The apps I've seen, like the Apple/Google one, would have enough correlation info to identify super-spreaders retrospectively. Though it would also alert some people unnecessarily.https://twitter.com/Pinboard/status/1263611492017958912 …
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Replying to @colmmacc
You can't assume superspreading events are linked to people and not location, or circumstance, or some combination of all three
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Replying to @Pinboard
But it'd help figure all of those possibilities out too. Ultimately it's a recording of infection pairings. Infected persons can still be interviewed, their histories traced, etc. It's easy to build an infection proximity graph too.
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Replying to @colmmacc
Easier to just collect the data upstream, the way we already do
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Replying to @Pinboard
Why not both? The bluetooth data gives much more granular physical proximity data; ~10cm fidelity. Nothing else I know of can duplicate that.
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Replying to @colmmacc
You don't need this kind of fidelity if you're chasing superspreading events. 100 meters is fine.
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Replying to @Pinboard
When there's uncertainty in which models are correct; more data seems better than less, and more granular better than less granular. Can't go from coarse data to fine-grained. But also, I am very very skeptical of your reasoning. There's like 10 houses within 100m of me.
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Replying to @colmmacc
My reasoning is that you can semi-automate contact tracing by giving epidemiologists a list of potential contacts derived from coarse-grained surveillance data, cutting down the number of people they need to contact by a factor of a lot
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Surely that process would be even more efficient with more granular data.
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