Monitoring System for Detecting Starts and Declines of Influenza Epidemics

Abstract

The aim is to detect an influenza outbreak as soon as possible. Data are weekly reports of number of patients showing influenza-like symptoms. At each additional observation we decide whether a change has occurred or not. The methodology of statistical surveillance is used to construct an outbreak detection system. The report also demonstrates measures that reflect timeliness, such as the probability of successful detection within a specified time and the predictive value at different time points. A new non-parametric approach is used. The cycles are estimated using only monotonicity restrictions. Also different approaches regarding the intensity of the change-process are compared. The pros and cons of using an empirical intensity are evaluated. The time to an alarm is investigated, both for false and motivated alarms. When setting the alarm limit, there is a trade-off between the false alarms and the delay of motivated alarms. In the evaluation it must be considered that the performance of the system depends on when the outbreak begins, in relation to the start of the surveillance. If the outbreak begins at the same time as the surveillance is started, then the probability of early detection is 0.04, but if the surveillance is started 12 time points before the outbreak the detection probability is 0.43. The non-parametric approach avoids miss-specifications of the base line. Even a "normal" miss-specification results in serious delay. Another drawback is that alarms at late time points have low predictive value. An empirical prior for the intensity works well when the actual outbreak time agrees with the prior. But when the outbreak occurs "earlier than expected", the alarms are seriously delayed. A non-informative prior, however, works well.