  Epidemiologists use a variety of statistical algorithms for the early
detection of outbreaks. The practical usefulness of such methods highly depends
on the trade-off between the detection rate of outbreaks and the chances of
raising a false alarm. Recent research has shown that the use of machine
learning for the fusion of multiple statistical algorithms improves outbreak
detection. Instead of relying only on the binary output (alarm or no alarm) of
the statistical algorithms, we propose to make use of their p-values for
training a fusion classifier. In addition, we also show that adding additional
features and adapting the labeling of an epidemic period may further improve
performance. For comparison and evaluation, a new measure is introduced which
captures the performance of an outbreak detection method with respect to a low
rate of false alarms more precisely than previous works. Our results on
synthetic data show that it is challenging to improve the performance with a
trainable fusion method based on machine learning. In particular, the use of a
fusion classifier that is only based on binary outputs of the statistical
surveillance methods can make the overall performance worse than directly using
the underlying algorithms. However, the use of p-values and additional
information for the learning is promising, enabling to identify more valuable
patterns to detect outbreaks.
