Surveillance to detect emerging space-time clusters

DOI10.1016/j.csda.2008.10.032zbMath1453.62033OpenAlexW2066530714MaRDI QIDQ961714

Renato M. Assunção, Thais R. Correa

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.10.032

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from spatial processes (62M30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics in engineering and industry; control charts (62P30)

Related Items (7)

Space-time border analysis to evaluate and detect clusters ⋮ \textit{MAD-STEC}: a method for multiple automatic detection of space-time emerging clusters ⋮ An up-to-date review of scan statistics ⋮ A Space-Time Conditional Intensity Model for Invasive Meningococcal Disease Occurrence ⋮ Bayesian Estimation and Prediction for Inhomogeneous Spatiotemporal Log-Gaussian Cox Processes Using Low-Rank Models, With Application to Criminal Surveillance ⋮ Editorial. Spatial statistics: methods, models \& computation ⋮ Prospective space-time surveillance with cumulative surfaces for geographical identification of the emerging cluster

Uses Software

• R
• Surveillance
• SaTScan

Cites Work

• Unnamed Item
• Count data regression charts for the monitoring of surveillance time series
• A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters
• Surveillance: An R package for the monitoring of infectious diseases
• Optimal detection of a change in distribution
• Geographic and network surveillance via scan statistics for critical area detection
• A Small Sample Size Comparison of the Cusum and Shiryayev-Roberts Approaches: Changepoint Detection
• A diffusion process and its applications to detecting a change in the drift of Brownian motion
• On the Detection of Disorder in a Manufacturing Process. I.
• Prospective Time Periodic Geographical Disease Surveillance Using a Scan Statistic
• Monitoring Point Patterns for the Development of Space–Time Clusters
• Applied Spatial Statistics for Public Health Data
• Statistical Surveillance. Optimality and Methods
• Monitoring Changes in Spatio-temporal Maps of Disease
• Space‐time cluster identification in point processes

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