Researchers use an algorithm to diagnose infectious disease a continent away.
“Real world information is often vague, minimal, and at times contradictory, so the challenge is to find ways to make good inferences (disease identifications) from such limited data,” says epidemiologist Stephen Morse of Columbia University, one of the paper’s co-authors and creator of the ProMED-mail site.
But the potential to detect outbreaks much faster through the use of statistical models applied to field reports is clear. These tools will find their greatest value in places where deadly pathogens are numerous, diagnostic equipment is hard to find, and time is short.
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The network of diagnosed outbreaks of diseases with the potential to cause encephalitis (colored) and outbreaks of encephalitis where the cause was removed (white). The inner network describes the strength and relationship of individual outbreaks to each other. The outer ring gives the composition of the 7 communities of disease that were found by the detection algorithm. Each circle represents one outbreak report. Lines connecting two nodes indicate shared traits between two reports.