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. Size of posterior dataset is calculated automatically;
2. Require `num_adaptation_iterations` in config file;
3. Set progress bar to measure iterations.

CHANGES:

1. Kernel builder functions moved to `mcmc_kernel_factory.py`
2. Windowed adaptive MCMC a la STAN implemented in `inference.py`
3. Prior on beta1 tightened to improve stability.
4. Depends on `gemlib`@develop branch for tf-nightly and tfp-nightly

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.