# EpidemicKabu a new method to identify epidemic waves and their peaks and valleys
***Kabu*** is a new methodology to identify waves, peaks, and valleys from epidemic curve. The algorithm in explain in **doi:..** as well as some examples.
## Description of files in this repository:
1. `epidemickabu/` contains the modules of the package:
* `kabu.py` is the main module. It makes the necessary calculations for the subsequent identification of waves, and peaks and valleys. The main input is a dataset with two variables (i.e., **cases**, and **dates**) and the **kernels** to smooth both the epidemic curve and its first derivative with a Gaussian filter.
* `kabuWaves.py` is a module to estimate the waves. You could set an optional **threshold** to filter the days delimiting the waves. There are some examples in **examples/** that give you and idea of the magnitude of this value. You can also filter the waves changing the **kernel**'s value.
* `kabuPeaksValleys.py` is a module to estimate the Peaks and Valleys of each identified wave.
2. `examples/` contains the files to replicate examples of how to use the library. The examples are made with COVID-19 data for 15 countries:
* `data/` is the input data used in all the research.
* `dataframes/` is created to save the output dataframes.
* `plots/` is created to save the output plots.
* `exampleUseLibrary.ipynb` shows basic examples to use the library.
* `exploringLibrary/.ipynb` explores attributes and methods from the classes in the library.
* The other files show the steps for some analysis made with the results obtained with the library for COVID-19 data.
3. `test/` contains the files to test the code.
4. `additional/` contains some notebooks showing the step by step of the algorithm.
## Installation in Python
**NOTE:** *This project was made in* ***Python 3.10.6***
1. Install the library using `pip`
```sh
pip install epidemickabu
```
2. Import the library
```sh
import epidemickabu as ek
```
## Installation in R
1. Install and load the package `reticulate` which provides an interface between R and Python
```sh
install.packages("reticulate")
```
and
```sh
library(reticulate)
```
2. Configure Python Environment
```sh
use_python("/path/to/python")
```
or
```sh
use_virtualenv("/path/to/your/virtualenv")
```
3. Install and import the package `EpidemicKabu`
```sh
py_install("epidemickabu")
```
and
```sh
ek <- import("EpidemicKabu")
```
## Contributing
This project is in progress and it requires some improvments. Therefore, if you have any suggestion that would make this better, please fork the repository and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks!
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/improvments`)
3. Commit your Changes (`git commit -m 'Adding some improvment`)
4. Push to the Branch (`git push origin feature/improvments`)
5. Open a Pull Request
## Contact
* [Lina M Ruiz G](https://co.linkedin.com/in/lina-marcela-ruiz-galvis-465896209) - lina.ruiz2@udea.edu.co
## Acknowledgments
* [Anderson Alexis Ruales Barbosa](https://co.linkedin.com/in/anderson-alexis-ruales-b27638199?original_referer=https%3A%2F%2Fwww.google.com%2F)
* [Oscar Ignacio Mendoza Cardozo](https://loop.frontiersin.org/people/2156647/overview)
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"description": "# EpidemicKabu a new method to identify epidemic waves and their peaks and valleys\n\n***Kabu*** is a new methodology to identify waves, peaks, and valleys from epidemic curve. The algorithm in explain in **doi:..** as well as some examples.\n\n## Description of files in this repository:\n\n1. `epidemickabu/` contains the modules of the package:\n\n * `kabu.py` is the main module. It makes the necessary calculations for the subsequent identification of waves, and peaks and valleys. The main input is a dataset with two variables (i.e., **cases**, and **dates**) and the **kernels** to smooth both the epidemic curve and its first derivative with a Gaussian filter.\n\n * `kabuWaves.py` is a module to estimate the waves. You could set an optional **threshold** to filter the days delimiting the waves. There are some examples in **examples/** that give you and idea of the magnitude of this value. You can also filter the waves changing the **kernel**'s value.\n\n * `kabuPeaksValleys.py` is a module to estimate the Peaks and Valleys of each identified wave.\n \n2. `examples/` contains the files to replicate examples of how to use the library. The examples are made with COVID-19 data for 15 countries:\n * `data/` is the input data used in all the research.\n * `dataframes/` is created to save the output dataframes.\n * `plots/` is created to save the output plots.\n * `exampleUseLibrary.ipynb` shows basic examples to use the library.\n * `exploringLibrary/.ipynb` explores attributes and methods from the classes in the library.\n * The other files show the steps for some analysis made with the results obtained with the library for COVID-19 data.\n\n3. `test/` contains the files to test the code.\n\n4. `additional/` contains some notebooks showing the step by step of the algorithm.\n\n## Installation in Python\n\n**NOTE:** *This project was made in* ***Python 3.10.6***\n\n1. Install the library using `pip`\n ```sh \n pip install epidemickabu\n ```\n2. Import the library\n ```sh \n import epidemickabu as ek\n ```\n\n## Installation in R\n\n\n1. Install and load the package `reticulate` which provides an interface between R and Python\n ```sh \n install.packages(\"reticulate\")\n ```\n and\n ```sh \n library(reticulate)\n ```\n2. Configure Python Environment\n ```sh \n use_python(\"/path/to/python\")\n ```\n or\n ```sh \n use_virtualenv(\"/path/to/your/virtualenv\")\n ```\n3. Install and import the package `EpidemicKabu`\n ```sh \n py_install(\"epidemickabu\")\n ```\n and\n ```sh \n ek <- import(\"EpidemicKabu\")\n ```\n\n## Contributing\n\nThis project is in progress and it requires some improvments. Therefore, if you have any suggestion that would make this better, please fork the repository and create a pull request. You can also simply open an issue with the tag \"enhancement\". Don't forget to give the project a star! Thanks!\n\n1. Fork the Project\n2. Create your Feature Branch (`git checkout -b feature/improvments`)\n3. Commit your Changes (`git commit -m 'Adding some improvment`)\n4. Push to the Branch (`git push origin feature/improvments`)\n5. Open a Pull Request\n\n## Contact\n\n* [Lina M Ruiz G](https://co.linkedin.com/in/lina-marcela-ruiz-galvis-465896209) - lina.ruiz2@udea.edu.co\n\n## Acknowledgments\n* [Anderson Alexis Ruales Barbosa](https://co.linkedin.com/in/anderson-alexis-ruales-b27638199?original_referer=https%3A%2F%2Fwww.google.com%2F)\n* [Oscar Ignacio Mendoza Cardozo](https://loop.frontiersin.org/people/2156647/overview)\n\n \n \n \n \n \n\n\n",
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