A new interpretation of the inference test for the spatial scan statistic

Spatial cluster analysis is considered an important technique for the elucidation of disease causes and epidemiological surveillance. Kulldorff's spatial scan statistic, defined as a likelihood ratio, is the usual measure of the strength of geographic clusters. The circular scan, a particular case of the spatial scan statistic, is currently the most used tool for the detection and inference of spatial clusters of disease.

May 02, 2019

A Zero-Inflated Poisson Based Spatial Scan Statistic

The spatial scan statistic proposed by Kulldorff has been widely used in spatial disease surveillance and other spatial cluster detection applications. In one of its versions, such scan statistic was developed for inhomogeneous Poisson process. However, the underlying Poisson process may not be suitable to properly model the data. Particularly, for diseases with very low prevalence, the number of cases may be very low and zero excess may cause bias in the inferences.

May 02, 2019

Changes in the Spatial Distribution of Syphilis

Public health officials and epidemiologists have been attempting to eradicate syphilis for decades, but national incidence rates are again on the rise.

May 02, 2019

Online surveillance of multivariate small area disease data: A Bayesian approach

The ability to rapidly detect any substantial change in disease incidence is of critical importance to facilitate timely public health response and, consequently, to reduce undue morbidity and mortality. Unlike testing methods (1, 2), modeling for spatio-temporal disease surveillance is relatively recent, and this is a very active area of statistical research (3). Models describing the behavior of diseases in space and time allow covariate effects to be estimated and provide better insight into etiology, spread, prediction and control.

May 02, 2019

Significant multiple high and low risk regions in event data maps

The Voronoi Based Scan (VBScan)[1] is a fast method for the detection and inference of point data set space-time disease clusters. A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance Minimum Spanning Tree (MST) linking the cases.

May 02, 2019

Spatial Scanning Tips and Tricks for Practical Outbreak Detection

For its January 2011 Literature Review, the ISDS Research Committee invited Daniel B. Neill, PhD, Assistant Professor of Information Systems at Carnegie Mellon University, to present his paper, "An Empirical Comparison of Spatial Scan Statistics for Outbreak Detection," published in the International Journal of Health Geographics.

Presenter

Daniel B. Neill, PhD, Assistant Professor of Information Systems, Carnegie Mellon University

Date and Time

Thursday, January 27, 2011

Host

October 20, 2017

The influence of address errors on detecting outbreaks of campylobacteriosis

Mandatory notification to public health of priority communicable diseases (CDs) is a cornerstone of disease prevention and control programs. Increasingly, the addresses of CD cases are used for spatial monitoring and cluster detection and public health may direct interventions based on the results of routine spatial surveillance. There has been little assessment of the quality of addresses in surveillance data and the impact of address errors on public health practice.

June 20, 2019

Sample size and spatial cluster detection power

Prior work demonstrates the extent to which sampling strategies reduce the power to detect clusters.1 Additionally, the power to detect clusters can vary across space.2 A third, unexplored, effect is how much the sample size impacts the power of spatial cluster detection methods. This research examines this effect.

Objective

June 24, 2019

Spatial cluster detection in schools using school catchment information

The H1N1 outbreak in the spring of 2009 in NYC originated in a school in Queens before spreading to others nearby. Active surveillance established epidemiological links between students at the school and new cases at other schools through household connections. Such findings suggest that spatial cluster detection methods should be useful for identifying new influenza outbreaks in school-aged children. As school-to-school transmission should occur between those with high levels of interaction, existing cluster detection methods can be improved by accurately characterizing these links.

June 24, 2019

Discriminative Random Field Approach to Spatial Outbreak Detection

Spatial scan finds the most anomalous region that has shown increase in observed counts when compared to the expected baseline. As there can be infinitely many regions to search for, most state-of-the-art algorithms assumes a specific shape of the attack region (circles for Kulldorff and rectangles for Ultra-Fast Spatial Scan Statistics). This assumption might reduce the detection power as real world attacks don't follow standard geometric shapes.

 

Objective

July 30, 2018

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