Geographic patterns can help predict how disease spreads

WASHINGTON - Disease statistics buried within patient records or detailed in newspaper clippings can be sorted and organised to depict geographic patterns, allowing the discovery of trends that were previously overlooked, says a new study.“The use of interactive maps and graphs, combined with word search interfaces, can lead to greater insight into complex events like the spread of Swine flu,” said Frank Hardisty, research associate and geographer at Penn State University’s GeoVISTA Centre.The GeoViz Toolkit is a user-friendly application that combines text mining with geographical mapping. It allows users to publicly search available data to identify and visualise data patterns for their own interests or concerns.The GeoViz application allows users to easily manipulate the software to change time and location, as well as how the data is viewed. The user can thus visualize the pattern of how the disease spreads and determine how quickly it progresses from one area to the next.The flexible software package allows someone with no programming experience to navigate the application, while also providing different components and analytical tools for experienced analysts.“Potential applications range from research in public health — infectious disease dynamics, cancer etiology, surveillance and control — through analysis of socioeconomic and demographic data, to exploration of patterns of incidents related to terrorism or crime,” said Hardisty.Many sources for disease and crime statistics — newspaper articles for example — are in a semi-structured format that do not clearly present the data in a table or graph, but rather bury it within the text of the document.To obtain high-quality, relevant information from these documents, researchers use “text analytics” or “text mining” allowing them to retrieve only applicable information, like the date and description of a disease-related death, from the flood of information usually included in a newspaper clipping.“An example would be searching a database of H1N1 flu reports for ‘child’ or ‘children’ and seeing if there is spatial clustering in the relative frequency of those reports,” Hardisty told attendees at the 2010 Association of American Geographers Annual Meeting in Washington, D.C.

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