Covid-19

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.

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 calculator

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 I_e = I + μA, in which asymptomatic individuals contribute less to the overall number of infectious than symptomatic cases.

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

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

Using rit tunnel

After installing rit, config your tunnel repo:

Remotely:

$ rit config tunnel add repo https://github.com/altjohndev/fabiommendes-covid-19-tunnel --name opascovid
$ rit config tunnel default set opascovid --path .

Locally:

$ git clone https://github.com/altjohndev/fabiommendes-covid-19-tunnel ../fabiommendes-covid-19-tunnel
$ rit config tunnel add local ../fabiommendes-covid-19-tunnel --name opascovid
$ rit config tunnel default set opascovid --path .

Examples of usage:

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

Build the development image:

$ rit tunnel run calculator development build

Fetch the development docker-compose:

$ rit tunnel run calculator development fetch compose

Run the test pipeline:

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

Build the production image:

$ rit tunnel run calculator production build

Fetch the production docker-compose:

$ rit tunnel run calculator production fetch compose

caiosba/covid-19