Deploy county cases and hospitalizations forecasting model with features

A central goal of our COVID-19 forecasting efforts is to deploy a that provides tangible value to public policy officials. In order to do this we need the following steps completed

[ ] Add models that effectively generalize. Including coming out of lockdown, resuming lockdown, giant pool parties, bars opening, etc. Possible approaches to this include Neural ODE's, probabilistic models, auto encoders (i.e. Uber method) and other models.
[ ] Create automatic module to compare model performance to California county baseline models.
[ ] Have epidemiologist evaluate the model's learned features and give feedback.
[ ] Create continuing evaluation mode in flow-forecast repository
[ ] Create inference mode in flow-forecast repository
[ ] Create Docker containers for flow-forecast deploy.
[ ] Create Airflow DAG to run deployed Docker container and persist predictions.
[ ] Create Web based app for epis to view predictions, relevant features, and different scenarios. (This will likely be several separate issues when we get there)
[ ] Create descriptions to analyze past model performance.

CoronaWhy / task-ts

Deploy county cases and hospitalizations forecasting model with features #73