Alex has been arguing for a “First Doses First” policy, and I find his views persuasive (while agreeing that “halfsies” may be better yet, more on that soon). There are a number of numerical attempts to show the superiority of First Doses First, here is one example of a sketched-out argument, I have linked to a few others in recent days, or see this recent model, or here, here is an NYT survey of the broader debate. The simplest numerical case for the policy is that 2 x 0.8 > 0.95, noting that if you think complications overturn that comparison please show us how. (Addendum: here is now one effort by Joshua Gans).
On Twitter I have been asking people to provide comparable back-of-the-envelope calculations against First Doses First. What is remarkable is that I cannot find a single example of a person who has done so. Not one expert, and at this point I feel that if it happens it will come from an intelligent layperson. Nor does the new FDA statement add anything. As a rational Bayesian, I am (so far) inferring that the numerical, expected value case against First Doses First just isn’t that strong.
Show your work people!
One counter argument is that letting “half-vaccinated” people walk around will induce additional virus mutations. Florian Kramer raises this issue, as do a number of others.
Maybe, but again I wish to see your expected value calculations. And in doing these calculations, keep the following points in mind:
a. It is hard to find vaccines where there is a recommendation of “must give the second dose within 21 days” — are there any?
b. The 21-day (or 28-day) interval between doses was chosen to accelerate the completion of the trial, not because it has magical medical properties.
c. Way back when people were thrilled at the idea of Covid vaccines with possible 60% efficacy, few if any painted that scenario as a nightmare of mutations and otherwise giant monster swarms.
d. You get feedback along the way, including from the UK: “If it turns out that immunity wanes quickly with 1 dose, switch policies!” It is easy enough to apply serological testing to a control group to learn along the way. Yes I know this means egg on the face for public health types and the regulators.
e. Under the status quo, with basically p = 1 we have seen two mutations — the English and the South African — from currently unvaccinated populations. Those mutations are here, and they are likely to overwhelm U.S. health care systems within two months. That not only increases the need for a speedy response, it also indicates the chance of regular mutations from the currently “totally unvaccinated” population is really quite high and the results are really quite dire! If you are so worried about hypothetical mutations from the “half vaccinated” we do need a numerical, expected value calculation comparing it to something we already know has happened and may happen yet again. When doing your comparison, the hurdle you will have to clear here is very high.
When you offer your expected value calculation, or when you refuse to, here are a bunch of things you please should not tell me:
f. “There just isn’t any data!” Do read that excellent thread from Robert Wiblin. Similar points hold for “you just can’t calculate this.” A decision to stick with the status quo represents an implicit, non-transparent calculation of sorts, whether you admit it or not.
g. “This would risk public confidence in the vaccine process.” Question-begging, but even if true tell us how many expected lives you are sacrificing to satisfy that end of maintaining public confidence. This same point applies to many other rejoinders. It is fine to cite additional moral values, but then tell us the trade-offs with respect to lives. Note that egalitarianism also favors First Doses First.
h. “We shouldn’t be arguing about this, we should be getting more vaccines out the door!” Yes we should be getting more vaccines out the door, but the more we succeed at that, as likely we will, the more important this dosing issue will become. Please do not try to distract our attention, this one would fail in an undergraduate class in Philosophical Logic.
i. Other fallacies, including “the insiders at the FDA don’t feel comfortable about this.” Maybe so, but then it ought to be easy enough to sketch for us in numerical terms why their reasons are good ones.
j. All other fallacies and moral failings. The most evasive of those might be: “This is all the more reason why we need to protect everyone now.” Well, yes, but still show your work and base your calculations on the level of protection you can plausibly expect, not on the level of protection you are wishing for.
At the risk of venturing into psychoanalysis, it is hard for me to avoid the feeling that a lot of public health experts are very risk-averse and they are used to hiding behind RCT results to minimize the chance of blame. They fear committing sins of commission more than committing sins of omission because of their training, they are fairly conformist, they are used to holding entrenched positions of authority, and subconsciously they identify their status and protected positions with good public health outcomes (a correlation usually but not always true), and so they have self-deceived into pursuing their status and security rather than the actual outcomes. Doing a back of the envelope calculation to support their recommendation against First Doses First would expose that cognitive dissonance and thus it is an uncomfortable activity they shy away from. Instead, they prefer to dip their toes into the water by citing “a single argument” and running away from a full comparison.
It is downright bizarre to me — and yes scandalous — that a significant percentage of public health experts are not working day and night to produce and circulate such numerical expected value estimates, no matter which side of the debate they may be on.
How many times have I read Twitter threads where public health experts, at around tweet #11, make the cliched call for transparency in decision-making? If you wish to argue against First Doses First, now it is time to actually provide such transparency. Show your work people, we will gladly listen and change our minds if your arguments are good ones.