Bayesian compartmental models for COVID-19
We are experimenting with different Bayesian compartmental models. The basic ingredients are:
- classical compartmental models from epidemiology
- prior distributions on parameters
- models for time-varying dynamics
- models for partial/noisy observations of confirmed cases and deaths
- Bayesian inference using numpyro
Dr. Reich directs the CDC Influenza Forecasting Center of Excellence at UMass.
Our code depends on recent development versions of numpyro and jax. If you don't have these packages and don't care about tracking their versions, our installation routine will pull and install them as unversioned packages:
git clone https://github.com/dsheldon/covid cd covid pip install -e .
If you need to manually install jax and numpyro, here are rough instructions. More details can be found at the project sites.
Install jaxlib (more info here)
pip install --upgrade jaxlib
Install jax (more into here)
git clone https://github.com/google/jax cd jax pip install -e .
Install numpyro (more details here)
git clone https://github.com/pyro-ppl/numpyro.git cd numpyro pip install -e .[dev]