A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus

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dc.contributor.author Moon, Sifat A.
dc.contributor.author Cohnstaedt, Lee W.
dc.contributor.author McVey, D. Scott
dc.contributor.author Scoglio, Caterina M.
dc.date.accessioned 2019-06-12T15:37:26Z
dc.date.available 2019-06-12T15:37:26Z
dc.date.issued 2019-03-13
dc.identifier.uri http://hdl.handle.net/2097/39793
dc.description Citation: Moon, S. A., Cohnstaedt, L. W., McVey, D. S., & Scoglio, C. M. (2019). A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus. PLOS Computational Biology, 15(3), e1006875. https://doi.org/10.1371/journal.pcbi.1006875
dc.description.abstract West Nile virus (WNV)—a mosquito-borne arbovirus—entered the USA through New York City in 1999 and spread to the contiguous USA within three years while transitioning from epidemic outbreaks to endemic transmission. The virus is transmitted by vector competent mosquitoes and maintained in the avian populations. WNV spatial distribution is mainly determined by the movement of residential and migratory avian populations. We developed an individual-level heterogeneous network framework across the USA with the goal of understanding the long-range spatial distribution of WNV. To this end, we proposed three distance dispersal kernels model: 1) exponential—short-range dispersal, 2) power-law—long-range dispersal in all directions, and 3) power-law biased by flyway direction —long-range dispersal only along established migratory routes. To select the appropriate dispersal kernel we used the human case data and adopted a model selection framework based on approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC). From estimated parameters, we find that the power-law biased by flyway direction kernel is the best kernel to fit WNV human case data, supporting the hypothesis of long-range WNV transmission is mainly along the migratory bird flyways. Through extensive simulation from 2014 to 2016, we proposed and tested hypothetical mitigation strategies and found that mosquito population reduction in the infected states and neighboring states is potentially cost-effective.
dc.relation.uri https://doi.org/10.1371/journal.pcbi.1006875
dc.rights CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
dc.rights.uri https://creativecommons.org/publicdomain/zero/1.0/
dc.title A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus
dc.type Text
dc.date.published 2019
dc.citation.doi 10.1371/journal.pcbi.1006875
dc.citation.issn 1553-7358
dc.citation.issue 3
dc.citation.jtitle PLOS Computational Biology
dc.citation.volume 15
dc.citation Moon, S. A., Cohnstaedt, L. W., McVey, D. S., & Scoglio, C. M. (2019). A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus. PLOS Computational Biology, 15(3), e1006875. https://doi.org/10.1371/journal.pcbi.1006875
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