Covid19 Pizza-Risk Estimator

By Robin Hanson · April 12, 2020 7:40 pm · Comments (0)

To keep us from catching and spreading Covid19, most of us are on “lockdown”, limiting non-home contact. But we aren’t completely isolated; we all do a few things that risk outside contact. And households really are the better unit of risk here; if one of you gets sick, the rest face a much higher risk.

To help households estimate and manage risk, I’ve made the following table, listing risks for 19 activities, all relative to the first: accepting delivery of and eating a pizza, paid for online. These risk estimates came from ~1000 respondents to each of 18 Twitter polls. (Technically, risk estimates are medians of lognormal distributions fitted to poll response frequencies.) Yes, it would be better to get expert estimates, but I don’t have experts to poll. I hope experts who see these will publicly improve on them. Until they tell us better, we must act on what we know.

The above is actually a screenshot from this spreadsheet I’ve made to help you estimate household risk. (Anyone know how to embed it here, so each reader can edit their own copy?) On this sheet, you can combine these risk estimates with estimates of how often per week your household does each activity, and also any corrections for how they do it differently, to get your total household weekly “Pizza Risk”. That is, how many weekly risks you take equivalent to a pizza delivered & eaten.

In the spreadsheet, each row lists a risky activity, grouped into types. To use the sheet, consider the activity in each row and think of similar activities you do that risk outside contact. For each such activity, find the closest activity in the table, and for that row, enter how many times per week your household does that in the “Count” column. And if your activity seems to have a different risk from other households, such as because you do it for more or less time, or because it involves fewer or more outsiders, then enter a number other than 1 in the “Factor” column. For example, if doing it your way has twice the risk, enter the number 2.

If you mange to use this spreadsheet to get a Pizza Risk estimate, please complete the following two polls so we can learn about how Pizza Risk varies across households.

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Peter Doherty on Variolation

By Robin Hanson · April 12, 2020 12:10 pm · Comments (0)

Two noteworthy media mentions of variolation:

1. The New Yorker features Douglas Perednia talking about controlled infection and variolation, as its Exhibit A on on why conservative media shouldn’t presume to write on health/medicine, as they will only say stupid obviously terrible things:

After the Federalist tweeted it out, Twitter, which has been cracking down on coronavirus misinformation, temporarily locked the Federalist’s account. … He’d submitted it to a number of medical journals and blogs. “They all turned it down with no comment,” … tried the Federalist, almost at random. … The site accepted his article the next day, no questions asked. …

On the site … most commenters found Perednia’s idea absurd, dangerous, hilarious, or all three. … many angry e-mails and calls … Andrew Lover, an assistant professor of epidemiology at the University of Massachusetts-Amherst, told the Times that Perednia’s article was “exceedingly ill-advised and not evidence-based in any way shape or form.” …

Perednia [said] the way to adapt his idea to this reality was to make sure that the infecting was done with ‘the lowest possible dose’ … a concept known as variolation—which, he thought, would cut the death rate among those who chose to take part. (more)

2. A month ago, Adam Ford interviewed me on voluntary infection. Yesterday, Ford posted his interview with Nobel laureate Peter Doherty, author of Pandemics (2012), wherein Ford asked Doherty about variolation. Here are selected quotes from that discussion (fuller quotes below the fold):

47:10 Ford: “Controversially, in leu of actual vaccine that could come, hopefully in 9 months, but maybe even in 18 months if things go okay, if social isolation doesn’t work well enough too, would something like strategic or voluntary small dose low dose infection, like variolation, work in order to gain immunity, or nudge herd immunity? Is that something that we should be considering?

Doherty: (laughing) “Well let’s tell people what variolation was. … What they did was do this in young children, young children had a good immune response, generally survived smallpox, so what they were doing essentially is giving them smallpox, and they survived, whereas if they got it when they were older, they’d have a much worse disease. … So its not an unthinkable thing. …

52:40 With Covid19 I don’t know, but it would take a brave soul to be a test candidate, With younger people who are not severely affected, it’s possible. But you’d have to be enormously careful that they didn’t get any dose through their nose. But there would be ways of doing this. …

53:40 Ford: Is this something that could be achieved in the near term, if the vaccine timeline ends up looking like its going to be longer?

Doherty: If it was an absolutely catastrophic situation, if it was like the situation that is depicted in Contagion, where everyone who is within 100 feet of the virus gets it and dies, yes it could be reasonable. But I think for a virus where 80+% of people are definitely mildly infected at worse, or not sick enough to go into hospital, I don’t think you would take risk of that. The thing about a vaccine is that you have to give it to large numbers of normal people. You can’t take risks with vaccines.

You can take risks with end stage therapy. If someone is very very sick, and you’ve got something you think might work, you can try it pretty easily. People will approve that, … But you can’t take risks with vaccines. And the magnitude of the severity of this threat is not great enough to do that. You could say, … we’ll take a vaccine that looks a bit risky, maybe, and we’ll give it to the elderly. These are the people who are at risk, they can try it. … People like me, say would volunteer, I certainly would. I’d give it a go, and see if that works. But I wouldn’t want to be giving a vaccine that had any risk at all to younger people. You know, these are all theoretical arguments. But there is no way anything is ever given to anybody in this sense without going through extremely thorough review processes. … I think it is pretty unlikely.

So Doherty accepts “variolation” as a term that applies outside the context of smallpox. He thinks it could work, but oddly seems to see the main concept as infecting the young, rather than controlling dose, delivery vector, or strain. And he sees it only as justified in extreme circumstances, which Covid19 will never be, as it isn’t deadly enough. Even if the Great Suppression crashes the economy worse than the Great Depression, and even if millions will likely die from accidental infections, in his eyes and those of regulators that’s no excuse for letting healthy people voluntarily take substantial personal risks. Continue reading "Peter Doherty on Variolation" »

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Variolation Test Design

By Robin Hanson · April 8, 2020 4:00 pm · Comments (0)

Okay, what the variolation concept needs most now is a trial/test/experiment ASAP. So to help get the ball rolling, let me sketch a tentative plan. I’m NOT saying this plan is now good enough. I’m saying let’s talk together about how to make it better. (Not so interested here in those ever popular “this can never work” comments.)

As with most projects, the obvious first top issue is staffing, especially leaders. This needs leaders who not only have the ability and expertise to execute it, but who can also inspire confidence in its other staff, subjects, patrons, sponsors, and audiences. (The most I’ve ever led is an assistant, so alas I don’t seem a good candidate.) The main point here is to inspire audiences to action, and that won’t happen if audiences don’t believe the project’s purported results, nor if they find its people too odious to associate with.

So the main purpose of this post is to try to attract participants, especially leaders, to pick up this ball and run with it. I’ll run with you, but I can’t run it by myself. When someone makes a good suggestion, such as in the comments, I’m likely to edit this post to include it. You are warned. Continue reading "Variolation Test Design" »

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Reply to Cowen On Variolation

By Robin Hanson · April 6, 2020 8:00 pm · Comments (2)

In the last nine days I’ve done two online debates on variolation, with Zvi Mowshowitz and Gregory Cochran. In both cases my debate partners seemed to basically agree with me; disagreements were minor. Last night Tyler Cowen posted 1000+ words on “Why I do not favor variolation for Covid-19”. Yet oddly he also doesn’t seem to disagree with my main claims that (1) we are likely to need a Plan B for all-too-likely scenario where most of the world seems likely to get infected soon, and (2) variolation is simple, mechanically feasible, and could cut Covid19 Deaths by a factor of 3-30.

Tyler lists 8 points, but really makes 11. If he had one strong argument, he’d have focused on that, and then so could I in my response. Alas, this way I can’t respond except at a similar length; you are warned. Continue reading "Reply to Cowen On Variolation" »

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Beware R0 Variance

By Robin Hanson · April 2, 2020 12:20 pm · Comments (3)

The big push now re Covid19 is to use “social distancing” to cut “R0”, the rate at which infection spreads. More precisely, R0 is the average number of other people that one infected person would infect, if they were not already infected. With no efforts to reduce it, estimates for natural R0 range from 2 to 15, with a best estimate perhaps around 4. The big goal is to get this number below 1, so that the pandemic is “suppressed” and goes away, and stays away, until a vaccine or other strong treatment, allowing most to escape infection. In contrast, if R0 stays above 1 we might “flatten the curve”, so that each infected person can get more medical resources when they are sick, but soon most everyone gets infected.

Apparently even with current “lockdown” efforts, all of 11 European nations studied now still have best estimate R0 over 2, with a median ~3.7. So they must do a lot more if they are to suppress. But how much more? My message in this post is that it is far from enough to push median R0 down below 1; one must also push down its variance.

Imagine a population composed of different relatively-isolated subpopulations, each with a different value of R0. Assume that few are infected, so that subpopulation pandemic growth rates are basically just R0. Assume also that these different R0 are distributed log-normally, i.e., the logarithm of R0 has a Gaussian distribution across subpopulations. This is (correctly) the usual distribution assumption for parameters bounded by zero below, as usually many small factors multiply together to set such parameters. The total effective R0 for the whole population is then found simply by integrating (via a lognormal) the effective growth over R0 subpopulations.

For example, assume that the R0 lognormal distribution has log mean (mu) -2 and sigma 1. Here the mode of the distribution, i.e., the most common R0 number, is 0.05, the median R0 is 0.14, only 5% of subpopulations have R0 above 0.70, and only 2% have R0 >1. Even so, if each of these subpopulations maintain their differing R0 over ten infection iterations, the mean growth factor R0 of the whole population is 20 per iteration!

As another example (for log mean -1, sigma 0.5), the R0 mode is 0.29, the median is 0.37, only 5% of subpopulations have an R0 over 0.85, only 2% have R0>1. Yet over ten infection iterations maintaining these same R0 factors per subpopulation, the mean growth factor R0 of the whole population is 1.28 per iteration. That is, the pandemic grows.

Of course these growth numbers eventually don’t apply to finite subpopulations, once most everyone in them gets infected. Because when most of a population is infected, then R0 no longer sets pandemic growth rates. And if these subpopulations were completely isolated from each other, then all of the subpopulations with R0<1 would succeed in suppressing. However, with even a modest amount of interaction among these populations, the highly infected ones will infect the rest.

The following graph tells a somewhat more general story. On the x-axis I vary the median value of R0 among the subpopulations, which sets the log-mean. For each such value, I searched for the log-sigma of the lognormal R0 distribution that makes the total average R0 for the whole population (over ten iterations) exactly equal to 1, so that the pandemic neither grows nor shrinks. Then on the graph I show the standard deviation, in R0 terms, that this requires, and the fraction of subpopulations that grow via R0>1.

As you can see, we consistently need an R0 standard deviation less than 0.21, and the lower the median R0, the lower a fraction of subpopulations with a positive R0 we can tolerate.

So, as long as there is substantial mixing in the world, or within a nation, it is far from enough to get the R0 for the median subpopulation below 1. You also need to greatly reduce the variation, especially the fraction of subpopulations in which the pandemic grows via R0>1. For example, when the median R0 is 0.5, you can tolerate less than 3% of subpopulations having an R0>1, just to hold the pandemic at a constant overall level. And to suppress in limited time, you need to go a lot further.

Different subpopulations with differing R0 seems plausible not just because our world has different nations, classes, cultures, professions, industries, etc., but because Covid19 policy has mostly been made at relatively local levels, varying greatly even within nations. In addition, most things that seem log-normally distributed actually have thicker than-lognormal tails, which makes this whole problem worse.

All of which is to say that suppressing a pandemic like this, with high R0 and many asymptomatic infected, after it has escaped its initial size and region, is very hard. Which is also to say, we probably won’t succeed. Which is to say: we need to set up a Plan B, such as variolation.

Spreadsheet for all this here.

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Variolation (+ Isolation) May Cut Covid19 Deaths 3-30X

By Robin Hanson · March 30, 2020 12:00 am · Comments (14)

(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. (As others have been also been suggesting.) Continue reading "Variolation (+ Isolation) May Cut Covid19 Deaths 3-30X" »

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Crush Contrarians Time?

By Robin Hanson · March 27, 2020 11:45 am · Comments (0)

If you are a contrarian who sees yourself as consistently able to identify contrary but true positions, covid19 offers the exciting chance to take contrary positions and then be proven right in just a few months. As opposed to typically taking decades or more to be shown right.

But, what if non-contrarian conformists know that (certain types of) contrarians can often be more right, but conformists see that they tend to win by getting more attention & affirmation in the moment by staying in the Overton window and saying stuff near what most others think at the time?

In that case conformists may usually tolerate & engage contrarians exactly because they know contrarians take so long to be proven right. So if conformists see that now contrarians will be proven right fast, they may see it as in their interest to more strictly shun contrarians.

Consider Europe at WWI start. Many had been anti-war for decades, but that contrarian view was suddenly suppressed much more than usual. Conformists knew that skeptical views of war might be proven right in just a few years. Contrarians lost on average, even though proven right.

Humans may well have a common norm of liberally tolerating contrarians when the stakes are low and it would take decades to be proven right, but shunning and worse to contrarians when stakes are high and events are moving fast.

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How Many Judges?

By Robin Hanson · March 27, 2020 7:45 am · Comments (0)

“Wow, that was sure a long slow legal process we just went through to get X punished for Y. Surely many such cases are never punished, because this process is just too hard.”

“I’ve heard that in some places it is much simpler and faster. If you have a complaint, you call over the local police officer, and he or she soon looks into it, and then makes a decision, usually that day. Fast and easy, no need for lawyers, courts, etc. Doesn’t that sound better?”

“No, that sounds terrible! What if that local police is corrupt, or biased, or stupid? Our checks and balances help correct for such problems.”

“Well in our system, after a slow expensive complex process, judges usually make the final decision. So what stops judges from being as corrupt, biased, or stupid as police?”

“Well there are a lot fewer judges than police, so we can focus our attention on a smaller number of them. For example, we can send in people undercover to try to bribe them, and arrest those who accept bribes.”

“But we almost never actually do that with judges. And we could also do that with police.”

“With judges we have an appeals system, where appeals judges fix other judges’ mistakes. And the process is public, so anyone can point to problems.”

“We could do an appeals system with police too – if there’s a complaint, call nearby police to see if they want to come make a quick appeals decision. And that process could be public.”

“We elect judges, or those who appoint them. That holds them accountable to citizens.”

“So why can’t we elect police, or those who appoint them?”

“Judges are more prestigious than police. They are picked for being the lawyers who are most respected by other lawyers.”

“Our actual police are also the most respected among people who apply to police academy.”

“Yeah but overall lawyers are more prestigious than police. They go to college, know big words, make more money.”

“And that makes them less corruptible or biased, and more just?”

“Well elites are more eager to conform, and are better able to conform, so either they will almost all be corrupt and biased or almost none will be.”

“Not sure I feel better about that. And aren’t they better at knowing how to tell when they can get away with things, so that they will be better at finding the loopholes where we are not checking, to be more corrupt and biased there? And doesn’t their conformity better help them coordinate to get away with stuff together?”

“Look, humans have long chosen to be ruled by prestigious elites, its our nature. So it must work somehow. We pick prestigious lawyers to run law, prestigious doctors to run medicine, and prestigious academics to run teaching and research. And those work well, right?”

“Okay, if it is better to be ruled by a smaller group of more prestigious people, making judges better than police, why isn’t it even better to be ruled by one most prestigious of all dictator? Who appoints and fires police or judges as they want?”

“No no, that’s terrible too! That’s too much concentration of power. This dictator could rule with impunity, because even if some of us know of his/her corruption or bias, we’ll be afraid to say so in public. He/she could crush us for our opposition.”

“But can’t judges crush us for opposing them?”

“No, that never happens. When have you ever heard of judges crushing opponents?”

“In a dictatorship, would you actually hear of the dictator crushing opponents?”

“I’m sure I would. And dictators don’t tend to be the most prestigious; they tend to be brutal thugs.”

“But won’t everyone say they are prestigious, out of fear of retaliation? And if it is better to spread out a dictator’s power, among many judges, why isn’t it even better to spread out that power among even more police?”

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Know When To Fold `Em

By Robin Hanson · March 26, 2020 12:45 pm · Comments (4)

In the 18th century, Daniel Bernoulli—the son, nephew and brother of mathematicians Johann, Jacob and Nicolaus II Bernoulli, respectively—made one of the first great mathematical contributions to infectious disease control. While formally trained in medicine, Bernoulli is known for his research in biomechanics, hydrodynamics, economics, and astronomy. He also played an important role in the eradication of smallpox from Europe, which was likely introduced there in the early 16th century and was endemic (maintained constantly) by the 18th century. Variolation is an inoculation technique whereby a scab or pus from an individual with a mild smallpox infection is introduced into the nose or mouth of healthy individuals. This practice began as early as 1000 AD in China and India and was introduced into England in 1717, where it was initially controversial. While variolation reduced the mortality probability of infected individuals from 30% to 1%, there was a small chance that the procedure would lead to death from a full-blown case of smallpox.

Bernoulli developed a mathematical model with which he argued that the gain from variolation in life expectancy through the eradication of smallpox far out-weighed associated risks … Using overall survivorship estimates calculated by Edmund Halley (of comet fame), he then used equation (2) to predict the mortality rates in every age class in a steady-state population with a birth class of size 1300. Inoculation via variolation of all newborns would confer widespread immunity, yet entail some mortality due to variolation-induced smallpox. Bernoulli compared the annual mortality rates and average life expectancy predicted by his model to those predicted assuming universal inoculation and found that variolation saves lives even if the mortality rate associated with variolation is quite high (with his parameters, as high as 10.6%).

Bernoulli’s calculations clarified the benefits of widespread inoculation even when there are significant risks. England began widely administering variolation in the 1750’s, and upon the development of the smallpox vaccine in 1796, mandated the inoculation of all infants. Thanks to these efforts, smallpox was eradicated from England by the end of the 19th century. (more)

The method was first used in China and the Middle East before it was introduced into England and North America in the 1720s in the face of some opposition. The method is no longer used today. It was replaced by smallpox vaccine, a safer alternative. This in turn led to the development of the many vaccines now available against other diseases. (more)

For the last few weeks I’ve spent a lot of time trying to more carefully make the case for deliberate infection plus immediate isolation, via the effect of flattening the curve to avoid overwhelming the medical system. (See my model, and models by Zach Hess and Kevin Simler.) Especially via exposing the voluntary young and healthy while isolating the old and sick. However, a few days ago I realized there’s a much stronger reason for deliberate infection: mortality varies greatly by the size of initial infectious dose, and deliberate infection allows for much lower doses. As the above indicates, this practice is a thousand years old.

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 effect is widely known and acknowledged by experts, and it is one of the main rationales given for wearing masks, gloves etc. That’s not just to cut the chance of an infection, but also to cut the initial dose.

Many studies have found big effects of initial virus dose on many outcomes. For covid19 we know that patients with more viruses in their blood (higher “viral load”) show more severe symptoms. And for other viruses we see that such patients also die more often. But in terms of the most direct sort of evidence, I’ve only been able to find these empirical studies connecting initial virus dose size to human death rates:

  1. Deliberate infection with low doses of smallpox is reported to have cut death rates of infected from 30% to 1-2%, or from 1 in 5-6 to 1 in 50.
  2. Among 126 African kids infected with measles, the first in a family to get it had a 14x lower death rate relative to other kids in the same families. Presumably that first kid gets it from outside the family, via a low dose, while other kids in the same family are infected at home, via a larger dose.
  3. In a Hong Kong high-rise, one resident infected many others with SARS, possibly via aerosols, but those who lived physically closer got a higher dose, and saw 3x the death rate.
  4. This New Yorker article mentions 2 more cases, but I can’t yet find cites to studies.

The first case, of a deliberate low dose infection, saw effects in the range 8-30x, while the other two cases of observing a natural difference in dose saw effects of 3x and 14x, giving only lower bounds on deliberate dose effects. So while we can’t at all be sure of the deliberate dose effect for Covid19, we have good reason to expect it to be at least a factor of 3. And maybe a factor of 30 or more.

I hope to include this dose effect in my spreadsheet model soon, but clearly this effect adds greatly to other benefits of deliberate infection, including not just flattening the curve, but also creating good places for controlled experiments and medical training.

The articles quoted above says that this policy was opposed and controversial back in the 1700s, and I’ve seen how many react badly when I’ve tweeted it. Twitter recently locked the account that linked to an op-ed making a similar suggestion. Many have told me privately that I should not write on this in public. But it seems to be far too important to suppress.

We are today proud of having anesthetics, and would think it cruel to do surgery on someone without it. But long ago they had no anesthetics, and so had to be cruel. And today we’d choose to be cruel again if surgery were essential but anesthetics were unavailable, such as on a battlefield. Similarly, humanity is proud of having replaced variolation (i.e, deliberate low-dose infection) with less-cruel vaccines. But in this crisis we don’t have vaccines, while variolation remains quite feasible. We should thus stand ready to swallow our pride, and use variolation if that’s our best remaining option. (As others have been also been suggesting.)

Dose effects seem good candidates for explaining much of the wide variation in observed Covid19 death rates across regions and subpopulations, in addition to age, comorbidity, selection effects, virus strain variations, genetic susceptibility differences, and overwhelming of medical systems. Medical workers plausibly get high doses, and the first few cases in a region would be from travelers who were likely infected with low doses. Places where large families live together closely would have higher doses. And lockdowns that limit non-family contact may similarly induce higher doses, raising death rates.

If lockdowns increase typical infection doses, that makes more difficult the tradeoff between on the one hand (a) that higher-dose mortality cost, (b) large costs of lockdown economic and social disruption, and (c) risks from centralizing power and losing freedoms, and on the other hand benefits of: (1) more time to grow medical resources, (2) flattening the curve of medical demand over time, and (3) hope for a complete suppression until a strong medical treatment arrives, so that most are never infected.

This tradeoff isn’t obvious to me; I’d like to see more detailed cost-benefit analyses. (The very idea of which apparently offends many.) But the bigger the mortality cuts from deliberate infection, the more I’m tempted to take that bird in the hand, rather than gamble on the two bush birds of being able to achieve even larger gains via complete suppression until a vaccine.

We now sit at a great pandemic poker table, playing a huge hand with nature. We could fold and lose the ante we’ve put in, accepting many regrettable deaths due to deliberate infection. Or we can push in 3-30 times as many chips as we have so far, in the form of human lives at risk, hoping for full suppression until strong treatment, just to win back our ante (no bigger pot at stake). Do you feel lucky, punk? I don’t.

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Do You Feel Lucky, Punk?

By Robin Hanson · March 19, 2020 12:00 pm · Comments (2)

A recent influential report posed the key Covid-19 issue today as: to mitigate or suppress? Should we focus on “flattening the curve” under the assumption that most everyone will get it soon, or adopt even stronger measures in an attempt to squash it, so most never get it.

Some simple obvious considerations are:

  1. if successful, squashing saves many more lives,
  2. you have to do a lot more to squash than to flatten,
  3. while flattening policies need be maintained only for a few months, squashing policies must be maintained until a strong treatment is available, probably years, and
  4. squashing is far easier when you have only a few infected and when your trading, travel, and physical neighbors don’t have many infected.

Several nations, mostly Asian, seem to have successfully squashed so far, though they started when they had few infected. China and perhaps S. Korea are the main examples of squashing more than a tiny number of infected, though even they had far fewer than we do now in the West where so many are suddenly eager to squash. China had much recent experience with mass surveillance, controlling population movements, and enforcing strict rules. Even so, they screwed up badly early on, and it isn’t at all obvious that China’s squashing will keep working as they let people go back to work, or when many big neighbors get highly infected.

The main point I want to make in this post is that trying to get your Western government to suppress Covid-19 in the usual way is making a big bet on the their quality, and the quality of typical neighboring governments. And also of your public’s commitment. As in the famous Dirty Harry (non-)quote, I ask: “Do you feel lucky, punk?”

Western government agencies and expert communities so far have had a bad record dealing with Covid-19. At first they criticized China’s strong measures and focused on signaling political correctness. The US government badly screwed up the generation and regulation of tests and masks, and the West continues to fail to cut regulation preventing rapid expansion of medical personnel and resources. Western governments only changed policies when public opinion changed, and even now seem more focused on handing out cash to allies, and symbolic but useless acts like banning bicycles.

As with most policy, you must expect that the details matter a lot. So even if you see China policy as a success, you shouldn’t have high hopes if your government merely copies a few surface features of China policy. That only works if this is a simple problem, with simple solutions, and few problems are that simple. This is not just a problem of insufficient moral fervor.

You should have higher hopes if they copied the whole China policy package relatively exactly, and even higher if the Chinese officials who managed their policy implementation personally came to manage implementation here. Even then climate, cultural, or infrastructure differences might mean their policies don’t work here. But no government seems even interested in copying the exact China package, and in my recent poll, 80% of 927 opposed this last idea of Chinese management.

Dear Western citizen, your government has already demonstrated incompetence at dealing with this in the absence of public pressure, and public pressure will mainly push them to do what they guess they would be most blamed by the public for not doing if things go badly. Regardless of whether that actually works; the public may never learn what actually works.

This pandemic has already been allowed to get much bigger than any that has ever been squashed before, and it is harder to squash than most, passing via the air, living on surfaces for days, and with infected folks showing no symptoms for a week. And in contrast to China, your government doesn’t have much recent experience with the mass surveillance, movement controls, and strict rule enforcement.

And yet now at this late date, you are considering if to authorize these same governments to oversee not just large efforts to flatten the curve, but the more extreme efforts required to squash it. Even knowing that to make it work you’ll need very strong public support in a far less-communal culture than those that have so far managed to squash.

Mind you, you are now considering this not because you have great confidence in your government’s competence, or your public’s support. But mostly, it seems, because it would look morally bad for you to give up hope on the millions who will die even if we flatten the curve well. Really, do you feel lucky, punk?

Also consider: even if your local government manages to successfully squash its internal infections temporarily, what happens if half of its neighbors fail, and become mostly infected? Or what if they succeed for a while, but half of their neighbors fail? What will it take to keep external infections from overwhelming you then? Or what will it take for your government and others to coordinate to ensure that most governments succeed? Remember, these are the governments who have so far largely failed to prevent massive illegal immigration, and who continue to fail to coordinate to limit global warming, war, and ocean overfishing, or to promote global innovation.

This wouldn’t matter much if the policies for squashing looked much like the policies to flatten, so we could actually flatten but pretend for a while that we were trying to squash. But there are policies that could help to flatten that look obviously bad for squashing, such as deliberate exposure, which might cut 3/4 of life-years lost. And locking down the economy and social contacts for many years at a level that looks at all like it might succeed in squashing is going to involve enormous costs to the economy and your freedoms.

In my recent polls, 73% and 74% of 393 and 533 respondents predicted US and world (respectively) will become >25% infected before an >80% effective treatment was given to >80% of world. So 3 in 4 agree that global containment just isn’t going to happen. Yet, to show that they care, most governments are giving lip service to squashing as their goal, not flattening. How far will we all go in paying huge costs to pretend that this is at all likely?

Before we all jump off this cliff together, can we at least collect and publish some honest estimates of our chances of success? Such as perhaps via conditional betting markets? If you aren’t willing to exactly copy the whole China policy, or have them manage it, how serious could you really be about succeess?

Look, this is like starting a war. Its not enough to ask “would it be nice to win such a war”, we need to ask “can we actually win?” Don’t start what you can’t finish.

I fear suppression is a monkey trap; afraid to let go the nut of saving everyone, we’ll be trapped in the gourd of not saving nearly as many as we could have.

Added 20Mar: Note that the many responses defending suppression talk about how many lives could be saved, and how they can imagine a plan that would work, but none address the issue of how competent is our government to implement such plans. Amazing how easily people slip from “it could be done” to “my government could do this”.

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