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Monitoring system for detecting start and declines of influenza epidemics.

Journal article
Authors Eva M. Andersson
Published in MMWR supplement (Morbidity and mortality weekly report)
Volume 53
Publication year 2004
Published at Statistical Research Unit
Language en
Subject categories Statistics


Introduction: A nonparametric surveillance system was constructed for early detection of influenza outbreaks. The system uses weekly data on the number of influenza cases. Objectives: For this analysis, a nonparametric method of surveillance was compared with the likelihood ratio method, which is optimal because it yields a minimal expected delay for a fixed false-alert probability. The evaluation was conducted by using probability of successful detection within a specified time and predictive value at different time points. The optimal surveillance method requires knowledge of the parametric model for the given process (i.e., the influenza cycle). Influenza cycles differ in shape and amplitude from one season to the next. Therefore, finding a parametric model based on influenza data from previous seasons is difficult. Also, using data from previous seasons might lead to misspecification of the cycles. Methods: In the nonparametric method, the influenza cycles were estimated under monotonicity restrictions (i.e., monotonically increasing during the outbreak and monotonically decreasing during the outbreak’s decline). The surveillance system was evaluated in a theoretical simulation study. The performance of the nonparametric method was compared with that of the optimal method. The effect of a misspecification of the parametric model was also studied. Results: For most surveillance methods, the probability of successful detection of an influenza outbreak within 1 week depends on when the outbreak began relative to the start of the surveillance. The predictive value depends on when the alert is generated (Table). Conclusions: The nonparametric method has lower detection probability then the optimal method when the outbreak begins immediately after surveillance is started. However, the nonparametric method avoids misspecifications. A parametric method with a misspecification results in poor detection probability for early outbreaks and low predictive value for late alerts.

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