With the number of daily new cases plateauing if not declining, some people are questioning all those models which predicted millions of deaths and therefore our understanding of the threat from this virus. Some others on the other hand are attributing the decline to the lockdowns and other similar measures. This blog by Peter Attia, who runs a medical practice, is about how the truth, like always, lies in the middle i.e, both arguments are partially valid. Whilst highlighting the limitations of models and their ability to predict with accuracy, Peter argues why we shouldn’t be debunking models either, instead use them as tools to help us a find a way forward by refining the inputs into the model as our understanding improves.
“Today I suspect American fatalities from COVID-19 will be more in line with a very bad, perhaps the worst, season of influenza (The last decade saw flu deaths in the U.S. range from 12,000 to 61,000, so you can imagine how much variability exists). This suggests COVID-19 will kill tens of thousands in the U.S. this year, but likely not hundreds of thousands, and definitely not millions, as previously predicted.
What accounts for my different outlook today? There are really only two first-order explanations for why I can say the early projections were incorrect:
“Today I suspect American fatalities from COVID-19 will be more in line with a very bad, perhaps the worst, season of influenza (The last decade saw flu deaths in the U.S. range from 12,000 to 61,000, so you can imagine how much variability exists). This suggests COVID-19 will kill tens of thousands in the U.S. this year, but likely not hundreds of thousands, and definitely not millions, as previously predicted.
What accounts for my different outlook today? There are really only two first-order explanations for why I can say the early projections were incorrect:
- Either the models were wrong because they incorrectly assigned properties about the biology of the virus pertaining to its lethality and/or ability to spread, or
- The models were correct, but as a society we changed our behavior enough in response to the threat of the virus to alter the outcome. In other words, the models were correct in assuming R_0 was high (north of 2.25 and in some estimates as high as 3), but aggressive measures of social distancing reduced R_0 to <1, thereby stopping the spread of the virus, despite its lethal nature.
It is, of course, most likely to be a combination of these two conditions.”
How much of each? Peter goes on to suggest a way of figuring it out.
“…find out (via blood testing for antibodies) how many people were already infected that weren’t captured as “confirmed cases.”…we should test as large a cross-section of the asymptomatic NYC population as possible, and extrapolate from the results.
These data are enormously important. If 5% of asymptomatic persons in NYC have been infected, while 95% have not, it would imply the IFR (Infected fatality rate) for COVID-19 is approximately 2.4%. This is a deadly virus, approximately 25x more deadly than seasonal influenza in NYC. It would also imply that efforts to contain the spread have been effective and/or the R_0 of the virus (the reproduction number) is much lower than has been estimated (2.2 to 2.6).
Conversely, if the asymptomatic prevalence in NYC is, say, 30%, it would imply that the IFR for COVID-19 is approximately 0.4%. This is a far less deadly virus than previously suggested, although still approximately 4x more deadly than influenza in NYC.3 It also implies that the disease is far more widespread than previously suggested. If 30 percent of New Yorkers have been infected, then efforts to prevent its spread have not been very successful, but NYC is approaching herd immunity..
… The sooner we know how the virus behaved in the most hard-hit city in the country (and likely the world), the sooner we can start making better decisions with at least some modicum of confidence, versus blind certainty in models that don’t have the humility to incorporate a margin of error or degree of uncertainty.
Testing broadly, especially asymptomatic people, to better estimate the true fatality rate is an essential part of any strategy to move forward. Doing so, especially if we can add more elaborate tools for contact tracing, can give us real data on the most important properties of the virus: how rapidly it spreads and how harmful it is to all people, not just the ones we already know about. And that data, in turn, will help us build better and more accurate models.
But we shouldn’t look at models to give us the “answers.” How many people will be hospitalized, how many people will die, and so on. That’s our natural, lazy inclination. Instead we should look to the models to show us how to change the answers. That’s why they are important, and why it is so important that those models a) accept uncertainty, and b) are based on the best data we can obtain. The model is not a prophet. It is simply a tool to help us understand the biology of what is happening, and to help us figure out what we have to do next.
Go back in time to March 1: Knowing what we knew then, quarantine and extreme social distancing was absolutely the right thing to do because we didn’t even know what we didn’t know, and we needed to slow the clock down. It was like taking a timeout early in the first quarter after your opponent has just scored two lightning touchdowns in a rapid succession. It may have seemed unnecessary to some, but we needed to figure out what was going on.
The mistake was not taking the timeout. The mistake was not using our timeout to better understand our opponent. We failed to scale up testing and gather the essential information outlined here that would have helped us create better, more nuanced and hopefully more accurate models, rather than having to essentially guess at our data inputs (and hence at the outcomes). Now, six weeks later, we are still in the dark because we didn’t do the broad testing that we should have done back then. We still don’t know fully how many people contract this virus and come out relatively unscathed.
We still have time to reduce the health and economic damage done by this virus and our response to it, but we can’t waste another timeout sitting around looking at each other and guessing.”
How much of each? Peter goes on to suggest a way of figuring it out.
“…find out (via blood testing for antibodies) how many people were already infected that weren’t captured as “confirmed cases.”…we should test as large a cross-section of the asymptomatic NYC population as possible, and extrapolate from the results.
These data are enormously important. If 5% of asymptomatic persons in NYC have been infected, while 95% have not, it would imply the IFR (Infected fatality rate) for COVID-19 is approximately 2.4%. This is a deadly virus, approximately 25x more deadly than seasonal influenza in NYC. It would also imply that efforts to contain the spread have been effective and/or the R_0 of the virus (the reproduction number) is much lower than has been estimated (2.2 to 2.6).
Conversely, if the asymptomatic prevalence in NYC is, say, 30%, it would imply that the IFR for COVID-19 is approximately 0.4%. This is a far less deadly virus than previously suggested, although still approximately 4x more deadly than influenza in NYC.3 It also implies that the disease is far more widespread than previously suggested. If 30 percent of New Yorkers have been infected, then efforts to prevent its spread have not been very successful, but NYC is approaching herd immunity..
… The sooner we know how the virus behaved in the most hard-hit city in the country (and likely the world), the sooner we can start making better decisions with at least some modicum of confidence, versus blind certainty in models that don’t have the humility to incorporate a margin of error or degree of uncertainty.
Testing broadly, especially asymptomatic people, to better estimate the true fatality rate is an essential part of any strategy to move forward. Doing so, especially if we can add more elaborate tools for contact tracing, can give us real data on the most important properties of the virus: how rapidly it spreads and how harmful it is to all people, not just the ones we already know about. And that data, in turn, will help us build better and more accurate models.
But we shouldn’t look at models to give us the “answers.” How many people will be hospitalized, how many people will die, and so on. That’s our natural, lazy inclination. Instead we should look to the models to show us how to change the answers. That’s why they are important, and why it is so important that those models a) accept uncertainty, and b) are based on the best data we can obtain. The model is not a prophet. It is simply a tool to help us understand the biology of what is happening, and to help us figure out what we have to do next.
Go back in time to March 1: Knowing what we knew then, quarantine and extreme social distancing was absolutely the right thing to do because we didn’t even know what we didn’t know, and we needed to slow the clock down. It was like taking a timeout early in the first quarter after your opponent has just scored two lightning touchdowns in a rapid succession. It may have seemed unnecessary to some, but we needed to figure out what was going on.
The mistake was not taking the timeout. The mistake was not using our timeout to better understand our opponent. We failed to scale up testing and gather the essential information outlined here that would have helped us create better, more nuanced and hopefully more accurate models, rather than having to essentially guess at our data inputs (and hence at the outcomes). Now, six weeks later, we are still in the dark because we didn’t do the broad testing that we should have done back then. We still don’t know fully how many people contract this virus and come out relatively unscathed.
We still have time to reduce the health and economic damage done by this virus and our response to it, but we can’t waste another timeout sitting around looking at each other and guessing.”
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