Some Thoughts About Covid-19

I thought I'd share some of my thoughts on the pandemic. My overall outlook is optimistic. It seems to be far less fatal than early reports would have indicated.

Reported Mortality Rates Are Biased High

There are varying estimates of the deadliness of the virus. One that's almost certainly biased high is the "case fatality rate", the CFR. This is literally the number of confirmed cases divided by the number of confirmed deaths. (See this piece for a useful discussion of "case fatality rate" versus "infection fatality rate"; the latter is the true measure of deadliness of the disease.) This number can be alarmingly high. See the CFR by country here. A 12% CFR for Italy? Does this mean you have a 12% chance of dying if you catch the virus? No, of course not. It is only the most severe cases of illness that are typically brought to our attention. This is particularly true in the early stages of a pandemic. The denominator in the CFR is biased low. In my opinion, this bias is extreme and dramatically overstates the true mortality rate. Some irresponsible journalists have reported the CFR as if it's the true fatality rate for the virus, and some (in my opinion, equally irresponsible) public health officials have under-corrected this number and gotten a still-too-high number. What bothers me is that everyone knows that the number of official cases is an under-count, and I hear daily reminders of all the reasons why. How much grousing (very much justified grousing, I should say) do we hear about how there isn't adequate testing? How many complaints have your heard from frustrated local hospitals and public health officials that they can't test patients even when they have symptoms that match the profile of Covid-19? How many (possibly alarmist) stories have you seen about how quickly the virus spreads, how long it lives on surfaces, how long the incubation period is (during which you may be asymptomatic but still contagious), how "exponential growth" implies that the virus has already expanded beyond our ability to contain it? The CFR might be a useful benchmark at the onset of an outbreak, a basis for extrapolating to what the true mortality will eventually turn out to be. (The fatality rate per infected individuals is called the infection fatality rate, or IFR; see the link above.) But unless you test every single individual in the entire population with a reasonably reliable test (one with low rates of false positives and false negatives), we won't know about the mild or asymptomatic cases of the disease.

Like other viruses, and like almost any phenomenon we're interested in, the severity of illness follows some sort of distribution. There are mild cases, there are severe cases requiring hospitalization and ICU attention, there are permanent injuries and ongoing conditions following recovery, and there are deaths. Extrapolating from the CFR to get the IFR requires some assumptions about what that distribution looks like, but it seems clear that there are a lot of asymptomatic and mild cases of Covid-19. See John Ioannidis' discussion of the virus here.

The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.
 Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.

The IFR of the seasonal flu is about 0.1%. So if we took the central estimate of 0.125% to be about right, Covid-19 is only slightly deadlier than the flu. I have read that there were ultimately 9 deaths from this population, so scale his numbers up by an appropriate factor to correct for this. (That would place the central estimate at about 0.16 rather than 0.125.) On the other hand, this puts some kind of limitation on claims that "asymptomatic people just haven't had a bad outcome yet". Based on the Diamond Princess experience, we can rule out that everyone who is infected eventually gets horrifically sick.

See also the data from Iceland, which has done some large-scale testing of its population (apparently not fully random testing, but the data is still useful). Jacob Sullum explains here:

The deCODE sample is self-selected, so it may not be nationally representative. But assuming the sample's infection rate applies to the general population, it means that something like 3,400 Icelanders already have been infected, implying a current CFR (including mild and asymptomatic cases) of around 0.1 percent, which would make COVID-19 in Iceland about as deadly as the seasonal flu. Even assuming that Iceland in a month has as many COVID-19 deaths, as a proportion of its population, as the United States does now, the CFR would still be remarkably low.

I hesitate to conclude definitively that the true fatality rate is close to 0.1% based on either the Diamond Princess or the Iceland experience. One is a small sample of people, the other is a small island whose experience may not be generalizable. Also, note the younger population of Iceland, which Sullum points out in his piece. Any crude mortality estimate must be age adjusted or, better yet, broken out by age groups to be meaningfully informative. Granting those caveats, we should take these data points seriously. Iceland's testing is much closer to a random sampling of the population than the (extremely) biased sampling that yields Covid-19 counts for various nations and states reporting. And the Diamond Princess is a little petri dish where we know 1) who was infected and 2) who has died, basically down to a person.

See also the example of Gangelt, Germany, which is getting a fatality rate of around 0.37% after doing extensive testing of the population. (Keep in mind that age distributions are a big deal because of the very steep age-mortality curve for Covid-19, as well as for everything else. Germany has an average age of 46 years, while Iceland has an average age of 36.5 years. It's hard to tell without detailed demographic data, but it's possible that the data from Gangelt is consistent with the data coming out of Iceland w.r.t. mortality rates.) It would be nice to see more of this done, say for an entire US state or large European country. If testing the entire population isn't feasible, then representative sampling would be extremely informative right now.

Comparisons to the Seasonal Flu

I have seen attempts to compare Covid-19 to the seasonal flu. I have also seen some of these comparisons met with apoplectic outrage or sneering sarcasm. I want to argue that comparisons to the flu are totally appropriate, even if it's to say something like "Covid-19's fatality rate is X times higher than the seasonal flu" or "Covid-19 is only slightly deadlier than a typical seasonal flu, but it's worse because so many more people get it." Or "It would be terrible if five times as many people got the seasonal flu, all within a compressed flu season, because our hospitals would be overrun." The seasonal flu is a useful baseline, because it's something we're all used to. It's something that kills tens of thousands of people a year, but we're inured to it because the risk is somewhat inevitable. It's inadequate to say we can't compare Covid-19 to the flu because it's different in some dimension. It's more helpful to point out in what ways it's different so we can think clearly about how we should respond. Hell, maybe this kind of reflection will lead us to treating the seasonal flu more seriously. Maybe our default rule is wrong and we just don't bother to think about it. That's the kind of thought we'd never get to explore if we reflexively shout down anyone who brings up the topic.

It's possible that the fatality rate of Covid-19 is much higher than the seasonal flu. (Note the confidence intervals given by Ioannidis above.) In fact, it is probably prudent at this point to act as if it is deadlier, say a true IFR of 0.3% or 0.5%. But there are limits on what's reasonable here. There is no "precautionary principle", no "safe" default rule, when mistakes in any direction are costly.

See this page by the CDC on recent flu seasons. The worst season in the past decade was 2017-2018, where about 61,000 people died of the flu. Every death is a tragedy, but it is worth reflecting on how we mostly accept this as a fact of life. People die of the flu. We don't shut down society because of a bad flu season. In fact, you probably don't even read a single news story about it. Did you know that 2017-2018 was a ten-year high for flu deaths? I hadn't.

Another thing I hadn't realized is this: a "bad" flu season isn't one where the disease itself is particularly deadly in terms of having a high IFR. A bad flu season is one where a lot more people get it. Spot-checking for a few years, it looks like almost all flu seasons have an IFR of around 0.1-0.2%. But in years where a lot more people get it, a lot more people die. (Exceptions are the Spanish flu and the 1957-58 flu season, where the IFR was exceptionally high.)

So maybe it's no deadlier on a per-infection basis, but a lot more people get it. The CDC link implies that a typical flu season sees ~30 million symptomatic cases. (Or ~45 million in 2017-2018; only ~9 million in the relatively mild 2011-2012 season.) Let's call it about 10% of the population. Maybe Covid-19 is so good at spreading that it infects 50% of the population in a single season. Is that "five times as bad?" I'm not sure what the right way to think about that is, but I don't recall anyone warning us all to be concerned about the 2017-2018 flu season because it was five times worse than the 2011-2012 season. A simple utilitarian response is, "Yes, it's five times as bad because there is five times as much illness." But a response that applies the existing default rule is something like, "No, this is a risk that we're all inured to and that we all find acceptable. It's like bemoaning that traffic fatalities increased because we're driving more."

I don't know how individuals should feel about this. "I'm five times as likely to get fatally ill this year, so I should worry five times as much" or "This hazard is below the threshold for risks that typically concern me, so I shouldn't worry too much." The knock-on effects of everyone getting sick at once is certainly worth our concern. But I'd like to see more flesh on this argument. How much can they really do for you if you get a really bad case? How much does medicine (in the broad sense of the word) actually help? How costly is it to "flatten the curve" compared to the cost of letting the virus rip through? It was darkly amusing to watch a Twitter thread in which Robin Hanson pointed out the very high mortality rate of people placed on ventilators, implying that they didn't help many people survive. He had claimed that something like 90% of people put on ventilators didn't recover (this was from a particular sample from a particular study). Some chimed in to correct him, "No, no, no, it's more like 60% who die on ventilators." And I've seen lower figures for this claim, too, but by all accounts a large percentage of patients on ventilators never recover. It's worth considering what kinds of medical attention actually help, and to what extent will people miss out on actual life-saving healthcare if the system is over-crowded.

What Are Public Officials Trying to Accomplish?

This has been my greatest point of frustration regarding the policy response to Covid-19: What end-game do you have in mind? Are you trying to "flatten the curve" in the sense of slowing the rate of growth? Or do you think you can completely halt the spread of the virus? I think state epidemiologists need to communicate much more clearly what their goals are and how those goals are achieved by any particular policy response. (Like school closures or shuttering businesses.) They should be explicitly tying these decisions to the output of an epidemiology model. I could cut them a little slack if they shuttered schools and businesses for a couple of weeks to buy some time. If they just needed a little time to get enough test kits or masks or gloves, a blunt approach might make sense without any kind of formal modeling. But beyond a short shut-down, we should all be demanding explicit justification for such measures. We should be demanding that epidemiologists share their models and various modeling assumptions so that informed members of the public can comment on and critique them. "Flatten the curve" is just rhetoric unless some epidemiological model shows that our efforts are actually flattening (and achieving that flattening at an acceptable cost).

Robin Hanson puts it quite well in this post. Many policy responses look as though they are intended to completely halt the spread, and that's probably the wrong approach. We are taking a giant risk here. Suppose we shut down parts of the economy for two months, but find out that the virus comes roaring back as soon as we open up again for business. If the spread of the virus is inevitable, if, say, 50-80% of the population is eventually going to get it anyway, then we shouldn't be in lock-down mode. We should take a more realistic approach that slows the spread but allows the spreading to happen. I just wish our governors and mayors would be straight with us. "Yes, we're trying to squash. If the virus comes back after we re-open the economy, we're going to go through this all again." Or "We ran a sophisticated epidemiology model, and it said this was the optimal time to close schools. It also tells us when to reopen them, which will be..." They need to be telling us clearly their basis for making these decisions, because they might be wrong in many dimensions. Their epidemiology model might be using the wrong assumptions, or perhaps we're being mislead by something as banal as a coding error. There needs to be more public scrutiny of how these decisions are made.

Another serious problem that I haven't seen anyone else point out: their epidemiology models might be solid, but their values and goals could be misaligned with ours. Public health officials tend to take a puritanical "avoid deaths at all costs" approach to policy, which  is grotesquely misaligned with what the public actually values. A young person might be willing to take a small risk of getting very sick to keep earning his income, and older people might be willing to risk getting sick to see their grandchildren. Public health officials who are merely counting bodies and trying to minimize deaths are not taking our values and preferences seriously. We need to remind the public health bureaucracy that it's isn't their job to tell us what they think we should want. They shouldn't be picking policy goals and back-fitting the modeling and the messaging to it. They should be acting more like engineers, telling us what will happen under various scenarios and policies. A government epidemiologist who is trying to decide policy, rather than merely inform it, is going beyond her mandate. Note that the FDA has been puritanical about e-cigarettes; they insist on decreasing the amount of vaping at all costs, even though this almost certainly leads to more smoking and more total deaths. The CDC has been puritanical about the use of opioids. The fact that some (very small) fraction of patients end up abusing their pain medication is seen as completely unacceptable. In both cases, our public health institutions are over-ruling the actual preferences of individuals. They are actively advocating and crafting policies to compel us to make the choices they think we should make. They are obsessed with the tableau that population-level statistics presents to the world. (Thus and increasing trend of teen vaping or opioid-related deaths "looks bad," and that is seen as a justification to overrule people's private decisions.) I don't think they're likely to represent our values or make the right trade-offs during a pandemic.

So let's be clear about what we're trying to do. Are we moving from the red curve to the blue curve? Or are we just postponing the red curve, which will rip through the population once the edicts have been lifted? Politicians really need to keep their powder dry. There is a limited budget for pulling the "shutter society" lever. If we've closed schools and shuttered businesses without a substantial fraction of the population getting sick (and thus acquiring immunity), what have we accomplished? And where do we go from there? People will eventually tire of stay-at-home orders and begin to flout them. And working parents who can't work from home will not tolerate indefinite school closures. We risk the possibility that the public becomes exhausted with social distancing protocols and start ignoring them, perhaps at exactly the wrong moment.

Maybe people's private efforts are adequate to slow the rate of spreading. We have a lot of tools at our disposal as individuals, like voluntary distancing, sanitation, masks, gloves, and less frequent ventures outdoors. Businesses can voluntarily implement protocols that slow the spread of the virus, like cleaning surfaces, cancelling large gatherings, allowing telecommuting for people who can work from home, and keeping different shifts from overlapping with each other. Obviously, businesses are already doing all of these things. Individuals and businesses have many different kinds of arrows in their quiver. If the virus gets worse, there are levers of control that they can pull harder. If it starts to recede, they can afford to relax and be less cautious. Government actions should be justified based on some kind of "value added" calculation that accounts for this private action. Maybe private efforts get us 90% of the way to optimum curve-flattening. Or maybe those efforts are totally inadequate to get population-level flattening. I don't know, because they're really not telling us.