Belief state approaches to signaling alarms in surveillance systems.

Surveillance systems have long been used to monitor industrial processes and are becoming increasingly popular in public health and anti-terrorism applications. Most early detection systems produce a time series of p-values or some other statistic as their output. Typically, the decision to signal an alarm is based on a threshold or other simple algorithm such as CUSUM that accumulates detection information temporally.We formulate a POMDP model of underlying events and observations from a detector. We solve the model and show how it is used for single-output detectors. When dealing with spatio-temporal data, scan statistics are a popular method of building detectors. We describe the use of scan statistics in surveillance and how our POMDP model can be used to perform alarm signaling with them. We compare the results obtained by our method with simple thresholding and CUSUM on synthetic and semi-synthetic health data.