Resolving outbreak dynamics using approximate bayesian computation for stochastic birth–death models

dc.contributorAalto Universityen
dc.contributor.authorLintusaari, Jarnoen_US
dc.contributor.authorBlomstedt, Paulen_US
dc.contributor.authorRose, Brittanyen_US
dc.contributor.authorSivula, Tuomasen_US
dc.contributor.authorGutmann, Michael U.en_US
dc.contributor.authorKaski, Samuelen_US
dc.contributor.authorCorander, Jukkaen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProfessorship Kaski Samuelen
dc.contributor.groupauthorProbabilistic Machine Learningen
dc.contributor.groupauthorHelsinki Institute for Information Technology (HIIT)en
dc.contributor.groupauthorProfessorship Vehtari Akien
dc.contributor.groupauthorFinnish Center for Artificial Intelligence, FCAIen
dc.contributor.organizationUniversity of Helsinkien_US
dc.contributor.organizationUniversity of Edinburghen_US
dc.contributor.organizationUniversity of Osloen_US
dc.description| openaire: EC/H2020/742158/EU//SCARABEE
dc.description.abstractEarlier research has suggested that approximate Bayesian computation (ABC) makes it possible to fit simulator-based intractable birth–death models to investigate communicable disease outbreak dynamics with accuracy comparable to that of exact Bayesian methods. However, recent findings have indicated that key parameters, such as the reproductive number R, may remain poorly identifiable with these models. Here we show that this identifiability issue can be resolved by taking into account disease-specific characteristics of the transmission process in closer detail. Using tuberculosis (TB) in the San Francisco Bay area as a case study, we consider a model that generates genotype data from a mixture of three stochastic processes, each with its own distinct dynamics and clear epidemiological interpretation. We show that our model allows for accurate posterior inferences about outbreak dynamics from aggregated annual case data with genotype information. As a byproduct of the inference, the model provides an estimate of the infectious population size at the time the data were collected. The acquired estimate is approximately two orders of magnitude smaller than assumed in earlier related studies, and it is much better aligned with epidemiological knowledge about active TB prevalence. Similarly, the reproductive number R related to the primary underlying transmission process is estimated to be nearly three times larger than previous estimates, which has a substantial impact on the interpretation of the fitted outbreak model.en
dc.description.versionPeer revieweden
dc.identifier.citationLintusaari, J, Blomstedt, P, Rose, B, Sivula, T, Gutmann, M U, Kaski, S & Corander, J 2019, ' Resolving outbreak dynamics using approximate bayesian computation for stochastic birth–death models ', Wellcome Open Research, vol. 4, 14 .
dc.identifier.otherPURE UUID: 7179185c-ceaf-453f-a9e5-ba953f291fa1en_US
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dc.publisherWellcome Trust
dc.relation.ispartofseriesWellcome Open Researchen
dc.relation.ispartofseriesVolume 4en
dc.subject.keywordApproximate Bayesian computationen_US
dc.subject.keywordDeath processen_US
dc.subject.keywordOutbreak dynamicsen_US
dc.subject.keywordStochastic birthen_US
dc.titleResolving outbreak dynamics using approximate bayesian computation for stochastic birth–death modelsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi