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

Detalhes bibliográficos
Autor(a) principal: Salami, Donald
Data de Publicação: 2020
Outros Autores: Sousa, Carla Alexandra, Martins, Maria do Rosário Oliveira, Capinha, César
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/116605
Resumo: The 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.
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spelling Predicting dengue importation into Europe, using machine learning and model-agnostic methodsArtificial IntelligenceInfectious DiseasesSDG 3 - Good Health and Well-beingThe 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.Instituto de Higiene e Medicina Tropical (IHMT)Global Health and Tropical Medicine (GHTM)Vector borne diseases and pathogens (VBD)Population health, policies and services (PPS)RUNSalami, DonaldSousa, Carla AlexandraMartins, Maria do Rosário OliveiraCapinha, César2021-05-01T22:51:03Z2020-06-162020-06-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttp://hdl.handle.net/10362/116605eng2045-2322PURE: 19603466https://doi.org/10.1038/s41598-020-66650-1info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:59:15Zoai:run.unl.pt:10362/116605Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:09.575047Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting dengue importation into Europe, using machine learning and model-agnostic methods
title Predicting dengue importation into Europe, using machine learning and model-agnostic methods
spellingShingle Predicting dengue importation into Europe, using machine learning and model-agnostic methods
Salami, Donald
Artificial Intelligence
Infectious Diseases
SDG 3 - Good Health and Well-being
title_short Predicting dengue importation into Europe, using machine learning and model-agnostic methods
title_full Predicting dengue importation into Europe, using machine learning and model-agnostic methods
title_fullStr Predicting dengue importation into Europe, using machine learning and model-agnostic methods
title_full_unstemmed Predicting dengue importation into Europe, using machine learning and model-agnostic methods
title_sort Predicting dengue importation into Europe, using machine learning and model-agnostic methods
author Salami, Donald
author_facet Salami, Donald
Sousa, Carla Alexandra
Martins, Maria do Rosário Oliveira
Capinha, César
author_role author
author2 Sousa, Carla Alexandra
Martins, Maria do Rosário Oliveira
Capinha, César
author2_role author
author
author
dc.contributor.none.fl_str_mv Instituto de Higiene e Medicina Tropical (IHMT)
Global Health and Tropical Medicine (GHTM)
Vector borne diseases and pathogens (VBD)
Population health, policies and services (PPS)
RUN
dc.contributor.author.fl_str_mv Salami, Donald
Sousa, Carla Alexandra
Martins, Maria do Rosário Oliveira
Capinha, César
dc.subject.por.fl_str_mv Artificial Intelligence
Infectious Diseases
SDG 3 - Good Health and Well-being
topic Artificial Intelligence
Infectious Diseases
SDG 3 - Good Health and Well-being
description The 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.
publishDate 2020
dc.date.none.fl_str_mv 2020-06-16
2020-06-16T00:00:00Z
2021-05-01T22:51:03Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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url http://hdl.handle.net/10362/116605
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2045-2322
PURE: 19603466
https://doi.org/10.1038/s41598-020-66650-1
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eu_rights_str_mv openAccess
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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