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Right now I don't think the problem can be solved. Deaths are easy to count and because they were ill first most all were tested, but the number of infected people is determined by testing, which has varied widely by location and indication. Until there has been surveillance testing in a large community to know the incidence and prevalence of infection, I don't think you can estimate probabilities needed for Bayesian inference.
I posted a notebook tying the SEIR model to the Logistic model:
I really appreciate the job you did.
I use to use statistics in my professional activity, and I'm quite familiar with problems concerning the "bayesian calibration of computer code outputs". Since a few weeks I'm analysing the data about the CoVid19 outbreak and one of my main question is "how can we infer the value of the mortality rate" ?
This is not a trivial problem and I think that bayesian inference (I don't know if you are familiar with this topic?) could enable estimating the distribution of "credible" mortality rates.
Do you have some knowledge about works concerning this approach?
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Thanks for your input! Well-explained!
Thanks a lot Robert! I really enjoy reading it. I wonder why you have used interpolation to get dataDE in your function logisticDEFit? For example, look at this notebook where I compare my suggestion with your interpolation data: