Public shared code for doing scenario forecasting and creating reports for various governmental entities.

Please see the Wiki for this repository for updated instructions on how to clone the repository and push/pull changes.

If making changes to this repository, please do it directly instead of through the submodule of another repository.

After cloning the repository (see Wiki for instructions on how to clone a repository with submodules) create setup in a subfolder of the data using the notebook data/build-model-input.ipynb. This creates two files:

• mobility.txt : mobility matrix from us commute, as in An Economic Geography of the United States: From Commutes to Megaregions by Garrett Dash Nelson and Alasdair Rae processed by Ed King on Kaggle
• geodata.csv : specification of the spatial nodes, with at least column for the index, the geoid or name, the population.

Then copy the main_template.py from this repo into the root of the SPATIAL_SETUP_REPO folder. Changes the first line and you're ready to run it.

if you haven't installed python packages, run

pip3 install -r requirements.txt

You can now run the code:

nohup python3 main.py > out.txt &

creates a figure/ and a SCENARIO_model_output_TIMESTAMP folder.

A containerized environment is a packaged environment where all dependencies are bundled together. This means you're guaranteed to be using the same libraries and system configuration as everyone else and in any runtime environment. To learn more, Docker Curriculum is a good starting point.

A pre-built container can be pulled from Docker Hub via:

docker pull hopkinsidd/covidscenariopipeline:latest

To start the container:

docker run -v ~/mysrcdir:/home/app/src -it hopkinsidd/covidscenariopipeline:latest

Replace mysrcdir with where the code is mounted on your machine; it will be available in the /home/app/src directory inside the container.

You'll be dropped to the bash prompt where you can run the Python or R scripts (with dependencies already installed).

Run docker build . if you ever need to rebuild the container.

The pipeline now uses a configuration file to set simulation parameters. A template can be found in config.yml. The easiest way to specify this config file to jobs is to use the CONFIG_PATH environment variable:

$ CONFIG_PATH=/path/to/config.yml python COVIDScenarioPipeline/simulate.py -s Wuhan
    [...]
$ CONFIG_PATH=/path/to/config.yml Rscript hosp_run.R

The Python simulation supports profiling as a command-line option with the --profile flag. To write output to a specific file, use the --profile-output command line option. If you're profiling, it's a good idea to run single-threaded (-j 1) to capture the statistics that would be lost within the child processes.

Here's an example to run 10 simulations while profiling the simulation and outputting to ~/profile.output.

$ ./simulate.py -n 10 --profile --profile-output $HOME/profile.output -j 1