PUNCHLunch: Inferring disease dynamics of COVID-19 (Jonas Dehning - Max Planck Institute for Dynamics and Self-Organization)


The COVID-19 pandemic has required to estimate quickly the speed of the spreading and the effectiveness of interventions. We will talk about how Bayesian methods helped to measure important parameters, introduce one of the most used algorithms, Hamiltonian Monte Carlo, and discuss the difficulty to aggregate and work with data from widely different sources. As an outlook, we will see which challenges have to be overcome to allow a fast estimation of the spreading pattern across age groups and regions for a potential future pandemic. 

ZOOM meeting:
Meeting ID: 919 1665 4877
Passcode: 481572

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The agenda of this meeting is empty