.. COVIDNPIs documentation master file, created by
   sphinx-quickstart on Wed Sep  9 10:48:13 2020.
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Welcome to COVIDNPIs' documentation!
=====================================

This is the documentation for the `COVIDNPIs project <https://github.com/epidemics/COVIDNPIs/tree/revisions>`_, Bayesian modelling the impact of non-pharmaceutical interventions (NPIs) on the rate of transmission of COVID-19 in 41 countries around the world. See the paper `The effectiveness of eight nonpharmaceutical interventions against COVID-19 in 41 countries <https://www.medrxiv.org/content/10.1101/2020.05.28.20116129v3>`_ by Brauner et. al. for details of the model.

COVIDNPIs provides a :ref:`data_preprocessor` for converting time-series case and death data along with NPI activation indicators to :ref:`PreprocessedData<preprocessed_data>` objects, ready to use for inference in any of several :ref:`NPI models<cm_model_zoo>`. In addition, the :ref:`model_parameters` module provides utilities for computing delay distributions, which can then be provided as initialisation parameters to the NPI models.

The :ref:`examples` walk through using the PreprocessedData object, initialising and running a model with custom delay parameters. Many pre-defined :ref:`experiments` are can also be run as scripts.

Installation
============

Install dependencies, then activate the virtual environment:

.. code-block::

    poetry install

.. code-block::

    poetry shell

Minimal Example
===============

.. seealso::
    `Default Model Example`_

.. _Default Model Example: examples/CM_Model_Examples.ipynb


The following steps are sufficient to run the default model with the dataset ``notebooks/double-entry-data/double_entry_final.csv`` and save the NPI reduction trace to ``CMReduction_trace.txt`` which can be loaded with :code:`numpy.loadtext`


.. code-block::

    from epimodel.preprocessing.data_preprocessor import preprocess_data
    from epimodel.pymc3_models.models import DefaultModel
    from epimodel.pymc3_models.epi_params import EpidemiologicalParameters, bootstrapped_negbinom_values
    import pymc3 as pm

    data = preprocess_data('../notebooks/double-entry-data/double_entry_final.csv')

    with DefaultModel(data) as model:
        model.build_model()

    with model.model:
        model.trace = pm.sample(2000, tune=1000, cores=4, chains=4, max_treedepth=12)

    numpy.savetext('CMReduction_trace.txt',model.trace['CMReduction'])



Table of Contents
=================

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   examples/examples
   module_documentation/module_documentation
   experiments/experiments
   reproduction/reproduction



Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`