An improved state filter algorithm for SIR epidemic forecasting

Simple item page
dc.contributor.authorHuang, Weipeng
dc.contributor.authorProvan, Gregory
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2017-03-13T12:11:19Z
dc.date.available2017-03-13T12:11:19Z
dc.date.issued2016-01
dc.date.updated2017-03-13T10:51:46Z
dc.description.abstractIn epidemic modeling, state filtering is an excellent tool for enhancing the performance of traditional epidemic models. We introduce a novel state filter algorithm to further improve the performance of state-of-the-art approaches based on Susceptible-Infected-Recovered (SIR) models. The proposed algorithm merges two techniques, which are typically used separately: linear correction, as seen in the Ensemble Kalman Filter (EnKF), and resampling, as used in the Particle Filter (PF). We compare the inferential accuracy of our approach against the EnKF and the Ensemble Adjustment Kalman Filter (EAKF), using algorithms employing both an uncentered co-variance matrix (UCM) and the standard column-centered covariance matrix (CCM). Our algorithm requires O(DN) more time than EnKF does, where D is the ensemble dimension and N denotes the ensemble size. We demonstrate empirically that our algorithm with UCM achieves the lowest root-mean-square-error (RMSE) and the highest correlation coefficient (CORR) amongst the selected methods, in 11 out of 14 major real-world scenarios. We show that the EnKF with UCM outperforms the EnKF with CCM, while the EAKF gains better accuracy with CCM in most scenarios.en
dc.description.sponsorshipScience Foundation Ireland (SFI Grant Number SFI/12/RC/2289)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHuang, W. and Provan, G. (2016) ‘An improved state filter algorithm for SIR epidemic forecasting’, (from proceedings of the Twenty-second European Conference on Artificial Intelligence (ECAI 2016),The Hague, The Netherlands, 29th August - 2nd September), Frontiers in Artificial Intelligence and Applications, 285, pp. 524-532. doi:10.3233/978-1-61499-672-9-524en
dc.identifier.doi10.3233/978-1-61499-672-9-524
dc.identifier.endpage532en
dc.identifier.issn0922-6389
dc.identifier.journaltitleFrontiers in Artificial Intelligence and Applicationsen
dc.identifier.startpage524en
dc.identifier.urihttps://hdl.handle.net/10468/3769
dc.identifier.volume285en
dc.language.isoenen
dc.publisherIOS Pressen
dc.relation.ispartofECAI 2016: 22nd European Conference on Artificial Intelligence
dc.relation.urihttp://www.ecai2016.org/
dc.rights© 2016, the Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).en
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectEnsemble Kalman Filteren
dc.subjectLagrangian data assimilationen
dc.subjectParticle filtersen
dc.subjectInfluenzaen
dc.subjectModelsen
dc.titleAn improved state filter algorithm for SIR epidemic forecastingen
dc.typeConference itemen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2620.pdf
Size:
787.69 KB
Format:
Adobe Portable Document Format
Description:
Published Version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Insight Centre for Data Analytics - Conference Items
Computer Science - Conference Items