CHANGES:

1. `thin_samples['init_state']` --> `thin_samples['initial_state']`
2. `thin_samples['events']` --> `thin_samples['seir']`
3. add time dimension to next generation matrix
4. update downstream scripts to changes.

CHANGES

1. Most tasks touched to use netCDF4 inferencedata file.
2. Added netCDF4 metadata copy to covid.util
3. Updated model_spec.py to use xarray
4. Updated covid.ruffus_pipeline to use netCDF4 files, adding pipeline metadata
5. Updated pyproject.toml
6. Removed chunk specification in inference.py.  Defer to automatic chunking for now.

CHANGES:

1. Replaced `read_phe_cases` with `CasesData`
2. Column-name fix in `case_exceedance`
3. Fix column heading in `summarize.infec_incidence`
4. Add Rt_exceed to `summarize.rt`
5. Add layer option to `summary_geopackage`
6. Update `ruffus_pipeline.py` to reflect above changes
7. Update `template_config.yaml` to reflect above changes

Note: in the actual analysis, the effect of December Tiers was
removed.

CHANGES:

1. Re-introduced Tier lockdown covariates.  National lockdown covariate is set to 0 because
   we cannot identify this from the time-varying baseline force of infection.

2. Added inference on variance parameter for the temporal Gaussian process on beta.

CHANGES:

1. Implemented log-linear model for weekday effect in I->R hazard;
2. Updated summary.py and within_between.py to match;
3. Implemented linear random walk for new gamma0 parameter.
4. Implemented per-parameter data structure in output HDF5 file.

CHANGES:

1. Re-implemented trace_fn
2. Implemented new Posterior class in `gemlib`

MCMC update kernels in `interface.py` changed to reflect new
AdaptiveRandomWalkMetropolis interface.

==============================

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.

CHANGES:

1. Added `data/example_cases.py`, `data/uk_clip.py`, `covid/summary.py`, `summary.py`
2. `summary.py` computes summary values required by reports to SAGE
3. Small changes to `inference.py` and `simulate.py` to reflect baked-in `nu` parameter in `model_spec.py`.

Removed redundant code from `inference.py` (duplicated in `model_spec.py`). Small change to `xi_freq` parameter in `model_spec.py`.

Changes:

1. Created a TFP JointDistribution to represent full probability model;
2. Renamed CovidUKStochastic --> DiscreteTimeStateTransitionModel;
3. DiscreteTimeStateTransitionModel now inherits from tfp.Distribution.