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Getting Started

.. automodule:: covid19_inference

Installation

There exist three different possibilities to run the models:

  1. Clone the repository, with the latest release:
git clone --branch v0.1.8 https://github.com/Priesemann-Group/covid19_inference
  1. Install the module via pip
pip install git+https://github.com/Priesemann-Group/covid19_inference.git@v0.1.8

3. Run the notebooks directly in Google Colab. At the top of the notebooks files there should be a symbol which opens them directly in a Google Colab instance.

First Steps

To get started, we recommend to look at one of the currently two example notebooks:

  1. SIR model with one german state
    This model is similar to the one discussed in our paper: Inferring COVID-19 spreading rates and potential change points for case number forecasts. The difference is that the delay between infection and report is now lognormal distributed and not fixed.
  2. Hierarchical model of the German states
    This builds a hierarchical Bayesian model of the states of Germany. Caution, seems to be currently broken!

We can for example recommend the following articles about Bayesian modeling:

As a introduction to Bayesian statistics and the python package (PyMC3) that we use: https://docs.pymc.io/notebooks/api_quickstart.html

This is a good post about hierarchical Bayesian models in general: https://statmodeling.stat.columbia.edu/2014/01/21/everything-need-know-bayesian-statistics-learned-eight-schools/