Support Vector Subset Scan for Spatial Outbreak Detection
نویسندگان
چکیده
منابع مشابه
Support Vector Subset Scan for Spatial Outbreak Detection
Introduction Neill’s fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The penalized fast subset scan (PFSS)3 provides a flexible framework for adding soft constraints to the fast subset scan, rewarding or penalizing inclusion of indivi...
متن کاملFast subset scan for spatial pattern detection
We propose a new ‘fast subset scan’ approach for accurate and computationally efficient event detection in massive data sets. We treat event detection as a search over subsets of data records, finding the subset which maximizes some score function. We prove that many commonly used functions (e.g. Kulldorff’s spatial scan statistic and extensions) satisfy the ‘linear time subset scanning’ proper...
متن کاملFast Multidimensional Subset Scan for Outbreak Detection and Characterization
Objective We present Multidimensional Subset Scan (MD-Scan), a new method for early outbreak detection and characterization using multivariate case data from individuals in a population. MD-Scan extends previous work on multivariate event detection by identifying the characteristics of the affected subpopulation, and enables more timely and accurate detection while maintaining computational tra...
متن کاملAn empirical comparison of spatial scan statistics for outbreak detection
BACKGROUND The spatial scan statistic is a widely used statistical method for the automatic detection of disease clusters from syndromic data. Recent work in the disease surveillance community has proposed many variants of Kulldorff's original spatial scan statistic, including expectation-based Poisson and Gaussian statistics, and incorporates a variety of time series analysis methods to obtain...
متن کاملFast subset scan for multivariate event detection.
We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time clusters even when the numbers of spatial locations and data streams are large. For two variants of the multivariate subset scan,...
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ژورنال
عنوان ژورنال: Online Journal of Public Health Informatics
سال: 2017
ISSN: 1947-2579
DOI: 10.5210/ojphi.v9i1.7599