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Epidemics Modelling

Table of Contents

About The Project

Interactive Epidemics Modelling dashboard created using Streamlit and deployed on an Amazon Web Services (AWS) EC2 Linux Instance. In this dashboard, has been used COVID-19 as example case study (in order to provide a practical demonstration) but by changing the modelling parameters (eg. SIR model), parts of this dashboard toolkit can be used also to model many other types of epidemics in general.

This dashboard is currently available live at this link (the dashboard might not be anymore publicly available online after May 2020).

Getting Started

This dashboard was created using Python 3.6. To run the code locally, start by installing PyTorch as per the docs (PyTorch installation is just required if you want to run all the Jupyter Notebooks, not the web application). Then run in terminal the commands below:

# Clone the repository.
git clone https://github.com/pierpaolo28/Epidemics-Modelling.git
cd Epidemics-Modelling

# (Windows Optional) Create a new Python environment and activate it.
python -m venv .env
.env\Scripts\activate

# (Ubuntu Optional) Create a new Python environment and activate it.
python3 -m venv .env
source .env/bin/activate

# Install the dependencies.
pip install -r requirements.txt

# Run dashboard in localhost (a web-broswer page is automatically going to be opened)
streamlit run src/main.py

Docker Support

In case you want to use this application in a Docker container, just follow the next few steps:

# Clone the repository.
git clone https://github.com/pierpaolo28/Epidemics-Modelling.git
cd Epidemics-Modelling

# Build the container
docker build -f Dockerfile -t app:latest .

# Run the container
docker run --name Epid_App -p 8501:8501 app:latest

At this point, the web application should be up and running at the following URL in your browser: http://localhost:8501/.

Contributing

In case you want to contribute to this open source project, please use the following instructions:

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the MIT License.

Contact

Your Name - @Pier_Paolo_28 - LinkedIn

Project Link: https://github.com/pierpaolo28/Epidemics-Modelling

Acknowledgments

[1] Pearl, Judea, and Dana Mackenzie. The book of why: the new science of cause and effect. Basic Books, 2018.

[2] Olsen, Jørn, et al. An introduction to epidemiology for health professionals. 2010.

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