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Nevua

Overview

Nevua is an advanced platform designed for forecasting and monitoring disease outbreaks, with a specific focus on COVID-19. It leverages high-quality, third-party data to provide insights and predict potential hotspots by analyzing growth trends rather than total case counts. This project is a continuation of earlier efforts to track the pandemic, updated to meet the standards required for academic publication.

Features

  • Interactive Dashboard: Visualizes growth trends and identifies hotspots in real-time, helping public health officials and researchers prioritize response efforts.
  • Data-Driven Insights: Utilizes data from the New York Times' COVID-19 reports.
  • Research-Ready: Aims to provide a robust tool for epidemiological research and public health strategy development.

Data Sources

  • COVID-19 Data: Directly integrated with the New York Times COVID-19 dataset for U.S. counties and states.

Installation

Nevua can be easily installed using Python's pip package manager. This package includes all necessary dependencies to run the application.

pip install nevua

Usage

To start the Nevua application, use the following command:

nevua up

For production environments, it is recommended to run Nevua on a WSGI server. The application's WSGI entry is configured under the module nevua.app:SERVER, and uWSGI is suggested for robust performance.

Example Command for WSGI Server:

uwsgi --http :8080 --wsgi-file nevua/app.py

Development

To contribute to Nevua or customize it for specific needs, you can clone the repository and set up a development environment. This is ideal for developers looking to add features or integrate different datasets.

Setup Development Environment:

git clone https://github.com/jbenjoseph/nevua.git
cd nevua
poetry install

License and Credits

Nevua is Apache licensed and was initially forked from the corona-dashboard project developed by B.Next. It retains the same primary author, JJ Ben-Joseph.

Additional Information

  • Predictive Model: Utilizes AutoARIMA for forecasting, configured with custom hyperparameters to optimize performance.
  • Technologies Used: Python, Dash by Plotly for the frontend, and Pandas for data manipulation.
  • Contribution: Contributions are welcome!