Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease
Q1
- Autores:
-
Ledien, Julia
Cucunubá, Zulma M.
Parra-Henao, Gabriel
Rodríguez Mongui, Eliana
Dobson, Andrew P.
Adamo, Susana B.
Basáñez, María-Gloria
Nouvellet, Pierre
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2022
- Institución:
- Pontificia Universidad Javeriana
- Repositorio:
- Repositorio Universidad Javeriana
- Idioma:
- eng
- OAI Identifier:
- oai:repository.javeriana.edu.co:10554/63601
- Acceso en línea:
- https://journals.plos.org/plosntds/article/authors?id=10.1371/journal.pntd.0010594
http://hdl.handle.net/10554/63601
https://doi.org/10.1371/journal.pntd.0010594
- Palabra clave:
- Rights
- License
- Atribución-NoComercial 4.0 Internacional
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Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease |
| title |
Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease |
| spellingShingle |
Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease |
| title_short |
Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease |
| title_full |
Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease |
| title_fullStr |
Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease |
| title_full_unstemmed |
Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease |
| title_sort |
Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease |
| dc.creator.fl_str_mv |
Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez Mongui, Eliana Dobson, Andrew P. Adamo, Susana B. Basáñez, María-Gloria Nouvellet, Pierre |
| dc.contributor.author.none.fl_str_mv |
Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez Mongui, Eliana Dobson, Andrew P. Adamo, Susana B. Basáñez, María-Gloria Nouvellet, Pierre |
| dc.contributor.corporatename.spa.fl_str_mv |
Pontificia Universidad Javeriana. Facultad de Medicina. Departamento de Epidemiología Clínica y Bioestadística |
| dc.contributor.javerianateacher.none.fl_str_mv |
Cucunubá, Zulma M. |
| description |
Q1 |
| publishDate |
2022 |
| dc.date.created.none.fl_str_mv |
2022-07-19 |
| dc.date.accessioned.none.fl_str_mv |
2023-03-07T16:11:44Z |
| dc.date.available.none.fl_str_mv |
2023-03-07T16:11:44Z |
| dc.type.local.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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https://journals.plos.org/plosntds/article/authors?id=10.1371/journal.pntd.0010594 |
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1935-2727 / 1935-2735 (Electrónico) |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10554/63601 |
| dc.identifier.doi.spa.fl_str_mv |
https://doi.org/10.1371/journal.pntd.0010594 |
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instname:Pontificia Universidad Javeriana |
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reponame:Repositorio Institucional - Pontificia Universidad Javeriana |
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repourl:https://repository.javeriana.edu.co |
| url |
https://journals.plos.org/plosntds/article/authors?id=10.1371/journal.pntd.0010594 http://hdl.handle.net/10554/63601 https://doi.org/10.1371/journal.pntd.0010594 |
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1935-2727 / 1935-2735 (Electrónico) instname:Pontificia Universidad Javeriana reponame:Repositorio Institucional - Pontificia Universidad Javeriana repourl:https://repository.javeriana.edu.co |
| dc.language.iso.spa.fl_str_mv |
eng |
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eng |
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1 |
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19 |
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PLoS Neglected Tropical Diseases |
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16 |
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7 |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
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PDF |
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application/pdf |
| dc.coverage.spatial.spa.fl_str_mv |
Colombia |
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1998-2014 |
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Pontificia Universidad Javeriana |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/http://purl.org/coar/access_right/c_abf2Ledien, JuliaCucunubá, Zulma M.Parra-Henao, GabrielRodríguez Mongui, ElianaDobson, Andrew P.Adamo, Susana B.Basáñez, María-GloriaNouvellet, PierrePontificia Universidad Javeriana. Facultad de Medicina. Departamento de Epidemiología Clínica y BioestadísticaCucunubá, Zulma M.Colombia1998-20142023-03-07T16:11:44Z2023-03-07T16:11:44Z2022-07-19https://journals.plos.org/plosntds/article/authors?id=10.1371/journal.pntd.00105941935-2727 / 1935-2735 (Electrónico)http://hdl.handle.net/10554/63601https://doi.org/10.1371/journal.pntd.0010594instname:Pontificia Universidad Javerianareponame:Repositorio Institucional - Pontificia Universidad Javerianarepourl:https://repository.javeriana.edu.coPDFapplication/pdfengLinear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas diseaseArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Q1Q1Background: Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues. Methodology/principal findings: We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty. Conclusions/significance: The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.https://orcid.org/0000-0002-8165-3198Revista Internacional - IndexadaA1No119PLoS Neglected Tropical Diseases167ORIGINALLinear and Machine Learning modelling for spatiotemporal disease predictions Force-of-Infection of Chagas disease.pdfLinear and Machine Learning modelling for spatiotemporal disease predictions Force-of-Infection of Chagas disease.pdfArtículoapplication/pdf2289633http://repository.javeriana.edu.co/bitstream/10554/63601/1/Linear%20and%20Machine%20Learning%20modelling%20for%20spatiotemporal%20disease%20predictions%20Force-of-Infection%20of%20Chagas%20disease.pdf31bcc35c0984bc9107f3f0912ace6c1bMD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-82603http://repository.javeriana.edu.co/bitstream/10554/63601/2/license.txt2070d280cc89439d983d9eee1b17df53MD52open accessTHUMBNAILLinear and Machine Learning modelling for spatiotemporal disease predictions Force-of-Infection of Chagas disease.pdf.jpgLinear and Machine Learning modelling for spatiotemporal disease predictions Force-of-Infection of Chagas disease.pdf.jpgIM Thumbnailimage/jpeg13224http://repository.javeriana.edu.co/bitstream/10554/63601/3/Linear%20and%20Machine%20Learning%20modelling%20for%20spatiotemporal%20disease%20predictions%20Force-of-Infection%20of%20Chagas%20disease.pdf.jpgbd64b5ac24d36e68b6cb721261610cfeMD53open access10554/63601oai:repository.javeriana.edu.co:10554/636012023-03-08 03:04:58.673Repositorio Institucional - Pontificia Universidad Javerianarepositorio@javeriana.edu.coTElDRU5DSUEgWSBBVVRPUklaQUNJw5NOIERFIExPUyBBVVRPUkVTIFBBUkEgUFVCTElDQVIgWSBQRVJNSVRJUiBMQSBDT05TVUxUQSBZIFVTTy4KClBhcnRlIDEuIFTDqXJtaW5vcyBkZSBsYSBsaWNlbmNpYSBnZW5lcmFsIHBhcmEgcHVibGljYWNpw7NuIGRlIG9icmFzIGVuIGVsIHJlcG9zaXRvcmlvIGluc3RpdHVjaW9uYWwKQ29tbyB0aXR1bGFyIChlcykgZGVsIGRlcmVjaG8gZGUgYXV0b3IsIGNvbmZpZXJvIChlcmltb3MpIGEgbGEgUG9udGlmaWNpYSBVbml2ZXJzaWRhZCBKYXZlcmlhbmEgdW5hIGxpY2VuY2lhIG5vIGV4Y2x1c2l2YSwgbGltaXRhZGEgeSBncmF0dWl0YSBzb2JyZSBsYSBvYnJhIHF1ZSBzZSBpbnRlZ3JhcsOhIGVuIGVsIFJlcG9zaXRvcmlvIEluc3RpdHVjaW9uYWwsIHF1ZSBzZSBhanVzdGEgYSBsYXMgc2lndWllbnRlcyBjYXJhY3RlcsOtc3RpY2FzOgphKSAgICAgIEVzdGFyw6EgdmlnZW50ZSBhIHBhcnRpciBkZSBsYSBmZWNoYSBkZSBpbmNsdXNpw7NuIGVuIGVsIHJlcG9zaXRvcmlvLCBwb3IgdW4gcGxhem8gZGUgNSBhw7FvcywgcXVlIHNlcsOhbiBwcm9ycm9nYWJsZXMgaW5kZWZpbmlkYW1lbnRlIHBvciBlbCB0aWVtcG8gcXVlIGR1cmUgZWwgZGVyZWNobyBwYXRyaW1vbmlhbCBkZWwgYXV0b3IuIEVsIGF1dG9yIHBvZHLDoSBkYXIgcG9yIHRlcm1pbmFkYSBsYSBsaWNlbmNpYSBzb2xpY2l0w6FuZG9sbyBhIGxhIFVuaXZlcnNpZGFkIHBvciBlc2NyaXRvLgpiKSAgICAgIEF1dG9yaXphIGEgbGEgUG9udGlmaWNpYSBVbml2ZXJzaWRhZCBKYXZlcmlhbmEgYSBwdWJsaWNhciBsYSBvYnJhIGVuIGRpZ2l0YWwsIGNvbm9jaWVuZG8gcXVlLCBkYWRvIHF1ZSBzZSBwdWJsaWNhIGVuIEludGVybmV0LCBwb3IgZXN0ZSBoZWNobyBjaXJjdWxhIGNvbiB1biBhbGNhbmNlIG11bmRpYWwuCmMpICAgICAgTG9zIGF1dG9yZXMgYWNlcHRhbiBxdWUgbGEgYXV0b3JpemFjacOzbiBzZSBoYWNlIGEgdMOtdHVsbyBncmF0dWl0bywgcG9yIGxvIHRhbnRvIHJlbnVuY2lhbiBhIHJlY2liaXIgYmVuZWZpY2lvIGFsZ3VubyBwb3IgbGEgcHVibGljYWNpw7NuLCBkaXN0cmlidWNpw7NuLCBjb211bmljYWNpw7NuIHDDumJsaWNhIHkgY3VhbHF1aWVyIG90cm8gdXNvIHF1ZSBzZSBoYWdhIGVuIGxvcyB0w6lybWlub3MgZGUgbGEgcHJlc2VudGUgbGljZW5jaWEgeSBkZSBsYSBsaWNlbmNpYSBkZSB1c28gY29uIHF1ZSBzZSBwdWJsaWNhLgpkKSAgICAgIExvcyBhdXRvcmVzIG1hbmlmaWVzdGFuIHF1ZSBzZSB0cmF0YSBkZSB1bmEgb2JyYSBvcmlnaW5hbCBzb2JyZSBsYSBxdWUgdGllbmVuIGxvcyBkZXJlY2hvcyBxdWUgYXV0b3JpemFuIHkgcXVlIHNvbiBlbGxvcyBxdWllbmVzIGFzdW1lbiB0b3RhbCByZXNwb25zYWJpbGlkYWQgcG9yIGVsIGNvbnRlbmlkbyBkZSBzdSBvYnJhIGFudGUgbGEgUG9udGlmaWNpYSBVbml2ZXJzaWRhZCBKYXZlcmlhbmEgeSBhbnRlIHRlcmNlcm9zLiBFbiB0b2RvIGNhc28gbGEgUG9udGlmaWNpYSBVbml2ZXJzaWRhZCBKYXZlcmlhbmEgc2UgY29tcHJvbWV0ZSBhIGluZGljYXIgc2llbXByZSBsYSBhdXRvcsOtYSBpbmNsdXllbmRvIGVsIG5vbWJyZSBkZWwgYXV0b3IgeSBsYSBmZWNoYSBkZSBwdWJsaWNhY2nDs24uCmUpICAgICAgQXV0b3Jpem8gKGFtb3MpIGEgbGEgVW5pdmVyc2lkYWQgcGFyYSBpbmNsdWlyIGxhIG9icmEgZW4gbG9zIMOtbmRpY2VzIHkgYnVzY2Fkb3JlcyBxdWUgZXN0aW1lbiBuZWNlc2FyaW9zIHBhcmEgcHJvbW92ZXIgc3UgZGlmdXNpw7NuLgpmKSAgICAgIEFjZXB0byAoYW1vcykgcXVlIGxhIFBvbnRpZmljaWEgVW5pdmVyc2lkYWQgSmF2ZXJpYW5hIHB1ZWRhIGNvbnZlcnRpciBlbCBkb2N1bWVudG8gYSBjdWFscXVpZXIgbWVkaW8gbyBmb3JtYXRvIHBhcmEgcHJvcMOzc2l0b3MgZGUgcHJlc2VydmFjacOzbiBkaWdpdGFsLgpnKSAgICAgIEF1dG9yaXpvIChhbW9zKSBxdWUgbGEgb2JyYSBzZWEgcHVlc3RhIGEgZGlzcG9zaWNpw7NuIGRlbCBww7pibGljbyBlbiBsb3MgdMOpcm1pbm9zIGF1dG9yaXphZG9zIGVuIGxvcyBsaXRlcmFsZXMgYW50ZXJpb3JlcyBiYWpvIGxvcyBsw61taXRlcyBkZWZpbmlkb3MgcG9yIGxhIHVuaXZlcnNpZGFkIGVuIGxhcyDigJxDb25kaWNpb25lcyBkZSB1c28gZGUgZXN0cmljdG8gY3VtcGxpbWllbnRv4oCdIGRlIGxvcyByZWN1cnNvcyBwdWJsaWNhZG9zIGVuIFJlcG9zaXRvcmlvIEluc3RpdHVjaW9uYWwgUFVKICwgY3V5byB0ZXh0byBjb21wbGV0byBzZSBwdWVkZSBjb25zdWx0YXIgZW4gaHR0cDovL3JlcG9zaXRvcnkuamF2ZXJpYW5hLmVkdS5jby8KClNJIEVMIERPQ1VNRU5UTyBTRSBCQVNBIEVOIFVOIFRSQUJBSk8gUVVFIEhBIFNJRE8gUEFUUk9DSU5BRE8gTyBBUE9ZQURPIFBPUiBVTkEgQUdFTkNJQSBPIFVOQSBPUkdBTklaQUNJw5NOLCBDT04gRVhDRVBDScOTTiBERSBMQSBQT05USUZJQ0lBIFVOSVZFUlNJREFEIEpBVkVSSUFOQSwgRUwgKExPUykgQVVUT1IoRVMpIEdBUkFOVElaQShNT1MpIFFVRSBTRSBIQSBDVU1QTElETyBDT04gTE9TIERFUkVDSE9TIFkgT0JMSUdBQ0lPTkVTIFJFUVVFUklET1MgUE9SIEVMIFJFU1BFQ1RJVk8gQ09OVFJBVE8gTyBBQ1VFUkRPLgo= |