore throat (7). In this study, we used the GP and GOPC sentinel surveillance data in 9 annual infl uenza seasons from 1998-1999 to 2006-2007, stratifi ed by 4 geographic regions in Hong Kong-Hong Kong Island, Kowloon, New Territories East, and New Territories West-resulting in 8 separate data streams (Figure).Each month a median of 1,555 specimens (interquartile range 1,140-2,740), primarily from hospitals, were sent to the Government Virus Unit of the Department of Health (7). We calculated the highest proportion of positive infl uenza isolations each season, and used these laboratory data to defi ne the onset of each peak activity period when the proportion of positive infl uenza A or B isolates exceeded 30% of the maximum seasonal level (7).Dynamic linear models (8) were used to generate alerts (online Technical Appendix, available from www.cdc.gov/ EID/content/13/7/1154-Techapp.pdf). We determined that an aberration had occurred when the current observation fell outside a forecast interval generated by the model. For methods based on monitoring of single data streams only, an aberration triggers an alert. For simultaneous monitoring of all 8 data streams, we monitored separate aberrations as above and generated alerts based on the fi rst occurrence of any aberration (M1), 2 simultaneous aberrations (M2), the fi rst occurrence of 3 simultaneous aberrations (M3), any 2 aberrations within a 2-week period (M4), and any 3 aberrations within a 2-week period (M5). In the multistream analyses, we compared alerts produced by univariate models, which effectively assumed independence between the data streams, and multivariate models, which allowed for correlation between the data streams (online Technical Appendix).Alerts were compared...

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