This is the underlying SEIR model without the machine learning layer to learn the parameters. We do include "best" parameter sets learned by the full model. This simulator does not use any published data. As a result, this tool is meant to generate simulations, not projections.
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Because this simulator has very little dependencies (only NumPy), it is fast to run, works right out of the box, and is easily modifiable. I've purposefully designed the simulator to be as lean and simple as possible (since all of the computation is run on my laptop).
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Our full model simply runs thousands of parameter sets through this simulator. It then compares the resulting simulations with the observed data, and combines the most accurate simulations to generate a single projection.
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You can use this simulator to create interesting scenarios like: - How much can we reduce infections/deaths if people started social distancing x days earlier - What if y% of individuals self-quarantine after symptom onset - What if reopening was pushed back by z days
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Thank you to all for being patient - it took a while to find the time to put this together. I am committed to open and transparent research. I'll try my best to answer any questions. Apologies in advance if I don't get to everyone.
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I also encourage everyone to also read our Model Details and Assumptions pages to get a better high-level idea before diving into the source code: http://covid19-projections.com/model-details/ http://covid19-projections.com/about/#assumptions …
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Very nice. Is there a parameter in there for "DEGREE_OF_DENIAL"? Maybe slip it in after ["DAYS_UNTIL_LOCKDOWN_FATIGUE", 28]?
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Actually, we stopped using lockdown fatigue for US projections because the data does not support this. Reopening is the main factor that drives an increase in R.
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This is super cool, too bad i have no idea what a SEIR is
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