Implement a simple SEIR model #2446
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38 tasks
| for attempt in range(100): | ||
| samples = model.generate({"R0": args.basic_reproduction_number, | ||
| "rho": args.response_rate}) | ||
| obs = samples["obs"][:args.duration] | ||
| S2I = samples["S2I"] | ||
| new_I = samples.get("S2I", samples.get("E2I")) |
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what's going on here?
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In the SIR models, S2I is noisily observed. In SEIR models, E2I is noisily observed. This one-liner works for either model class.
| self.data = data | ||
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| series = ("S2E", "E2I", "I2R", "obs") | ||
| full_mass = [("R0", "rho")] |
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should this be passed through infer like num_quant_bins?
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Indeed it is already passed like num_quant_bins.
This class attribute merely sets the default value.
martinjankowiak
approved these changes
Apr 26, 2020
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Thanks for reviewing @martinjankowiak ! |
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Addresses #2426
This adds a simple SEIR model with
Binomialtransitions and likelihoods. I plan to follow this up with an overdispersed SEIR model withBetaBinomialinfections, but that will require deeper modifications from theSimpleSIRs model.@martinjankowiak you should be able to test mixing by adding the
-ecommand line arg e.g.Tested