Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks.
New methods that improve the overall detection capabilities of these systems can have a broad practical impact. However, most surveillance systems do not hold up when there are shifts in health care utilization such as caused by public-health crises and major public events, such as the Olympic Games.
AdvertisementIn this modeling paper Ben Reis and colleagues, from Harvard Medical School, developed models, known as network models, that were able to detect localized outbreaks better and which were more robust to unpredictable shifts in healthcare utilization.
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