Expectation-based scan statistics for monitoring spatial time series data

Research output: Contribution to journalArticlepeer-review


We consider the simultaneous monitoring of a large number of spatially localized time series in order to detect emerging spatial patterns. For example, in disease surveillance, we detect emerging outbreaks by monitoring electronically available public health data, e.g. aggregate daily counts of Emergency Department visits. We propose a two-step approach based on the expectation-based scan statistic: we first compute the expected count for each recent day for each spatial location, then find spatial regions (groups of nearby locations) where the recent counts are significantly higher than expected. By aggregating information across multiple time series rather than monitoring each series separately, we can improve the timeliness, accuracy, and spatial resolution of detection. We evaluate several variants of the expectation-based scan statistic on the disease surveillance task (using synthetic outbreaks injected into real-world hospital Emergency Department data), and draw conclusions about which models and methods are most appropriate for which surveillance tasks.

Original languageEnglish (US)
Pages (from-to)498-517
Number of pages20
JournalInternational Journal of Forecasting
Issue number3
StatePublished - Jul 2009


  • Biosurveillance
  • Event detection
  • Pattern detection
  • Spatial scan statistics
  • Time series monitoring

ASJC Scopus subject areas

  • Business and International Management


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