Minutes for epinowcast meeting: 2022-11-24
Attendees: @FelixGuenther, @samabbott, @adrianlison
This turned out a richer meeting than expected! We discussed:
- Assessing how much nowcasts are informed by the observations vs. the expectation model
- Delayed entry of symptom onset information in line lists and implications for modeling missing symptom onsets
- Methods for estimation of reporting delays under right truncation and relationship with nowcasting
- Renewal models & growth rate models: their relationship and pros & cons
- An epinowcast instance for mastodon
Details for the individual topics are below. Our next meeting will take place December, 15, 1pm UK time (Sam will create an invitation with zoom link). 
During the meeting, we have made a couple of commitments for the next weeks, including implementation, mission statement/paper drafts, blog posts etc. So stay tuned!
How much are nowcasts informed by observations?
See here.
Handling delayed entry of symptom onset dates in line lists
See here.
Methods for estimation of reporting delays under right truncation
Basically see @samabbott post above. Together with @sangwoopark, they have been working on estimation of delay distributions from right-truncated and possibly censored data. The methods used are closely related to nowcasting, and there may be some hidden potential to avoid a joint model of the epidemic curve and the reporting delay in certain situations, and use much more efficient models instead. Currently, Sam is still in a, quote, “state of confusion” about this, so this hunch has to be developed further in the future.
There may also be something going on with insights about the individual-level relationship between forward and backward delays that we could use to improve our renewal models in the future (e.g. modeling changes in the generation time distribution).
Renewal models & growth rate models
We had a short but lively discussion on the pros and cons of renewal models vs. growth rate models. Main points:
- If you want to be negative, you can view (daily) growth rate models as renewal models with per default misspecified generation interval distribution (fixed, one day generation interval). For any given R trajectory, they require a more complex time series smoothing prior to account for this misspecification (which may also have distributional assumptions). In fact, if you use an AR process to model the growth rate, this becomes a renewal model in which the generation time distribution is also estimated.
- By being “misspecified per default” and trying to account for this through an appropriate time series prior, growth rate models can in principle be more robust compared to simple renewal models which assume a known and fixed generation interval distribution.
- It can be harder to identify very sharp changes in transmission dynamics from a growth rate model. Especially if you have some additional assumptions about transmission (e.g. further covariates, changepoints etc.) renewal models are better suited.
- Growth rate models are probably faster to fit, however there may be issues if you have outlier days with unusually high or low infections. Renewal models have some natural smoothing over such outliers, growth rate models may get stuck more easily. This may be relaxed in both cases with stochastic modeling of infections.
- Independent of the modeling, growth rates may be useful to compare between models using different generation time assumptions.
An epinowcast instance for mastodon
Sam is planning to host a mastodon instance and dedicate it to the epinowcast platform. This would likely be an invite-only space without moderation. If you are interested, reply to this thread or get in touch directly.