More consideration on IHR priors?
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Description
Given that this is a big model to compute (as in a lot of compartments), and we want to do Bayesian analysis, then its probably a good idea to make strong priors where possible.
I think the priors for infection-hospitalisation-ratio (IHR) are too vague and should probably reflect known age structure in IHR?
The prior range here looks like prior mean 4.8% IHR with 2.5-97.5% range: 0.005% - 22.7% with no age variation.
For me thats too vague and doesn't help the sampler (or indeed the reasoning); I think we can tighten those priors and add age structure.
NB: Pre-apologies if I'm not getting something here!
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Research direction
Start by reviewing mechanistic_model/mechanistic_inferer.py around lines 130–131 and the surrounding Bayesian inference setup. Determine how the current IHR prior is represented and what age-structured information is available in the model. Done means proposing and implementing tighter, age-varying IHR priors with evidence that the resulting assumptions better support sampling and interpretation.
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Assessment
- Tech stack
- python
- Domain
- backend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100