- From: Adrian Gropper <agropper@healthurl.com>
- Date: Tue, 7 Apr 2020 13:35:22 -0400
- To: Christopher Allen <ChristopherA@lifewithalacrity.com>
- Cc: Credentials Community Group <public-credentials@w3.org>
- Message-ID: <CANYRo8i5ktS5tZUW3Qaomxb04N-Fn07L2UMiSnYmbyDSntXt7w@mail.gmail.com>
I agree. Also, (1) A lot can be done quickly by having a licensed clinician issue the credential (maybe by picking from a handful of assertions that have been standardized and summarized by a couple of experts. This speeds things by taking the lab's API and ID management (maybe paper or proprietary) and letting any accountable clinician issue the VC. Different verifiers will treat the assertions and the clinician's credentials differently but we would at least be on our way. (2) Prepare to have multiple serology tests over time for the same patient as well as the history of symptoms (because serology results are highly time-dependent over weeks to months). Along the same lines, there will be an evolving series of serology tests with different sensitivity and specificity which will mean that they will need to be interpreted differently based on local conditions like prevalence. - Adrian On Tue, Apr 7, 2020 at 1:21 PM Christopher Allen < ChristopherA@lifewithalacrity.com> wrote: > As was discussed briefly in the call today, if we are going to talk about > #Covid19 technology solutions, we must partner with health & > epidemiological experts to do it right. > > For instance, it has been proposed that we support some kind of digital > immunity certificate. Even if we ignore its possible human-rights & privacy > risks, it can have still have risky public health care choices: > > https://unherd.com/2020/04/how-far-away-are-immunity-passports/ > > “If you issue immunity passports on this basis, *barely a third *of the > people you give them to will actually be immune. “There’s nothing peculiar > about this statistically,” Kevin McConway, an emeritus professor of > statistics at the Open University, told me. “It’s just Bayes’ theorem > <https://en.wikipedia.org/wiki/Bayes%27_theorem>.” The likelihood of you > having had Covid-19, if you’ve had a positive test, depends not just on the > accuracy of the test but on the prevalence in the population you’re looking > at. > … > In the end, that’s going to be a horribly cold-blooded calculation. If you > let people out when they’re 90% likely to be immune, that means one person > in 10 is going to be at risk of getting and spreading the disease. Is that > risk a price worth paying for reducing the real costs (economic, social, > physical, mental) of isolation? I don’t know and I’m glad I don’t have to > work it out. But someone has to. And they’ll have to start by getting a > reasonably effective test, and testing hundreds of thousands of people, to > see how many of us have had it.” > > — Christopher Allen >
Received on Tuesday, 7 April 2020 17:35:50 UTC