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Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data

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dc.contributor.authorPenedos, Ana Raquel
dc.contributor.authorFernandez-Garcia, Aurora
dc.contributor.authorLazar, Mihaela
dc.contributor.authorRalh, Kajal
dc.contributor.authorWilliams, David
dc.contributor.authorBrown, Kevin E
dc.contributor.funderUK Health Security Agency (Reino Unido)es_ES
dc.contributor.funderWorld Health Organization (WHO/OMS)es_ES
dc.date.accessioned2022-08-05T09:49:53Z
dc.date.available2022-08-05T09:49:53Z
dc.date.issued2022-05
dc.description.abstractBackground: Assessing relatedness of pathogen sequences in clinical samples is a core goal in molecular epidemiology. Tools for Bayesian analysis of phylogeny, such as the BEAST software package, have been typically used in the analysis of sequence/time data in public health. However, they are computationally-, time-, and knowledge-intensive, demanding resources that many laboratories do not have available or cannot allocate frequently. Methods: To evaluate a faster and simpler alternative method to support the routine interpretation of sequence data for epidemiology, we obtained sequences for two regions in the measles virus genome, N-450 and MF-NCR, from patient samples of genotypes B3, D4 and D8 taken between 2011 and 2017 in the UK and Romania. A mathematical model incorporating time, possible shared ancestry and the Poisson distribution describing the number of expected substitutions at a given time point was developed to exclude epidemiological relatedness between pairs of sequences. The model was validated against the commonly used Bayesian phylogenetic method using an independent dataset collected in 2017–19. Findings: We demonstrate that our model, using time and sequence information to predict whether two samples may be related within a given time frame, minimises the risk of erroneous exclusion of relatedness. An easy-to-use implementation in the form of a guide and spreadsheet is provided for convenient application. Interpretation: The proposed model only requires a previously calculated substitution rate for the locus and pathogen of interest. It allows for an informed but quick decision on the likelihood of relatedness between two samples within a time frame, without the need for phylogenetic reconstruction, thus facilitating rapid epidemiological interpretation of sequence data.es_ES
dc.description.peerreviewedSíes_ES
dc.description.sponsorshipThis work was funded by the United Kingdom Health Security Agency (UKHSA). The World Health Orga- nization European Regional Office funded Aurora Fernández-García and Mihaela Lazar training visits to UKHSAes_ES
dc.format.page103989es_ES
dc.format.volume79es_ES
dc.identifier.citationEBioMedicine. 2022 May;79:103989.es_ES
dc.identifier.doi10.1016/j.ebiom.2022.103989es_ES
dc.identifier.e-issn2352-3964es_ES
dc.identifier.journalEBioMedicinees_ES
dc.identifier.pubmedID35398788es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/14867
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.ebiom.2022.103989es_ES
dc.repisalud.centroISCIII::Centro Nacional de Microbiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMeasleses_ES
dc.subjectOutbreakes_ES
dc.subjectEliminationes_ES
dc.subjectEpidemiologyes_ES
dc.subjectMolecular epidemiologyes_ES
dc.subjectClinical virologyes_ES
dc.subject.meshMeasleses_ES
dc.subject.meshBayes Theoremes_ES
dc.subject.meshDisease Outbreakses_ES
dc.subject.meshGenotypees_ES
dc.subject.meshHumanses_ES
dc.subject.meshModels, Statisticales_ES
dc.subject.meshMolecular Epidemiologyes_ES
dc.subject.meshPhylogenyes_ES
dc.titleMind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology dataes_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationf9be60b8-8b65-45a9-a731-348ced4e1019
relation.isAuthorOfPublication.latestForDiscoveryf9be60b8-8b65-45a9-a731-348ced4e1019
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