Tag Archives: Covid-19

The Opportunity of Skewed Coronavirus Transmission

30 Monday Mar 2020

Posted by in economic growth, Pandemic

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People talk about the transmission rate or reproduction rate (R0) of Covid-19 as if it’s a single number that applies to the entire population. John Cochrane emphasizes the huge implications of this misperception for how best to prevent the spread of the virus, and at lower cost, and for how best to “restart” the economy.

First, however, lets dispense with the absolutist position that there can be no compromise on virus mitigation in favor of economic activity. I am not opposed to the “lockdown” we are now living, but it will have significant and unnecessary costs if it goes on too long: the lost output is a huge blow not only to our current lifestyles but to our ability to grow in the future, or even to afford better health care in the future. Beyond that, the lockdown has immediate negative impacts of its own on public health: economic stress leads to all kinds of terrible health outcomes like heart disease and even suicide. About the latter, the President is absolutely correct: if you need research to prove it, see here, here, here, and here, all respected journals (the links all courtesy of The Federalist.) Economic stress and isolation is quite likely to promote poor dietary habits, lethargy, and possibly family dysfunction as well. Don’t pretend there aren’t real tradeoffs between the economy, virus interventions, and public health. The trick is to improve those tradeoffs. A balance can and must be struck, and depending on policy actions, the tradeoff can be made better or worse.

Back to the virus reproduction rate: the R0 values we see quoted are estimates of the average number of other people infected by each infected person. A value of three means that each person infected with the virus passes it on to three others, on average. If R0 is greater than one, an epidemic grows. If R0 is less than one, a contagion recedes. It becomes a “non-epidemic” if R0 remains less than one. It does not have to be zero (and probably cannot be zero).

But not everyone is the same: my R0 is different from your R0 if only because we have different occupational exposure to others and different levels of social engagement. We also differ physiologically, which probably leads to differences in our “personal” R0 values. And an individual’s R0 will differ by time and place, depending on random circumstances like which way the wind is blowing. But here is where it gets interesting. Cochrane describes an extreme version of the skewed distribution shown at the top of this post:

“Suppose there are 100 people with a 0.5 reproduction rate, and 1 super-spreader with a 100 replication rate. The average reproduction rate is 1.5. Clearly, locking everyone down is wildly inefficient. It’s much more important to find the 1 super-spreader and lock him or her down, or change the business or behavior that’s causing the super-spreading.

This is exaggerated, but not far off the mark. I have not seen numbers on the distribution of reproduction rates across people, but it is a fair bet that it has an extremely fat tail. Most of us are washing our hands, social distancing, work in businesses that are shut down or are taking great steps to limit contact. And a few people and activities contribute to most of the spread.

This wide and fat-tailed dispersion is ignored in a lot of simulations I’ve seen. They take the average reproduction rate as the same for everyone. That’s a big mistake.

The danger: we waste a huge amount of time and money moving you and me from a 0.5 reproduction rate to an 0.4 reproduction rate. …  The opportunity: focus on the super-spreaders, and the super-spreading activities, and you bring down the reproduction rate at much lower cost. “

There are many ways to reduce R0. Cochrane gets a little more specific about this and the policy implications of the skewed R0 distribution across individuals:

“All we need is to get the transmission rate under one. Activities with possible but very low transmission rates, and high economic benefits should go on. Don’t separate to ‘essential’ and ‘non-essential.’ Separate into ‘high likelihood of transmission’ and ‘low likelihood of transmission.’

Why are we not using masks everywhere? Sure, they’re not perfect. Sure, an old hankerchief might only cut the chance of transmission by half. We’re not all surgeons. Cutting by half is enough to stop the virus. 

Conversely, why did they close the state parks? Really? Just how dangerous is it to drive the dog to a hiking trail and stay 6 feet away from other people? Parks, ski areas, golf courses, all sorts of businesses that surely can be run with a reproduction rate far less than one are just shut down. I met a realtor on our dog walk yesterday. They’re totally shut down. Just how hard is it to run a realty business with a 0.5 reproduction rate? One family in the house at a time, don’t touch anything, an hour between showings, stay 6 feet from the realtor… But instead the whole business is just shut down.”

The beginning of that last paragraph echoes a point I made in my last post about public park closures and the health benefits of getting outside generally.

Cochrane goes on to discuss several other policy options, including the potential benefits of simple kinds of testing and the overemphasis on false negatives and positives in policy discussions. Imperfect tests should not be discouraged by these concerns. If you’re worried about that, you shouldn’t use a thermometer either!

“Stay-at-home” or “shelter-in-place” orders will increasingly be tested by private parties if they remain in effect too long. That will be encouraged by the seemingly arbitrary distinctions some orders make between “essential” and “non-essential” activities. If workers or small businessmen judge themselves to be at low risk, they will take matters into their own hands to the extent they can. I believe that’s already happening where the specifics of “lockdown” orders have gone too far.  Workers at the low end of the income spectrum are especially hard hit by these orders. One can hardly blame them for trying to earn what they can if they believe, and their customers believe, their activities and interactions are of low risk.

Ultimately, the entire distribution of R0s will slide to the left. That will occur even at low levels of “herd immunity” and anything that offers at least weak prophylaxis. Broadly speaking, the latter includes maintaining distance, refusing admittance to venues with a fever, avoiding handshakes, wearing masks, and potentially chloroquine, which is already in widespread use by physicians treating coronavirus patients. Ultimately, a vaccine will slide the distribution far to the left, but the economy need not be held hostage until that time. To paraphrase Cochrane, we can get the transmission rate below one and keep it there without stopping the world permanently. There are many options, and now is the time for business and government to start planning for that.

Coronavirus “Framing” Update

28 Saturday Mar 2020

Posted by in Pandemic

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This is an update of my coronavirus “framing” post from early last Sunday morning, March 22. Before I say anything about the experience since then, there is great alarm in the media about the absolute number of diagnosed cases, and some parties are doing their best to exploit that alarm. So please, at least as a start, DIVIDE BY COUNTRY POPULATION if you want to make accurate cross-country comparisons, as in the illustration below from Business Insider, or put the absolute number of cases in a normalized context, as I did in my post last weekend. The numbers below are for confirmed cases, and it takes 10,000 per million to reach 1% of the population. So all major countries are well below that level. Things are much less certain if you want to think in terms of total infections, including the asymptomatic or as yet undiagnosed. Estimates range from 5 to nearly 20 times the number of confirmed cases, so you can multiply by 10 as a start.

I was getting case numbers from a “dashboard” at Insights & Outliers, but this week they had trouble because Johns Hopkins stopped reporting a certain data element, and they seem to have stopped updating the dashboard. I’ve reverted to taking the daily totals directly from Johns Hopkins. I try to take the number relatively late in the evening, usually no earlier than 11 p.m. EDT, but it’s possible that an audit would find that my numbers have a few cases shifted to the next day…. except for tonight, when I didn’t get the number until 12:45 a.m. EDT on Saturday. I was watching a good movie!! If you want to do a deep dive on Covid-19 data, there are now a number of very good sources and dashboards available.

The daily number of new confirmed cases of coronavirus in the U.S. has accelerated since last Saturday. That was expected given the slow start of testing in the U.S.; eliminating the backlog of qualified test requests might still be constrained by bottlenecks in processing results, but let’s hope not. On Wednesday evening, Dr. Deborah Birx of President Trump’s Coronavirus Task Force stated that the backlog might be eliminated very soon. I hope we’ll catch-up within just a few days, and that might be accompanied by a decrease in daily cases, which would also be a very good sign the spread won’t be as severe as in many other countries. Continued acceleration in the daily number of new cases for more than another week would be worrisome, leaving more uncertainty about the ultimate breadth of the spread.

 Updated versions of the chart and data I posted last Sunday appear below. The actual number of confirmed cases (the red line) has climbed above what I called the “very good” scenario. This time, I “zoomed in” on the chart to get a better view of actual cases relative the two extreme scenarios.

Just to review: day zero in the chart was March 6th. The “very good” scenario (green line) would ultimately involve a maximum rate of confirmed diagnoses in the U.S. of 0.017% of the U.S. population, or 0.17% if 90% of infections are undiagnosed. That was 2.5x the South Korean experience as of last weekend. The “very bad” scenario (blue line) implies a maximum rate of diagnosis of 0.077% of the population, or 0.77% including undiagnosed cases, which was about 4x the Italian experience as of six days ago. I’ll update those extremes next time as well.   

The daily growth rate of confirmed cases in the U.S. has declined from about 40% a week ago to about 22.5% on Friday, despite increasing numbers of new cases. (I will put the growth rate on the chart next time.) The red curve in the chart will start to bend to the right as the growth rate continues to decline, but we don’t know how soon that will happen. This uncertainty is exacerbated by the presence of any remaining backlog.

The following is a screen shot of an interactive chart showing an epidemiological model of coronavirus infection prevalence. It is shown here for the U.S. under “weak” global mitigation. At the site, you can select other countries and different levels of global mitigation. Curves are shown for different assumptions about the seasonal pattern of coronavirus as well as reductions in global air travel. Unfortunately, while extremely interesting, it leaves much to the imagination, such as what “moderate global mitigation” really means. Try the “moderate” setting if you’re curious to see how it changes.

I don’t want to overemphasize any of the numbers in this chart. My point in sharing it is that prevalence declines drastically in the late spring and early summer in all scenarios. Of course, I’m not sure whether the estimates of total prevalence, the seasonal effect, or the mitigation effect are at all accurate, but on the whole I found the range of scenarios available at the site reassuring. 

We might have early indications of the efficacy of certain treatments under testing within the next week or so, some of which were already being legally administered off-label. (Dr. Anthony Fauci of the President’s Task Force, who I find generally likable, misrepresented the facts by implying that the FDA had acted this week to allow the use of Chloroquine. It was already allowed off-label.) Those treatments might help limit the virus’s spread in some cases (the prophylactic effect) and otherwise treat the infection.

The U.S. coronavirus mortality rate, which is now about 1.3% of confirmed cases, remains low in the U.S. relative to most other countries (see chart below, which is one day old). Of course, we don’t know the “real” mortality rate because so many undiagnosed cases are missing from the denominator. But one thing we know for certain is the real mortality rate is much lower than what we can measure by dividing deaths by confirmed cases.

Here’s more food for thought: most coronavirus deaths involve individuals with serious co-morbidities like diabetes, respiratory problems, and heart disease. Most fatalities are of advanced age. Mortality among these groups is high to begin with, so it’s worthwhile to ask about the marginal effect of coronavirus on mortality rates. This article does just that. There is certainly overlap between coronavirus deaths and the set of individuals who would have died anyway during any time period. That doesn’t mean coronavirus doesn’t cost lives, but it’s a pertinent question. 

 

Coronavirus: Framing the Next Few Weeks

19 Thursday Mar 2020

Posted by in Pandemic

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See my March 28 update here.

What follows is an exercise intended to put the coronavirus in perspective as we move through a crucial phase in the U.S. I believe it’s more informative than speculative. However, I’m relying on several pieces of information in establishing bounds on how the caseload and mortality rate will play out over the next few months: the experience abroad; domestic developments thus far; risk mitigation; prospective treatments; and some mathematics.

Pandemic Progressions

Despite all the warnings we’ve heard about exponential growth in the number of infections, that is something characterizing only the earliest stages of epidemics. These episodes invariably follow a growth curve that accelerates for a time before tapering. The total number of individuals infected eventually flattens, like the top of the dashed red line in the chart below. Think of the blue line’s height as showing the number of new positive diagnoses each day. New cases (blue) peak as the slope of the red line, total positive diagnoses, begins to decrease. The blue line is the one we’d like to flatten. That’s because once the number of new cases exceeds a certain threshold, medical resources can no longer handle the load. Nevertheless, I’ll focus on the red line and how it’s growth accelerates and then decelerates. Where are various countries on that curve? 

I’ve been using the interactive tool at the Insights and Outliers web site to view curves like the red line above, by country. China and South Korea are at the top, where the line is flat, though I discount the Chinese numbers as quite likely manipulated. Italy is somewhere in the middle of the red curve — one hopes it will enter the deceleration phase soon, but it’s not clear from the numbers.

Setting Context

The U.S. caseload is accelerating; it will continue to accelerate until the availability of tests catches up with demand from individuals meeting the qualifications for testing. The delay in the availability of tests, which I mentioned in an earlier post, will exaggerate the acceleration in the number of diagnosed cases for another week, or perhaps a bit more. Some of those cases already existed, and we should have known about them before now. However, after starting high, the U.S. death rate from the virus is already well below the global death rate, suggesting that either 1) our testing is actually well ahead of the rest of the world; 2) our Covid-19 mortality is, and will be, lower than the rest of the world; or 3) deaths in the U.S. will increase much faster than new diagnoses over the next few weeks. I seriously doubt the latter given the high quality of U.S. health care, the time of year, and the promising treatments that have recently been approved for use.

I expect the daily number of new cases in the U.S. to fall after the “catch-up” in testing. That’s based on a combination of things: first, the time from infection to first symptoms can be up to about 14 days, but the mean is just five days. Second, in the U.S., we began to practice “social distancing” and “self-quarantine” in earnest just this past week. Among those infected before this week, those who develop symptoms serious enough to notice will know before the end of March. But people are still out trying to take care of business. Some of those people will catch the virus, and there will be secondary infections of family members or others in close proximity to individuals diagnosed earlier. It will take an additional week, accounting for overlap, for infections among that cohort to mature. Nevertheless, over the next three weeks, the number of infections transmitted to new “hosts” will fall drastically with social distancing, as each of us comes into contact with fewer people. 

Third, the transmissibility of the virus will decrease with rising temperatures, more direct sunlight, and higher absolute humidity. See my post on the topic here. I know, I know, skeptics wag their fingers and say, “Covid-19 is not the same as the flu virus”, and they’re right! It has some similarities, however: a so-called “envelope” lipid membrane, transmissability via fine aerosols or larger droplets expelled into the air by infected individuals, and symptoms that are similar, except for shortness of breath with Covid-19. And like the flu, the new virus seems to be more virulent in cold, dry environments. If you cannot avoid contact with other individuals during your workday, if you have a large family at home, or if you live in quarters with a number of other individuals, it might be a good idea to keep your humidifier on or don’t air-condition aggressively. That’s an implication of this study and this study:

“The current spread suggests a degree of climate determination with Coronavirus displaying preference for cool and dry conditions. The predecessor SARS-CoV was linked to similar climate conditions.”

Bounding Expectations

How high will the numbers go? I’ll start by establishing some “very good” and “very bad” scenarios for total confirmed cases. South Korea (where masks were used widely) has had an excellent experience thus far. The county’s cumulative confirmed cases flattened out at less than 0.017% (0.00017) of the total population. Assuming that 90% of cases are asymptomatic and undiagnosed, that would mean 0.17% (0.0017) of the South Korean population has been infected. If the U.S. experience is the same, we’d have a total of about 60,000 confirmed infections when our curve flattens. But I won’t be that optimistic — we’re at about 25,000 cases already and I think we’ll be at 60,000 cases within a week. Instead, I’ll define the “very good” scenario as 2.5x the South Korean outcome, or 150,000 confirmed cases. 

For a “very bad” scenario one might look to Italy. Unfortunately, it’s impossible to say how much higher Italy’s case load will go before flattening. If it had flattened today (it didn’t), the rate of confirmed cases for the country would be 0.077% (0.00077). That yields 0.77% of the population including the undiagnosed at 90% of cases. Applying the same percentage to the U.S. would mean just over 250,000 confirmed cases. But again, for a really bad scenario, and because we don’t yet know how Italy’s experience will play out, let’s suppose Italy’s confirmed cases quadruple: For the U.S., using the same percentage of the population would imply just over 1 million confirmed cases, or about 1.6% of the population. That yields a total infected population of 10 million.  

I’ve illustrated these “very good” and “very bad” scenarios in the chart below as Gompertz curves, a form of sigmoid function or s-curve. First, a couple of caveats: Please note that this represents a “first wave” of the virus, as it were. I do not dismiss the possibility of a second wave should we relax our nonprescription safeguards prematurely, and obviously the chart does not speak to a return of the virus in the fall. Also, these are just two examples that allow us to examine the implications of extreme outcomes. I could have varied the timing, growth, and end-outcome in other ways, but I think the following is instructive.

The chart shows cumulative confirmed cases for each scenario. It also shows the actual confirmed case total through March 21st, which is the shorter red line at the bottom. The data plotted begins on March 6, when there were 248 cases confirmed. The horizontal axis shows days elapsed since then. The accompanying table shows the same information through March 28th, a week from today. 

There are a few things to note about the chart and table:

  1. The actual curve is still below the “very good” curve. If our experience proves to be marginally worse than the “very good” scenario, then we’ve already “caught-up” in terms of testing for Covid-19: the actual increase today is larger than the highest daily increases under that scenario. If our experience approaches the “very bad” scenario, then we are eight days behind in our testing. That is, today’s actual increase is about what that scenario would have predicted eight days ago.
  2. Under the “very good” scenario, the daily increase in cases would peak by Friday, March 27. We’ve already exceeded that level of daily increase, but it will be encouraging if the daily increase doesn’t accelerate much more over the next few days. Under the “very bad” scenario, and given a full “catch-up”, the daily increase would peak about a week from now at approximately 37,000. That would be delayed if the catch-up process is more protracted.
  3. New cases flatten out within a couple of months under both scenarios. Under the “very good” scenario, new cases fall below 1,000 per day by April 21. Under the “very bad’ scenario, they don’t reach that level until Mid-May.
  4. In the next week (by March 28th), we will know a lot more about where we’re trending relative to these scenarios. I plan to provide an update later in the week or next weekend. 

While it can’t be seen from the chart or the table, once the “catch-up” ends, if there really is a catch-up involved, the daily increase in cases might fall abruptly. That would be encouraging.

Other Thoughts

It’s also important to note that the experience within the U.S. might be as varied as what we see around the globe. For example, New York and Washington state seem to be hot spots. Cities with large international ports and flights are more likely to suffer relatively high infection rates. In contrast, St. Louis probably won’t have a comparable incidence of infection, as there are so few international flights terminating there. 

The global death rate from Covid-19 has been widely quoted as somewhere around 4%, but that came into question with the revelation of low mortality aboard the Diamond Princess, despite the many seniors aboard. And it appears that the death rate from coronavirus is declining in the U.S. This was noted here at Endless Metrics several days ago. It’s also discussed in some detail by Aaron Ginn in this excellent this Medium article. Again, the death rate will decline as our testing “catches-up”, if indeed it must, and it will decline with spring weather as well as treatments if they are effective. Ultimately, I’ll be surprised if it comes in at more than 1% of confirmed cases in the U.S., and I won’t be surprised if it’s much less. At 1% under the “very bad” scenario, the U.S. would have about 10,000 deaths associated with coronavirus, the large majority of which would be individuals older than 70 years of age with significant co-morbidities.  

Conclusion

I hope this exercise proves useful to others in establishing a framing for what will ensue over the next few weeks. However, even the “very bad” scenario discussed above involves an infected share of the population of much less than we’ve heard we’re in for. Yet that scenario is far worse than Italy’s experience thus far, which most people consider pretty bad. For this reason, I am increasingly convinced that this pandemic will not prove to be the widespread calamity we’re still being told to expect. Those warning us might be alarmists, or perhaps they simply lack a sufficient level of numeracy.

This post was partly inspired by The Math of Epidemics by Willis Eschenbach, as well as Scott Alexander’s March 2nd post at Slate Star Codex. Also, see the Medium article by Aaron Ginn. It is a thorough examination of many aspects of the pandemic and very much aligned with my views. (Something is wrong with the link … I’ll try to fix it later.)

 

 

at WUWT 

Covid-19: Killing It With Sunshine, Fresh Air

14 Saturday Mar 2020

Posted by in Health Care, Pandemic

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Update: also see “Don’t Be Cowed: Shelter, But Get Outside”

Patients with viral and bacterial infections seem to respond better if exposed to sunshine and fresh air. In fact, anyone hoping to keep infections at bay would do well to get outside in the sun for a while every day. A friend’s post alerted me to this fascinating article in Medium.com: “Coronavirus and the Sun: a Lesson from the 1918 Influenza Pandemic“, by Richard Hobday. It is well-sourced, though the references aren’t hyperlinked. Here’s the main point:

“... records from the 1918 pandemic suggest one technique for dealing with influenza — little-known today — was effective. … Put simply, medics found that severely ill flu patients nursed outdoors recovered better than those treated indoors. A combination of fresh air and sunlight seems to have prevented deaths among patients; and infections among medical staff. There is scientific support for this. Research shows that outdoor air is a natural disinfectant. Fresh air can kill the flu virus and other harmful germs. Equally, sunlight is germicidal and there is now evidence it can kill the flu virus.

On the last assertion, see here. Viruses always ebb as the weather warms in the spring. Light conditions improve, which might be more important than temperature: UV light is thought to kill germs of many kinds. Moreover, Vitamin D is generally protective against infections, and a deficiency is thought to increase Covid-19 risk.

Hobday goes on to describe the Open Air Factor, which probably is related to the presence of ozone, but maybe other curatives:

“Doctors who had first-hand experience of open-air therapy at the hospital in Boston were convinced the regimen was effective. It was adopted elsewhere. If one report is correct, it reduced deaths among hospital patients from 40 per cent to about 13 per cent. …

Patients treated outdoors were less likely to be exposed to the infectious germs that are often present in conventional hospital wards. They were breathing clean air in what must have been a largely sterile environment. We know this because, in the 1960s, Ministry of Defence scientists proved that fresh air is a natural disinfectant. Something in it, which they called the Open Air Factor, is far more harmful to airborne bacteria — and the influenza virus — than indoor air. They couldn’t identify exactly what the Open Air Factor is. But they found it was effective both at night and during the daytime. 

I’m not sure they were able to control for the relative absence of germs in fresh air, as opposed to the presence of something beneficial, but it’s certainly intriguing.

So whether you’re still on the “office team” or otherwise on the job, try to get outside! Whether you’re in a Covid-19 self-quarantine or worried about catching it, get outside if you can. Get some sun and fresh air, especially after a thunderstorm, when the air is rich with ozone. But drink plenty of fluids and don’t get burned! I’ll be hanging out in my back yard.

The Federal Reserve and Coronatative Easing

09 Monday Mar 2020

Posted by in Monetary Policy, Pandemic

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Laughs erupted all around when the Federal Reserve reduced its overnight lending rate by 50 basis points last week: LIKE THAT’LL CURE THE CORANAVIRUS! HAHAHA! It’s easy to see why it seemed funny to people, even those who think the threat posed by Covid-19 is overblown. But it should seem less silly with each passing day. That’s not to say I think we’re headed for disaster. My own views are aligned with this piece by Michael Fumento: it will run its course before too long, and “viruses hate warm weather“. Nevertheless, the virus is already having a variety of economic effects that made the Fed’s action prudent.

Of course, the Fed did not cut its rate to cure the virus. The rate move was intended to deal with some of the economic effects of a pandemic. The spread of the virus has been concentrated in a few countries thus far: China, Iran, Italy, and South Korea. Fairly rapid growth is expected in the number of cases in the U.S. and the rest of the world over the next few weeks, especially now with the long-awaited distribution of test kits. But already in the U.S., we see shortages of supplies hitting certain industries, as shipments from overseas have petered. And now efforts to control the spread of the virus will involve more telecommuting, cancellation of public events, less travel, less dining out, fewer shopping trips, missed work, hospitalizations, and possibly widespread quarantines.

The upshot is at least a temporary slowdown in economic activity and concomitant difficulties for many private businesses. We’ve been in the midst of a “flight to safety”, as investors incorporate these expectations into stock prices and interest rates. Firms in certain industries will need cash to pay bills during a period of moribund demand, and consumers will need cash during possible layoffs. All of this suggests a need for liquidity, but even worse, it raises the specter of a solvency crisis.

The Fed’s power can attempt to fill the shortfall in liquidity, but insolvency is a different story. That, unfortunately, might mean either business failures or bailouts. Large firms and some small ones might have solid business continuation plans to help get them through a crisis, at least one of short to moderate duration, but many businesses are at risk. President Trump is proposing certain fiscal and regulatory actions, such as a reduction in the payroll tax, wage payment assistance, and some form of mandatory paid leave for certain workers. Measures might be crafted so as to target particular industries hit hard by the virus.

I do not object to these pre-emptive measures, even as an ardent proponent of small government, because the virus is an externality abetted by multiplicative network effects, something that government has a legitimate role in addressing. There are probably other economic policy actions worth considering. Some have suggested a review of laws restricting access to retirement funds to supplement inadequate amounts of precautionary savings.

Last week’s Fed’s rate move can be viewed as pre-emptive in the sense that it was intended to assure adequate liquidity to the financial sector and payment system to facilitate adjustment to drastic changes in risk appetites. It might also provide some relief to goods suppliers who find themselves short of cash, but their ability to benefit depends on their relationships to lenders, and lenders will be extremely cautious about extending additional credit as long as conditions appear to be deteriorating.

In an even stronger sense, the Fed’s action last week was purely reactive. Scott Sumner first raised an important point about ten days before the rate cut: if the Fed fails to reduce its overnight lending target, it represents a de facto tightening of U.S. monetary policy, which would be a colossal mistake in a high-risk economic and social environment:

“When there’s a disruption to manufacturing supply chains, that tends to reduce business investment, puts downward pressure on demand for credit. That will tend to reduce equilibrium interest rates. In addition, with the coronavirus, there’s also a lot of uncertainty in the global economy. And when there’s uncertainty, there’s sort of a rush for safe assets, people buy treasury bonds, that puts downward pressure on interest rates. So you have this downward pressure on global interest rates. Now while this is occurring, if the Fed holds constant its policy rate, it targets the, say fed funds rate at a little over 1.5 percent. While the equilibrium rates are falling, then essentially the Fed will be making monetary policy tighter.

… what I’m saying is, if the Fed actually wants to maintain a stable monetary policy, they may have to move their policy interest rate up and down with market conditions to keep the effective stance of monetary policy stable. So again, it’s not trying to solve the supply side problem, it’s trying to prevent it from spilling over and also impacting aggregate demand.”

The Fed must react appropriately to market rates to maintain the tenor of its policy, as it does not have the ability to control market rates. Its powers are limited, but it does have a responsibility to provide liquidity and to avoid instability in conducting monetary policy. Fiscal actions, on the other hand, might prove crucial to restoring economic confidence, but ultimately controlling the spread of the virus must be addressed at local levels and within individual institutions. While I am strongly averse to intrusions on individual liberty and I desperately hope it won’t be necessary, extraordinary measures like whole-city quarantines might ultimately be required. In that context, this post on the effectiveness of “non-pharmaceutical interventions” such as school closures, bans on public gatherings, and quarantines during the flu pandemic of 1918-19 is fascinating.