Publication:
Travel distance and human movement predict paths of emergence and spatial spread of chikungunya in Thailand

dc.contributor.authorS. Chadsuthien_US
dc.contributor.authorB. M. Althouseen_US
dc.contributor.authorS. Iamsirithawornen_US
dc.contributor.authorW. Triampoen_US
dc.contributor.authorK. H. Grantzen_US
dc.contributor.authorD. A.T. Cummingsen_US
dc.contributor.otherSouth Carolina Commission on Higher Educationen_US
dc.contributor.otherNaresuan Universityen_US
dc.contributor.otherThailand Ministry of Public Healthen_US
dc.contributor.otherUniversity of Washington, Seattleen_US
dc.contributor.otherUniversity of Floridaen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherJohns Hopkins Bloomberg School of Public Healthen_US
dc.contributor.otherNew Mexico State University Las Crucesen_US
dc.contributor.otherInstitute for Disease Modelingen_US
dc.contributor.otherCentre of Excellence in Mathematics CHEen_US
dc.date.accessioned2019-08-23T11:42:48Z
dc.date.available2019-08-23T11:42:48Z
dc.date.issued2018-10-01en_US
dc.description.abstract© Cambridge University Press 2018. Human movement contributes to the probability that pathogens will be introduced to new geographic locations. Here we investigate the impact of human movement on the spatial spread of Chikungunya virus (CHIKV) in Southern Thailand during a recent re-emergence. We hypothesised that human movement, population density, the presence of habitat conducive to vectors, rainfall and temperature affect the transmission of CHIKV and the spatiotemporal pattern of cases seen during the emergence. We fit metapopulation transmission models to CHIKV incidence data. The dates at which incidence in each of 151 districts in Southern Thailand exceeded specified thresholds were the target of model fits. We confronted multiple alternative models to determine which factors were most influential in the spatial spread. We considered multiple measures of spatial distance between districts and adjacency networks and also looked for evidence of long-distance translocation (LDT) events. The best fit model included driving-distance between districts, human movement, rubber plantation area and three LDT events. This work has important implications for predicting the spatial spread and targeting resources for control in future CHIKV emergences. Our modelling framework could also be adapted to other disease systems where population mobility may drive the spatial advance of outbreaks.en_US
dc.identifier.citationEpidemiology and Infection. Vol.146, No.13 (2018), 1654-1662en_US
dc.identifier.doi10.1017/S0950268818001917en_US
dc.identifier.issn14694409en_US
dc.identifier.issn09502688en_US
dc.identifier.other2-s2.0-85049569258en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/46307
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049569258&origin=inwarden_US
dc.subjectMedicineen_US
dc.titleTravel distance and human movement predict paths of emergence and spatial spread of chikungunya in Thailanden_US
dc.typeArticleen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049569258&origin=inwarden_US

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