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Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease

dc.rights.licenceAtribución-NoComercial 4.0 Internacional*
dc.contributor.authorLedien, Julia
dc.contributor.authorCucunubá, Zulma M.
dc.contributor.authorParra-Henao, Gabriel
dc.contributor.authorRodríguez Mongui, Eliana
dc.contributor.authorDobson, Andrew P.
dc.contributor.authorAdamo, Susana B.
dc.contributor.authorBasáñez, María-Gloria
dc.contributor.authorNouvellet, Pierre
dc.coverage.spatialColombiaspa
dc.coverage.temporal1998-2014
dc.date.accessioned2023-03-07T16:11:44Z
dc.date.available2023-03-07T16:11:44Z
dc.date.created2022-07-19
dc.identifierhttps://journals.plos.org/plosntds/article/authors?id=10.1371/journal.pntd.0010594spa
dc.identifier.issn1935-2727 / 1935-2735 (Electrónico)spa
dc.identifier.urihttp://hdl.handle.net/10554/63601
dc.formatPDFspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleLinear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas diseasespa
dc.type.hasversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.description.quartilewosQ1spa
dc.description.quartilescopusQ1spa
dc.identifier.doihttps://doi.org/10.1371/journal.pntd.0010594spa
dc.description.abstractenglishBackground: 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.spa
dc.type.localArtículo de revistaspa
dc.contributor.corporatenamePontificia Universidad Javeriana. Facultad de Medicina. Departamento de Epidemiología Clínica y Bioestadísticaspa
dc.identifier.instnameinstname:Pontificia Universidad Javerianaspa
dc.identifier.reponamereponame:Repositorio Institucional - Pontificia Universidad Javerianaspa
dc.identifier.repourlrepourl:https://repository.javeriana.edu.cospa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.description.orcidhttps://orcid.org/0000-0002-8165-3198spa
dc.relation.citationstartpage1spa
dc.relation.citationendpage19spa
dc.relation.ispartofjournalPLoS Neglected Tropical Diseasesspa
dc.contributor.javerianateacherCucunubá, Zulma M.
dc.description.indexingRevista Internacional - Indexadaspa
dc.relation.citationvolume16spa
dc.relation.citationissue7spa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.description.publindexA1spa
dc.description.esciNospa


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