Semiparametric surveillance of outbreaks

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, for example, epidemics. 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 while 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 six years. The advantage of this semiparametric surveillance method, which does not rely on an estimated baseline, is illustrated by a Monte Carlo study. The proposed method is successively accumulating the information. Such accumulation is not made by the commonly used approach where the current observation is compared to a baseline. The advantage of information accumulation is illustrated.

Introduction: Model selection is important and different adaptive and model-free approaches have been suggested (see e.g. .Without including available assumptions on the shape of the regression, the estimates would be unnecessary inefficient. On the other hand, wrong assumptions might cause wrong conclusions from the data. Thus, limited constrains on a regression, focused on the issues that are important for the application, are of interest.One important aim in public health surveillance is to detect disease outbreaks. An outbreak can be characterised as a change from a constant level to a monotonically increasing incidence. Outbreak detection is an important part of surveillance for bioterrorism as well as of surveillance for the detection of new diseases such as the recent SARS and avian flu. Outbreaks are also important in the study of ordinary influenza. For likelihood-based surveillance methods ([2], [3]) maximum likelihood estimates are needed. Such estimators will be given in this article. However, this article will not deal with the sequential issues of surveillance.

Author: Marianne Frisén,Eva Andersson

Source: Göteborg University

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