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Keeping track of subjects under mandatory quarantine is a complex task. There are simply not enough resources to ensure that every individual is compliant and even a single rogue individual can spread the virus in a way that can spiral out of control. Take Korea’s patient 31 as an example, a super spreader that caused a successful, globally appraised, strategy to fail. (more info)

The Korean clusters — How coronavirus cases exploded in South Korean churches and hospitals — Reuters

The intuitive approach is to keep track of an individual’s location and automatically launch an alert to the control team when they leave their designated location. And what better way to do so than keeping track of their cellphones?. This is Korea’s approach and has been successful. They legally prohibit subjects from leaving their home, track their cellphones and perform official checks on them 2 times a day.

But it might be a little more difficult to implement when individuals are actively trying to escape the quarantine and trying to devise ways to trick the system, something we foresee in less compliant cultures. A drug addict (or any individual with high enough motivation to leave) would wait for the official to call him to leave the house without the phone and get his fix.

Another big issue is that every government is attempting to reinvent the wheel by developing their own similar solutions. Everyone is starting from scratch and no collaborative efforts have been established (that I’m aware of).

This is why we designed BienAcá, an open-source solution that enables any country or organization, to create their own solution out of a centralized codebase. The tool includes strategies to actively discourage rogue subjects with little to no extra effort from the authorities.

Using smartphone fingerprint scanners, we introduce biometrics checks to the standard solution to ensure the subject, and not somebody else, is with the device. But that’s not enough; we introduce randomness to prevent the user to figure out and play, the system.

How does it work?

  1. An app is installed on the subject’s mobile phone. This app sends location information to a central system
  2. If there is no contact with the device (the device loses connectivity, is turned off, etc) or the device leaves the designated area a pre-alert is sent to the subject asking them to solve the issue.
  3. If the cause of the pre-alert is not found out, (return to the area, connect to the internet, etc..) the system notifies the control center for them to deal with the incident by calling the subject or use whatever action they see fit.
  4. Several times a day, randomly, the app requires the user to identify himself using the fingerprint scanner. If the user fails to do so, an alert is sent to the control center.

Behavioral Design Aspects.

The role of randomness

After a biometrics check, there is no way that the user knows how long it will take for the next one to appear. This uncertainty makes the checks more effective. A random check works better than a predictable one because we are adverse to uncertainty. For example, when choosing between two bets, we are more likely to choose the bet for which we know the odds, even if the odds are poor, than the one for which we don’t know the odds (link) . We are more likely to take a risk that we know (even a high one) over an uncertain one. So by making the possibility to get caught more uncertain we expect to increase compliance.

Pre alert system.

The system has been designed to pre alert the user whenever a notification to the center of control is going to be sent. This serves two purposes, it allows the user to solve the issue (return to the quarantine zone, reconnect to the internet, restart the app) with no outside intervention, optimizing resources. But it also serves as a deterrence.

No technology is fail proof so the system will likely generate some false positives. GPS coordinates sometimes jump around, a heartbeat message between the app and the server can be lost, or the app can freeze. By notifying the user, even when we are not totally sure, we reinforce the sense of being controlled. In a previous study (Optimización de estrategias de castigo en el tránsito) we have found that increasing the notion of being controlled is as effective as actually controlling, and way more effective than increasing the penalties.

This project is open source (link)


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