File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TCYB.2015.2489702
- Scopus: eid_2-s2.0-84979765749
- WOS: WOS:000388923100009
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history
Title | Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history |
---|---|
Authors | |
Keywords | Identifiability infectious diseases metapopulation networks process identification spatial spread |
Issue Date | 2016 |
Publisher | Institute 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, 2016, v. 46 n. 12, p. 2782- 2795 How to Cite? |
Abstract | Spatial 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 Identifier | http://hdl.handle.net/10722/230499 |
ISSN | 2023 Impact Factor: 9.4 2023 SCImago Journal Rankings: 5.641 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, JB | - |
dc.contributor.author | Wang, L | - |
dc.contributor.author | Li, X | - |
dc.date.accessioned | 2016-08-23T14:17:23Z | - |
dc.date.available | 2016-08-23T14:17:23Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | IEEE Transactions on Cybernetics, 2016, v. 46 n. 12, p. 2782- 2795 | - |
dc.identifier.issn | 2168-2267 | - |
dc.identifier.uri | http://hdl.handle.net/10722/230499 | - |
dc.description.abstract | Spatial 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.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221036 | - |
dc.relation.ispartof | IEEE Transactions on Cybernetics | - |
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.subject | Identifiability | - |
dc.subject | infectious diseases | - |
dc.subject | metapopulation | - |
dc.subject | networks | - |
dc.subject | process identification | - |
dc.subject | spatial spread | - |
dc.title | Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history | - |
dc.type | Article | - |
dc.identifier.email | Wang, L: lwang14@hku.hk | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1109/TCYB.2015.2489702 | - |
dc.identifier.scopus | eid_2-s2.0-84979765749 | - |
dc.identifier.hkuros | 261160 | - |
dc.identifier.volume | 46 | - |
dc.identifier.issue | 12 | - |
dc.identifier.spage | 2782 | - |
dc.identifier.epage | 2795 | - |
dc.identifier.isi | WOS:000388923100009 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2168-2267 | - |