2/ Like most people working on COVID-19, I am of the strong belief that mass gatherings during a pandemic are a bad idea. When this paper came out, the huge figures immediately hit the "I Told you So!" button in my & many people’s brains.https://www.iza.org/publications/dp/13670/the-contagion-externality-of-a-superspreading-event-the-sturgis-motorcycle-rally-and-covid-19 …
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3/ The first red flag is the huge number itself-it doesn't pass the sniff test.pic.twitter.com/fpwEBqXKpY
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4/ The rally had an estimated 350-460K attendees, spread over 10 days. To reach such a huge # of 266K over 4 weeks, a LOT of transmission would have to occur both at the rally & in home counties (where cases were counted). No transmission model is mentioned, however.pic.twitter.com/bM4NwHO4ce
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5/ It requires some mental gymnastics to imagine a scenario where over 200K event attendees became infected at the rally itself. Even with bleak assumptions of 1 % of attendees already infectious (spread over 10 days), yet well enough to ride a motorcycle to South Dakota...pic.twitter.com/ioUZugyhKY
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6/ & all of them were “superspreaders,” passing their infection along to another 10 people, back-of-the-envelope math makes it hard to get in the ballpark of this number of infections that could have happened at the rally.pic.twitter.com/uwPN9ZnvO1
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7/ What about the attendees spreading the infection upon returning home? Recall the authors measured increased cases in *home* counties (defined by cell-phone pings pre-Sturgis). This was a motorcycle rally, with some "high inflow" counties far away like CA, NV & FL.
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8/ Many attendees likely rode their bikes home & the lure of the open road in August after months of worldwide lockdown may have even induced many riders to take a meandering path home. (Some may have flown-I've since learned there are bike transport services for such events).pic.twitter.com/xVlJF4NUkl
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9/ Even accounting for people leaving early or going directly home, this leaves v. little time for so many infected riders to get home, infect others, incubate, get tested (with delays),& have these infections show up in county statistics by Sept. 2, just 2 weeks after the rally.pic.twitter.com/SlZbBo6vi8
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10/ How could such seemingly implausible numbers be estimated? The authors don't track individuals or contacts, but use county-level case data from home counties, & employ a "diff-in-diff" analysis--challenging under the best of times, more so when modeling epidemic spread.
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11/ See the
@Slate article above for more on the challenges of diff-in-diff, as well as this insightful thread(s) from@RexDouglass:https://twitter.com/RexDouglass/status/1303379252742479872?s=20 …Show this thread -
12/ Suffice to say the paper's own figures don't inspire confidence in the assumption of parallel trends:pic.twitter.com/yOwqLB7ELF
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13/ Even taking the model at face value, the precision of the estimates suggests some caution is warranted with bold headline numbers as well:pic.twitter.com/hdPvmXZ3bF
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14/ It will be hard to know exactly what went wrong with the analyses until others replicate, which I understand from
@thehauer is proving a challenge:https://twitter.com/thehauer/status/1304166170652745729?s=21 …
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