Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches
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Summary
When the degree of spatial autocorrelation is low, the univariate method is generally better at detecting changes in rates that occur in a small number of regions; the multivariate is better when change occurs in a large number of countries.
- Type
- article
- Published
- 2004-07-30
- Cited by
- 105
- References
- 37
- OpenAlex
- https://openalex.org/W15236425
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:42341717
Keywords
Creep, Materials science, Physics, Metallurgy
References
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- ON THE APPLICATION TO STATISTICS OF AN ELEMENTARY THEOREM IN PROBABILITY
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- The national center for health statistics
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- Shrinkage Estimators for Covariance Matrices
- Multivariate CUSUM Quality- Control Procedures
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- A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism.
- Breast cancer clusters in the northeast United States: a geographic analysis.
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- Efficient change detection methods for bio and healthcare surveillance
- Nouveaux outils et nouvelles données pour la surveillance des maladies infectieuses
- Statistical monitoring of the hand, foot and mouth disease in China
- Approaches to the evaluation of outbreak detection methods
- Syndromic Surveillance, An Article for The Encyclopedia for Quantitative Risk Assessment
- A Spatial‐EWMA Framework for Detecting Clustering
- Robust distribution-free multivariate CUSUM charts for spatiotemporal biosurveillance in the presence of spatial correlation
- Spatial and temporal aberration detection methods for disease outbreaks in syndromic surveillance systems
- Poisson regression charts for the monitoring of surveillance time series
- Spatio-Temporal Disease Surveillance: Forward Selection Scan Statistic
- Identifying localized changes in large systems: Change-point detection for biomolecular simulations
- Detecting patterns of anomalies
- Review of methods for space–time disease surveillance
- A stack-based prospective spatio-temporal data analysis approach
- Count data regression charts for the monitoring of surveillance time series
- Spatiotemporal surveillance methods in the presence of spatial correlation
- A one‐sided MEWMA chart for health surveillance
- Monitoring Highly Correlated Multivariate Processes Using Hotelling's T2 Statistic: Problems and Possible Solutions
- A Review of Healthcare, Public Health, and Syndromic Surveillance
- Statistical approaches to the monitoring and surveillance of infectious diseases for veterinary public health.
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