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Model accurately predicts peak intensity, timing of local flu outbreaks
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The geographic spread of influenza may be accurately predicted using a model that incorporates data on influenza-like illness, lab-confirmed cases and travel habits of people within a local area.
“The system could also be adapted for use with other respiratory viruses, and with some modification, for infectious diseases more broadly,” Sen Pei, PhD, a postdoctoral scientist of environmental health sciences in the Mailman School of Public Health at Columbia University, said in a press release.
To create, assess and corroborate the efficacy of a disease forecasting system that predicts the spatiotemporal spread of influenza, the researchers implemented human mobility data and a metapopulation model. The researchers assessed the accuracy of the forecasting system with retrospective state-level forecasts that they conducted for 35 states. These analyses were conducted for influenza seasons from 2008-2009 through 2012-2013.
Pei and colleagues applied strategies typically found in weather forecasting, which include data collected from the Department of Defense on the incidence of influenza-like illness on a local scale and lab-confirmed cases. This information was then enhanced with data collected from U.S. Census data regarding commuting patterns in the area, which include specific adjustments for population during the day and at night, as well as instances of irregular travel.
When using the model for retrospective analysis, local influenza outbreaks accurately predicted onset — the week that influenza incidence increases above the baseline threshold — up to 6 weeks before the event. Furthermore, the system was able to predict peak timing and intensity more accurately than isolated location-specific systems.
“Influenza, like many infectious diseases, is spread from person to person and as people move from place to place," Jeffrey Shaman, PhD, associate professor of environmental health sciences at the Mailman School of Public Health, said in the release. “By assimilating information on commuting patterns, we have taken a big step forward and improved our ability to accurately forecast where the flu might crop up next.” – by Katherine Bortz
Disclosures: Pei reports serving as a consultant for SK Analytics. Please see the study for all other authors’ relevant financial disclosures.
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