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Very nice!
Our models are very similar, right down to some of the assumptions! I see that your fits suffer from the same problems as mine: a long exponential tail on the confirmed cases (aka, infected) and the recovered rises too slowly.
Bob
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Thank you for your comment, Bob!
The purpose of that notebook is to show the general workflow with a simple model.
There are multiple ways to make the SEI2R model more comprehensive. The directions I have in mind can be found in the repository Coronavirus propagation dynamics.
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My work is posted now: [Notebook] Epidemiological Models for Influenza and COVID-19
I’ll get a a look at your repo later today.
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One possible issue is from an oversimplification. In actuality the beta parameters are going to be functions of time and space. To some extent quarantining can account for this, at least the temporal aspect, if you can model it by some plausible kinetics.
Another issue is in the data itself. There are believed to be many mild cases. For these, recovery=infected (up to a time lag) and if both go unreported, this will have the effect of making recovery seem slower than it actually is.
That stated, both posts are really nice.
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Nice live-stream today!
Thanks, Dan!
Probably refined models can be made that have more population compartments and have time-dependent rates that reflect the medical observations and enacted policies.
Many thanks for the effort you put into this. I also much enjoy your website and learn a lot from it.
Regards,
Robert
The notebook here is also (without output) placed in GitHub: SystemModeling/Projects/Coronavirus-propagation-dynamics/WL-notebooks/Basic-experiments-workflow-for-simple-epidemiological-models.nb at master · antononcube/SystemModeling · GitHub .
Thank your kind words!
I put a fix into the notebook – I forgot to run it with the most recent package updates.