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we aim to provide researchers and public health analysts with a review and demonstration of software packages for space-time disease surveillance. we aim to facilitate expanded use of these methods by providing a means to quickly determine the software options and to identify the ways in which programs differ. our work is limited to methods that use both space and time, rather than purely temporal or spatial analysis.

reconstructed maps of historical disease outbreaks were generated using software developed by the association of schools of public health and the center for communicable disease dynamics. the software program, d3.js, was used to combine data from a number of sources into a single map. this website provides downloadable, multilingual software for mapping historical disease outbreaks.

a 2013 study investigating the software options for space-time disease surveillance found that in addition to the limitations of software availability, there is also a lack of training in these methods. as mentioned above, the traditional statistical software packages that are widely used in public health and epidemiology do not include statistical methods for space-time disease surveillance. some of these software packages are available as extensions to software programs like r and s-plus. others are available on the commercial market. however, these programs can be difficult to learn, and, as noted in the 2013 study, they may be limited to primarily english speakers.

clusterseer is one of the few programs that is designed specifically for cluster analysis of time-series data. clusterseer is able to identify clusters of time-series data and to test for clustering in different ways. these features make clusterseer a useful tool for cluster analysis of space-time disease data. clusterseer also has a built-in visualization feature that is designed to provide a graphic representation of clusters. the user can interact with these clusters directly, changing the resolution of the visualization and the number of clusters identified. furthermore, the user can export the clusters to other data visualization programs, such as google earth, arcgis, and leaflet. therefore, clusterseer can be used to explore space-time disease clusters and visualize clustering results in a variety of ways. clusterseer also has advanced data output functions that can be exported with data to new files for further examination inside statistical or gis software.




software programs were designed to support their specific users, and may not be easy to use for alternative tasks. this is the case with most of the software packages used in surveillance, and the same analysis may be supported by different packages. each program, however, had a unique set of technical issues, many of which are related to the specific nature of data analysis. for example, the surveillance package was designed to support temporal aggregation of cases over the course of a specified time period, which is a unique scenario. the packages we reviewed for spatial analysis of health event data are summarized in table 3. all software packages have spatial tools to analyze the spatial distribution of cases. data are typically collected at an address level, which must be spatially aggregated to the appropriate administrative level. the first step is to aggregate the data to a spatial shapefile, typically a polygon that represents the boundaries of the geographic area to be analyzed. in addition, the geographic area must be defined. in some cases, the geographic area is defined by the spatial extent of the event data, while in others, the geographic area is simply the geographic area of interest (e.g., the state). this is important, as the geographic area to be analyzed must be defined. the data on the net book order form included a column for comments. these comments were not systematically used in any of the software programs. comments are an important part of the surveillance process, and we recommend their systematic use in future software updates. comments are also important for evaluating the accuracy of a set of geocoded points. the table in appendix b includes a complete list of comments for each set of points. each comment can be used as an easily identified placeholder for the error in a set of points. 5ec8ef588b


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