covid-models
pypi i covid-models

covid-models

Python package for simulating COVID-19 outbreaks

by pydemic

0.1.2 (see all)
pypi i covid-models
Readme

========

Covid-19

.. image:: https://gitlab.com/pydemic/covid-19/badges/master/pipeline.svg :target: https://github.com/pydemic/covid-19/commits/master

.. image:: https://gitlab.com/pydemic/covid-19/badges/master/coverage.svg :target: https://github.com/pydemic/covid-19/commits/master

This is a Python library that simulates COVID-19 outbreaks. The main focus is on Brazil, but it includes demographic data about other countries and can be adapted with relative ease. This library implements the RSEICHA model (yet to be published, we will link the preprint here). One version, covid.models.RSEICHADemografic considers demographic information and the other, covid.models.RSEICHA just uses generic compartments.

WARNING!

This library is deprecated and will be replaced by several projects in the Pydemic organization. If you want to use the library or contribute to its development, please contact the developers first.

Usage

You can run models from the command line::

$ python -m covid.models.seichar

Or, more typically, from Python code

from covid.models import SEICHAR m = SEICHAR(region='Italy') run = m.run() run.plot() print(run)

Calculator

To serve the app calculator, use::

$ inv run

Installation

Either clone this repository and install locally using flit install -s or use pip install covid-models. If you do not have flit, install it using either your distribution package manager or use pip install flit --user.

The model

SEICHAR is a compartmental model with 8 compartments: Recovered, Fatalities, Susceptible, Exposed, Infectious, Critical (require ICU care), Hospitalized (or requires hospitalization) and Asymptomatic.

It is governed by the following dynamics:

Basic theoretical results

If we ignore the "Exposed" compartment, it is easy to derive R0 for this model. We must, however, consider the number of equivalent infectious :math:I_e = I + \mu A, in which asymptomatic individuals contribute less to the overall number of infectious than symptomatic cases.

When :math:S \simeq N, this quantity experience an exponential growth and we can associate R0 with :math:R_0 = \frac{\beta}{\gamma}\left[1 - (1 - \mu) p_s\right]

Default parameters

+------------------+----------------------+------------------------------------+ | Parameter | Default value | Reference | +==================+======================+====================================+ | | | | +------------------+----------------------+------------------------------------+

Parameters and references

Epidemiological parameters

Clinical parameters

Required medical resources

Development

Testing

Simply perform::

$ inv test

Managing i18n

To update messages files::

$ inv makemessages

To compile messages files::

$ inv compilemessages

To update and compile messages files::

$ inv i18n

Using rit tunnel

After installing rit <https://gitlab.com/ritproject/cli#installation>_, config your tunnel repo:

Examples of usage:

  • If you use docker and docker-compose, you can:

    • Build the development image::

      $ rit tunnel run apps calculator development build

    • Fetch the development docker-compose::

      $ rit tunnel run apps calculator development fetch compose

    • Run the test pipeline::

      $ rit tunnel run apps calculator development test up $ rit tunnel run apps calculator development test sync $ rit tunnel run apps calculator development test all $ rit tunnel run apps calculator development test down

    • Build the production image::

      $ rit tunnel run apps calculator production build

    • Fetch the production docker-compose::

      $ rit tunnel run apps calculator production fetch compose

GitHub Stars

12

LAST COMMIT

2yrs ago

MAINTAINERS

1

CONTRIBUTORS

5

OPEN ISSUES

2

OPEN PRs

2
VersionTagPublished
0.1.2
2yrs ago
0.1.1
2yrs ago
0.1.0
2yrs ago
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