A spatio-temporal individual-based network framework for West Nile virus in the USA: parameter estimation and spreading pattern selection using approximate Bayesian computation

dc.contributor.authorMoon, Sifat Afroj
dc.date.accessioned2018-11-06T15:47:31Z
dc.date.available2018-11-06T15:47:31Z
dc.date.graduationmonthDecember
dc.date.issued2018-12-01
dc.description.abstractWest 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.description.advisorCaterina M. Scoglio
dc.description.degreeMaster of Science
dc.description.departmentDepartment of Electrical and Computer Engineering
dc.description.levelMasters
dc.description.sponsorshipThe authors gratefully acknowledge the financial support provided by the National Science Foundation under Grant Award CIF-1423411, United States Department of Agriculture under the Grant No. 2015-67013-23818 (NIFA) and 3020-32000-008-04-S (ABADRU).
dc.identifier.urihttp://hdl.handle.net/2097/39240
dc.language.isoen_US
dc.publisherKansas State University
dc.rights© the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectNetwork
dc.subjectParameter estimation
dc.subjectmodel comparison
dc.subjectspreading
dc.titleA spatio-temporal individual-based network framework for West Nile virus in the USA: parameter estimation and spreading pattern selection using approximate Bayesian computation
dc.typeReport

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