If it's raining outside but modelling tells you it's sunny, do you believe what you can see and the clear data? Triangulation of modelling, observational data, & common sense is vital. More complex doesn't mean more accurate.https://www.bmj.com/content/369/bmj.m1567 …
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If you have very few people testing positive, positivity is <0.5%, genetic sequencing shows that 1st strains were eliminated, what does this tell you about level of COVID? Or believe complex, untransparent mathematical models w/ predetermined assumptions?https://www.heraldscotland.com/news/18931916.scotland-close-eliminating-coronavirus-travel-brought-new-strains/ …
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Some modellers also saying last Jan 2020 that 80% of Chinese population would get COVID-19 and this would be uncontrollable. Guess what? China controlled it. So did Taiwan, Singapore, Hong Kong, S. Korea, Australia, NZ -> the list goes on. Public health measures can and do work.
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The mistake modelling made was not to account for value of TIME. Buying time for science saved thousands of lives in countries that went for 'no COVID tolerance'. Now they will vaccinate their populations & escape this pandemic with few deaths & faster economic recovery.
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Replying to @devisridhar
Yes but. That's not totally fair to modeling. Not taking time into account in models is not a problem with modeling, it's a problem of *those* models that don't. Here's one specifically about value of time in interventions—concludes buying time is key. https://arxiv.org/pdf/2004.02209.pdf …
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I wrote an article earlier in the pandemic urging people to interpret models with a time and action-focused lens, rather than predictions: i.e. what we can do now to chose our branch. Don't disagree we did treat them as predictions, and many weren't great. https://www.theatlantic.com/technology/archive/2020/04/coronavirus-models-arent-supposed-be-right/609271/ …pic.twitter.com/GILq0gSGV3
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