Awesome

Pandemic

An agent model in which commuting, compliance, testing and contagion parameters drive infection in a population of thousands of millions. Agents follow Ornstein-Uhlenbeck processes in the plane and collisions drive transmission. Results are stored at <a href="https://www.swarmprediction.com/about.html">SwarmPrediction.com</a> for further analysis, and can be retrieved by anyone.

Motivaton

Covered in this post with the followup here where possible improvements are also discussed and acknowledgements are made. See also the SwarmPrediction.Com list of articles. The author is not an epidemiologist. The model expresses no opinions on the health aspects of COVID-19. The model offers a novel motion model with some interesting analytic properties also discussed in the article referenced above and presented in a more technical working paper shared on Overleaf.

Basic Usage

pip install pandemic
>> from pandemic import run
>> run()

See also <a href="https://github.com/microprediction/pandemic/tree/master/examples">examples</a> of library use and <a href="https://github.com/microprediction/pandemic/tree/master/examples_of_surrogate_use">examples of using the public database of simulations</a> generated by this model.

Other entry points.

• pandemic.simulation.simulate is the main routine
• pandemic.client offers an alternative in object oriented style

Modifying

See pandemic/client.py for examples of extending the newer style to include data storage, lockdown and so forth.

Docker

Pandemic can be run in a docker container.

docker run xtellurian/pandemic

Crowd-sourced surrogate model

See <a href="https://www.swarmprediction.com/about.html">SwarmPrediction.com</a> for an explanation of a SETI-like project to crowd-source a surrogate model.

Basic elements of the model

Covered in detail in this article. As noted some analytical properties of special cases of the model are discussed in a working paper.

Contributing

Opinions and issues are most welcome.