Welcome to the Johns Hopkins University Infectious Disease Dynamics COVID-19 Working Group's COVID Scenario Pipeline, a flexible modeling framework that projects epidemic trajectories and healthcare impacts under different suites of interventions in order to aid in scenario planning. The model is generic enough to be applied to different spatial scales given shapefiles, population data, and COVID-19 confirmed case data. There are multiple components to the pipeline, which may be characterized as follows: 1) epidemic seeding; 2) disease transmission and non-pharmaceutical intervention scenarios; 3) calculation of health outcomes (hospital and ICU admissions and bed use, ventilator use, and deaths); and 4) summarization of model outputs.

We recommend that most new users use the code from the stable master branch. Please post questions to GitHub issues with the question tag. We are prioritizing direct support for individuals engaged in public health planning and emergency response.

For more information on getting started, please visit our wiki at HopkinsIDD/COVID19_Minimal. We are trying to keep this page up-to-date for use with the master branch.

For more details on the methods and features of our model, visit our preprint on medRxiv.

This open-source project is licensed under GPL v3.0.

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

replacing mysrcdir with the path where the code is mounted on your machine. This code will then 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 Python SEIR 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