We are faced with a crisis of unknown severity and scale. What is the appropriate amount of money to spend on the chance it might be important enough to justify the costs?

From the moment rumors of “quarantine” began circulating, there has been push-back from those who believe that the social cost of attempting it will outweigh the social benefits, either because the benefits are low, or because the potential for successful suppression of infection rates could be low. Full disclosure: I was one of those who so believed.

After all, markets were already on very shaky legs even before COVID-19 was discovered, and the threat of severe recession was already not insignificant. Slashing production and consumption with social distancing measures is all but guaranteed to initiate and exacerbate the imminent correction.

Furthermore, we are also faced with serious public debts and deficits which will result in higher and higher proportions of national income going to interest payments.

And, let’s not forget about the Global Climate Change crisis, whose major protest movements were just gathering steam before they all had to go and stay home (reducing emissions and consumption, though!).

These concerns are not trivial, and they are urgent. Yes, the COVID-19 epidemic may be very destructive, but can we really afford to spend trillions of dollars on our response?

Unfortunately, the answer is that we cannot afford not to.

Let’s talk about the range of possible deaths.

We will start by talking about the high range of estimates. Please try not to panic. Make sure to look at the low estimates, and the reasons why we expect the low estimates to be more likely than the high estimates.

Taking published mortality rates by age group and applying them to U.S. age demographics, if everyone catches this extremely contagious, airborne, waterborne, plasticborne, clothborne, often asymptomatic, and anyway long incubation period virus, we are talking about a 1.8% mortality rate (substantially lower than the global average of 3.4%!).

1.8% of the U.S. population is 6 million people. Catastrophic.

Those mortality rates are based on instances where the vast majority of people in critical condition were able to be hospitalized. It is unknown what the mortality rate would be if hospitals were completely overloaded, but...

According to this research, one in three people in U.S. intensive care with COVID-19 do not survive. From the accounts of medical staff, it seems unlikely that the other two out of three would survive without care. Of the remaining three out of four patients who are hospitalized but not in intensive care, it is less clear what additional proportion may perish without care, although it would presumably be much, much less than those in intensive care.

It is therefore not unreasonable to suppose that mortality rates could as much as triple for those who are crowded out of hospital capacity.

Our hospital capacity is between 12.5 to 200 times too small to serve everyone at the same time, depending on what true hospitalization rates turn out to be, meaning that if we fail to flatten the curve of exponential growth of new cases, and the virus spreads rapidly enough, 92% to 99.5% of those with severe cases could end up facing that tripled mortality rate.

6 million x 3 = 18 million American deaths, or 5.4% of the population.

The difference between flattening the curve perfectly vs. doing nothing could approach 12 million American lives.

The difference between stalling long enough to develop a cure or vaccine vs. doing nothing could approach 18 million American lives.

For comparison, everything else that kills Americans combined only made it to 2.8 million last year.

We can cut the red bar and the yellow bar by the proportion of the population we can keep from being exposed.

We can cut the yellow bar by the proportion of the population for whom we can guarantee hospital space.

With so many lives theoretically at stake, spending trillions of dollars may seem like an insufficient amount.

Alright, that’s the high end of the spectrum. Don’t panic. These numbers are almost definitely very inflated. Now let’s talk about the low end.

We have a lot of reason to expect that those numbers are way too high, because we know our detection rate for mild and asymptomatic cases is very, very poor. This study found that perhaps 86% of cases go undetected!

So, let’s give ourselves even a little bit of an optimistic buffer on that and presume that a nice round 90% of cases might be undetected, because then we can slash the projected mortality rate by an even factor of 10.

If everyone catches it (and probably not EVERYONE would catch it in any scenario), we are now talking about “only” 600,000 deaths with perfect hospitalization, and 1.8 million deaths with no hospitalization. That’s a bit over half a year’s worth of mortality, although a lot of that will likely overlap and, indeed, eat into general mortality for the next several years by wiping out so many of peoples family members who were already enduring or recovering from something serious. (Reminder: those people whom others are praying and paying to see alive and well are those whom we are trying to give a fighting chance.)

Once again, we can cut the red bar and the yellow bar by the proportion of the population we can keep from being exposed.

We can cut the yellow bar by the proportion of the population for whom we can guarantee hospital space.

With numbers this much smaller, a closer reckoning of the money spent in prevention is fully appropriate. Furthermore, suppose we authorize gigantic sums of money for quarantine, and then we fail to flatten the curve enough to matter anyway! Our attempts could be largely discounted by insufficient ability and/or social will to fight the spread, no matter how much money we spend.

Thus, the low-end estimates for the number of lives saved from protective social distancing measures compared to doing nothing could actually cap out at 1 million if detection estimates are accurate and potentially approach 0 if protective measures fail.

Okay, now let’s compare the optimistic and pessimistic models:

That’s a pretty big spread. Maybe our efforts would save a few thousand lives to a million, maybe up to 18 million, depending on your assumptions. A lot of room for a lot of different opinions, there, so be gentle with each other and keep an open mind. Be patient as researchers scramble to produce trustworthy science. The actual numbers are almost certainly somewhere in between those two extremes, based on the size of the difference between our detection rates and the actual infection rate. Where in between? Probably a lot closer to the low end, but we need more study to answer with good confidence. A presumptuous attitude could easily cost millions of lives.

How does a policymaker choose with such divergent, deeply uncertain, potentially cataclysmic outcomes? One popular choice is the “expected value” calculation. That is, you decide how likely each outcome is and how devastating it would be and then you take an average of the expected outcomes weighted for how likely each is, and you compare that to how much each option would cost, and you get a good idea of whether you’re gambling with the odds or against them. (Any choice is a gamble, so this is the safest strategy.)

We currently have a big problem with that approach. How likely are all the different outcomes, and what, even, are all the possible outcomes? This virus is very new, and we are still at the early stage of estimating what our estimates are probably eventually going to be when we have had more time to collect and analyze data. We need more time.

The key element here is the deep uncertainty caused by our knowing so little about this virus and how it affects people. The first cases appeared in December, and the number of cases has grown exponentially since then.

What this means is that until February, there were very limited numbers of cases to study even in China, and really only in March did most of the world start to have any data to work with. The virus has a 3–6 week recovery time, so most of the cases outside of China have actually not even been resolved yet.

That means that many kinds of analysis, for example international comparative studies, have only had a few weeks of data with sufficient sample size, if that, to be of a lot of use at predicting global outcomes. The biggest studies have just barely started collecting data. Let’s not even start to talk about the limitations on longitudinal studies and meta-analyses. We need more time!

Meanwhile, COVID-19 is a very time-sensitive disease, because it is able to spread incredibly efficiently among humans. That is, if we do not check the spread, it will continue to grow exponentially and very rapidly reach everybody that it is going to reach. Furthermore, the more it has spread, the more difficult it becomes to prevent it from spreading.

We are thus in a position where we will be ratcheted into any decision to allow it to spread, no matter how devastating our research ultimately proves its spread will be, whereas there is much less permanence in implementing strong social distancing measures other than the known costs. It would be really easy for us to stop social distancing measures as soon as we have enough data to justify giving up on containment. It would be extremely challenging to reverse a decision to allow the virus to spread if more data prove that we should have been containing it all along. We need more time to know what we are dealing with before allowing any potentially catastrophic, irreversible outcomes.

Effective social distancing measures are very expensive. Yet, we know the costs and we can estimate the economic damage. However, they are also mostly linear and discretionary. The benefits of buying a few more months to study the disease before we allow our whole population to be the lab rats could be negligible, or the benefits could be in the millions of lives saved. That is a gamble too big to accept.

Every month we contain the virus is another month to train and hire hospital staff; build and equip additional intensive care wards; increase productive capacity of medical supplies; develop additional treatments, vaccines, or a cure; or at the very least know with more confidence the actuarial cost of allowing uncontrolled spread, before we capitulate.

Time and research may prove that measures to suppress the virus were an inefficient use of money, after all, in hindsight. Maybe even an egregiously inefficient use, in hindsight, predicted by some “prophetic” people who used low estimates for the disease that turned out to be correct, in hindsight. But, where we are now, with so many unknowns and such incredibly high stakes, there is no other responsible choice besides maximum containment. Buying even just two more months of time will likely more than triple the amount we know about the virus.

It is imperative that we pay for the time to study and understand and prepare for this potential disaster before we can even think about maybe surrendering to it.