\textit{MAD-STEC}: a method for multiple automatic detection of space-time emerging clusters

DOI10.1007/s11222-016-9673-yzbMath1384.62205OpenAlexW2428728971MaRDI QIDQ1703826

Thais R. Correa, Bráulio M. Veloso, Andréa I. Tavares, Gabriel F. Oliveira, Marcos Oliveira Prates

Publication date: 7 March 2018

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-016-9673-y

zbMATH Keywords

surveillancepoint patternprospective spacetime surveillancespacetime clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to social sciences (62P25)

Related Items (3)

Space-time border analysis to evaluate and detect clusters ⋮ An up-to-date review of scan statistics ⋮ MAD-STEC

Uses Software

• Surveillance
• SaTScan

Cites Work

• Count data regression charts for the monitoring of surveillance time series
• Spatial scan statistics adjusted for multiple clusters
• A spatial scan statistic for multiple clusters
• Surveillance to detect emerging space-time clusters
• Surveillance: An R package for the monitoring of infectious diseases
• Adaptive likelihood ratio approaches for the detection of space-time disease clusters
• Prospective space-time surveillance with cumulative surfaces for geographical identification of the emerging cluster
• Nonanticipating estimation applied to sequential analysis and changepoint detection
• A Space-Time Scan Statistic for Detecting Emerging Outbreaks
• Multiple Spatio-Temporal Cluster Detection for Case Event Data: An Ordering-Based Approach
• A spatial scan statistic
• Prospective Time Periodic Geographical Disease Surveillance Using a Scan Statistic
• Monitoring Point Patterns for the Development of Space–Time Clusters
• Bayesian Estimation and Prediction for Inhomogeneous Spatiotemporal Log-Gaussian Cox Processes Using Low-Rank Models, With Application to Criminal Surveillance
• Monitoring Changes in Spatio-temporal Maps of Disease
• Space‐time cluster identification in point processes

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