Avoiding use of time varying estimation for small datasets

[adamkucharski]

For datasets with relatively small number of cases/deaths (like Ebola 1976 example in README), the estimate_time_varying function can be unstable, as it's distributing expected timings based on a small number of discrete events, rather than estimating the trend over events occuring daily (like the COVID timeseries).

Therefore, we should limit usage to estimate_static for these smaller datasets, e.g. in the README, as the earlier iteration of this package did: https://github.com/adamkucharski/ebola-cfr/blob/main/scripts/main_script.R

If we want to show a figure, we could show how the static CFR calculation changes as more and more data are included. This was in the above script: CFR_figure.pdf and in earlier version of this package as a plot, but may have been deprecated to prevent confusion between using an expanding time window of data to fit a static CFR (i.e. this PDF), and fitting a time-varying CFR to a fixed time window of data (i.e. estimate_time_varying ). However, given it nicely illustrates the difference between the naive and time-adjusted statitic method, maybe we should include again in the README.

[pratikunterwegs]

Thanks - happy to re-add that function as before if the naming could be sorted.

[pratikunterwegs]

This issue now potentially tackled in the PR #49, happy to hear feedback!