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Prospective Time Periodic Geographical Disease Surveillance Using a Scan Statistic
Abstract
Summary: Most disease registries are updated at least yearly. If a geographically localized health hazard suddenly occurs, we would like to have a surveillance system in place that can pick up a new geographical disease cluster as quickly as possible, irrespective of its location and size. At the same time, we want to minimize the number of false alarms. By using a space–time scan statistic, we propose and illustrate a system for regular time periodic disease surveillance to detect any currently ‘active’ geographical clusters of disease and which tests the statistical significance of such clusters adjusting for the multitude of possible geographical locations and sizes, time intervals and time periodic analyses. The method is illustrated on thyroid cancer among men in New Mexico 1973–1992.
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Citations
Cited by
Mathematical modeling of infectious disease dynamics
Siettos, C. I., & Russo, L. (2013) · Virulence · doi:10.4161/viru.24041 · Cited by 466
Desjardins, M. R., Hohl, A., & Delmelle, E. M. (2020) · Applied Geography · doi:10.1016/j.apgeog.2020.102202 · Cited by 365
A Review and Discussion of Prospective Statistical Surveillance in Public Health
Sonesson, C., & Bock, D. (2003) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/1467-985x.00256 · Cited by 314
Spatiotemporal Data Mining: A Computational Perspective
Shekhar, S., Jiang, Z., Ali, R., Eftelioglu, E., Tang, X., Gunturi, V., & Zhou, X. (2015) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi4042306 · Cited by 206
Zeng, D., Cao, Z., & Neill, D. B. (2021) · Artificial Intelligence in Medicine · doi:10.1016/b978-0-12-821259-2.00022-3 · Cited by 183
Metadata
- Authors
- Kulldorff, M.
- Venue
- Journal of the Royal Statistical Society Series A: Statistics in Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-01-01
- Volume
- 164
- Issue
- 1
- Pages
- 61-72
- ISSN
- 0964-1998 (ISSN-L)
- 1467-985X
- OpenAlex
- W1991370590
- Cited by
- 779
- References
- 0
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
