Skip to main content


Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted.

The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit

Updates regarding government operating status and resumption of normal operations can be found at

Grant Details

Grant Number: 9R01CA165057-05 Interpret this number
Primary Investigator: Kulldorff, Martin
Organization: Harvard Pilgrim Health Care, Inc.
Project Title: SATSCAN: Spatial Scan Statistic Surveillance Software II
Fiscal Year: 2012
Back to top


DESCRIPTION (provided by applicant): SaTScan is a free statistical disease surveillance software package implementing the spatial, temporal and spatio-temporal scan statistics. It is used by many scientists and public health officials across the United States and around the world for geographical disease cluster detection and evaluation, and for the early detection of disease outbreaks. With the software, it is possible to determine whether disease cases are randomly distributed over space and/or time, or whether there are statistically significant spatial, temporal and/or spatio-temporal clusters with more (or fewer) cases than expected. Critically, it adjusts for the multiple testing inherent in the many possible cluster locations and sizes evaluated, as well as for covariates. As the number of users increase, there is an increasing number of requests for new SaTScan features and functionalities, including integration with geographical information systems and statistical software packages, graphical output functionalities, power evaluation tools, more general analysis options, and easy to use training material. In this project, we propose to further develop and maintain the SaTScan software to fulfill many of these needs. PUBLIC HEALTH RELEVANCE: The SaTScan disease surveillance software has around 13,000 registered users, including over 800 at federal (CDC), state and local public health departments across the United States. Additional features, functionalities and training material will enable users to apply the software in new and innovative ways, as well as more efficiently.

Back to top


Daily Reportable Disease Spatiotemporal Cluster Detection, New York City, New York, Usa, 2014-2015
Authors: Greene S.K. , Peterson E.R. , Kapell D. , Fine A.D. , Kulldorff M. .
Source: Emerging Infectious Diseases, 2016 Oct; 22(10), p. 1808-12.
PMID: 27648777
Related Citations

Local spatial clustering in youths' use of tobacco, alcohol, and marijuana in Boston.
Authors: Duncan D.T. , Rienti M. , Kulldorff M. , Aldstadt J. , Castro M.C. , Frounfelker R. , Williams J.H. , Sorensen G. , Johnson R.M. , Hemenway D. , et al. .
Source: The American Journal Of Drug And Alcohol Abuse, 2016 Jul; 42(4), p. 412-21.
PMID: 27096932
Related Citations

Public domain small-area cancer incidence data for New York State, 2005-2009.
Authors: Boscoe F.P. , Talbot T.O. , Kulldorff M. .
Source: Geospatial Health, 2016-04-18 00:00:00.0; 11(1), p. 304.
EPub date: 2016-04-18 00:00:00.0.
PMID: 27087033
Related Citations

Comments on 'a critical look at prospective surveillance using a scan statistic' by T. Correa, M. Costa, and R. Assunção.
Authors: Kulldorff M. , Kleinman K. .
Source: Statistics In Medicine, 2015-03-30 00:00:00.0; 34(7), p. 1094-5.
PMID: 25754922
Related Citations

Geographical Clusters Of Rape In The United States: 2000-2012
Authors: Amin R. , Nabors N.S. , Nelson A.M. , Saqlain M. , Kulldorff M. .
Source: Statistics And Public Policy (philadelphia, Pa.), 2015; 2(1), p. 87-92.
PMID: 28078318
Related Citations

Relative risk estimates from spatial and space-time scan statistics: are they biased?
Authors: Prates M.O. , Kulldorff M. , Assunção R.M. .
Source: Statistics In Medicine, 2014-07-10 00:00:00.0; 33(15), p. 2634-44.
EPub date: 2014-07-10 00:00:00.0.
PMID: 24639031
Related Citations

Influence of spatial resolution on space-time disease cluster detection.
Authors: Jones S.G. , Kulldorff M. .
Source: Plos One, 2012; 7(10), p. e48036.
PMID: 23110167
Related Citations

Back to Top