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In recent decades there has been an extraordinary growth in and acceptance of automatic data systems that collect official and popular reports of epidemic occurrence. While different systems employ one or another proprietary algorithms to collect and parse disease reports all include, at a minimum, spatial locators, the date of a report, and the number of individual cases reported. These systems have been increasingly vital in both the study of individual epidemics and the exposition of expanding epidemics in real time. To date, however, there has been little analysis of the nature and quality of the data collected in these “big-net” programs or the degree to which redundancies and uncertainties may limit their utility. Here data on the 2009 H1N1 Type-A influenza epidemic gathered by a single system, healthmap.org, is parsed to determine where problems exist and how they might be rectified.
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