Comment by jgraham | Hacker News Reader

Comment by jgraham | Hacker News Reader

Is there a good discussion anywhere of the anticipated impact in effacity on various choices around privacy here? A simple toy model for the fraction of infection chains stopped might be something like

fraction stoppped = (usage fraction in population)2 * (fraction of infections recorded as contacts) * (fraction of detected contacts who follow up correctly)

That's obviously a massive over simplification; for example real population dynamics don't give full random mixing between all members of the population. But every factor there is potentially depressed because of opt in and anomnymity; the first obviously so, the third because people are less likely to followup correctly if there's no consequence for not doing so, and the second because it will be impossible to tune the detection parameters without any data to work from. In that — again oversimplified — model if you get 50% o the population using the app (compared to 80% smartphone usage in western Europe), it detects 80% of transmissions and (say) 50% of people follow up correctly before themselves passing the disease on, you end up with 10% of infection chains traced. That doesn't seem like it's going to have a big impact.

So assuming that model's not wildly wrong — which it could be, again, I'm really looking for a link to an expert discussion of this, I can see a few possibilities: * Digitial contract tracing doesn't have much impact on our ability to control the virus * Governments seek to outsource the privacy-invasion needed to companies by e.g. requiring evidence that people are using the contact tracing software and have followed up on potential transmission events before allowing people to buy food etc. * Ineffective opt-in contact tracing and a second wave of pandemic deaths/lockdown is used to bounce people into accepting the need for mandatory, non-anonymous contact tracing with fewer privacy concessions than you'd get right now.

To be clear I'm very concerned with the idea of govenments and in particular the current UK government having access to non-anonymised contact data; it's already being run by someone who considers population-level manipulation using data science and social media to be his core skillset. But I'd also like to understand what tradeoffs are being made in terms of disease control.

While of course a valid question I feel like some of your assumptions might have a different impact in reality. For Germany "50% of people follow up correctly" for example seems unrealistic due to a few factors:

- The proposed centralized solution would have to rely on malice to easily identify people that did not follow up correctly since there was no means to do that in it. The only upside you would have had was more trivial means for data collection on population scale to validate random epidemiological models and validate follow up.

- 50% of people failing to follow up correctly seems like an unreasonably low number given that it's not that different from breaking quarantine with the existing process and there's few enough cases of that to still garner high profile media attention here.

- Even if 50% wrong behaviour was a correct assumption, that would be a slippery slope in most models. If you lose, say, 10% of the overall population because it's allergic to the historical and privacy implications of the system design, misbehaviour of 50% of people left using it can be pretty irrelevant.

- AFAIR adoption in Singapore, which is often used as a reason to use this model in the first place, so far has not been anywhere close to the 50% of 80% of phone users. Many people seem to suggest looking at WhatsApps growth rates for realistic adoption time frames.

Fraser et al have a few general articles and calculated through scenarios on the matter that might be interesting, maybe you get something out of those:

https://science.sciencemag.org/content/early/2020/04/09/scie...

https://045.medsci.ox.ac.uk/files/files/report-effective-app...

Many discussions w.r.t. the epidemiological impact of these trade-offs at the moment seem anecdotal because they lack proper validation. I do not think any of them so far directly address the one you are looking at here.

> "50% of people follow up correctly" for example seems unrealistic due to a few factors

My assumption was that if the R0 is 3-5, that's small compared to the total number of contacts over the infectious period. That means that the false positive rate is going to be rather high. Given a high false positive rate and some inconvenience with following up, needing to go get tested or go into isolation, either of which mean taking time off work at short notice, people will "take a chance" more often than you'd like and delay getting tested until there are symptoms. But certainly it's not a confident estimtate.

Also, Germany is likely cultrally different, but opt-in social distancing / lockdown lasted fully 3 days in the UK before it became clear that it wasn't going to have the necessary effecity. I can imagine the same thing for compliance with contact tracing recommendations.

> The proposed centralized solution would have to rely on malice to easily identify people that did not follow up correctly since there was no means to do that in it

Right, but this is (aiui) different to the systems in countries like South Korea which use location tracking to ensure that you don't break quarantine. It's a point in the possibility space that must be considered to understand tradeoffs.

> AFAIR adoption in Singapore, which is often used as a reason to use this model in the first place, so far has not been anywhere close to the 50% of 80% of phone users. Many people seem to suggest looking at WhatsApps growth rates for realistic adoption time frames.

You'd hope a massive public information campaign could speed up uptake here. I think I've heard that whatsapp is on 75% of devices in Germany (but I haven't verified that number) which if you assume 80% of the population owning a smartphone, leads to 60% of the population opting in. So that doesn't change the results of the toy model too much (if it was 75% of the population rather than 75% of smartphone owners, that would roughly double the fraction of infection chains terminated compared to the 50% assumption).

These numbers still seem pretty low to me, but again I've got precisely zero expertise here.

> Fraser et al have a few general articles and calculated through scenarios on the matter that might be interesting, maybe you get something out of those

Thanks! I'll read those.

> Many discussions w.r.t. the epidemiological impact of these trade-offs at the moment seem anecdotal because they lack proper validation.

That's worrying. I think there's a possibility here that we're in the zone where privacy-preserving contact tracing has too low effacity to be significant in saving lives, but solutions that are mandatory and come with enforcement are effective. If that turns out to be true, there's a clear tradeoff between individual privacy and saving lives / rescuing countries from economic ruin. If I were the sort of person who wanted to significantly change the narrative around privacy to make it look unacceptably selfish, this might be the sort of crisis I'd see as an oppertunity. And given that this is less information than Google and Apple can access as a mattter of course, constructing the narrative that it should be shared with the health service is easy, if people go that way.

I hope there are people thinking about the case where there's popular support for the tracking being mandatory and non-anonymous, so that there can be proper legal — rather than technical — safeguards to ensure the data is only used for its intended purpose and is destroyed promptly when it's no longer useful for that purpose. The optimistic point of view is that this process will give us the ability to shape the fuure of privacy regulation so that we accept that some entities (Google, Facebook, maybe the Government) have more personal data than we're confortable with, but there are stronger controls on how long that data can last and what it can be used for.

opt-in social distancing / lockdown lasted fully 3 days in the UK before it became clear that it wasn't going to have the necessary effecity.

Citation?

https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_t...

On March 16th there was a request to avoid "non-essential travel and contact with others", on March 20th pubs and resturants were forcibly closed, and by 23rd the restrictions on movement were being put into law.

The three-day period I was thinking of was the 20th-23rd; the number of people out and about on the weekend of 21st/22nd was widely reported as the reason for the stricter rules the following Monday e.g. https://www.theguardian.com/world/2020/mar/23/boris-johnson-...

If, however, you mean "what's the proof that people were ignoring the pre-lockdown restrictions in sufficient numbers to make the stricter provisions necessary", all I can do is point out that the people with the best access to that data did chose to require the stricter provisions in response to what they saw. For more you're going to have to wait for the inevitable public enquiry (and I am deeply deeply uninterested in having a discussion about whether lockdowns are the only or most effective policy here).

> effecity

is that a thing/word?

efficacy. Can't spell it and made a typo :)

> https://045.medsci.ox.ac.uk/files/files/report-effective-app...

This is pretty much what I was looking for, thanks. So in their model you need 80% of smartphone users (56% of the population) to opt in to the contact tracing for it to be effective. But iiuc they also assume what — to me — looks like a really high compliance rate; it assumes that people self-isolate for up to two weeks with 2% daily drop-out rate. That seems fine for people with trivial WFH jobs, or those in industries with good job protections, but pretty unlikely for people whose choice is between ignoring the guidelines and losing their income. I imagine with the excuse of "the whole country is in lockdown" gone, employers will be less tolerant of absence, even if — as in the models — it can be a significant fraction of the country in self-isolation at any time.

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