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dc.contributor.authorChakraborty, Prithwish
dc.contributor.authorLewis, Bryan
dc.contributor.authorEubank, Stephen
dc.contributor.authorBrownstein, John S.
dc.contributor.authorMarathe, Madhav
dc.contributor.authorRamakrishnan, Naren
dc.date.accessioned2018-10-15T18:46:20Z
dc.date.available2018-10-15T18:46:20Z
dc.date.issued2018-10-12
dc.identifier.urihttp://hdl.handle.net/10919/85375
dc.description.abstractAccurate and timely influenza (flu) forecasting has gained significant traction in recent times. If done well, such forecasting can aid in deploying effective public health measures. Unlike other statistical or machine learning problems, however, flu forecasting brings unique challenges and considerations stemming from the nature of the surveillance apparatus and the end utility of forecasts. This article presents a set of considerations for flu forecasters to take into account prior to applying forecasting algorithms.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.publisherPLOSen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleWhat to know before forecasting the fluen_US
dc.typeArticleen_US
dc.title.serialPLOS Computational Biologyen_US
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1005964
dc.identifier.volume14en_US
dc.identifier.issue10en_US
dc.type.dcmitypeText


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Attribution 4.0 International
License: Attribution 4.0 International