Publication:
SEIQR disease transmission on GA-network

dc.contributor.authorW. Jumpenen_US
dc.contributor.authorS. Orankitjaroenen_US
dc.contributor.authorB. Wiwatanapathapheeen_US
dc.contributor.authorP. Boonkrongen_US
dc.contributor.otherMahidol Universityen_US
dc.date.accessioned2018-06-11T04:44:43Z
dc.date.available2018-06-11T04:44:43Z
dc.date.issued2012-12-01en_US
dc.description.abstractThis paper aims to present a local network model of Susceptible-Exposed-Infectious-Quarantined-Recovered disease transmission taking into account the community structure of the population. The population structure is generated by genetic algorithm based on the network modularity concept for the heterogeneous property. The basic reproductive number of the model is derived and used to predict the epidemiological situation. In numerical simulation, the disease transmissions within and across the communities are considered. The results show that this approach is able to capture the essential feature of epidemic spreading in human community.en_US
dc.identifier.citationInternational Journal of Mathematics and Computers in Simulation. Vol.6, No.5 (2012), 504-511en_US
dc.identifier.issn19980159en_US
dc.identifier.other2-s2.0-84875716479en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/14007
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84875716479&origin=inwarden_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleSEIQR disease transmission on GA-networken_US
dc.typeArticleen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84875716479&origin=inwarden_US

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