Online Temporal Clustering for Outbreak Detection

We hypothesize that epidemics around their onset tend to affect primarily a well-defined subgroup of the overall population that is for some reason particularly susceptible. While the vulnerable cohort is often well described for many human diseases, this is not the case for instance when we wish to detect a novel computer virus. Clustering may be used to define the subgroups that will be tested for over-density of symptom occurrence. The clustering slowly changes in response to changes in the population.

Objective

This paper describes a method of detecting a slowlygrowing signal in a large population, based on clustering the population into subgroups more homogeneous in their infectious agent susceptibility.