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dc.contributor.authorLee, Sophie A.
dc.contributor.authorEconomou, Theodoros
dc.contributor.authorLowe, Rachel
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2022-10-03T14:48:28Z
dc.date.available2022-10-03T14:48:28Z
dc.date.issued2022-09
dc.identifier.citationLee, S.A.; Economou, T.; Lowe, R. A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping. "Journal of the Royal Society. Interface", Setembre 2022, vol. 19, núm. 194, 20220440.
dc.identifier.issn1742-5662
dc.identifier.urihttp://hdl.handle.net/2117/373889
dc.description.abstractSpatial connectivity is an important consideration when modelling infectious disease data across a geographical region. Connectivity can arise for many reasons, including shared characteristics between regions and human or vector movement. Bayesian hierarchical models include structured random effects to account for spatial connectivity. However, conventional approaches require the spatial structure to be fully defined prior to model fitting. By applying penalized smoothing splines to coordinates, we create two-dimensional smooth surfaces describing the spatial structure of the data while making minimal assumptions about the structure. The result is a non-stationary surface which is setting specific. These surfaces can be incorporated into a hierarchical modelling framework and interpreted similarly to traditional random effects. Through simulation studies, we show that the splines can be applied to any symmetric continuous connectivity measure, including measures of human movement, and that the models can be extended to explore multiple sources of spatial structure in the data. Using Bayesian inference and simulation, the relative contribution of each spatial structure can be computed and used to generate hypotheses about the drivers of disease. These models were found to perform at least as well as existing modelling frameworks, while allowing for future extensions and multiple sources of spatial connectivity.
dc.description.sponsorshipS.A.L. was supported by a Royal Society Research Grant for Research Fellows. T.E. was funded by the European Union's Horizon 2020 research and innovation programme under Grant agreement no. 856612 and the Cyprus Government. R.L. was supported by a Royal Society Dorothy Hodgkin Fellowship.
dc.format.extent11 p.
dc.language.isoeng
dc.publisherThe Royal Society
dc.relation.urihttps://rs.figshare.com/collections/Supplementary_material_from_A_Bayesian_modelling_framework_to_quantify_multiple_sources_of_spatial_variation_for_disease_mapping_/6186135
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut::Impacte ambiental
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut::Medicina::Medicina comunitària i salut pública
dc.subject.lcshInfectious diseases
dc.subject.lcshModeling and simulation in science, engineering & technology
dc.subject.lcshBayesian analysis
dc.subject.lcshEpidemiology
dc.subject.otherHierarchical modelling
dc.subject.otherSpatial connectivity
dc.subject.otherInfectious disease dynamics
dc.subject.otherVector-borne disease
dc.subject.otherSpatial analysis
dc.subject.otherSpatial epidemiology
dc.titleA Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping
dc.typeArticle
dc.subject.lemacSimulació per ordinador
dc.identifier.doi10.1098/rsif.2022.0440
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://royalsocietypublishing.org/doi/10.1098/rsif.2022.0440
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
local.citation.other20220440
local.citation.publicationNameJournal of the Royal Society. Interface
local.citation.volume19
local.citation.number194
dc.relation.datasetAll data used in this study are open access and available freely on the internet; see the methods section for more details. Data and code used to produce this analysis is available from Zenodo (https://doi.org/10.5281/zenodo.7054457)
dc.identifier.pmid36128702


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