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Analysis of COVID-19 tracking tool in India: case study of Aarogya Setu mobile application
Gupta R., Bedi M., Goyal P., Wadhera S., Verma V.  Digital Government: Research and Practice 1 (4): 1-8, 2020. Type: Article
Date Reviewed: Jul 11 2022

Gupta et al. focus on the development of tests, methods, and reference data, and the reliability of information used in proximity sensing for health and safety-related software, such as the Aarogya Setu mobile application provided by the government of India to track the spread of COVID-19. Assessing the likelihood of a COVID-19 infection based on the user’s self-reported symptoms and other relevant information [1,2] such as recent travel, age, and gender--once registered with a mobile phone number via one-time passcode (OTP) verification--has been developed by a density-based HDBSCAN function (based on the density-based spatial clustering of applications with noise, DBSCAN, algorithm) [3].

Gupta et al. infer that the Aarogya Setu app likely used a protocol called iBeacon for Bluetooth proximity data collection. Belonging to Apple’s class of iBeacon-enabled hardware transmitters, beacons broadcast Bluetooth low energy (BLE) and its unique identifier to nearby portable electronic devices, achieving higher sensitivity and specificity than other existing systems [4]. Because “location systems, such as global positioning systems (GPS), are inaccurate inside buildings,” [5] the BLE application programming interface (API) protocol implemented in smartphones may notwithstanding affect “how likely a smartphone can reliably detect and classify contacts,” a probability which is linked in turn with a correct neighbor discovery (ND) mechanism [6,7].

In Section 3.2, “Comparative Analysis With Other Apps,” Aarogya Setu is compared with other similar applications such as TraceTogether in Singapore and Private Kit: Safe Paths developed at the Massachusetts Institute of Technology (MIT). Already developed mobile apps to track people and determine subsequent follow-up actions such as non-pharmaceutical interventions (NPIs), that is, “social distancing, hygiene care and self-isolation,” are helpful in highlighting how Aarogya Setu functions differently from the above apps. The app generates alerts related to the potential risk of infection, offered in gradually increasing color codes (green, yellow, orange, red), following that IDs and personal travel history may be self-reported in 12 Indian languages to self-circumscribe the situations of contagion.

Both beacon placement strategies and the API protocol able to support different applications push the authors to discuss further the app’s tradeoffs between utility and privacy. Malicious misuse of Aarogya Setu’s assurance levels when it comes to transparency and/or accountability may not be effectively countered by existing Indian legislation, which is currently fragmented on the point (see Section 3.4, “Privacy Concerns & Features”). The authors then identify the app’s potential limitations, for instance, because of threats such as duplication, eavesdropping, and online guessing, among others. On the other hand, the app’s authentication system--the generation of a unique device identification number that is exchanged between devices without the user’s name and phone number--is its best assurance of personal privacy. Additionally, because of its storage limitation, “location information on the app is deleted on a rolling 30-day cycle.”

Gupta et al.’s paper is novel as far as it enhances knowledge and interest in something that ordinary citizens may only understand peripherally yet use in daily life. They also identify how a population may help overcome the crisis by being informed and responsible. The intended audience includes people who want to stay up-to-date with biomedical information about their own geographical areas and those interested in how continuous advancements in technologies around the world can help with this.

Reviewer:  Romina FucĂ  Review #: CR147469
1) Figaro, M. An assessment of self-reported COVID-19 related symptoms of 227,898 users of a social networking service in Japan: Has the regional risk changed after the declaration of the state of emergency? The Lancet Regional Health - Western Pacific 1, (2020), https://doi.org/10.1016/j.lanwpc.2020.100011.
2) Adorni, F.; Prinelli, F.; Bianchi, F.; et al. Self-reported symptoms of SARS-CoV-2 infection in a nonhospitalized population in Italy: cross-sectional study of the EPICOVID19 web-based survey. JMIR Public Health and Surveillance 6, 3 (2020), http://doi.org/10.2196/21866.
3) Xu, S.; Xiao, Y.; Wang, F. Artificial intelligence for COVID-19: battling the pandemic with computational intelligence. Intelligent Medicine 2, 1(2022), 13–29.
4) Barthe, G.; De Viti, R.; Druschel, P.; Garg, D.; Gomez-Rodriguez, M. Listening to Bluetooth beacons for epidemic risk mitigation. Scientific Reports 12, 1(2022), 1–19.
5) Kindt, P. H.; Chakraborty, T.; Chakraborty, S. How reliable is smartphone-based electronic contact tracing for COVID-19? . Communications of the ACM 65, 1(2022), 56–67.
6) Singhal, B.; Vorster, C.; Meng, D. A machine learning approach to digital contact tracing: TC4TL challenge. 2022 https://arxiv.org/pdf/2203.04307.pdf.
7) Figaro, M. The potential of location data for contact tracing and understanding individual mobility during the COVID-19 pandemic, master’s thesis. University of Central Florida, 2021. https://stars.library.ucf.edu/etd2020/863.
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