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Predicting dengue importation into Europe, using machine learning and model-agnostic methods

dc.contributor.authorSalami, Donald
dc.contributor.authorSousa, Carla Alexandra
dc.contributor.authorMartins, Maria do Rosário Oliveira
dc.contributor.authorCapinha, César
dc.date.accessioned2020-09-30T09:36:08Z
dc.date.available2020-09-30T09:36:08Z
dc.date.issued2020
dc.description.abstractThe geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model's predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country's dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSalami, D., Sousa, C. A., Martins, M. D. R. O., & Capinha, C. (2020). predicting dengue importation into europe, using machine learning and model-agnostic methods. Scientific reports, 10, 9689. https://doi.org/10.1038/s41598-020-66650-1pt_PT
dc.identifier.doi10.1038/s41598-020-66650-1pt_PT
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10451/44459
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationGHTM – UID/Multi/04413/2013pt_PT
dc.relationModeling of the Spatiotemporal Distribution Patterns and Transmission Dynamics of Dengue, For an Early Warning Surveillance System
dc.relationCentre of Geographical Studies
dc.relation.publisherversionhttps://www.nature.com/articles/s41598-020-66650-1#Ack1pt_PT
dc.subjectDenguept_PT
dc.subjectImportationpt_PT
dc.subjectMachine learningpt_PT
dc.subjectModel-agnostic methodspt_PT
dc.titlePredicting dengue importation into Europe, using machine learning and model-agnostic methodspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleModeling of the Spatiotemporal Distribution Patterns and Transmission Dynamics of Dengue, For an Early Warning Surveillance System
oaire.awardTitleCentre of Geographical Studies
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//PD%2FBD%2F128084%2F2016/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00295%2F2020/PT
oaire.citation.issue1pt_PT
oaire.citation.startPage9689pt_PT
oaire.citation.titleScientific Reportspt_PT
oaire.citation.volume10pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameCapinha
person.givenNameCésar
person.identifier.ciencia-id7714-2A88-CDE3
person.identifier.orcid0000-0002-0666-9755
person.identifier.ridK-6439-2017
person.identifier.scopus-author-id32867555000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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