Please use this identifier to cite or link to this item: https://doi.org/10.1109/SIS.2009.4937852
DC FieldValue
dc.titleKey node selection for containing infectious disease spread using particle swarm optimization
dc.contributor.authorFu, X.
dc.contributor.authorSonja, L.
dc.contributor.authorWang, L.
dc.contributor.authorLee, G.
dc.contributor.authorMa, S.
dc.contributor.authorWong, L.
dc.contributor.authorXiao, G.
dc.date.accessioned2013-07-04T08:38:25Z
dc.date.available2013-07-04T08:38:25Z
dc.date.issued2009
dc.identifier.citationFu, X.,Sonja, L.,Wang, L.,Lee, G.,Ma, S.,Wong, L.,Xiao, G. (2009). Key node selection for containing infectious disease spread using particle swarm optimization. 2009 IEEE Swarm Intelligence Symposium, SIS 2009 - Proceedings : 109-113. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/SIS.2009.4937852" target="_blank">https://doi.org/10.1109/SIS.2009.4937852</a>
dc.identifier.isbn9781424427628
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41896
dc.description.abstractIn recent years, some emerging and reemerging infectious diseases have grown into global health threats due to high human mobility. It is important to have intervention plans for containing the spread of such infectious diseases. Among various intervention strategies, screening infected people is an efficient way for evaluating the infection scale and controlling the spread of infectious diseases. Considering the cost in manpower and limited screening machines available, we face to challenges for selecting the optimal nodes (sites) in order to obtain better screening and control effects. In this paper, particle swarm optimization technique is used to determine key nodes for controlling infectious disease spread, through evaluating the number of people captured at each key node. The research example is shown on evaluating the screening control over train stations in Singapore. The optimization algorithm and control concept can be easily extended to large-scale infectious disease control in other kinds of key nodes and in other geographical regions. The selection for optimal control set of the multi objective optimization problem is done using particle swarm optimization. Numerical simulation shows the effectiveness of the proposed algorithm. © 2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/SIS.2009.4937852
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/SIS.2009.4937852
dc.description.sourcetitle2009 IEEE Swarm Intelligence Symposium, SIS 2009 - Proceedings
dc.description.page109-113
dc.identifier.isiutNOT_IN_WOS
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