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README.md

Pandemic Central


An application of machine learning in forecasting U.S. COVID-19 cases

We utilize socioeconomic, mobility, and epidemiological datasets to forecast COVID-19 cases at the US county/county-equivalent level. This repository contains the code we use to generate this information.

See our latest predictions at our website and check us out on Twitter.

Project Status: Active – The project has reached a stable, usable state and is being actively developed. License

Disclaimer

We hope to serve as a valuable resource for understanding trends in the ongoing pandemic and raise awareness about COVID-19 at the community level, something which is desperately needed in our attempts to lower the curve. However, we strongly advise against over-interpreting our predictions. Machine learning models are only as good as the data that trains them. We use the best quality data that is available to us, but we acknowledge that error in our predictions is unavoidable.

Getting Started

Follow these instructions to get the project up and running on your local machine.

Prerequisites

These are what you must install before using our project.

  1. NumPy, Pandas and Matplotlib

  2. Scikit-learn

Optional:

  1. TensorFlow (release ≥ 2.0.0) and TensorFlow_docs

  2. Plotly including: plotly, chart_studio, and Plotly Orca

  3. psutil

Your local machine must also have Python 3 (≥ 3.7) installed beforehand.

Run

To run this project, first clone this repository.

git clone https://github.com/solveforj/pandemic-central.git

For a basic usage, use this command

python covid.py -d

or

python covid.py --default

This command should download the data from sources, preprocess them, train, and export predictions.


For full list of available commands, use

python covid.py --help

GitHub

Make sure you always clone and pull the latest version from Pandemic Central. Our repository can always be found at https://github.com/solveforj/pandemic-central.

Authors

Support

Since this is still in its earliest versions, bugs and incompletions are unavoidable. Please feel free to comment or contact our developers! Your contributions are very valuable to us and this project.

For technical support, please email our developers: jgalasso@udallas.edu (Joseph) or dcao@udallas.edu (Duy). Thank you for your patience.

Versioning

Our latest version is v2.0.0. For version details, see Releases tags.

Credits

Our project can not be completed without these great sources. We do not own any data; all input data we use are open-source or permission-granted. More details about how we process this data may be found in generate_data.py and preprocess.py.

Here is a list of datasets we have used so far:

  1. Apple Maps Mobility Trends Reports

  2. Google Community Mobility Reports

  3. Facebook Movement Range

  4. Johns Hopkins CSSEGISandData

  5. COVID Tracking Project

  6. MIT Projections

  7. IHME Datasets

  8. Rt.live Reproduction Rate

  9. CCVI Index

  10. US Census Population Data

  11. USDA FIPS Code List

We also thank Plotly, TensorFlow and Python communities for very detailed and helpful documentations.

Please check out these resources for yourself!

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An application of machine learning to forecast COVID-19 cases at the US county/county-equivalent level.

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