Tag Archives: Pandemic

A Perfect Storm of Inflexibility

Most biological species specialize for particular ecological niches. But some species are generalists, “specializing” in doing acceptably well in a wider range of niches, and thus also in rapidly changing niches. Generalist species tend to be more successful at generating descendant species. Humans are such a generalist species, in part via our unusual intelligence.

Today, firms in rapidly changing environments focus more on generality and flexibility. For example, CEO Andy Grove focused on making Intel flexible:

In Only the Paranoid Survive, Grove reveals his strategy for measuring the nightmare moment every leader dreads–when massive change occurs and a company must, virtually overnight, adapt or fall by the wayside–in a new way.

A focus on flexibility is part of why tech firms tend more often to colonize other industries today, rather than vice versa.

War is an environment that especially rewards generality and flexibility. “No plan survives contact with the enemy,” they say. Militaries often lose by preparing too well for the last war, and not adapting flexibly enough to new context. We usually pay extra for military equipment that can function in a wider range of environments, and train soldiers for a wider range of scenarios than we train most workers.

Centralized control has many costs, but one of its benefits is that it promotes rapid thoughtful coordination. Which is why most wars are run from a center.

Familiar social institutions tend to be run by those who have run parts of them well recently. As a result, long periods of peace and stability tend to promote specialists, who have learned well how to win within a relatively narrow range of situations. And those people tend to change our rules and habits to suit themselves.

Thus rule and habit changes tend to improve performance for rulers and their allies within the usual situations, often at the expense of flexibility for a wider range of situations. As a result, long periods of peace and stability tend to produce fragility, making us more vulnerable to big sudden changes. This is in part why software rots, and why institutions rot as well. (Generality is also often just more expensive.)

Through most of the farming era, war was the main driver pushing generality and flexibility. Societies that became too specialized and fragile lost the next big war, and were replaced by more flexible competitors. Revolutions and pandemics also contributed.

As the West has been peaceful and stable for a long time now, alas we must expect that our institutions and culture have been becoming more fragile, and more vulnerable to big unexpected crises. Such as this current pandemic. And in fact the East, which has been adapting to a lot more changes over the last few decades, including similar pandemics, has been more flexible, and is doing better. Being more authoritarian and communitarian also helps, as it tends to help in war-like times.

In addition to these two considerations, longer peace/stability and more democracy, we have two more reasons to expect problems with inflexibility in this crisis. The first is that medical experts tend to think less generally. To put it bluntly, most are bad at abstraction. I first noticed this when I was a RWJF social science health policy scholar, and under an exchange program I went to the RWJF medical science health policy scholar conference.

Biomed scholars are amazing in managing enormous masses of details, and bringing up just the right examples for any one situation. But most find it hard to think about probabilities, cost-benefit tradeoffs, etc. In my standard talk on my book Age of Em, I show this graph of the main academic fields, highlighting the fields I’ve studied:

Academia is a ring of fields where all the abstract ones are on one side, far from the detail-oriented biomed fields on the other side. (I’m good at and love abstractions, but have have limited tolerance or ability for mastering masses of details.) So to the extent pandemic policy is driven by biomed academics, don’t expect it to be very flexible or abstractly reasoned. And my personal observation is that, of the people I’ve seen who have had insightful things to say recently about this pandemic, most are relatively flexible and abstract polymaths and generalists, not lost-in-the-weeds biomed experts.

The other reason to expect a problem with flexibility in responding to this pandemic is: many of the most interesting solutions seem blocked by ethics-driven medical regulations. As communities have strong needs to share ethical norms, and most people aren’t very good at abstraction, ethical norms tend to be expressed relatively concretely. Which makes it hard to change them when circumstances change rapidly. Furthermore we actually tend to punish the exceptional people who reason more abstractly about ethics, as we don’t trust them to have the right feelings.

Now humans do seem to have a special wartime ethics, which is more abstract and flexible. But we are quite reluctant to invoke that without war, even if millions seem likely to die in a pandemic. If billions seemed likely to die, maybe we would. We instead seem inclined to invoke the familiar medical ethics norm of “pay any cost to save lives”, which has pushed us into apparently endless and terribly expensive lockdowns, which may well end up doing more damage than the virus. And which may not actually prevent most from getting infected, leading to a near worst possible outcome. In which we would pay a terrible cost for our med ethics inflexibility.

When a sudden crisis appears, I suspect that generalists tend to know that this is a potential time for them to shine, and many of them put much effort into seeing if they can win respect by using their generality to help. But I expect that the usual rulers and experts, who have specialized in the usual ways of doing things, are well aware of this possibility, and try all the harder to close ranks, shutting out generalists. And much of the public seems inclined to support them. In the last few weeks, I’ve heard far more people say “don’t speak on pandemic policy this unless you have a biomed Ph.D”, than I’ve ever in my lifetime heard people say “don’t speak on econ policy without an econ Ph.D.” (And the study of pandemics is obviously a combination of medical and social science topics; social scientists have much relevant expertise.)

The most likely scenario is that we will muddle through without actually learning to be more flexible and reason more generally; the usual experts and rulers will maintain control, and insist on all the usual rules and habits, even if they don’t work well in this situation. There are enough other things and people to blame that our inflexibility won’t get the blame it should.

But there are some more extreme scenarios here where things get very bad, and then some people somewhere are seen to win by thinking and acting more generally and flexibly. In those scenarios, maybe we do learn some key lessons, and maybe some polymath generalists do gain some well-deserved glory. Scenarios where this perfect storm of inflexibility washes away some of our long-ossified systems. A dark cloud’s silver lining.

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Covid19 Pizza-Risk Estimator

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, these 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 more, we must act on what we know.

The above is actually part of a screenshot from this spreadsheet (copy it to edit it) that I’ve made to help you estimate household risk. (Anyone know how to embed it here, so each reader can edit their own version here?) 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 you 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 activity 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.

FYI, this post was up just 33 hours after I first tweeted the idea for this project.

Added 14Apr: Commentor Roman Kuksin did an explicit analysis of many of these risks, and finds a 0.74 correlation with the above risk estimates.

Added 18Apr: Here is another set of activity risk estimates: HT @diviacaroline.

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Peter Doherty on Variolation

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

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

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

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

(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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Do You Feel Lucky, Punk?

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 quality of they and 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 over 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 also 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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Expose The Young

23Mar: Sorry for doing this, but I found errors, & can’t stand to let them stand, so I’ve edited this substantially to fix them. The more features one adds to a sim, the more chances for bugs!

In my previous analysis of (voluntary) deliberate exposure (plus immediate isolation), I assumed that people were quarantined and exposed at random. But there seem to be good arguments for preferring either the young & health or the old & sick early. Exposing the old early gives them access to a less overwhelmed medical system, while exposing the young early allows the population to more quickly reach “herd immunity”, saving the old from later exposure.

To see which effect is stronger, I’ve changed my spreadsheet model to let the population be split into two groups who can differ in their death rate, and in whether they are quarantined or deliberately exposed.

As before, I start with one random contagious person in a US-sized population of 327M uninfected. After 7 days each contagious person becomes visibly sick, 10% of these need an average of 7 ICU days of help, and after 7 days some fraction of sick folks die, while the rest recover and are immune. Sick folks are added onto the usual 10K people who need ICU help each day, and their death rate goes as the log of the daily total number of people who need ICU help. If only 10K people total need ICU help, only 0.4% of sick folks die, but if 50K per day people need ICU help, then 3% of them die. (See below for alt death rate function.)

The number of infected people who become contagious each day is proportional to the product of the uninfected count times the contagious count. Except that there is a quarantine that always holds 10M people, with a proportion of contagious vs. uninfected the same as the larger population from which it is drawn. People in quarantine have only 2% leakage of the usual rate of infecting others. (See below for alt leakage rate.) The infection rate parameter is set so that, early on, the death so far count doubles about every 6 days. 

To allow an old and a young group, I found US estimates on the population fraction and life-years remaining for different age and gender combinations, and also on how COVID19 death rates change by age, gender, and comorbidity.  From these I’ve defined 72 groups which vary in age, gender, and a binary health-or-not, and produced estimates for each group on life-years remaining and COVID19 death rate. Sorting these groups by death rate, I can split them many ways into two groups: “young” with a lower death rate and higher remaining life-years, and “old” with a higher death rate and lower life-years. Depending on the cutoff between young and old, the ratio of their death rates varies from 36 to 6781, and the ratio of life-years remaining varies from 1.7 to 10.4. 

I compared 8 options with varying combinations of who is quarantined: all (= random), young only, or old only, and also who is deliberately exposed: none, all (=random), old only, and young only. As before, I assumed a quarantine big enough to hold 10M, and that higher death rates induce higher needed ICU days. For each option I crudely searched to minimize the total lost life years by varying the young vs. old cutoff, the number of days of deliberate exposure (E days), and the fraction of the quarantine used for those deliberately exposed (D % of Q).

The following table shows my results, as do 3 graphs at the end of this post.

The 4th and 5th columns (% Deaths and % Adj. Deaths) give the percent of deaths, and life-year-adjusted deaths for each option relative to the first (baseline) option. Note that both choosing to deliberate exposed random folks, and choosing to quarantine the old, each save about 18% of life-years lost. Combining those policies, via quarantine the old and deliberately expose the young, saves about 43%.

For all but the last two options in the table, the optimal age cutoff had 24% being “old”: healthy men 60+ and women 65+, and also unhealthy men 40+ and women 50+. For the last two options, the optimal age cutoff had 26% being “old, adding in only healthy women 60-64.

The next three columns give percent chances of dying relevant for the deliberately exposed. % Lives/E is about the average % chance of deaths avoided in others per deliberately exposed person, relative to the same quarantine policy but no one deliberately exposed. % Die not E and % Die if E are about the % chances that someone would die if they die not or did become deliberately exposed. The last column Lives/Risk gives the ratio of the decreased chances of death in others over the personal increased chance of death in the deliberately exposed.

In the last two options which exposing the young, one can offer the young a dramatic motivation for choosing to become deliberately exposed. In addition to any cash compensation, or perhaps medical priority for loved ones, each volunteers to choose deliberate exposure saves someone else a 6.5-7.2% chance of death while themselves suffering only an additional 0.20-0.21% chance. And they can brag about having passed strict health requirements to be eligible. Much like a soldier at war, they can credibly claim to be an elite who made personal sacrifices to cause much larger community gains. (E.g., US soldiers in Iraq in ’06 suffered 1/255 death rate.)

Okay, I tried some variations to test robustness. I encourage you to try variations as well:

  1. I tried increasing the quarantine leakage from 2% to 10%.  Here quarantine old, expose none, gives 82% of baseline loss, while quarantine old and expose young has 77%. So a much leakier quarantine gives smaller, but still positive, gains to deliberate exposure.
  2. I tried doubling the quarantine size, and found losses, relative to baseline, of 72.7% for quarantine old, expose none, 80.9% for expose & quarantine at random, and 40.7% for expose young quarantine old. So the gains of deliberate exposure increase with the size of the quarantine.
  3. I replaced the log death function with a two-state function: The first 10K ICU cases per day get a low death rate of 0.4%, while any more get a high death rate of 3%. Results are quite similar. The optimal young/old cutoff turn out to be the same. Adjusted deaths for quarantine old is 82.4% of baseline, for exposing at random is 87.2%, and for expose young and quarantine old is 61.5%. In that last option, each young deliberately exposed saves 1.7% of another life, paying a personal added risk of .07% chance of death. So while the ratio is similar, both amounts are smaller.
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Vouching Fights Pandemics

As I’ve pitched vouching as a general solution to both law and medicine, the looming coronavirus pandemic offers a good and challenging concrete test; how well could vouching handle that?

If you recall, under a law vouching system, each person is required to get a voucher who stands ready to cover them for any large legal liability, including fines as punishment for crimes. Under a medical vouching system, each person gets a voucher to pay for all their medical treatments, and also to pay large amounts to a third party when that person becomes disabled, in pain, or dead. Voucher-client contracts can specific physical punishments like torture or jail, co-liability with associates, and limits on freedoms, such as re travel, privacy, or risky behaviors. 

Regarding a looming pandemic, your voucher would know that it must pay for your medical treatment, your lost salary if you stop working, and large fines if you die or get hurt. So it would offer large premium discounts to gain powers to limit your travel and contacts, and to penetrate your privacy enough to see what contagion risks you might incur. And it would have good incentives to make risky medical choices expertly, such as if to try an experimental treatment, or to accept early deliberate exposure. 

When you live with others who you might infect, or who might infect you, you’d probably also be offered premium discounts to let the same voucher cover all of you together. But there would remain key externalities, i.e., risks of infecting or being infected by others who are not covered by the same voucher.

The straightforward legal remedy for such externalities is to let people sue others for infecting them. In the past this remedy has seemed inadequate for two reasons: 

  1. It has often been expensive and hard to learn and prove who infected who, and
  2. Ever since we stopped holding family members liable for each other, and selling debtors into slavery, most folks just can’t pay large legal debts.

The vouching system directly solves (2), as everyone has a voucher who can pay lots. And the key to (1) is ensuring that the right info is collected and saved.

First, consider some new rules that would limit people’s freedoms in some ways. Imagine people were required to keep an RFID tag (or visible QR code) on their person when not at home, and also to save a sample of their spit or skin once a week? Then phones could remember a history of the tags of people near that phone, and lawsuits could subpoena to get surveillance records of possible infection events, and to see if spit/skin samples on nearby dates contain a particular pathogen, and its genetic code if present. We might also adopt a gambled lawsuit system to make it easier to sue for small harms.

Together these changes could make it feasible to, when you discovered you had been infected, sue those who likely infected you. First, your voucher could collaborate with vouchers of others who were infected nearby in space and time, by a pathogen with a similar code. By combining their tag records and local surveillance records, this group of vouchers could collect a set of candidates of who might plausibly have infected you when and where. 

(Yes, collaboration gains from voucher groups might give vouchers more market power, but not too much, as this can work okay even when there are many competing voucher groups.)

You could then sue all these possible infectors via gambled lawsuits. For the winning lawsuits, your voucher could subpoena their split/skin to see if their pathogen codes match the code of the pathogen that infected you. When a match was found, a lawsuit could proceed, unless they settled out of court. Sharing verdict and settlement info with collaborating vouchers could make it easier for them to figure out who to sue.  

Okay, yes, there is the issue of who would agree to keep RFID tags and sufficient spit/skin samples, if this weren’t required by law. I’ve proposed that the amount awarded in a lawsuit be corrected for how the chances of catching someone varies with the freedoms they keep. Such chances would be estimated by prediction markets. The lower the estimated chance of catching a particular harm for a given set of freedoms, then the higher would be the award amount if they are caught. 

So if, given the choice, some people choose not to use RFID tags or keep spit/skin samples, they may be harder to catch, but they would pay more when they do. (Which is part of why most might choose less privacy.) As a result, clients and their vouchers will know that on average they will pay for the full cost of infecting others. Which could be huge amounts if they infect many others with deadly pathogens. Which would push vouchers to work to ensure that their clients take sufficient care to avoid that. 

And that’s my concept. During the early stages of a pandemic, a system of law/med vouchers would have incentives to try the sort of aggressive case tracing that public health professionals now try. And if such professionals existed, they could collaborate with vouchers. Once the pandemic escaped containment, this vouching system would encourage people to isolate themselves to avoid infecting others, and to avoid being infected. Their freedoms of travel and privacy would become more limited, more like the limits that an aggressive government might impose. 

But exceptions would be allowed when other costs loomed larger, just as economic efficiency demands. Compared to a centralized aggressive government, a voucher system could much more easily and flexibly take into account individual differences in inclinations, vulnerability, and preferences. The choice of freedoms would be made more practical and local, and less symbol.

With vouchers and lawsuits for infections working well to get people to internalize the infection externalities, pandemics might be limited and contained at nearly the level that a cost-benefit analysis would suggest. 

Added 07Mar: Early in a pandemic it is easier to trace who infected you, and it would make sense to let you sue someone who infected you not only for the damages you suffered, but also for the damages you had to pay others who you infected. This could create very large incentives to contain pandemics early.

Later in a pandemic people sued might reasonably argue that they should only have to pay for the harm from someone being infected earlier than they would otherwise have been, which might be no harm at all during a period before the peak when medical resources are becoming spread increasingly thin.

Added 10Mar: If later in an infection it becomes too hard to trace who infected who, even with the above reforms, then it might make sense to have more general crime-law-based rules limiting social contact. Vouching can also do well at enforcing such rules.

Added 20Apr: See my more flexible and general approach to requiring that info be collected and saved. Also, if cases are common where you can narrow an infection down to 1 of 5 sources, but can’t prove which one, it makes sense to make them each pay 20% of the damages. This way we don’t need to be >50% confident of each particular infection link.

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