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A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease
Abstract
C. P. Farrington, N. J. Andrews, A. D. Beale, M. A. Catchpole, A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease, Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 159, No. 3 (1996), pp. 547-563
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PAGE, E. S. (1954) · Biometrika · doi:10.1093/biomet/41.1-2.100 · Cited by 5,739
Methods for current statistical analysis of excess pneumonia-influenza deaths
Serfling, R. E. (1963) · Public Health Reports (1896-1970) · doi:10.2307/4591848 · Cited by 597
The GLIM System: Release 4 Manual.
Francis, B., Green, M., & Payne, C. (1995) · Biometrics · doi:10.2307/2533370 · Cited by 449
The Surveillance of Communicable Diseases of National Importance
Langmuir, A. D. (1963) · New England Journal of Medicine · doi:10.1056/nejm196301242680405 · Cited by 436
The Distribution of the Size of the Maximum Cluster of Points on a Line
Naus, J. (1965) · Journal of the American Statistical Association · doi:10.1080/01621459.1965.10480810 · Cited by 305
A Statistical Problem in Space and Time: Do Leukemia Cases Come in Clusters?
Ederer, F., Myers, M. H., & Mantel, N. (1964) · Biometrics · doi:10.2307/2528500 · Cited by 216
A significance test for cyclic trends in incidence data
Roger, J. (1977) · Biometrika · doi:10.1093/biomet/64.1.152 · Cited by 155
A TEST FOR DETECTION OF CLUSTERING OVER TIME
Wallenstein, S. R. (1980) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a112908 · Cited by 152
Detection of aberrations in the occurrence of notifiable diseases surveillance data
Stroup, D. F., Williamson, G. D., Herndon, J. L., & Karon, J. M. (1989) · Statistics in Medicine · doi:10.1002/sim.4780080312 · Cited by 145
WEATHERALL, J. A. C., & Haskey, J. (1976) · British Medical Bulletin · doi:10.1093/oxfordjournals.bmb.a071321 · Cited by 90
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Metadata
- Authors
- Farrington, C. P., Andrews, N. J., Beale, A. D., & Catchpole, M. A.
- ORCID
- Andrews, N. J.
- Venue
- Journal of the Royal Statistical Society. Series A (Statistics in Society)
- Publisher
- Oxford University Press (OUP)
- Published
- 1996
- Volume
- 159
- Issue
- 3
- Pages
- 547
- ISSN
- 0964-1998 (ISSN-L)
- OpenAlex
- W2001449174
- Cited by
- 417
- References
- 16
- Citations by year
- 2026: 21 · 2025: 13 · 2024: 24 · 2023: 19 · 2022: 26 · 2021: 31 · 2020: 39 · 2019: 16 · 2018: 10 · 2017: 10 · 2016: 18 · 2015: 15 · 2014: 18 · 2013: 19 · 2012: 12
- FWCI
- 1.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Advanced Statistical Process Monitoring
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- outbreak detection, infectious disease surveillance, statistical algorithms, epidemic detection
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