remove dependency to using Rstudio in weekly processing workflow

[ManuelaRunge]

• estimate Rt using epyestim in python instead of epiestim in R
• other plotting scripts are the iteration comparison plot and the recent seroprevalence plot, that are optional
• the fitting script could also be rewritten in python (lower priority)

[ManuelaRunge]

In this pull request:

Rfiles changed to python:
get_Rt_forCivisOutputs.R -> estimate_Rt_forCivisOutputs.py
get_Rt_from_LineListData.R -> estimate_Rt_from_LL.py
compare_simulation_iterations.R -> iteration_comparison.py

Rfiles not yet changed to python (lower priority)
seroprevalence_plot.R
fit_to_data_spatial.R
bar_plot_script.R
county_populations.R

Note on EpiEstim versus epyestim

• Epyestim seems to not translate 1:1 to Epiestim, it uses bootsrap instead of MCMC and the confidence intervals depend more on the number of cases as for EpiEstim.
• Epyestim further includes infection-to-delay distribution, while it was not clear whether this was included in EpiEstim.
• EpiEstim with a smoothing window of 13 resulted in smoother estimates than epyestim with the same window (in epyestim one can specify smoothing window for new cases, as well as rolling average window for rt estimates). To get closest Rt estimates over time, epyestim was run with a smoothing window of 18 days (see plot).
• Serial interval distributions are similar, but sampled in Epiestim and fixed in epyestim.

Nevertheless moving to epyestim seems beneficial as it removes dependencies to the R-software.

Both 14 day window (Epiestim 13)

13 versus 28 day window

Serial interval distributions
EpiEstim (custom specified mean at 4.6, based on Nishiura et al )

epyestim (default for COVID, mean at 4.3 based on Flaxman et al)