https://github.com/bendingspoons/epidemic-suppression-model

https://github.com/bendingspoons/epidemic-suppression-model

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• Host: GitHub
• URL: https://github.com/bendingspoons/epidemic-suppression-model
• Owner: BendingSpoons
• License: gpl-3.0
• Created: 2021-03-26T16:53:59.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2023-04-03T15:23:27.000Z (almost 2 years ago)
• Last Synced: 2024-04-24T08:56:43.827Z (8 months ago)
• Language: Python
• Size: 67.4 KB
• Stars: 4
• Watchers: 20
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Epidemics Suppression Model

[![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg)](https://www.python.org/downloads/release/python-370/)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

This repository implements a mathematical model describing the suppression of an epidemic due to measures that identify and

isolate infected individuals, possibly with the help of a mobile app.

The output of the algorithm is the time evolution

of the relative suppression of the effective reproduction number due to these measures.

The model is described in the paper Maiorana, A., Meneghelli, M. & Resnati, M. 

Effectiveness of isolation measures with app support to contain COVID-19 epidemics: a parametric approach. 

*J. Math. Biol.* **83,** 46 (2021). https://doi.org/10.1007/s00285-021-01660-9

([arXiv preprint](https://arxiv.org/abs/2011.02349)).

In section 4 of the paper we described the application of the model to the COVID-19 

epidemic, and we reported the results of some computations that were made running the 

code contained in the package `examples` of this repository.

We refer to the paper for details about the model, the interpretation of the parameters

appearing here, and the sources of the epidemiological features of COVID-19 that 

are used as an input of the model. Note that in this repository, like in the 

computations made in the paper, the "default" (i.e. without measures) effective 

reproduction number is taken constantly equal to 1, as we are interested in studying 

the relative suppression only.

## Set-up and usage

To use the algorithm you should clone the repository by running

```sh

git clone https://github.com/BendingSpoons/epidemic-suppression-model.git

cd epidemics-suppression-model

```

The library runs on Python 3.8. The dependencies of the library are provided in the

file `pyproject.toml`. To install them, you first need to

install [Poetry](https://python-poetry.org/docs/), and then run

```sh

poetry install

```

## Examples

The directory `examples` contains some functions that, when executed, run the algorithm

with certain specific choice of the input parameters, printing the results

and generating the plots appearing in the paper.

There are also some scripts running only small pieces of the algorithm to

illustrate how they work, or displaying the epidemic data used by the algorithm.

## How to cite this repository

Maiorana A and Meneghelli M 2021. Epidemics Suppression Model. 

https://github.com/BendingSpoons/epidemic-suppression-model.

```

@misc{mm2021epidemics,

    author = {Maiorana, Andrea and Meneghelli, Marco},

    title = {Epidemics Suppression Model},

    year={2021},

    publisher = {GitHub},

    journal = {GitHub repository},

    howpublished = "\url{https://github.com/BendingSpoons/epidemic-suppression-model}"

}

```