1. Early on I was skeptical that R0 was >3 instead of <2. This was anchoring on flu directly, and also in looking at the epidemic curve. You can't tell from the epidemic curve whether you have higher R0 and longer generation interval, or lower R0 and shorter generation interval.
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COVID had a longer pre-infectious latent period and a much longer infectious period than I expected, again thinking about influenza. That means fewer generations than expected after time t, and thus higher R0 than one might expect.
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2. Even after we knew that airborne transmission was very important, I remained concerned about fomite transmission through July 2020. (I still don't have solid evidence to rule it out, but to date it seems minimal at most.) Again, anchoring on flu.
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3. I considered it unlikely—though not impossible—that we'd see increases in transmissibility of the scale of B.1.1.7 or B.1.617.2 within the first few years of circulation. Here I was both anchoring on flu and comparing to the greater genetic variation available to flu.
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4. Through fall 2020 I thought that a very best case scenario for vaccine effectiveness was 85%. Again, anchoring on flu to some degree, even though I knew that the antigenic variation we see in flu would not be there for COVID.
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In retrospect, where I've been wrong about things for COVID, it's been not so much because of poor inferences from available data, but rather because my priors were not flat enough.
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Why? Probably because I spent the 2000s thinking about how prepare for a flu pandemic and clearly this—along with knowledge of the epidemiology of other human respiratory RNA viruses—influenced my priors around COVID too strongly.
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Replying to @CT_Bergstrom
This is very interesting to hear! I had a lengthy discussion about this with
@PaulSaxMD (because I had written about this difference, Western countries on flu playbook while East Asia on SARS/MERS). + https://academic.oup.com/ofid/article/8/3/ofab117/6178359 …pic.twitter.com/CePXk7DsDF
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If you get out of the flu playbook, by February 2020, you can see what Dr. Oshitani of Japan has for example: key role of presymptomatic spread, airborne transmission, clustering (just Diamond princess plus Wuhan epi data works). I link to his papers here: https://www.theinsight.org/p/the-gaslighting-of-science …pic.twitter.com/KSUIQKCo5K
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yes, these were definitely key factors shaping the whole reality of this thing, and i can definitely imagine the diamond princess saga shape Japanese response more significantly than in the west
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Not just that though, if you read the rest of the article I link above. The anchoring on flu (and incorrect assumptions about droplets versus airborne transmission) is key, and we had our own examples here to work from.
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mmm, i see, that does seem to explain a lot of the bias in the response.
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