Thanks for the comments @adrianlison!
Collaborating more closely with the survival analysis field sounds very sensible!
Yeah I am really keen on this idea more generally.
WP3 in terms of mixing generation times and incubation periods - is this more in the sense of directly modeling a convolution of two parametric distributions
Yeah I realise now I should really have been clearer about this. The mitey etc approach is a mixture of convolved delays and it is that which we would want to take advantage of for generation time estimation (i.e. data might have different delays from infection to report in the data). Good point!
Will you achieve this simply by supporting uncorrelated case counts (broad or improper prior for daily cases) or in a different way?
Yeah I agree this is interesting. So I thought this would be one option to play around with but I was also thinking about a different likelihood to be only used retrospectively that depends on the final counts (i.e multinomial).
I didn’t fully get what you mean by priors for the infection process here and how this is different from joint modeling.
Here I mean not having an infection process model i.e. like the work we have been doing in epidist. There we can inform the first events prior with a growth rate etc and I think this makes sense as a first pass generation time estimation model.
I guess guidance on how to decide what a good fit is and what to report will be really crucial.
Yes agree.
and the correlation between those parameters is often not reported
yes this is a good point. I would like to make some tooling around this for epidist to make it a bit easier.
But where do all these modelers come from
haha I think a lot of it is CI calls!