In an op-ed at Bloomberg infectious disease authorities Arturo Casadevall and Liise-anne Pirofski try to explain why it’s hard to predict who will live and who will die after contracting COVID-19:

Among individuals in the same risk group — the same age, say — differences in infection outcome can result from five different variables outside their control.

The first of these is microbial dosage or inoculum, the number of viral particles that cause infection. Small numbers of viral particles are more likely to be contained effectively by the body’s defenses. Then, infection may cause no symptoms or only mild disease. In contrast, a large number of particles can lead to increased viral growth, overwhelming the immune system and causing more severe disease.

Genetics may also influence susceptibility to severe infection. Viruses often gain access to host cells via surface proteins, which vary in presence and nature from person to person. Someone with no such surface proteins may be resistant to infection. In the case of HIV, for example, some people lack the receptors needed for viral infection and are not susceptible to the virus.

A third variable that influences infection outcome is the route by which a virus enters the body. It’s possible that virus inhaled in the form of aerosolized droplets triggers different immune defenses than does virus acquired by touching contaminated surfaces and then touching one’s face. The nose and the lung differ in local defenses, so the route of infection could significantly affect the outcome.

The fourth variable is the strength of the coronavirus itself. Viruses differ in virulence — their capacity to damage host tissues or immunity — even when they are all the same species. This is why flu seasons vary in severity from year to year. The varieties of a virus such as coronavirus differ depending on small genetic characteristics and how these affect the interaction with human hosts. As the coronavirus spreads from person to person, it may undergo unique changes in its genetic structure that enhance or attenuate its capacity to do harm. Strains that are more virulent could lead to more severe disease.

Finally, people’s immune status — especially their history of prior infectious diseases — crucially determines how they respond to a new infection. The immune system remembers previous encounters with microbes, and that affects how it fights and responds to new ones. In the case of dengue, infection with one type of the virus can make the individual more susceptible to infection with a different type of the same virus. In other situations, a recent infection with a virus can affect susceptibility to an unrelated new infection. For example, having had the flu before coronavirus infection could change the course of Covid-19 disease in unpredictable ways. When a person’s immune system has no memory of an infectious agent, it may be unable to rapidly respond, and this may allow the invader to escape detection, giving it more time to cause damage.

And then there’s the interactions among these factors.

It’s not entirely clear to me why that explanation is actually better than attributing it Providence or fate. They’re about equally predictive.

Rather than fisking Ross Douthat’s latest New York Times column, I want to focus on one relatively short section:

Four key measures, on top of treating the ill and maintaining physical distancing, must be in place not just to slow the rise of Covid-19 cases, but also to bend the curve downward. These are: test widely, isolate the infected, trace the contacts of those infected and quarantine appropriately.

Of all these measures, the one that will require the greatest adaptation on the part of the American public is isolation and quarantine. Where this has been most successful it has required separate designated facilities to accommodate and monitor those isolated with mild illness and those subject to quarantine. We call this approach “smart isolation and quarantine” or “smart quarantine.”

The United States needs to adopt smart quarantine as soon as possible. It will require us to endure new and difficult challenges. But the long-term benefits — fewer infections and deaths, a quicker return to work and “normalcy” — will far outweigh the short-term hardships.

There are three main challenges to building a smart quarantine system in the United States. First, we must vastly increase our capacity for testing and tracing contacts.

Second, we must create — and at times mandate — humane quarantine processes. With considerable success, China, South Korea and Singapore have tested far more of their populations and concurrently mandated tiered isolation and quarantine.

Third, American families will be asked to endure separations that are more difficult than what many have currently experienced. Family units are the hotbed of viral spread, and doing the right thing for your family simultaneously does the right thing for the community.

First, ignore the Chinese experience. We simply do not have enough information to know what it was. How many people have contracted COVID-19 there? How many died? How many were tested? What was the testing regime? We don’t know the answer to any of those questions.

Second, to the extent that there is any relationship between testing and cases or deaths, it’s a positive correlation.

Third, he’s flat out wrong in this claim:

the one that will require the greatest adaptation on the part of the American public is isolation and quarantine

We were already more isolated than any of the other countries before the outbreak.

Fourth, we are already nearing the number of tests per million population of South Korea, the most frequently cited success story. No country to date has tested its entire population. If that’s the plan, it presents a daunting logistical problem. Producing enough tests, administering them, processing them, reporting the results, and so on for a population of 330 million people are all enormous tasks. Not to put too fine a point on it but if that’s what’s needed we should declare defeat. We won’t accomplish it.

Quite in contradiction to Mr. Douthat’s claim, I think the most daunting problem of his four steps is contact tracing. We do not have the legal or civil infrastructure to support such a thing. Oddly enough, Google and Facebook are much better prepared for that than the federal government is.

I would propose a somewhat different plan:

1. Stop testing for diagnostic purposes or, at least, limit such testing.
2. Test a sample of the population sufficient to gauge the spread and intensity of the contagion. That is probably in the thousand rather than the millions.
3. Test more intensively within “hot spots”. We don’t have the political or legal infrastructure for that. We should develop it quickly.
4. Strongly discourage the media from spreading alarmist disinformation. Examples of this from recent days include the claim that grocery workers are becoming sick and dying of COVID-19 (the contagion or death rates among them is actually no higher than the general population), and spreading the lie that COVID-19 was created by the United States.
5. Use the Defense Production Act more lavishly to produce protective equipment, ventilators, tests, and the materials to make more of them.

Experts, whether actual expert or self-proclaimed, need to adjust themselves to the facts. The United States is not a small, compact, densely populated, socially cohesive country in which the people will meekly comply with directives. We are a vast, sprawling, fractious country that prefers to confront problems head-on and throw material at them until something sticks.

The Illinois Department of Public Health (IDPH) has published a web site that provides detailed statistics on COVID-19 cases and deaths to it, right down to the zip code level. Roughly 28,000 people live in my zip code and fewer than 40 cases have been diagnosed here with no deaths.

The most heavily promoted news here in Illinois is that there is a marked racial disparity in the number of deaths due to COVID-19: relative to their numbers blacks are dying at a much higher rate. The official explanation is that there has been “disinvestment” in health care in the neighborhoods in which most blacks live. I’d like to see the numbers. I suspect that it’s a lot more complicated than that. It’s true that there are fewer hospitals in black neighborhoods than there used to be. However, it’s also true that the age-adjusted rate of obesity among blacks is nearly 50% and that conditions like type 2 diabetes and heart disease are among the relevant “comorbidities” contributing to death due to COVID-19.

If I had to hazard a guess, I’d say that “disinvestment”, predisposition to obesity, and behavior all play a role.

Update

Another factor: smoking. The prevalence of smoking among blacks in Chicago is 50% higher than for whites or Hispanics.

In his latest column in the Miami Herald Dave Barry writes:

Anyway, I think now we’re supposed to wear masks. When I walk my dog, Lucy, around my neighborhood I’m seeing more and more people in masks. We all keep our distance, of course. We eye each other warily, like gunslingers in a Western, ready to react instantly if the other person draws a gun, or — much scarier — coughs.

But I think I’m getting eyed more warily lately because I’m not wearing a mask, and neither is Lucy.

If I could, I would buy a mask, but that is of course ridiculous. It’s like saying “If I could, I would fly like a bird” or “If I could, I would buy toilet paper.” So I thought maybe I could make a mask. It would be something to do, and I’m desperate for things to do. Several days ago, when our bank statement arrived in the mail, my reaction — seriously — was “All RIGHT! Now I can BALANCE THE CHECKING ACCOUNT!”

I’m thinking of taping a spatula to my forehead in solidarity. Read the whole thing. You can probably use it.

In a Wall Street Journal op-ed Scott Gottlieb reports on prospective treatments for COVID-19:

Even if new cases start to stall in the summer heat, the virus will return in the fall, and so will fresh risk of large outbreaks and even a new epidemic. People will still be reluctant to crowd into stores, restaurants or arenas. Schools may remain closed. The public’s fears won’t relent simply because there are fewer new cases. We’ll be running an 80% economy.

The only way out is with technology. Aggressive surveillance and screening can help warn of new infection clusters that could turn into outbreaks, but that won’t be enough. A vaccine could beat the virus, but there won’t be one this year. The best near-term hope: an effective therapeutic drug. That would be transformative, and it’s plausible as soon as this summer. But the process will have to move faster.

Americans would have the confidence to return to work, even if the virus is still circulating in the fall, if they knew that a robust screening system is in place to identify and arrest new outbreaks and medication can significantly reduce the chance of becoming severely ill. Kevin Warsh, a former Federal Reserve governor, estimates that such a drug could restore at least $1 trillion in economic activity.

Given the enormous public-health and economic stakes, it is worth doing whatever it takes to move such a drug to market. There are two promising approaches, and both could be available soon if government and private industry do things right. It’s time to place some firm bets and put resources behind these experimental treatments.

One approach involves antiviral drugs that target the virus and block its replication. Think of medicines for treating influenza, HIV or cold sores. The drugs work by blocking the mechanisms that viruses use to replicate. Dozens of promising antiviral drugs are in various stages of development and could be advanced quickly. The one furthest along is remdesivir, from Gilead Sciences. There’s evidence from clinical experience with Covid-19 patients that it could be effective.

The other approach involves antibody drugs, which mimic the function of immune cells. Antibody drugs can be used to fight an infection and to reduce the risk of contracting Covid-19. These medicines may be the best chance for a meaningful near-term success.

Antibody drugs are based on the same scientific principles that make “convalescent plasma” one interim tactic for treating the sickest Covid-19 patients. Doctors are taking blood plasma from patients who have recovered from Covid-19 and infusing it into those who are critically ill. The plasma is laden with antibodies, and the approach shows some promise. The constraint: There isn’t enough plasma from recovered patients to go around.

Antibody drugs are engineered to do the same thing as convalescent plasma, but because they’re synthesized, they don’t depend on a supply of antibodies from healed patients. Biotech companies would manufacture them in large quantities using recombinant technology, the same approach behind highly effective drugs that target and prevent Ebola, respiratory syncytial virus and other infections. The antibodies can also be a prophylaxis given to those exposed to Covid-19, or to prevent infection in vulnerable patients, such as those on chemotherapy. These drugs could protect the public until a vaccine is available.

The biotech company Regeneron successfully developed an antibody drug to treat Ebola as well as one against MERS, a deadly coronavirus similar to Covid-19. Regeneron has an antibody drug that should enter human trials in June. Vir Biotechnology is also developing an antibody treatment for Covid-19 and says it could be ready for human trials this summer. Amgen recently started its own program with Adaptive Biotech and Eli Lilly has one as well. If these approaches work, the drugs can advance quickly, because much of the science and the safety is already well understood.

The greatest emphasis is likely to be on things that are patented or can be patented. If there is any prospect that something that isn’t patented or patentable has any applicability we should jump all over it. My point is not to boost one therapy over another, only that we shouldn’t sneer at inexpensive treatments.

Let many flowers bloom. A treatment effective in a minority of cases that can be approved and manufactured in quantity quickly is better than one that can’t be approved or manufactured on a timely basis, however effective it might be. You deploy the one until the other comes along.

It’s a science experiment!

In an op-ed in the New York Times Hong Kong epidemiologist provides a frank assessment of the status of our global efforts against COVID-19:

A formal framework is needed, with an explicit rationale grounded in science, for determining when and how and based on what factors to relax restrictions — and how to reapply some or all of them should another epidemic wave hit again.

Containment has failed everywhere. In some places — Wuhan in February; northern Italy in March — the epidemic spread so quickly that the relevant authorities had to focus mainly on mitigating its effects, on damage control. In other places, suppression has worked so far: Hong Kong, Singapore and Taiwan have not experienced sustained local epidemics. Not yet, at least.

and here’s his plan of action:

Here is a formal framework for how governments could monitor the state of this pandemic much more accurately than many seem to be doing now, and how then, acting on the evidence, they could tune their interventions quickly enough to stay ahead of the outbreak trajectory.

For starters, one needs robust data. Policy must not be determined based on the daily count of reported cases ­— the tallies you read about constantly in the news — because those are unreliable. What’s needed instead is the coronavirus’s real-time, effective reproduction number, or its actual ability to spread at a particular time. And one needs to understand that number properly, in context.

The rate at which a virus is transmitted — known as the R-naught (R0), or basic reproductive number — refers to the average number of people to whom an infected person passes on the virus in a population with no pre-existing immunity. The R0 can vary from place to place because of the population’s age structure and how frequently people come into contact with each other.

The “effective” version of that number, the Rt — or the reproductive number at time “t” — is the virus’s actual transmission rate at a given moment. It varies according to the measures to control the epidemic — quarantine and isolation protocols, travel restrictions, school closures, physical distancing, the use of face masks — that have been put in place.

Daily reported cases do not convey the true state of the virus’s spread. For one thing, there is so much heterogeneity in the per capita testing capacity of countries around the world that it would be foolhardy to try to draw any broad conclusion about the virus’s transmissibility from all that disparate data. For another, the figures for reported cases lag actual infections by at least 10 to 14 days.

That’s because the incubation period for Covid-19 is about six days. And because — partly given shortages of test kits in many countries — some people don’t ever get tested, and those who do probably don’t until they have displayed symptoms for a few days.

However, it is possible to bring the daily count of reported cases closer to the real-time Rt thanks to both statistical adjustments and digital analytics.

The School of Public Health at the University of Hong Kong has been estimating, and publishing, the real-time Rt for Hong Kong since early February. The chart is based on the epidemic curve corrected by established statistical methods to reduce the time lag between the onset of infection symptoms and the official reporting of new cases. (The result is called “nowcasting.”) We hope to soon be able to further enhance these estimates by incorporating location-based data from the Octopus card that many Hong Kongers use to pay for public transport or to shop.

In China, the location-based functions of the online payment platforms of Alibaba, Baidu and Tencent could be used to track people’s activity. In the West, data feeds from Facebook and Google could geo-code online searches and payments. Citymapper, a mapping and public transit app, follows people’s movements in major cities in real time.

Activity data mined from all these apps and platforms, as well as records from payment cards, could be used to determine how people mix — which in turn could be used to infer the likelihood of their passing the virus around. In a recent contribution to the journal Science, Caroline Buckee described how all this data could be marshaled to chart a real-time map analyzing how physical distancing policies are affecting people’s movements.

With a bit of ingenuity, existing digital tools can quickly be turned into epidemic-monitoring instruments — and without intruding into people’s lives. Those who, as a general matter, worry about invasions of privacy (and rightly so) need not in this case: The idea is to only study aggregate, and therefore anonymous, numbers — to look at big data, not at personal information or anyone’s identity.

Then, having determined what the Rt actually is, decision makers could more precisely adjust their interventions to keep that number at what is, for them and their constituencies, an acceptable level.

An Rt of 1 means that the epidemic is holding steady: For every person who is infected, another one becomes infected, and as the first one either recovers or dies, the second one replaces it; the size of the total pool of infected people remains the same. At a rate below 1, the epidemic will fade out. Above 1, it will grow, perhaps exponentially.

That said, an Rt of 1 or below will not do in all circumstances. Context matters, too.

An Rt of 1 might be acceptable in a place with 10 million people if, say, no more than a couple of dozen new infections are confirmed every day. But it wouldn’t be if an epidemic were raging there and several hundred or thousands of new cases occurred daily. In the face of an explosive outbreak, the authorities would first need to take a sledgehammer to the Rt to knock it down to a very low level — 0.1 or 0.2 — and maintain it there for as long as it took to bring the daily case count down to a manageable figure.

In other words: Each community must determine the real-time effective reproductive number it can accept given its own circumstances, in particular the stage of the epidemic it is at.

Still, for all communities that determination essentially requires doing the same thing: Figuring out the number of new daily infections that their health system can handle without imploding.

Imagine a city that has 1,000 beds in intensive care units. It cannot have more than 1,000 people on a respirator at any given time. If the average length of a patient’s stay in the I.C.U. is 14 days, this city cannot provide intensive care for more than about 71 new patients a day (1,000 / 14 = 71.42). Assuming that about 5 percent of all newly infected cases are so severe as to require intensive care, then the city cannot afford to have more than a total of about 1,420 new infections a day (71 x 20 = 1,420). This is the true number of infections, only a fraction of which are reflected in the officially reported count.

The authorities, having established the number of new infections the city’s emergency health facilities can support, can then determine what Rt they should aim for and tune their interventions to reach it.

Next, once it is clear what the health care system can bear, one must ask what the economy and, separately, what the people, can accept.

Even if the health care system can just about tolerate 1,420 new infections a day, would Wall Street? Would the financial markets — and, more important, the real economy — be spooked? Or react as they do during a bad flu season?

And how long can the population accept the restrictions required to maintain that level of infections? Will people stop complying? Are their mental and emotional well-being being jeopardized?

My first observation about that is that I think he’s whistling in the dark. At the present low volume of recoveries, no action to reduce spread will prevent the health care system from being overloaded. The best that can be expected is to delay that point long enough to expand the available resource and a lot of the barriers preventing that now are regulatory.

My second observation is that in the United States at least we reached the end of the line on his plan

And how long can the population accept the restrictions required to maintain that level of infections? Will people stop complying? Are their mental and emotional well-being being jeopardized?

before it even began. Compliance has never been and will never be good enough for any suppression strategy to work, at least not until the recovery time can be shortened substantially.

Now let me air a peeve. The present method being used to treat COVID-19 patients is not scientific. It’s based on commonsense and the standard of care. Does it work in the majority of cases? Nobody really knows. It certainly hasn’t been subjected to the kind of scrutiny that the medical Powers-That-Be are demanding of other alternative treatment. For one thing it would be unethical to do so.

What should we do? I have no idea but I can make some suggestions. First, enlist the American people in what they should do rather than relying on limiting what they are allowed to do. Get volunteers making masks in the tens or hundreds of millions. There are a half million 3D printers in the U. S. alone capable of making N95 masks. The limitation there will be materials. Use the Defense Production Act more aggressively to get companies producing the necessary materials, not just for masks and respirators but for all of the things that will be necessary to treat COVID-19. We don’t produce enough of those things domestically and we can’t rely on other countries for them. They all have problems of their own. Waive requirements including environmental requirements. Get all of the many levels of government pulling in the same direction. There’s a role for the federal government in that but if we depend on the federal government for it, it’s a cop out. We’ll fail.

Get the companies whose business model consists of gathering information to do what they do best—gather information. Dragoon them into it, if necessary.

We need to respond to COVID-19 in the American way which is not to endure the hardships it imposes but to refuse to endure the hardships it imposes and do something about them. Rely on passive compliance in places where that will work.

I don’t have much to say about it but there’s an op-ed well worth reading at the Washington Post, written by a physician whose job it is to clear the ventilators of COVID-19 patients in hospital:

It’s a powerless feeling, watching someone die. The oxygen level drops, the heart rate drops, the blood pressure drops. These patients are dying on the ventilator, and sometimes when they take away the body, the tube is still in the airway.

It isn’t that often that you read something approximating despair but this comes too close for comfort.

This prediction at STAT sounds about right to me:

Public health experts are increasingly worried that Americans are underestimating how long the coronavirus pandemic will disrupt everyday life in the country, warning that the Trump administration’s timelines are offering many a false sense of comfort.

It would be interesting to know the operative definition of “everyday life” or “returning to normal”. I think that at a first approximation we will never return to normal or, perhaps more precisely, what is perceived as normal has changed.

I found this grimly amusing:

Experts say even a return to normal could come with asterisks. Mina noted, for instance, that restaurants may need to put more space between tables. Others have suggested people in high-risk groups — those over 65 or 70 and people with chronic conditions — may need to practice physical distancing even after restrictions have loosened for others, at least until vaccine is ready.

Ignoring the legal problems in the last sentence, the “experts” appear to be unaware that restaurants (the ones that actually have tables) gauge their prospective revenue based on the number of people they seat and how many times per day that can be turned over.

Restaurants tend to operate on very tight margins at the best of times and reducing the prospective revenue by 10% is likely to mean the difference between a money-losing hobby and something from which one can make a living. My offhand guess is that sit-down restaurants may become a thing of the past, particularly in places with very high real estate costs per square foot, like New York or San Francisco. The hospitality sector in general could be, if not doomed, a shadow of its former self.

When you recognize how many of the “working poor” work in just those sectors, it really makes you wonder what “normal” will look like.

And this sounds like a free flight of fantasy to me:

“We need an army of contact tracers in every community in the U.S. to be ready to find every contact and warn them to care for themselves and stop spreading it to others,” he said.

We are not China. Regardless of the risks we will not have paid informers in every block tracking people. Who would pay for such a thing? It would necessarily be financed locally. Why should the residents of Marfa, Texas pay for contact tracing in New York City? Nine of the ten most densely-populated towns in the country are in the New York metro area (the other is in LA).

Last observations:

• I’d have a lot more confidence in that 18 month estimate for a vaccine if a vaccine for any coronavirus had ever been produced. What if a vaccine is never produced?
• The cost of contact-tracing increases exponentially with the population density.
• Just because you’re an expert in something does not mean you’re an expert in everything.
• We really need to start talking about risk tolerance. Different people have difference levels of tolerance for or ability to accept risk. In general the poorest have the least ability accept increased risk. Let’s not lose track of that in a panic-stricken rush towards a risk-free world.