Contact Tracing using DBSCAN algorithm

[sharayuanuse]

Is your feature request related to a problem? Please describe.

The challenge arises when trying to identify clusters of individuals who may have been exposed to an infectious person based on GPS data. Tracking large-scale, dynamic location data and isolating infected clusters manually is inefficient, which leads to difficulty in preventing further disease transmission.

Describe the solution you'd like

By implementing the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise) for contact tracing, we can efficiently group individuals based on their proximity over time. DBSCAN is ideal as it forms clusters based on density and distance without needing to predefine the number of clusters. This algorithm will:

• Detect clusters of individuals who have been in close proximity (within a defined distance, such as 2 meters) for a specified time.
• Tag individuals who fall within these clusters as at risk and notify them for testing, quarantine, or monitoring.

The approach will automate the clustering process, reduce manual effort, and allow for more rapid isolation of potential exposure cases. This solution adapts well to large datasets and effectively handles noise (individuals not in contact with others).

Describe alternatives you've considered

• K-Means Clustering: This method requires specifying the number of clusters beforehand, which is not practical in dynamic settings where the number of exposed individuals may vary over time.

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