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Adding forecasting functionality #14
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Thanks for flagging that functionality. Agree doesn't make sense to duplicate, but a couple of thoughts/questions:
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Re inconsistency possibly - I guess ideally this would all be done in the same generative model that jointly estimated the delays and make a forecast, rather than first making an estimate and then feeding it into a forward model. The
Ah, that would definitely be different. I think you'd still need a model for expected case incidence though and how it behaves in the future unless you'd only be interested in outcomes of already observed cases. Note that functionality to estimate delays from individual-level data have recently also been implemented e.g. in EpiLine and dynamicaltruncation (though both stan-based). |
Thanks, very useful. Have had a go at implementing It also made me aware there are two slightly different problems being tackled here. One is joint estimation of delay function and truncated infection dynamics (i.e. above packages) and another is estimation of CFR, adjusting for truncation (i.e. focus of datadelay). For small, noisy datasets (e.g. Ebola) the full joint distribution is often not identifiable. However, a generative model like your In the case where delay from onset-to-outcome is independent of outcome (i.e. delay to death and recovery the same, so quite a strong assumption), then individual-level data conditioned on known outcome wouldn't need any further adjustment, because just need to calculate the CFR directly as a ratio (as the generative process for both deaths and non-deaths is the same). On wider note, I noticed I spent a fair bit of time getting the right data structures, dependency versions, valid inputs etc. set up to compare these different outbreak models, so this would be a natural area for contributions if this functionality is a priority for some of the pipelines we're working on. |
Just checking to see whether this issue is also still relevant? Thoughts or suggestions for implementation? |
We've been exploring an EpiNow2 comparison in the case studies @CarmenTamayo has been working on (with static estimation). But had some stability issues recently - would be good to revisit @sbfnk to make sure the estimation function is still appropriate. We'll share the Rmd when Carmen is back. |
Related to epiverse-trace/epiparameter#250. Closing this issue as this will be covered in {howto} using {EpiNow2}. |
Once we have an estimate of CFR, which based on current estimation methods will typically run up to the most recent death, it would be possible to generate a forecast forward in time based on the estimated CFR, time from onset-to-outcome and recent case numbers.
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