The Utility of Space-Time Surveillance for Tuberculosis

Tuberculosis (TB) has reemerged as a global health epidemic in recent years. Although several researchers have examined the use of space-time surveillance to detect TB clusters, they have not used genetic information to verify that detected clusters are due to person-to-person transmission. Using genetic fingerprinting data for TB cases, we sought to determine whether detected clusters were due to recent transmission.

 

Objective

July 30, 2018

Algorithms to Characterize Syndromic Surveillance Spatial Alerts

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.

July 30, 2018

Effect of Work-related Mobility in the Simulation of Aerosol Anthrax Releases with BARD

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.

July 30, 2018

Geographically Meaningful Cluster Scanning Through Weak Link Correction

Many heuristics were developed recently to find arbitrarily shaped clusters (see  review  [1]). The most popular statistic is the spatial scan  [2]. Nevertheless, even if all cluster solutions could be known, the problem  of selecting the best cluster is ill posed. This happens because other measures, such as geometric regularity  [3-5] or topology  [6] must be taken intoconsideration. Most cluster finding  methods does not address  this last problem. A genetic multi-objective algorithm was developed elsewhere to identify irregularlyshaped clusters [5].

July 30, 2018

GIS Mapping of Occupational Health Visit Data from a Southeastern Ontario Tertiary Care Hospital

Health care workers (HCWs) have an increased risk of exposure to infectious agents including (among others) tuberculosis, influenza, norovirus, and Clostridium difficile as a consequence of patient care1,2 Most occupational transmission is associated with violation of one or more basic principles of infection control: handwashing; vaccination of HCWs; and prompt isolation.3 OH surveillance is paramount in guiding efforts to improve worker safety and health and to monitor trends and progress over time.4 GIS can assist in supporting health situation analysis and surveillance for the preventio

July 30, 2018

Anonymization of Spatial Data by Gaussian Skew: Is Re-Identification Possible?

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.

July 30, 2018

Identifying and Modeling Spatial Patterns of Heat-Related Illness in New York City

This paper describes the spatial pattern of New York City (NYC) heat-related emergency medical services (EMS) ambulance dispatches and emergency department visits (ED) and explores how this information can be used in planning for and response to heat-related health events.

July 30, 2018

Delineating Spatial Clusters with Artificial Neural Networks

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.

July 30, 2018

A Spatial Scan Statistic Scanning Only the Regions with Elevated Risk

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

July 30, 2018

Automated Generation of Hypothesis of Processes Causing Clusters

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.

July 30, 2018

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