Digital Contact Tracing for Covid 19
Chandresh Kumar Maurya, Seemandhar Jain, Vishal Thakre

TL;DR
This paper presents a GPS-based contact tracing system for COVID-19 that clusters user locations to identify potential exposure, offering a scalable, linear-time solution with additional hotspot and safe-route features, tested on simulated data.
Contribution
It introduces a novel, efficient GPS clustering method for contact tracing that handles static and dynamic user movement, with potential for hotspot detection and route safety analysis.
Findings
Runs in linear time O(n) for static users
Successfully tested on simulated data of 10 million users
Provides additional hotspot detection and safe-route recommendations
Abstract
The COVID19 pandemic created a worldwide emergency as it is estimated that such a large number of infections are due to human-to-human transmission of the COVID19. As a necessity, there is a need to track users who came in contact with users having travel history, asymptomatic and not yet symptomatic, but they can be in the future. To solve this problem, the present work proposes a solution for contact tracing based on assisted GPS and cloud computing technologies. An application is developed to collect each user's assisted GPS coordinates once all the users install this application. This application periodically sends assisted GPS data to the cloud. To determine which devices are within the permissible limit of 5m, we perform clustering over assisted GPS coordinates and track the clusters for about t mins to allow the measure of spread. We assume that it takes around 3 or 5 mins to get…
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Taxonomy
TopicsCOVID-19 Digital Contact Tracing · Data-Driven Disease Surveillance · Human Mobility and Location-Based Analysis
