A Space–Time Permutation Scan Statistic for Disease Outbreak Detection
By Martin Kulldorff, R. Heffernan, J. Hartman, Renato Martins Assunção, Farzad Mostashari. PLoS Medicine, 2005
1,139 citations recorded by the source corpus.
Background The ability to detect disease outbreaks early is important in order to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Many national, state, and local health departments are launching disease surveillance systems with daily analyses of hospital emergency department visits, ambulance dispatch calls, or pharmacy sales for which population-at-risk information is unavailable or irrelevant. Methods and Findings We propose a prospective space–time permutation scan statistic for the early detection of disease outbreaks that uses only case numbers, with no need for population-at-risk data. It makes minimal assumptions about the time, geographical location, or size of the outbreak, and it adjusts for natural purely spatial and purely temporal variation. The new method was evaluated using daily analyses of hospital emergency department visits in New York City. Four of the five strongest signals were likely local precursors to citywide outbreaks due to rotavirus, norovirus, and influenza. The number of false signals was at most modest. Conclusion If such results hold up over longer study times and in other locations, the space–time permutation scan statistic will be an important tool for local and national health departments that are setting up early disease detection surveillance systems.
Subfields: Epidemiology, Infectious Diseases, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/0ccc4b45c9a4fd6f964c3866be1237c5c9ebd81e
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Citations in the corpus
- Spatial data mining and geographic knowledge discovery - An introduction (2009, methodology): “The spatial scan statistics developed by Kulldorff (1997) and Kulldorff et al. (2005) calculates a likelihood ratio for each local area.”
- Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, methodology): “STC methods that are gaining popularity include ST-DBSCAN (Birant & Kut 2007) and space-time scan statistics (STSS) (Kulldorff et al. 2005, Cheng & Adepeju 2014).”
- Optimal detection of heterogeneous and heteroscedastic mixtures (2011, background): “Shorack and Wellner (2009)), HC∗ n ≈ √ 2 log log n, which grows to ∞ very slowly.”
- CASE: a framework for computer supported outbreak detection (2010, methodology): “Two algorithms from this application are used in CASE: SaTScan Poisson which uses the discrete Poisson SaTScan model to search for spatial clusters and SaTScan Space-Time Permutation , which searches for spatio-temporal clusters.”
- Mathematical modeling of infectious disease dynamics (2013, methodology): “73 SatScan has been also exploited by Gaudart et al.”
- Spatio-Temporal Data Mining (2017, methodology): “Generalization of the scan statistic for ST data, termed as space-time scan statistics, have been explored for studying disease outbreaks, crime hot-spots, and events in twitter data [Cheng and Wicks 2014; Kulldorff 2001; Kulldorff et al. 2005].”