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enqueue_pipeline.sh --> covid_pipeline.sge --> prepare_config.py --> inference.py --> summary.py

enqueue_pipeline.sh identifies the dataset and analysis period, launching 4 SGE jobs [P1, P1+2]\times[specimen_date, report_date]

Each SGE job runs the inference and summary scripts, using information contained in a config.yaml file.

The config.yaml file, together with results outputs, are stored in a results directory specified the config file
and over-ridden in the top-level enqueue_pipeline.sh script.

Also add a script to tune MH for event times

1. Formatting -- black style!
2. After profiling, implemented a better workaround for the
tfd.Categorical bug.  Replaced with an inherited Categorical2 class,
with overloaded log_prob function.
3. Fixed a  bug in EventTimeProposal where tfd.FiniteDiscrete was
being used on a non-strictly-increasing outcomes vector.
4. Increased number of meta-populations to 149 to mirror UK UTLA regions.

Added a "medium" simulation, which assumes 10 meta-populations of 1000 individuals each.

Implemented custom nametuple for tracing innerd of UncalibratedEventTimesUpdate

Got stochastic model fitting with new bounded convergence respecting adaptive proposal.  However, XLA appears now very slow.

1. chainbinom* file and methods renamed to discrete_markov* files and methods.

2. Implemented batched hazard rate calculation, adding functions to automatically broadcast rates to the Markov transition rate matrix.

3. Implemented test simulation and MCMC algorithm.

Updated ODE model with overall number of starting individuals parameter, and daily confirmed cases up to 27th March (PHE, whole of England).