I'd like to highlight a point about epistemology. Many people—scientists and journalists—with infectious disease experience immediately pegged this as the next potential SARS. It had all the hallmarks. Nothing was certain, but it wasn't that we had "no data." We always have data.https://twitter.com/DimaBabilie/status/1345861726370205696 …
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I'm not arguing here about any particular debate but the fallacy that's plagued a lot of this discussion. There is of course stronger/weaker and different types of evidence, different trade-offs and calculations etc. But "no data" is almost never true, and yet gets used a lot.
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I've been thinking that, epistemologically, that might be one of the most important lessons of last year. An implied philosophical frequentism that has plagued our analysis/action/communication.https://twitter.com/zeynep/status/1341452907175170050 …
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(And please don't @ me as if this is an argument about the merits of a particular vaccine scheduling or dosing. I'm talking about epistemology. There are people with impeccable credentials making important points on all sides of that & decisions will be made with imperfect data!)
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Yep. The BBC story then just repeats the Chinese official lie that there had "been no human-to-human transmission" whereas anyone familiar with these viruses, the region & the Chinese government patterns knew this to be likely false and acted accordingly.https://twitter.com/coreyspowell/status/1346107331847860230 …
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Also, on the vaccine debate, this is an excellent thread. (No, it won't give you an answer but explains why we are where we are, differences between individual results & population-level questions and makes a strong case for adapting fast as we go along).https://twitter.com/IDEpiPhD/status/1345176257995165696 …
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And then the WHO spent the next six odd months insisting that perfect data was needed to shift from the anti-mask position they'd taken on the basis of sweet bugger all :/
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Having very much dealt with those committees that refused to budge for so long, not only did they insist on perfect data, they outright dismissed and actively ignored any data that did not fit their priors. So it was worse than that, to be honest.
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