Teresa Scassa - Blog

Monday, 10 August 2020 08:58

How Will COVID Alert Measure Up?

Written by  Teresa Scassa
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Canada’s new exposure notification app – COVID Alert has launched in Ontario. This shifts the focus from whether to adopt an app and what type to how we will know if the app is a success.

COVID Alert is built upon the Google Apple Exposure Notification System (GAEN) which is a completely decentralized model. This means that none of the proximity data collected via the app is shared with public authorities. GAEN apps must be entirely voluntary. Users choose whether to download the app and whether to upload positive test results for COVID-19. If a user is notified that they have been in proximity to someone who has tested positive for COVID-19 the app will advise what steps to take – but it will be up to the user to take those steps. Although there are privacy risks with any app (and here, they would be predominantly ones related to security and the possibility of malicious attacks), this could be the app on most users’ phones that collects the least personal data. COVID Alert has been vetted by the Privacy Commissioner of Canada and by Ontario’s Privacy Commissioner. It will also be reviewed by privacy commissioners in those provinces that choose to deploy it.

All of this is good news. As we start returning to workplaces, bars, restaurants and public transit, our daily lives will involve more and more moments of proximity with strangers. If nearly everyone is using COVID Alert – and if COVID Alert actually works the way it should – then it should help alert us to potential exposure to COVID-19 so that we can take steps to get tested and/or to isolate ourselves from those we might harm.

Although it is likely to be useful, authorities are quick to point out it is only one tool among many. This is because there is much that is unknown about the actual performance of GAEN exposure notification apps. Such apps have only recently been launched in other countries. The threshold for recording a proximity event is one issue. For COVID Alert, a proximity event is recorded when two app users are within 2 metres of each other for 15 minutes or more. An EU guidance document describes this as “a starting point for the definition of a high-risk exposure contact”, but also indicates that “evaluation and calibration will be key to define the optimal time-and distance settings that adequately capture people at risk of infection.” The apps cannot detect whether people are separated by plexiglass or wearing masks or face shields, and may not function as well when phones are in purses or backpacks. These factors may impact the accuracy of the apps. People may receive exposure notifications due to contacts that are very unlikely to result in infection (on opposite sides of plexiglass, for example) but will experience stress and disruption (perhaps having to miss work while waiting for test results) as a result. These inconveniences might be disproportionately experienced by those whose work demands that they interact with the public or ride transit, and there may be problematic sociodemographic impacts as a result. On the other hand, for those who have to be out and about, the app may provide some level of comfort. There is much that we do not yet know, but that we need to learn. Noting some of the uncertainties around these types of apps, the Privacy Commissioner has recommended “that the government closely monitor and evaluate the app’s effectiveness once it is used, and decommission it if effectiveness cannot be demonstrated.”

One way to learn about the app and its impacts is to gather data and develop metrics to assess its performance. The highly decentralized GAEN model makes this more challenging, since no data is shared with governments via the app. The number of downloads can reveal how many people are willing to try the app. But it does not do much more than that. Useful data would include data about how many people who get tested do so because they received an app notification. It would be interesting to be able to correlate this data with positive or negative test results. In other words, what percentage of people who are prompted to get tested by the app actually test positive for COVID-19? It would also be useful to know how many of the people who receive exposure notifications are also separately contacted by contact tracers. Does the app amplify the reach of conventional contact tracing or does it largely duplicate it? Jurisdictions such as Australia, which has a centralized model, are beginning to collect and analyze such data. Alberta’s contact tracing app uses a centralized system and it might be particularly interesting to compare the domestic performance of a centralized app with the decentralized one. And, while the GAEN is fully decentralized, it does allow for additional data to be collected, with user consent, so long as this is separate from the exposure notification system. The Irish app, built on GAEN, has a voluntary user survey which allows consenting users to share data about the performance of the app. As provinces begin to deploy COVID Alert, both they and the federal government should be thinking about what data they need to evaluate this technology, and how they will gather it. According to the Privacy Commissioner’s assessment, the new Advisory Council established to oversee the use of the app will evaluate its effectiveness. Any such evaluation should be shared with the public.

As the app rolls out in Ontario, individuals will be asked to download it, and broad uptake will be important to its success. Using the app may provide individuals with added protection; it also means that they will be contributing to an experiment to assess the utility of this type of technology to assist in pandemic control. COVID Alert aims to help contain a disease which we know can spread wildly and at great personal and societal cost. Carefully calibrated metrics, and transparency about the successes or failures of the app should, and hopefully will, be part of this experiment.

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