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Robert Rimmer

[Notebook] Updated: coronavirus logistic growth model: China

Robert Rimmer
Posted 3 years ago
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MODERATOR NOTE: coronavirus resources & updates: https://wolfr.am/coronavirus

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POSTED BY: Robert Rimmer
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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:

POSTED BY: Mads Bahrami
Robert Rimmer
Robert Rimmer
Posted 3 years ago

POSTED BY: Robert Rimmer

Thanks for your input! Well-explained!
POSTED BY: Mads Bahrami

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POSTED BY: Moderation Team
Robert Rimmer
Robert Rimmer
Posted 3 years ago

I posted a notebook tying the SEIR model to the Logistic model: LogisticFromSEIR
POSTED BY: Robert Rimmer
Updating Name
Updating Name
Posted 3 years ago

Hi,

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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POSTED BY: Updating Name
Robert Rimmer
Robert Rimmer
Posted 3 years ago

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.
POSTED BY: Robert Rimmer
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