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Article: Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history

TitleIdentifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history
Authors
Issue Date2015
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221036
Citation
IEEE Transactions on Cybernetics, 2015 How to Cite?
AbstractSpatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: 1) anatomizing the whole spread process among all subpopulations into disjoint componential patches; 2) inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; and 3) recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.
Persistent Identifierhttp://hdl.handle.net/10722/230499
ISSN
2015 Impact Factor: 4.943
2015 SCImago Journal Rankings: 2.886

 

DC FieldValueLanguage
dc.contributor.authorWang, JB-
dc.contributor.authorWang, L-
dc.contributor.authorLi, X-
dc.date.accessioned2016-08-23T14:17:23Z-
dc.date.available2016-08-23T14:17:23Z-
dc.date.issued2015-
dc.identifier.citationIEEE Transactions on Cybernetics, 2015-
dc.identifier.issn2168-2267-
dc.identifier.urihttp://hdl.handle.net/10722/230499-
dc.description.abstractSpatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: 1) anatomizing the whole spread process among all subpopulations into disjoint componential patches; 2) inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; and 3) recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221036-
dc.relation.ispartofIEEE Transactions on Cybernetics-
dc.rightsIEEE Transactions on Cybernetics. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleIdentifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history-
dc.typeArticle-
dc.identifier.emailWang, L: lwang14@hku.hk-
dc.description.naturepostprint-
dc.identifier.doi10.1109/TCYB.2015.2489702-
dc.identifier.hkuros261160-
dc.publisher.placeUnited States-

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