Publication: Travel distance and human movement predict paths of emergence and spatial spread of chikungunya in Thailand
Issued Date
2018-10-01
Resource Type
ISSN
14694409
09502688
09502688
Other identifier(s)
2-s2.0-85049569258
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Mahidol University
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SCOPUS
Bibliographic Citation
Epidemiology and Infection. Vol.146, No.13 (2018), 1654-1662
Suggested Citation
S. Chadsuthi, B. M. Althouse, S. Iamsirithaworn, W. Triampo, K. H. Grantz, D. A.T. Cummings Travel distance and human movement predict paths of emergence and spatial spread of chikungunya in Thailand. Epidemiology and Infection. Vol.146, No.13 (2018), 1654-1662. doi:10.1017/S0950268818001917 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/46307
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Title
Travel distance and human movement predict paths of emergence and spatial spread of chikungunya in Thailand
Other Contributor(s)
South Carolina Commission on Higher Education
Naresuan University
Thailand Ministry of Public Health
University of Washington, Seattle
University of Florida
Mahidol University
Johns Hopkins Bloomberg School of Public Health
New Mexico State University Las Cruces
Institute for Disease Modeling
Centre of Excellence in Mathematics CHE
Naresuan University
Thailand Ministry of Public Health
University of Washington, Seattle
University of Florida
Mahidol University
Johns Hopkins Bloomberg School of Public Health
New Mexico State University Las Cruces
Institute for Disease Modeling
Centre of Excellence in Mathematics CHE
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.