epidemickabu 0.2.3

New methodology to identify waves, peaks, and valleys from epidemic curve

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

   pip install epidemickabu
   

2. Import the library

   import EpidemicKabu as ek
   

Installation in R

1. Install and load the package reticulate which provides an interface between R and Python

   install.packages("reticulate")
   

   and

   library(reticulate)
   

2. Configure Python Environment

   use_python("/path/to/python")
   

   or

   use_virtualenv("/path/to/your/virtualenv")
   

3. Install and import the package EpidemicKabu

   py_install("epidemickabu")
   

   and

   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 - [e-mail]

Acknowledgments

• Anderson Alexis Ruales Barbosa
• Oscar Ignacio Mendoza Cardozo