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: LogisticFromSEIR

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: