Contact Tracing Using Ball-Tree Algorithm

High-performance spatial contact tracing using Ball Tree data structures -- published in IEEE Xplore.

What It Does

This project implements contact tracing using the Ball Tree spatial data structure for efficient nearest-neighbor queries on large-scale location datasets. It handles 10,000+ data points with sub-linear query time, making it suitable for real-world contact tracing scenarios. The research behind this implementation has been published in IEEE Xplore.

Key Features

• Ball Tree Nearest Neighbor Search -- efficient spatial queries with sub-linear time complexity
• Scales to 10,000+ Data Points -- handles large datasets without performance degradation
• Proximity Detection -- configurable distance and time thresholds for contact identification
• Matplotlib Visualizations -- visual contact maps and spatial distribution plots
• CSV Export -- structured output of detected contact pairs
• IEEE Published -- peer-reviewed research backing the implementation

Tech Stack

Category	Technologies
Language	Python 3.x
Spatial Indexing	scikit-learn BallTree
Data Processing	pandas, NumPy
Visualization	Matplotlib
Performance	Cython, C extensions

Getting Started

git clone https://github.com/Jesseman-418/Contact-Tracing-using-Ball-tree-Algorithm.git
cd Contact-Tracing-using-Ball-tree-Algorithm
pip install scikit-learn pandas numpy matplotlib
python3 main.py

Publication

This work has been published in IEEE Xplore. The Ball-tree approach provides improved query performance over traditional brute-force methods for high-dimensional contact tracing data.

Related Work

See also: Contact Tracing using KD-Tree Algorithm -- the KD-Tree variant of this approach.

License

MIT