OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany

@article{Radev2021OutbreakFlowMB,
  title={OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany},
  author={Stefan T. Radev and Frederik Graw and Simiao Chen and Nico Tom Mutters and Vanessa M Eichel and Till W B{\"a}rnighausen and U. K{\"o}the},
  journal={PLoS Computational Biology},
  year={2021},
  volume={17}
}
Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventional measures during an ongoing outbreak. However, reliably inferring the epidemical dynamics by connecting complex models to real data is still hard and requires either laborious manual parameter fitting or expensive optimization methods which have… Expand

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