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dc.contributor.authorFairbanks, Emma
dc.contributor.otherDaly, Janet
dc.contributor.otherBaylis, Matthew
dc.contributor.otherTildesley, Michael
dc.date.accessioned2022-05-10T07:47:43Z
dc.date.available2022-05-10T07:47:43Z
dc.date.issued2022-05-10
dc.identifier.urihttps://rdmc.nottingham.ac.uk/handle/internal/9511
dc.description.abstractAn example randomly generated region-level spatial distribution and code associated with Emma L. Fairbanks, Matthew Baylis, Janet M. Daly, Michael J. Tildesley. Inference for a spatio-temporal model with partial spatial data: African horse sickness virus in Morocco, Epidemics, 2022, 100566, ISSN 1755-4365, https://doi.org/10.1016/j.epidem.2022.100566. (https://www.sciencedirect.com/science/article/pii/S1755436522000202) Abstract: African horse sickness virus (AHSV) is a vector-borne virus spread by midges (Culicoides spp.). The virus causes African horse sickness (AHS) disease in some species of equid. AHS is endemic in parts of Africa, previously emerged in Europe and in 2020 caused outbreaks for the first time in parts of Eastern Asia. Here we analyse a unique historic dataset from the 1989-1991 emergence of AHS in Morocco in a naïve population of equids. Sequential Monte Carlo and Markov chain Monte Carlo techniques are used to estimate parameters for a spatial–temporal model using a transmission kernel. These parameters allow us to observe how the transmissiblity of AHSV changes according to the distance between premises. We observe how the spatial specificity of the dataset giving the locations of premises on which any infected equids were reported affects parameter estimates. Estimations of transmissiblity were similar at the scales of village (location to the nearest 1.3 km) and region (median area 99 km2), but not province (median area 3000 km2). This data-driven result could help inform decisions by policy makers on collecting data during future equine disease outbreaks, as well as policies for AHS control. Keywords: Vector-borne disease; Spatio-temporal model; Bayesian inferenceen_UK
dc.language.isoenen_UK
dc.publisherThe University of Nottinghamen_UK
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1755436522000202en_UK
dc.rightsCC-BY*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subject.lcshAfrican horse sickness virusen_UK
dc.subject.lcshVirus diseases -- Transmissionen_UK
dc.subject.lcshBayesian statistical decision theoryen_UK
dc.titleInference for a spatio-temporal model with partial spatial data: African horse sickness virus in Morocco exampleen_UK
dc.typedataset
dc.identifier.doihttp://doi.org/10.17639/nott.7193
dc.subject.freeSpatial disitribution; Vector-borne disease; Spatio-temporal model; Bayesian inferenceen_UK
dc.subject.jacsVeterinary Sciences, Agriculture & related subjects::Animal science::Animal health::Animal pathologyen_UK
dc.subject.jacsVeterinary Sciences, Agriculture & related subjects::Animal science::Veterinary public healthen_UK
dc.subject.lcS Agriculture::SF Animal cultureen_UK
uon.divisionUniversity of Nottingham, UK Campusen_UK
uon.funder.controlledBiotechnology & biological Sciences Research Councilen_UK
uon.datatypeMathematical model code, Excel fileen_UK
uon.collectionmethodCreated in silico using MATLABen_UK
uon.preservation.rarelyaccessedtrue
dc.relation.doi10.1016/j.epidem.2022.100566en_UK


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