An improved state filter algorithm for SIR epidemic forecasting

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dc.contributor.author Huang, Weipeng
dc.contributor.author Provan, Gregory
dc.date.accessioned 2017-03-13T12:11:19Z
dc.date.available 2017-03-13T12:11:19Z
dc.date.issued 2016-01
dc.identifier.citation Huang, 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:10.3233/978-1-61499-672-9-524 en
dc.identifier.volume 285 en
dc.identifier.startpage 524 en
dc.identifier.endpage 532 en
dc.identifier.issn 0922-6389
dc.identifier.uri http://hdl.handle.net/10468/3769
dc.identifier.doi 10.3233/978-1-61499-672-9-524
dc.description.abstract In 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.sponsorship Science Foundation Ireland (SFI Grant Number SFI/12/RC/2289) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher IOS Press en
dc.relation.ispartof ECAI 2016: 22nd European Conference on Artificial Intelligence
dc.relation.uri http://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.uri https://creativecommons.org/licenses/by-nc/4.0/ en
dc.subject Ensemble Kalman Filter en
dc.subject Lagrangian data assimilation en
dc.subject Particle filters en
dc.subject Influenza en
dc.subject Models en
dc.title An improved state filter algorithm for SIR epidemic forecasting en
dc.type Conference item en
dc.internal.authorcontactother Gregory Provan, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: g.provan@cs.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2017-03-13T10:51:46Z
dc.description.version Published Version en
dc.internal.rssid 386981728
dc.internal.wokid WOS:000385793700062
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Frontiers in Artificial Intelligence and Applications en
dc.internal.copyrightchecked Yes en
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation The Hague, The Netherlands en
dc.internal.IRISemailaddress g.provan@cs.ucc.ie en


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© 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). Except where otherwise noted, this item's license is described as © 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).
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