Coronavirus, like cold and flu viruses, will perhaps remain prevalent in societies around the world, even with the available vaccinations and drug treatments. Despite the existing high-tech tools for tracking and preventing the spread of COVID-19, what unique features of contact-tracing tools should be enhanced to help restrain or eliminate this deadly virus? Gupta et al. examine the impacts of the Aarogya Setu (AS) COVID-19 tracking tool in India, and recommend effective algorithms, policies, and features for innovative virus detection and prevention tools.

The mobile AS COVID-19 tracking application uses the data produced by Bluetooth and the global positioning system (GPS) to provide: (a) information and self-appraisal of the likelihood of becoming infected with COVID-19; (b) current local and nationwide COVID-19 statistics; and (c) an electronic emergency mobility pass for people in India. The authors discuss the unique features and pitfalls of AS. Indeed, like any other coronavirus tracking tool, AS is prone to recommending quarantine for healthy people and failing to detect people with the virus, possibly due to inaccuracies in the communication protocols of different smartphones.

The authors clearly present the requirements for assessing the capabilities of alternative coronavirus tracing tools used in countries around the world: the ability to delete data within a realistic timeframe, the ability to protect data from intruders, openness of the implicit components of the tool, limitations on usage of the assembled data, and the nature of the inherent technology. Devoid of any experiment with a tracking tool, the authors discuss how artificial intelligence (AI) algorithms might be useful in creating ultramodern contact-tracing tools. Even though the analytical data of AS is missing, I recommend this enlightening paper for mobile app developers and data and computer scientists, especially those who can recommend effective strategies for engineering future virus tracking tools.