Spatial Analysis

An important problem in biosurveillance is the early detection and characterization of outdoor aerosol releases of B. anthracis. The Bayesian Aerosol Release Detector (BARD) is a system for simulating, detecting and characterizing such releases. BARD integrates the analysis of medical surveillance data and meteorological data. The existing version of BARD does not account for the fact that many people might be exposed at a location other than their residence due to mobility. Incorporation of a mobility model in biosurveillance has been investigated by several other researchers. In this paper, we describe a refined version of the BARD simulation algorithm which incorporates a model of work-related mobility and report the results of an experiment to measure the effect of this refinement.

Objective 

To refine the simulation algorithm used in the BARD so that it takes into account the work-related mobility and to compare the refined simulator with the existing one.

San Francisco has the highest rate of TB in the US. Although in recent years the incidence of TB has been declining in the San Francisco general population, it has remained relatively constant in the homeless population. Spatial investigations of disease outbreaks seek to identify and determine the significance of spatially localized disease clusters by partitioning the underlying geographic region. The level of such regional partitioning can vary depending on the available geospatial data on cases including towns, counties, zip codes, census tracts, and exact longitude-latitude coordinates. It has been shown for syndromic surveillance data that when exact patients’ geographic coordinates are used, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. While the benefits of using a finer spatial resolution, such as patients’ individual addresses, have been examined in the context of spatial epidemiology, the effect of varying spatial resolution on detection timeliness and the amount of historical data needed have not been investigated.

Objective

The objective of this study is to investigate the effect of varying the spatial resolution in a variant of space-time permutation scan statistic applied to the tuberculosis data on the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed for training the model.

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

We propose discriminative random field approach for detecting a disease outbreak. Given observed data on a spatial grid, the goal is to label each node as being under attack and non-attack.

Outbreaks of infectious diseases are identified in a variety of ways by clinicians and public health practitioners but not usually by analytic methods typically employed in syndromic surveillance. Systematic spatial-temporal analysis of statewide data may enable earlier detection of outbreaks and identification of multi-jurisdictional outbreaks.

Objective

Clusters of cases of individually-reportable infectious diseases were identified by a spatial-temporal retrospective analysis. Clusters were examined to determine association with previously reported outbreaks.

Multiple or irregularly shaped spatial clusters are often found in disease or syndromic surveillance maps. We develop a novel method to delineate the contours of spatial clusters, especially when there is not a clearly dominating primary cluster, through artificial neural networks. The method may be applied either for maps divided into regions or point data set maps.

Using New York Cityís dead bird surveillance for West Nile Virus (WNV), this paper presents two explorations of the spatial cluster detection problem in which lagged test results are available for a random subset of observations. First, we establish a framework for the direct evaluation of methods and identify the optimal parameterization over a large family of models. We then investigate ways in which the lagged test results and other covariates might be used prospectively to extend the family of models by refining the baseline.

Computational and statistical methods for detecting disease clusters, such as the spatial scan statistic, have become frequently used tools in epidemiology. However, they simply tell the user where a cluster is, and leave the analysis task to the user. Multivariate visualization tools provide one way for this analysis. The approach developed in this research is computational in nature, using computer vision techniques to analyze the shape of the cluster. Shapes are used here because different spatial processes that cause clusters, such as pollution along a river, create clusters with different shapes. Thus, it may be possible to categorize clusters by their respective spatial processes by analyzing the cluster shapes.

OBJECTIVE

There are plenty of computational and statistical methods for detecting spatial clusters, although the interpretation of these clusters is a task left to the user. This research develops computational methods to not just detect, but also analyze the cluster to hypothesize one or more potential causes.

To evaluate the robustness of a spatial anonymization algorithm for syndromic surveillance data against a triangulation vulnerability attack. `BACKGROUND We have published an anonymization algorithm that takes precise point locations for patients and moves them a randomized distance according to a 2D Gaussian distribution that is inversely adjusted by the underlying population density. Before such algorithms can be integrated into live systems, assurances are needed so that patients cannot be reidentified through systematic vulnerabilities. Here we investigate the ease with which a spatial anonymization algorithm can be compromised by triangulating the original points with multiple repeated data requests. Obfuscative and cryptographic algorithms may be susceptible to weakening when it is possible for an adversary to produce output from the algorithm according to adversary-provided input. Under this threat model, an adversary could use a syndromic surveillance system to request anonymized patient data from a RHIO or other health network several different times. If the anonymized results are produced each time they are requested, triangulation of original addresses may be possible or the anonymity afforded by the algorithm may be reduced.

This paper explores some visualization methods for characterizing spatial signals detected by SaTScan and discusses how these maps might aid in deciding whether to investigate a signal, as well as the scope and focus of the investigation.

To propose a new spatial scan statistic that has higher ability of pinpointing the true cluster.