SaTScan™ is a free software that analyzes spatial, temporal and space-time data using the spatial, temporal, or space-time scan statistics. It is designed for any of the following interrelated purposes: Perform geographical surveillance of disease, to detect spatial or space-time disease clusters, and to see if they are statistically significant. Test whether a disease is randomly distributed over space, over time or over space and time. Evaluate the statistical significance of disease cluster alarms. Perform repeated time-periodic disease surveillance for early detection of disease outbreaks. The software may also be used for similar problems in other fields such as archaeology, astronomy, botany, criminology, ecology, economics, engineering, forestry, genetics, geography, geology, history, neurology or zoology.

References in zbMATH (referenced in 23 articles )

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  1. Veloso, Bráulio M.; Correa, Thais R.; Prates, Marcos O.; Oliveira, Gabriel F.; Tavares, Andréa I.: \itMAD-STEC: a method for multiple automatic detection of space-time emerging clusters (2017)
  2. Maëlle Salmon; Dirk Schumacher; Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance (2016)
  3. Sharpnack, James; Arias-Castro, Ery: Exact asymptotics for the scan statistic and fast alternatives (2016)
  4. Sinha, Arun Kumar; Kumar, Mukesh: Poverty analysis using scan statistic methods (2016)
  5. Srivastava, Pankaj; Sinha, Arun Kumar: Spatial analysis of AFP surveillance strategy for polio eradication in India (2016)
  6. Markus Loecher; Karl Ropkins: RgoogleMaps and loa: Unleashing R Graphics Power on Map Tiles (2015)
  7. Forbes, Florence; Charras-Garrido, Myriam; Azizi, Lamiae; Doyle, Senan; Abrial, David: Spatial risk mapping for rare disease with hidden Markov fields and variational EM (2013)
  8. Wan, You; Pei, Tao; Zhou, Chenghu; Jiang, Yong; Qu, Chenxu; Qiao, Youlin: ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization (2012) ioport
  9. Tango, Toshiro; Takahashi, Kunihiko; Kohriyama, Kazuaki: A space-time scan statistic for detecting emerging outbreaks (2011)
  10. Cook, Andrea J.; Li, Yi; Arterburn, David; Tiwari, Ram C.: Spatial cluster detection for weighted outcomes using cumulative geographic residuals (2010)
  11. Jiang, Xia; Cooper, Gregory F.: A real-time temporal Bayesian architecture for event surveillance and its application to patient-specific multiple disease outbreak detection (2010) ioport
  12. Jiang, Xia; Neill, Daniel B.; Cooper, Gregory F.: A Bayesian network model for spatial event surveillance (2010) ioport
  13. Assunção, Renato; Correa, Thais: Surveillance to detect emerging space-time clusters (2009)
  14. Chan, Hock Peng: Detection of spatial clustering with average likelihood ratio test statistics (2009)
  15. Liang, Shengde; Carlin, Bradley P.; Gelfand, Alan E.: Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information (2009)
  16. Yamada, Ikuho; Rogerson, Peter A.; Lee, Gyoungju: \itgeosurveillance: a GIS-based system for the detection and monitoring of spatial clusters (2009) ioport
  17. Fraker, Shannon E.; Woodall, William H.; Burkom, Howard S.: A note on the Poisson likelihood ratio test statistic for Kulldorff’s scan methods (2008)
  18. Kim, Youngho; O’kelly, Morton E.: A bootstrap based space-time surveillance model with an application to crime occurrences (2008) ioport
  19. Loh, Ji Meng; Zhu, Zhengyuan: Accounting for spatial correlation in the scan statistic (2007)
  20. Zhang, Tonglin; Lin, Ge: A decomposition of Moran’s $I$ for clustering detection (2007)

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