Semiparametric Surveillance of Monotonic Changes

Abstract

The detection of a change from a constant level to a monotonically increasing (or decreasing) regression is of special interest for the detection of outbreaks of epidemics but is also of interest in other areas. A maximum likelihood ratio statistic for the sequential surveillance of an “outbreak” situation is derived. The method is semiparametric in the sense that the regression model is nonparametric whereas the distribution belongs to the regular exponential family. The method is evaluated with respect to timeliness and predicted value in a simulation study that imitates the influenza outbreaks in Sweden. To illustrate its performance, the method is applied to Swedish influenza data for 6 years. The advantage of this semiparametric surveillance method, which does not rely on an estimated baseline, is illustrated by a Monte Carlo study. The advantage of information accumulation is illustrated.

Keywords:

• Change-points
• Exponential family
• Generalized likelihood
• Monitoring
• Ordered regression
• Robust regression

Subject Classification:

• 62C10
• 62C20
• 62L15
• 62P10

ACKNOWLEDGMENTS

Linus Schiöler has provided expert technical and computational help. Kjell Pettersson has given constructive comments. The data were made available to us by the Swedish Institute for Infectious Disease Control, and we are grateful for discussions about the aims and the data quality. The work was supported by the Swedish Emergency Management Agency (grant 0314/206). The authors have declared no conflict of interest.

Notes

Recommended by Nitis Mukhopadhyay