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Review
. 2020;100(4):623-646.
doi: 10.1007/s41745-020-00201-5. Epub 2020 Nov 11.

GoCoronaGo: Privacy Respecting Contact Tracing for COVID-19 Management

Yogesh Simmhan  1 , Tarun Rambha  1 , Aakash Khochare  1 , Shriram Ramesh  1 , Animesh Baranawal  1 , John Varghese George  1 , Rahul Atul Bhope  1 , Amrita Namtirtha  1 , Amritha Sundararajan  1 , Sharath Suresh Bhargav  1 , Nihar Thakkar  1 , Raj Kiran  1
Affiliations
Review

GoCoronaGo: Privacy Respecting Contact Tracing for COVID-19 Management

Yogesh Simmhan et al. J Indian Inst Sci. 2020.

Abstract

The COVID-19 pandemic is imposing enormous global challenges in managing the spread of the virus. A key pillar to mitigation is contact tracing, which complements testing and isolation. Digital apps for contact tracing using Bluetooth technology available in smartphones have gained prevalence globally. In this article, we discuss various capabilities of such digital contact tracing, and its implication on community safety and individual privacy, among others. We further describe the GoCoronaGo institutional contact tracing app that we have developed, and the conscious and sometimes contrarian design choices we have made. We offer a detailed overview of the app, backend platform and analytics, and our early experiences with deploying the app to over 1000 users within the Indian Institute of Science campus in Bangalore. We also highlight research opportunities and open challenges for digital contact tracing and analytics over temporal networks constructed from them.

Keywords: Bluetooth; COVID; Digital contact tracing; Mobile apps; Temporal networks.

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Figures

Figure 1:
Figure 1:
Overall Design of GCG.
Figure 2:
Figure 2:
Identifier mapping during GCG App installation.
Figure 3:
Figure 3:
User interface and analytics in the GoCoronaGo v0.7 Android App.
Figure 4:
Figure 4:
Backend VMs, services and databases, and their interactions.
Figure 5:
Figure 5:
Signing service requests using device key.
Figure 6:
Figure 6:
Sample scan records for an interval and the corresponding interval graph.
Figure 7:
Figure 7:
Experimental setup for collecting RSSI values at fixed distances.
Figure 8:
Figure 8:
Distribution of RSSI at different distances for the Android phones.
Figure 9:
Figure 9:
A visualization of the contact graph of a subset of app users for a single day. The size of the nodes is proportional to their degree centrality.
Figure 10:
Figure 10:
Number of GCG installations at IISc over time.
Figure 11:
Figure 11:
Federated deployment of GCG across Institutions.
See this image and copyright information in PMC

References

    1. Ministry of Health and Familty Welfare, India (2020) Advisory on Social Distancing Measure in view of spread of COVID-19 disease. Tech rep
    1. World Health Organization (WHO) (2020) Contact tracing in the context of COVID-19: interim guidance. Tech rep
    1. World Health Organization (WHO) (2020) Coronavirus disease (COVID-19) advice for the public. Tech rep
    1. BBC (2020) Coronavirus: People-tracking wristbands tested to enforce lockdown. https://www.bbc.com/news/technology-52409893
    1. World Health Organization (WHO) (2020) Digital tools for COVID-19 contact tracing: annex: contact tracing in the context of COVID-19. Tech rep

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