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dc.contributor.authorLi, Yuting Ien
dc.contributor.authorTurk, Güntheren
dc.contributor.authorRohrbach, Paulen
dc.contributor.authorPietzonka, Patricken
dc.contributor.authorKappler, Julianen
dc.contributor.authorSingh, Rajeshen
dc.contributor.authorDolezal, Jakuben
dc.contributor.authorEkeh, Timothyen
dc.contributor.authorKikuchi, Lukasen
dc.contributor.authorPeterson, Josephen
dc.contributor.authorBolitho, Austenen
dc.contributor.authorKobayashi, Hidekien
dc.contributor.authorCates, Michaelen
dc.contributor.authorAdhikari, Ronojoyen
dc.contributor.authorJack, Roberten
dc.descriptionpython notebooks and data accompanying "Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19", generated using the pyRoss package. See the README file for a full description of the dataset.en
dc.description.sponsorshipThis work was undertaken as a contribution to the Rapid Assistance in Modelling the Pandemic (RAMP) initiative, coordinated by the Royal Society. This work was funded in part by the European Research Council under the Horizon 2020 Programme, ERC grant 740269, and by the Royal Society grant RP17002. The authors are also grateful for financial support from the EPSRC doctoral training programme, the Leverhulme Trust, the Cambridge Trust and Jardine foundation.en
dc.formatpython (jupyter) notebooks ( see ) that use the pyRoss package, ( )en
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.subjectepidemiological inferenceen
dc.titleData for "Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19"en
datacite.contributor.supervisorJack, Robert
dcterms.formatmd, pynb, pik, npy, csv, pdf, pyen
dc.contributor.orcidRohrbach, Paul [0000-0001-6240-6872]
dc.contributor.orcidPietzonka, Patrick [0000-0003-1744-3724]
dc.contributor.orcidSingh, Rajesh [0000-0003-0266-9691]
dc.contributor.orcidJack, Robert [0000-0003-0086-4573]

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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International