Show simple item record

dc.contributor.authorTabataba, Farzaneh Sadat
dc.contributor.authorChakraborty, Prithwish
dc.contributor.authorRamakrishnan, Naren
dc.contributor.authorVenkatramanan, Srinivasan
dc.contributor.authorChen, Jiangzhuo
dc.contributor.authorLewis, Bryan
dc.contributor.authorMarathe, Madhav
dc.date.accessioned2017-08-03T20:01:25Z
dc.date.available2017-08-03T20:01:25Z
dc.date.issued2017-05-15
dc.identifier.citationBMC Infectious Diseases. 2017 May 15;17(1):345en_US
dc.identifier.urihttp://hdl.handle.net/10919/78637
dc.description.abstractAbstract Background Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm. Results In this paper we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features when evaluated across error measures. As an alternative, we provide various Consensus Ranking schema that summarize individual rankings, thus accounting for different error measures. Since each Epi-feature presents a different aspect of the epidemic, multiple methods need to be combined to provide a comprehensive forecast. Thus we call for a more nuanced approach while evaluating epidemic forecasts and we believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.rightsCreative Commons Attribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.titleA framework for evaluating epidemic forecastsen_US
dc.typeArticle - Refereed
dc.date.updated2017-08-03T10:58:56Z
dc.description.versionPeer Reviewed
dc.rights.holderThe Author(s)en_US
dc.title.serialBMC Infectious Diseases
dc.identifier.doihttps://doi.org/10.1186/s12879-017-2365-1
dc.type.dcmitypeText


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International