(Here I try to put my recent arguments together into an integrated essay, suitable for recommending to others.)
When facing a new pandemic, the biggest win is to end it fast, so that few ever suffer. This prize makes it well worth trying hard to trace, test, and isolate those near the first few cases. Alas, for Covid-19 and the world, this has mostly failed, though not yet everywhere.
The next biggest win is to find a cheap effective treatment, such as a vaccine. And while hope remains for an early win, this looks to be years away. To keep most from getting infected, at this point the West must apparently develop and long maintain unprecedented expansions in border controls, testing, tracing, and privacy invasions, and perhaps also non-home isolation of suspected cases. Alas, these ambitious plans must be implemented by the same governments that have so far failed us badly.
Yes, there remains hope here, which should be pursued. But we also need a Plan B; what if most will eventually be infected without a treatment? The usual answer is “flatten the curve,” via more social distance to lower the average of (and increase the variance of) infection rates, so that more can access limited medical resources. Such as ventilators, which cut deaths by <¼, since >¾ of patients on them die.
However, extreme “lockdowns”, which isolate most everyone at home, not only limit freedoms and strangle the economy, they also greatly increase death rates. This is because infections at home via close contacts tend to come with higher initial virus doses, in contrast to the smaller doses you might get from, say, a public door handle. As soon as your body notices an infection, it immediately tries to grow a response, while the virus tries to grow itself. From then on, it is a race to see which can grow biggest fastest. And the virus gets a big advantage in this race if its initial dose of infecting virus is larger.
This isn’t just a theory. The medical literature consistently finds strong relations, in both animals and humans, between initial virus dose and symptom severity, including death. The most directly relevant data is on SARS and measles, where natural differences in doses were associated with factors of 3 and 14 in death rates, and in smallpox, where in the 1700s low “variolation” doses given on purpose cut death rates by a factor of 10 to 30. For example, variolation saved George Washington’s troops at Valley Forge.
Early on, it can be worth paying such high costs to end a pandemic. But once a pandemic seems likely to eventually infect most everyone, it becomes less clear whether lockdowns are a net win. However, the dose effect that lockdowns exacerbate, by increasing dose size, also offers a huge opportunity to slash deaths, via voluntary infection with very low doses.
Just as replacing accidental smallpox infections with deliberate low dose infections cut smallpox deaths by a factor of 10 to 30, a factor of 3-30 is plausible for Covid19 death rate cuts due to replacing accidental Covid19 infections with deliberate small dose infections. Observed mortality differences due to natural dose variations give only a lower bound on what is feasible via controlled doses. Of course we can’t be sure until we get more direct evidence. But systematic variolation experiments involving at most a few thousand volunteers seem sufficient to get evidence not only on death rates, but also on ideal infection doses and methods, and on the value of complementary drugs that slow viral replication (e.g., remdesivir).
This dose size advantage adds to several other substantial advantages of variolation. Not only does it offer controlled conditions for studying disease progression, and for training medical personnel, it can also help ensure consistent staffing of critical workers, by spacing out their infections.
Furthermore, the combination of variolation with immediate isolation until recovery “flattens the curve,” by spreading out medical demand over time, and also adding to the herd immunity that usually ends a pandemic. So even without a death rate cut due to lower doses, this strategy produces a net social gain.
This last claim may sound counter-intuitive, but it has in fact recently been confirmed in three independently developed simulations. For example, in a simulation where old and sick people are selected for isolation, while only the young and healthy are eligible for variolation, there are 40% fewer life years lost, compared to no variolation and random selection for isolation. Each variolation volunteer suffers only an additional 0.20% chance of death to save a random other person from a 6.5% chance. And these simulations ignore any benefits of low doses; they hold constant the infection and death rates, and the total quantity of social isolation, and thus expense.
Of course, if low doses cut death rates by a factor of two or more, variolation volunteers would actually cut their chance of death, perhaps greatly. Yes, the first few thousand volunteers could be less sure of such gains, but they could be compensated for this risk, just as we now consider compensating subjects in vaccine trials using live Covid19 viruses. We could pay variolation volunteers cash, offer their loved ones priority medical care, certify them as safe for work and social gatherings, and honor them like soldiers selected for their elite features who take risks to produce community gains.
So the scenario is this: Variolation Villages welcome qualified volunteers. Friends and family can enter together, and remain together. A cohort enters together, and is briefly isolated individually for as long as it takes to verify that they’ve been infected with a very small dose of the virus. They can then interact freely with each other, but not leave the village until tests show they have recovered.
In Variolation Village, volunteers have a room, food, internet connection, and full medical care. Depending on available funding from government or philanthropic sources, volunteers might either pay to enter, get everything for free, or be paid a bonus to enter. Health plans of volunteers may even contribute to the expense.
Those who work in medicine or critical infrastructure seem especially valuable candidates for early variolation; volunteers might be offered larger bonuses. Once they have recovered, they are more surely available to work near the pandemic peak, and can more easily risk social contact at work.
Note that this strategy of variolation plus isolation requires no government support, nor loss of personal freedom, just the sort of legal permission sometimes given to administrators and volunteers of vaccine trials. And this comparison with vaccine trial policy can be emphasized to those tempted to see this policy as repulsive. Variolation policy offers similar social gains, and may require similar voluntary personal sacrifices.
Note also that there is no minimum scale required to make this policy beneficial. Even variolation of only a few is still a social gain compared to none at all. A small early trial could generate much useful attention and discussion regarding this strategy, to inspire application in this and future pandemics. Furthermore, the optimal time to stop this practice for personal reasons is probably close to the optimal time to stop for social reasons, so choice of stopping date needn’t be heavily regulated.
Some fear that it is now too late to consider variolation, as the pandemic peak may be only a few weeks away. But lockdowns may succeed in substantially slowing Covid19 growth, and we may then be in for many months or years of alternating local waves of suppression and reappearance. Furthermore, if low doses cut death rates enough, variolation can make sense even at the pandemic peak, when medical resources are stretched most thin. For example, for a factor of 3 cut in death rates, variolation replaces three sick patients with one similarly sick patient, lowering total medical demand.
As variolation doesn’t much change the total number who are ever infected, it doesn’t give the virus more total chances to evolve. In fact, while accidental infections risk selection for versions that infect people more easily, voluntary infections avoid this problematic effect.
While you might think policy wonks would be eager to cut Covid19 death rates by a factor of 3-30, few have so far been attracted to discuss or pursue this concept. It seems to push the wrong buttons in many people. So if you are a rare exception who finds the concept plausible, you can get a disproportionate policy leverage by working on a neglected important option. You might help in one of these areas:
[more text at the link above]
We have much work to do if this Plan B is to be ready when needed.
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