In The Washington Post, Geoffrey A. Fowler described a smartphone app that alerts a user when the user has been in close contact with someone who reports a positive COVID-19 test result. The app uses Bluetooth technology to track which other smartphones one has been near (but not the physical locations of the contacts).
Fowler's article also mentioned the problem of false positives, a typical problem in the design of any warning system. The system is meant to alert a user if they have had a close contact with someone who tests positive. To do that, the app must define a "close contact" based on the signal strength (stronger = closer) and the duration of the contact. If these parameters are set too "low" so that weak signals and short encounters are considered "close contacts," then a user may get too many alerts (false positives); on the other hand, if the parameters are set too "high," then a user may not receive an alert about a risky encounter (a false negative).
The article quoted Jenny Wanger of the Linux Foundation Public Health, who said, "We are working as a community to optimize it and to figure out how to get those settings to be in the right place so that we do balance the risk of false positives with the getting notifications out to people who are at risk."
Note that an alert here is not a positive test result for the user, only a warning that one was near someone with a positive test result and thus may have been exposed. The costs of false positives and false negatives are subjective, of course. At this point in the pandemic, a false positive, which may cause a user to quarantine or limit his activities unnecessarily, may be more costly that a false negative for someone who is taking precautions while doing typical activities and is likely having many brief, low-risk encounters. This type of user may prefer to know only about really close contacts that have a higher risk of transmission.
The opposite may be true for someone who is significantly at risk for becoming seriously ill and has very few contacts in a typical day. Then, a more sensitive (but less specific) system may be more appropriate.
Thus, it would be useful for users to have the ability to set the warning threshold based on their risk preferences, similar to the way financial advisors ask investors about their risk tolerance.
