Treffer: Spatially Clustered Outbreak Detection Using the EWMA SCAN Statistics with Multiple Sized Windows
Title:
Spatially Clustered Outbreak Detection Using the EWMA SCAN Statistics with Multiple Sized Windows
Authors:
Source:
Communications in statistics. Simulation and computation. 41(8-10):1637-1653
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Estimation moyenne, Mean estimation, Estimación promedio, Moyenne mobile, Moving average, Promedio móvil, Méthode statistique, Statistical method, Método estadístico, Simulation numérique, Numerical simulation, Simulación numérica, Statistique balayage, Scan statistic, Average run length, EWMA, Monitoring, Spatial outbreaks
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
CSIRO Mathematics, Informatics and Statistics, Sydney, NSW, Australia
ISSN:
0361-0918
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.26164013
Database:
PASCAL Archive
Weitere Informationen
Spatio-temporal surveillance methods for detecting outbreaks of disease are fairly common in the literature with the SCAN statistic setting the benchmark. If the shape and size of the outbreaks are known in advance, then the SCAN statistic can be trained to efficiently detect these, however this is seldom true. Therefore, we want to devise plans that are efficient at detecting a number of outbreaks that vary in size and shape. This article examines plans which use the exponential weighted moving average statistic to build temporal memory into plans and tries to develop robust plans for detecting outbreaks of unknown shapes and sizes.