April 2020

The World Health Organization put us in a minor funk on Saturday with a tweet, promoted by the media in a most inflammatory way, that said “There is currently no evidence that people who have recovered from #COVID19 and have antibodies are protected from a second infection.” Of course, no antibodies means no vaccine, which means our road back to happiness and prosperity gets ever more treacherous.

Then our wife pointed out that “the WHO is usually wrong and, anyway, it is controlled by the Chinese,” and that cheered us right back up again.

And, sure enough, the WHO has backed off, having learned for itself that a tweet is just about the worst way to communicate a complicated point on which literally the entire world has pinned its hopes: “WHO Deletes Misleading Tweet That Spread Paranoia About COVID-19 Reinfection.”

So, yeah, “the experts” can be as dangerously misleading as Donald Trump. They may not do it as often or involve as much orange, but for better or worse more or less the entire world has learned to check with, say, the WHO when Donald Trump says something a little bit crazed off. Who do we check with when “the experts” tweet “you may as well kill yourself now, because this is as good as it gets”?

Last night we had Zoom cocktails — by which we mean the guzzling of Old Forester poured over one of those giant hipster ice cubes — with Tim Blair, columnist for News Corp Australia’s Daily Telegraph. Unlike the non-Murdoch media in the United States and, we expect, Oz as well, Tim and the Telegraph have been rather clear and, we dare say, scientific in their description of the origins of the Wuhan virus. That has earned them a couple of badges of honor unbelievably arrogant letters of demerit from the People’s Republic of China. Tim delicately deconstructed the Commie “pansygrams” here and here, both links worth your time.

That, however is not the best part. Here is the best part:

You want some “fearless” journalism? Not a bad example, right there. We’d love to see more of that in the corporate press, but much of it is entangled with conglomerates that want to sell products in China. AT&T owns CNN, Disney owns ABC, and so forth, and even The New York Times has to be worried about its employees. Is there any of them that aren’t compromised?

Probably not.

If you were to imagine that all of this were just Middle Kingdom indignation at “sweet and sour sick” and the like, you’d be, well, sadly misinformed. Before defending China became a totem of the anti-Trump tribe and most Westerners had heard of “wet markets,” China had launched a plan to dominate global media.

Beijing is buying up media outlets and training scores of foreign journalists to ‘tell China’s story well’ – as part of a worldwide propaganda campaign of astonishing scope and ambition…

[O]ver the past decade or so, China has rolled out a more sophisticated and assertive strategy, which is increasingly aimed at international audiences. China is trying to reshape the global information environment with massive infusions of money – funding paid-for advertorials, sponsored journalistic coverage and heavily massaged positive messages from boosters. While within China the press is increasingly tightly controlled, abroad Beijing has sought to exploit the vulnerabilities of the free press to its advantage.

In its simplest form, this involves paying for Chinese propaganda supplements to appear in dozens of respected international publications such as the Washington Post. The strategy can also take more insidious forms, such as planting content from the state-run radio station, China Radio International (CRI), on to the airwaves of ostensibly independent broadcasters across the world, from Australia to Turkey….

Since 2003, when revisions were made to an official document outlining the political goals of the People’s Liberation Army, so-called “media warfare” has been an explicit part of Beijing’s military strategy. The aim is to influence public opinion overseas in order to nudge foreign governments into making policies favourable towards China’s Communist party. 

The Guardian, more than a year ago. Read the whole thing. Oh, and bold emphasis added.

Somebody ought make a list of the American media outlets that have published paid PRC propaganda. Oh. Wait. They did.

Media corporations, left and right, really won’t be credible on the Covid-19 pandemic until they publish detailed disclosures of their financial and other entanglements with the People’s Republic. Otherwise, how do we know they aren’t Commie shills?

Imagine you wake up in a house you have never been in before, and it is pitch dark, and there is no electricity. You need to find your way to the bathroom, and then the kitchen, and eventually to the outside. Your only option is to tip toe – there might be a Lego block, or you might fall down the stairs – and grope the walls and furniture, and eventually make your way as you gather more tactile information in little bits, with still no understanding of the bigger picture.

Everyone in the world right now, from the most sage public health expert to the wisest head of state to the anxious citizen shopping for groceries, is tip-toeing through that dark unfamiliar house.

Since the SARS-COV-2 virus first emerged in China at some point last fall, all leaders and followers have been deciding and not deciding, and acting and not acting, without the information necessary to know whether their decisions and actions and omissions will be correct in retrospect, or not. Every politician who orders a business closed, or not, and every person who decides to touch a surface, or not, is acting at roughly the same level of information, which is very, very, low. So low, that we will all make a lot of decisions and take a lot of actions that we will have the opportunity to regret, or for which others might call us to account.

How dark is the house? Very. Even as of mid-April, with all the world’s medical and biological scientists at work on the problem, the list of things that we do not know about SARS-Cov-2 and the transmission, mechanism of action, and treatment of its disease, Covid-19, is very long. Among the things that we do not know to any level of certainty:

• Prevalence. We not only do not know how many current cases there have been in any given population, we don’t know how many there have been. Sure, certain experienced countries and homogeneous micro-states probably have better information than most other countries, but even when we have done extensive antibody testing, such as in California’s Santa Clara County, the extrapolations for the broader population have a huge margin of error.
• Transmission. We know that an infected person in contact or close proximity to another person can transmit Covid-19. Other than that, there is a great deal we do not know, including whether the amount of virus at exposure makes a big difference, or whether strolling on a beach in high wind poses any risk at all.
• R0, pronounced “R naught.” See here for the concept. R0 is the “basic reproduction rate” of the virus, which can be thought of as “the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection.” The estimated R0 for Covid-19 has a massive proposed range, between 1.4 and 5.7, per the obsessively updated Wikipedia page. If you play with any of the epidemiological models, you quickly learn that there is tremendous leverage, and therefore uncertainty, in this number.
• Rt. Rt is the actual transmission rate per case. In a nutshell, if “t” is above 1 then we are suffering an increasing rate of infection, and if it is below 1 we are gaining against the rate of infection. Click around this site for more, including purported Rt for each American state. The problem, of course, is that we can only measure Rt for cases we have confirmed, and we have very little idea how many cases there are that haven’t been confirmed.
• The mortality rate from Covid-19. Notwithstanding evidence of excess mortality and the problem of people who die suspiciously without being tested, we probably know the rough number of Covid-19 as well as we know anything. What we do not know is the percentage of infected people who die, and under what circumstances, because we really have no clue how many people are, or have been, infected.
• The mechanism and circumstances by which Covid-19 kills people. This is an excellent article on what we do and do not know about Covid-19’s mechanism of action, but only read it if you have a robust emotional state. There remains lots of mystery.
• Who is at risk of dying, and why? We know that old age and various co-morbidities, such as diabetes, hypertension, compromised lungs, and autoimmune diseases are correlated with much higher mortality rates than for young and healthy people. We don’t know why some otherwise healthy people crash and become very ill.
• Treatments. As the world’s doctors treat more patients and share their knowledge, we are learning more every day about how to respond to patients sick with Covid-19. In addition to practical interventional medicine, there are around 800 clinical trials around the world studying the safety and efficacy of old and new therapies. All this progress is great for people getting sick later rather than sooner, but we still have no clue whether we will have a robust cure in the near future.
• Immunity, antibodies, and the testing therefor. We do not know the extent to which survivors of Covid-19 develop antibodies, how long they persist, whether they usually confer substantial immunity or only do so sometimes, how long that immunity persists — weeks, months, years? — and whether a test for antibodies, even if it works to do that much, is therefore a test for immunity.
• Vaccines. There is a global scramble to develop a vaccine and test it sufficiently to green-light mass production and administration. Vaccines are moving along the developmental and regulatory process more quickly than under normal circumstances, probably in part because we are taking risks to accelerate their development. A vaccine would be like a light coming on in the dark house, showing us exactly the path to the front door. If we knew that the mortality rate from Covid-19 were very high, it might be wise to take even more risks to get a vaccine. But what if the mortality rate isn’t very high because the disease is far more prevalent than we now know? Do we want to take the risk of vaccinating the entire population with a short-cut vaccine if the mortality rate from Covid-19 is only, say, twice that of the seasonal flu?
• Oh. And the models. There has been a lot of controversy about the various epidemiological models and their predictions under various social-distancing measures. “Listen to the experts!” being both the ultimate admonition and excuse for our times, models are necessary — no, they are not crackpot conspiracies — so that people in authority have some rationale for making decisions that carry the force of law. However, because those models need to embed assumptions about all of the variables in the foregoing bullets that we know little or nothing about, they inherently have extremely wide margins of error. In other words, they barely improve the likelihood that those decisions will ultimately prove to be the optimal ones, even if models help us choose a direction. As we pick our way through the dark house, the models are, perhaps, a flash of light, maybe a single distant lightning strike, that plays through the windows for a fraction of a second. We might see the top of the staircase, but we won’t see a toy left on the third step from the top, or the bad guy lurking with his cudgel.

When it comes to Covid-19, we — by which we mean literally everyone — do not know what is the case. We do not know where we are in the dark house, and where we can find a bathroom, the kitchen, and a door to the outside.

In the absence of useful information about the disease, it is easiest to rest on that which we do know: That lots of people are in fact dying from Covid-19, that more people will die of other things if the local healthcare system is overwhelmed by Covid-19 hospitalizations, and that social distancing incrementally reduces Rt and therefore probably also reduces direct mortality and morbidity from Covid-19 and, in a few very sad places, from an overwhelmed healthcare system.

Well, if incremental social distancing probably reduces direct mortality and morbidity from Covid-19, doesn’t that fact tell us that we need to keep up maximum social distancing?

Well, no. Because one can never derive “what ought” from “what is.” Call it “Hume’s guillotine” if you want to condescend to your less learned interlocutors. It is simply not logical to say “the entire country should stay at home and avoid all contact if it saves just one life.” You need to explain why saving just one life, or even thousands, justifies shutting down the country.

We knew why it made sense to “flatten the curve,” and it had absolutely nothing to do with preventing the total number of infections, or “saving just one life.” We were worried that in some jurisdictions, the healthcare system would be overwhelmed if the surge in Covid-19 cases requiring hospitalization, intensive care unit intervention, or ventilation far exceeded the capacity. If the healthcare system in a given location is overwhelmed, not only will patients with Covid-19 die because they do not get the best attention, but other people will as well. How many people would die of strokes and heart attacks and anything else because the ambulances are busy and emergency rooms are choked with Covid-19 victims, or because they were too afraid to see a doctor in the first place? So we made the judgment, when we woke up in that house, to avoid the risk of further injury and stay in bed for a while.

Whether or not the curve is flattened in any place according to the ex ante predictions of the modelers, we now know that in many parts of the United States the healthcare system has not been overwhelmed, and it is unlikely to be if we maintain some of the social distancing policies and social norms that prevented the spike in Covid-19 hospitalizations we all feared. Click here and go to your state of choice to see the excess capacity, at least under the current circumstances.

So, the old rationale for the most aggressive social distancing policies in the jurisdictions with vacant healthcare capacity no longer obtains. Does that mean that loosening things up will not result in more infections? Of course it will. “Flattening the curve” was meant to buy time. The “area under curve” (or AUC to you data aficionados) was not going to be smaller, it was going to be spread out over time. However many people were going to be infected if we didn’t flatten the curve are still going to be infected in the many months before we have a vaccine, if we get one. They just will be more likely to survive because the system won’t be swamped and the world’s doctors are learning more every day about how to treat patients.

But loosening will also come with many benefits: Jobs, the avoided destruction of the work of many lives, the end of isolation for people who are being driven to despair because of it, and relief from the profound fear of dying alone. And, of course, the resumption of school for the many children whose parents are not up to doing a better job from home.

Back to our house. The social distancing maximalists — people who want to prioritize the saving of lives from Covid-19 above other considerations, virtually without qualification — are essentially saying “stay in the bed you woke up in, no matter how badly you have to go pee, get a drink of water, or get out of the house.” After all, that reduces the risks of an accident from fumbling in the dark to zero. Sure, you’ll wet the bed and you will be very anxious because you cannot explore this strange house you’ve never seen before, but at least you will run zero risk of falling down the stairs.

But what if the house is on fire, and you just don’t know it yet? Well, the house is on fire if you are out of work with no savings, or the business you spent a lifetime building is being destroyed. We don’t know for sure that we will die from fire, but we can smell a whiff of smoke and it is making us very anxious.

In any jurisdiction where the healthcare system has excess capacity and is likely to continue to do, there is now a new decision to make. We know that even a little reopening will cost lives to Covid-19 at the margin. We also know that relying only on that fact – that thing that is – to tell us what we ought to do next, is not intellectually honest. So how should our leaders decide how to tip toe forward in the dark?

One group of experts — “Harvard” is the tell in the linked story — says that it is not safe to reopen the economy until we are doing 20 million tests per day, up from around 150,000 today, and we have developed a network of 100,000 “contact tracers” to run down outbreaks and such.

One needn’t have the experience of having built up a large industrial organization to know that only professors, with secure jobs funded by a $40 billion endowment, could think this way. Even with an extraordinarily effective government agency in charge — think NASA in the moonshot years — this would take months to do. Months during which poverty deepens and the accomplishments of many lives are destroyed. And, it must be said, there is no subject on which Americans of all political parties agree (albeit for different reasons) more than that there is no such extraordinarily effective government agency.

The Harvard idea is to stay in bed, come what may, until the sun shines through the windows and we can see the hazards in this strange house. Essentially Harvard says, “assume the house isn’t on fire and we have all the time in the world.”

The fact is, in parts of the country where the healthcare system is not overwhelmed and is unlikely to be, there are other considerations that are appropriately weighed against the more than “just one life” that we will lose to Covid-19 when we loosen restrictions. For starters, there are the excess deaths and morbidity that spring directly from poverty and the despairing confinement of social distancing. That is a cheap and unbecoming answer though, because it implies simple math, measuring lives lost by one policy versus those lost by another.

In any case, the “simple math” will not be nearly so simple in advance, because we know so damned little about this virus for all the reasons above. We simply do not know how many lives will be cost by policy changes, changes in social norms, and the like under different circumstances, because the models have such massive margins of error because the assumptions in the models are backed by experienced guesswork more than scientific knowledge.

There are also other costs than lives, including the shattering of dreams and the loss of hope and the sheer wretchedness of poverty. And, no, we cannot simply solve all of that with more money from the federal government. That money may help, but it does not substitute for purpose and industriousness and pride of accomplishment. It also comes at the cost of burdening the standards of living of the rising generation, since the piper will eventually be paid in either painful inflation or very high taxes.

Maximalist social distancing destroys. We know that just as we know that it saves people from dying from Covid-19. How we and our leaders reconcile those trade-offs, and they are inescapable, will be well-understood only in retrospect. This we do know: If you believe either that we must all stay in bed in the dark and burning house until the sun comes up or that we should rush around in the dark scrambling for any exit at any risk to our life and limb, you are not being serious about the problem of making a necessary and unavoidable decision on very little useful information.

We believe that we Americans and our leaders will have to start exploring the house, little by little, and find our way to the front door. We will need to start tip toeing and groping our way, because we simply do not have the information we need to know that the right decision is to rush headlong for the exit, or explore the house comprehensively in leisure, or lie in bed until the we have perfect information.

Come what may.

We’ve slightly modified a Venn diagram that has been going around on social media…

Perhaps in a bit of ironic circularity, we offer this bit of wisdom in rough support of the same point:

One does not have to spend a lot of time on political Twitter or following the latest in punditry or among one’s engaged “friends” on Facebook to see that many if not most such people have precisely identified which politicians and which pandemic policy responses and omissions have been catastrophic failures or glorious successes. If you consort with “both sides” and read pundits with whom you rarely agree, you know it when you see it. There has been so much of this that it makes no sense to link examples.

Our own view is that it will be some time — probably after the United States presidential election, unfortunately — before we will know which policies will have worked at an acceptable cost, and which will have worked, or failed, at “too high” a cost. But as early studies do emerge, there will be a meta-struggle, mostly promoted by partisans seeking narrative dominance, about the best way way to measure relative success, failure, and cost.

Herewith, we propose a few measures of ultimate success or failure.

Ultimately, we will know three things in each applicable jurisdiction (itself a hot topic, of which a bit more below): (1) excess deaths (whether from the virus or the stress of the economy and confinement or from local collapse of the healthcare system) over the baseline experience, (2) excess morbidity and mental health problems, and (3) the decline in the jurisdiction’s GDP per capita against the baseline. Those three things in a given country or state will be the indicators of success or failure.

Now, there will be many arguments to be had in that big partisan quarrel to dominate the narrative. How to compare jurisdictions? How to weigh the value of excess lives lost vs. non-lethal physical and mental health and social problems vs. GDP? (And if you are one of those shallow thinkers who recoil with disgust at the idea that we would measure lives in dollars, please go read some other moron’s blog.) How to attribute the effects of public policy to overlapping jurisdiction in a federal system? How much credit should be given to cultural propensity to follow the direction of governmental, business, and social leaders? What was the role of private sector responses and innovation, and should political leaders get the credit for it?

Regarding jurisdictions, we’ve noticed that people who truly hate President Trump without fail want to compare the policy and social responses in the United States, as a whole, to Singapore, Taiwan, South Korea, Iceland and — if the milk of intellectual honesty compels the concession — Germany. Expect Republicans to compare red states with blue states, or the outcomes for Republican governors compared to Democrats (knowing full well that Republican governors preside over more wide open spaces), or New York State to Lombardy. All such comparisons would be — how you say? — rubbish. Or, if you prefer fruity metaphors, “apples to oranges.”

Having dabbled a bit in social science and having, er, gleaned more than enough from an erstwhile mother-in-law who was a professor of sociology, we say with at least the usual blogger’s expertise that the accepted method would be to identify “paired” jurisdictions with similar known parameters that turn out to matter. Competent social scientists can and will identify pairings among relevantly similar states and between countries to measure the effectiveness of policy responses. Suppose (for the sake of argument) it turns out that the attributes in a jurisdiction most closely correlated with excess mortality are average or median age, poverty, obesity, and density of the population in proximity to heavy travel flows from Europe and China. It might be silly to compare New York and Texas, but it might make sense to compare Florida and Texas. It might be pointless to compare Iowa and Illinois avec Chicago, but it might make sense to compare Iowa with Illinois excluding Chicago and the “collar counties.” Or Minnesota and Wisconsin or the Dakotas with each other. You get the point.

As for the rest of our questions and the many more we have not considered, file them away so you can detect partisan nonsense from actual scholarship in the months to come.