Think of the economy as being in the state that it was on December 7, 1941. The problem we faced then was not sustaining aggregate demand. It was the problem of converting from peacetime production to wartime production. We also had to anticipate a later problem of converting from wartime to peacetime, but note that the latter problem took care of itself quite easily. The Depression that Samuelson and others anticipated would follow the reduction in government spending in 1945 never materialized.

Right now, we don’t need TSA screeners, if we ever did. But Amazon and Wal-mart need people in order to ramp up their logistical capabilities. Jobs maintaining our infrastructure in health, electrical power production and distribution, and Internet capacity are essential. Jobs at amusement parks and casinos are not.

Production of more face masks and coronavirus test kits is essential. Some other production is less essential.

We are acting as if our biggest worry is how to get back to our “normal,” pre-war economy. Our biggest challenge instead is to win the war, after which we will transition to an economy that looks considerably different, just as the post-WWII economy was quite different from the pre-war economy.

For conversion, the government should spend where it is needed–on the health care supply chain, testing and development of treatments and vaccines, improving the logistical infrastructure for a social-distance economy (pay to ramp up 5G? Develop policies and systems to rapidly increase the use of drone delivery?), etc. It needs to cut spending on inessential services, such as TSA.

By worrying about the conversion back to peacetime now, we are getting ahead of ourselves. Once we get back to peacetime, there will be pent-up demand for. . .we don’t know exactly what right now. In the 1950s, we built Levittowns and Holiday Inns and K-Marts, none of which were anticipated in 1941.

When a war breaks out, one of the things you have to do is fire many of the peacetime generals and replace them with officers from lower down in the ranks. The problem can be explained using the Game One, Game Two framework.

Game 1 is figuring out a winning strategy and executing it.

Game 2 is figuring out what you need to do to get a promotion.

In peacetime, the generals who rise to the top are the ones who play Game 2. In wartime, you need to find the Game 1 players.

The peacetime bureaucrats seem to be causing a lot of difficulty for the folks who are trying to play Game 1 against the virus. You need to find a way to route around them. There should be a Game 1 player to head up each of the following:

1. Hospital Logistics. Their job is to get hospitals the equipment they need, whatever it takes to do it. Presumably someone with a military background, although there is some expertise at places like Amazon.

2. Treatment Protocols. They should issue a “default protocol” for doctors to use if they want to use it. But they should encourage doctors who want to try different protocols to try them and document the results. You want to revise the “default protocol” as new information comes in.

3. Testing Strategy. Their job is to see that testing yields useful overall information in addition to information that is useful for individual treatment decisions.

4. Vaccine R&D. Eliminate roadblocks, direct funding.

5. International liason. Ensure that we learn from other countries and help them as much as we reasonably can.

6. Public Communications. Make sure that communication is clear and credible.

7. Financial Maintenance. Make sure that the priority is forbearance that works its way through to individuals and businesses. Not following the standard rule book.

Etc.

1. Tyler Cowen on the Grand Princess data (not quite the complete sample you thought it was). UPDATE from a commenter: Diamond Princess was the complete sample. Grand Princess is the cruise in SF Bay.

2. Scott Alexander’s take.

Most of my rationalist friends self-isolated really early, before it was socially acceptable to do so

I cannot resist joking that nerds on the spectrum look for any excuse to self-isolate. But seriously, the people who are complaining about “panic” and “you’re trashing the economy for nothing” and “the flu is worse” strike me as less facile with math and data than those of us on the other side. Almost three weeks ago, Tyler Cowen talked about the debate between “growthers” and “base-raters.”

There are still base-raters out there. In a podcast interview with Nick Gillespie, Richard Epstein insists that outbreaks look geometric when they start, but then people adjust and they slow down. But I cannot think of a country where a slowdown occurred without strong government action. We’ve had effective government action in a few countries, and not-yet-effective government action in many others.

3. John Cochrane laments that economists are using old playbooks to deal with the current situation. I would say that there is a divide between the “stimulus” approaches and the “forbearance” approaches. He points out that the forbearance approach has not been carefully worked out. But that is not a good reason to prefer the stimulus approach. Doing the wrong thing because you know how to do it strikes me as the policy equivalent of looking for for the lost keys under the lamppost because that is where the light is.

Commenter Education Realist supplied interesting numbers on the cumulative path of deaths in Italy and the U.S. Let’s work with those.

Call the date at which there were 12 cumulative deaths in each country Day 0. The next number for Italy is 17, meaning that there were 5 deaths on Day 1. For the U.S. it is 15, meaning that we had 3 deaths on Day 1. So Italy is +2 on Day 1. Going forward, we have the following numbers for Italy minus the U.S.

0, 5, 8, 7, 19, 25, 33, 43

This is some combination of faster spread in Italy (starting from the same base in terms of total deaths) plus a breakdown in the ability to deliver care in Italy. Assuming it is mostly the latter, you want to take strong social-distancing measures sooner rather than later.

Yesterday, Tuesday, March 18 at 10 AM, the JHU web site said that there were 6519 cases in the U.S. Today, Wednesday, March 19, at 4 AM, it was showing 9415 cases. That is an increase of roughly 50 percent. That increase in known cases is a combination of two factors: increased testing (an artificial factor), which raises the number of known cases to the number of actual cases; and spreading of actual cases. I don’t know how much is due to each, but if you are looking for evidence that the virus is not spreading exponentially, an increase of 50 percent per day is not a good sign.

Now for some grim math. Let C be the number of known cases, H be the ratio of hospitalizations to known cases, and D be the ratio of deaths to hospitalizations. Then we have:

(1) total deaths = DxHxC

For example, if there are 1000 known cases (C=1000), 5 percent of these are hospitalized, and 20 percent of those who are hospitalized die, then deaths = 1000x.05x.20 = 10. Note that in this particular example, I assumed that no one dies who is not hospitalized. In reality some people will die without being hospitalized, and they will count in D.

Note that in this equation, HxC is the case mortality rate. In the numerical example, it is .05x.20 = .01, or one percent.

Next, we can do a logarithmic derivative approximation to write

(2) g = d + h + c

where g is the growth rate of deaths, d is the growth rate of D, h is the growth rate of H, and c is the growth rate of C. Note that this approximation only works for SMALL values of d, h, and c, not for big numbers like 50.

Suppose that cases grow at a rate of 4 percent (c = .04). Then if the hospitalization rate falls by 4 percent (h = -.04), that would offset the growth rate in cases.

Assume that soon the growth rate of cases will reflect true spreading, and the bump from increased testing will be behind us. Then going forward, there is reason for optimism in all three components of (2). The rate of death of hospitalized patients should fall as we get better treatment protocols and find useful drugs. The rate of hospitalization should fall as we get better at triage and we also find more effective treatment protocols that reduce time in hospital. It also could fall if we get better at protecting high-risk populations, so that more of the people who get the virus do not experience severe symptoms. Finally, the rate of growth of cases should fall as the effects of social distancing kick in.

If the rate of hospitalization does not fall fast enough (h turns sufficiently negative), then as long as c, the growth rate of cases, remains positive, we may at some point run out of facilities to treat seriously ill patients. The limiting factor in facilities might not be space and equipment–it could be the supply of health care workers. In any case, once we exceed capacity, that would cause a spike in d, the growth rate of deaths relative to hospitalizations. The growth rate in deaths would be high in such a scenario.

There are web sites that track total cases, C, and total deaths. What would help in this framework is to have H, the proportion of known cases that are hospitalized. As I searched for that data, at first I found what appears to be misinformation:

Up to 1 in 5 younger adults in the U.S. infected with coronavirus wind up in the hospital, according to a new analysis by the Centers for Disease Control and Prevention.

Baloney sandwich. What the report says is

Among 508 (12%) patients known to have been hospitalized, 9% were aged ≥85 years, 26% were aged 65–84 years, 17% were aged 55–64 years, 18% were 45–54 years, and 20% were aged 20–44 years. Less than 1% of hospitalizations were among persons aged ≤19 years

That is, 20 percent of those hospitalized were in the 20-44 year age group, not that 20 percent of the cases in that age group are hospitalized. Since 508 were hospitalized, that means that about 102 in the 20-44 age group were hospitalized.

As I understand it, at the time the report was run, there were 4226 cases, and 29 percent of these were in the 20-44 age group. That means that there were about 845 cases in that age group. So the rate of hospitalization within that age group was 102/845, or a bit under 12 percent. Still a big number, and an indication that letting this “low-risk” population all get infected soon may not be a good strategy. But see my final note.

Overall, dividing 508/4226 gives a value for H of just over 12 percent. With cases having more than doubled since the report was run, in order to hold steady we would need H to have fallen below 6 percent.

Final note: the value of H in the report is greatly overstated to the extent that people without severe symptoms did not get tested, and hence did not show up as cases. That could be a lot of 20-44 year-olds, which would make their H much lower.

I wish we had a dashboard that provided reliable numbers for H. I wish we were testing a random sample of the population so that we could estimate key numbers with more confidence.

There are a lot of people who are calling the social-distancing movement a “panic” that is needlessly wrecking the economy. But I thin we can all agree that it buys us some time by slowing the spread of the virus. Here is a list of advantages that I see from this.

1. We can produce more ventilators before the demand peaks.

2. We can keep our health care workers healthier longer.

3. We can evaluate treatment protocols.

4. We can test many more people, and we can analyze the data we obtain from doing so, before making further course corrections.

5. It is possible that the course correction that we need is even stronger quarantines. But we could never do that without first finding out that the milder social-distancing measures have been tried first and failed.