Data for "Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19"
Li, Yuting I
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Li, Y. I., Turk, G., Rohrbach, P., Pietzonka, P., Kappler, J., Singh, R., Dolezal, J., et al. (2021). Data for "Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19" [Dataset]. https://doi.org/10.17863/CAM.72839
python 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.
python (jupyter) notebooks ( see https://jupyter.org/ ) that use the pyRoss package, ( https://github.com/rajeshrinet/pyross )
Publication Reference: https://doi.org/10.1098/rsos.211065https://www.repository.cam.ac.uk/handle/1810/326632
This 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.
This record's DOI: https://doi.org/10.17863/CAM.72839