Software Review Space Time Surveillance

Software Review Space Time Surveillance - Robertson and...

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REVIEW Open Access Review of software for space-time disease surveillance Colin Robertson * , Trisalyn A Nelson Abstract Disease surveillance makes use of information technology at almost every stage of the process, from data collec- tion and collation, through to analysis and dissemination. Automated data collection systems enable near-real time analysis of incoming data. This context places a heavy burden on software used for space-time surveillance. In this paper, we review software programs capable of space-time disease surveillance analysis, and outline some of their salient features, shortcomings, and usability. Programs with space-time methods were selected for inclusion, limit- ing our review to ClusterSeer, SaTScan, GeoSurveillance and the Surveillance package for R. We structure the review around stages of analysis: preprocessing, analysis, technical issues, and output. Simulated data were used to review each of the software packages. SaTScan was found to be the best equipped package for use in an automated sur- veillance system. ClusterSeer is more suited to data exploration, and learning about the different methods of statis- tical surveillance. Introduction Disease surveillance is an ongoing process of informa- tion gathering, organizing, analyzing, interpreting, and communicating. It is the principal means by which pub- lic health information is generated and disseminated, informing policy, research, and response measures. For outbreaks of infectious disease, timely information on the spread of cases in space and time can facilitate action by public health officials [e.g., [1]]. For chronic and endemic diseases, monitoring space-time trends in d iseaseoccurrencecanh ighlight changing patterns in risk and help identify new risk factors [e.g., [2]]. Analysis of spatial-temporal patterns in public health data is an increasingly common task for public health analysts as more surveillance data become available. Surveillance datasets are often massive in size and complexity, and the availability and quality of software capable of analyz- ing space-time disease surveillance data on an ongoing basis is integral to practical surveillance [3-5]. Geo- graphic information systems (GIS) used for disease map- ping can visualize the spatial variation in disease risk. However, statistical methods are often required to detect changes in the underlying disease process. GIS are also poorly equipped to handle temporal data [6]. In Fall of 2008, a workshop on training priorities in the use of GIS in health research conducted in Victoria, British Columbia, polled 78 researchers, graduate stu- dents, faculty, and others working in health and GIS regarding barriers to the use of space-time disease sur- veillance [7]. Training and software availability were cited as the primary barriers to the uptake of space-time disease surveillance. Currently, statistical methods for
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This note was uploaded on 01/21/2012 for the course HUMBIO 156 taught by Professor Katzenstein,d during the Fall '11 term at Stanford.

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Software Review Space Time Surveillance - Robertson and...

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