PIcture credit: Aleph Blog, and the same for all the graphs and charts in this post. All liability for mistakes here is mine.

Since then I never pay attention to anything by “experts”. I calculate everything myself.

Dr. Richard Feynman, Part 5: “The World of One Physicist”, “The 7 Percent Solution”, p. 255, quoted here.

Outline

• Summary
• Introduction
• On the Limitations of this Model
• The Graphs — Second Wave, FInishing the First Wave, Coming to the Turning Point, Problem Children (Turkey, USA, Canada, Iran, UK, Brazil, and France)
• Closing

Summary

Though the USA model has lagged considerably, and a some of the other modeled countries have lagged a little, the central thesis still stands. Things are getting better faster than most of the politicians, policymakers and media have been forecasting.

Introduction

To those reading me for the first time, you should read the following articles to get up to speed. Those who have read me for a long time know that natively I am a pessimist, so it is unusual for me to be writing as I am doing now.

Before I go on, I want to explain what the two rightmost columns on the tables above and below mean.

• 7D Trend — the seven day sum of forecast errors as a ratio of the number of cases at the beginning of the seven days. Positive means the model has been underestimating. Negative, overestimating.
• Dir — Direction of the seven days of forecast errors — beta coefficient of the forecast errors versus time as a ratio of the number of cases at the beginning of the seven days.

The idea is to try to point out where the model is persistently missing, how large is it, and is it correcting or getting worse?

On the Limitations of this Model

All models have limitations. This model, being used to extrapolate, definitely has limitations. Extrapolation, as I have said, is dangerous. It’s dangerous because we know the past data with some degree of error, and the future not at all. Extrapolation, even if the underlying functional form used for estimation is right, assumes that all processes generating the values estimated are not shifting. If that is a good approximation to the reality that comes, luck will come marching in as genius.

This is a time series model. There are many structural models out there, and the “experts” estimate and use them. They have more data than I do. Those models suffer many of the same problems that complex economic models do. There are too many parameters to estimate, and they face the same problem I do with the future shifting, and errors in past data, as well as functional form issues. They are also subject to social and economic pressures that I don’t have to the same degree.

Scientists need the approval of their peers in order to publish and for general happiness. They need to be able to make money to survive. It is difficult to get tenure, and most scientists won’t take chances with that process.

It gets worse when science is being used for policy purposes, and gets picked up by the media. Caution is ordinarily a good thing,, but not when it misstates what is going on in order to achieve the ends of third parties. Tell the truth, and then let the politicians, policymakers, lawyers, businessmen, etc. figure out what they will do. The media almost always prefers sensational, sharp, easy-to-tell stories, over the complexity of what is true. The same is true of most average people who would rather not think hard, but just imitate the behavior of others.

Thus I tend to distrust “experts” whose ideas are used for political or policy purposes, and get trumpeted in the media. Their incentives are skewed — once you get close to fame, power, and maybe even money, you’d like to keep it, and that is a snare for many.

Practically, for this model, the difficulty comes around the middle, where the shift is happening — new cases peaking, growth in total cases decelerates. Why is it difficult? Small changes make a big difference to the shape and height of the curve. The prior day’s curve is anticipating a certain amount of deceleration in the growth in total claims. When the data arrives with more new cases than anticipated, the new curve will be taller and longer. VIce-versa if less new cases arrive. That is why for some areas that I modeled, the process seems to stall around 40-60%.

Now, there are raw data errors as well, like China on 2/12 and France on 4/3. There are nations like Iran where the political turmoil may have led to delayed reporting.

But for the most part, the models have worked well, just not so well for the US yet. We’ll get to that later. Before I do, I want to state a few things I have learned as I have gone through the modeling, which has been a learning process for me.

• Look at the percentage of population that the model is projecting for reported infections. If it’s not high enough, the model is wrong. Unless a nation jumped on the problem immediately, you won’t get results like South Korea.
• The nations that tested more as a percentage of population, particularly early in the epidemic, did a lot better.
• Repeated forecast errors in the same direction indicate the process for new claims is changing, and the model is trying to adjust.
• In that situation, if we?re only getting better at finding those infected, the upper part of the curve should tail off sharply.? On the other hand, if the if the ratio of new cases to total cases is protractedly rising because more infections are occurring, that?s an increase in the ultimate level of reported cases.
• Once a nation gets to the 10% point, getting to the 90% point takes three weeks or so. Getting to the 99% point takes 4-5 weeks.
• The markets will anticipate the end, with false starts, and a lot of noise.
• As Buffett says (something like): I’d rather be approximately right than precisely wrong… or, Rule 65: “The second-best plan that you can execute is better than the best plan that you can?t execute.” My goal was to get some idea of when the market might turn. In that sense, this has been a success.

The Graphs

As in prior posts, I will run through the graphs now.

Second Wave

Finishing the First Wave

I write this with a little concern that I might be early on Italy and Switzerland, but new cases have been slowly rapidly for the two of them and Austria. Note that all three of them did a lot more testing per capita than most nations. You can see that here. The table sorts itself if you click on the top of the columns.

Coming to the Turning Point

For Belgium, Germany, Netherlands, Portugal and Spain, new cases are declining, though not as rapidly as the model would predict. Even with that, it seems likely to me that all will pass the 90% point within a week.

Problem Children

Turkey

My problem with Turkey is that the expected total population infected is too low. They got to the game late, and the curve looks too sharp. I would expect this to not turn as quickly as the model says.

United States of America with some States and Cities

Yes, the USA has been slower than I expected, and I think I have a good reason for it. I gained the reason while trying to model the world as a whole for the COVID-19 pandemic. Using the logistic equation as my functional form, I could not even in the slightest achieve a positive pseudo-R-squared. Why?

If you add together a bunch of logistic curves with varying timing, height and sharpness, there is no guarantee that you will end up with a logistic curve. The US is a big place, and the population is spread out, with many different large population centers. Much as would have killed me timewise unless I had better software, I think it would have made more sense to model the US as a bunch of logistic curves state-by-state, and add them up.

Here’s a demonstration for the past week: if I take the forecast errors of New York State and New Jersey, they are roughly 65% of the forecast errors for the US as a whole. Together they have 47% of all reported COVID-19 cases.

There have been statements by some politicians that there will be a lot of new “hotspots” across the US, it’s a tempest in a teapot. The dense and large cities like New York City and Boston have a lot harder of a time preventing the spread of an epidemic. Areas that are smaller and less dense won’t have the same impact, not even proportionate to their sizes.

The US has been making progress, just not as fast the model predicted. I would be surprised if the US weren’t at the 90% point by what is normally tax day. It takes three weeks or so to get from 10% to 90% and another week or two to get to 99%. We will likely see the practical end of this in April. It’s just a question of when.

Canada

I place Canada in the same boat as Turkey. Too few ultimate cases. It will likely revise upward. That said, their population is more spread out, so it will likely have fewer cases per capita than the US.

Iran

After several weeks of having claims far higher than the model would predict, the curve for Iran has regained a normal shape. The expected ultimate number of reported cases is on the low side of reasonable, and the model is finally tracking well. This is a watch and see sort of thing because of the instability in Iranian society, particularly amid the epidemic.

United Kingdom

The UK is on the same path as the USA, only 5-11 days behind. Their new case rate is decelerating slowly, but it is decelerating.

Brazil

The expected ultimate number of reported cases is too low, and the model is too new. Conditions in Brazil are less than orderly, so I would expect this model to revise significantly upward.

France

On 4/3 of the French government announced that they had only been counting deaths in hospitals and as such reported 23,000 new cases. Since that time the model for France has been posting negative forecast errors, and is slowly returning to a normal shape. I would expect in a week that the curve will look normal, and that the crisis in France would end about the same time as for the US.

Closing

That’s all for now. For those talking about these posts on Facebook, please note that I don’t interact there much. It’s best to comment at my blog or email me if you want my attention.

PIcture credit: Aleph Blog, and the same for all the graphs and charts in this post. All liability for mistakes here is mine.

Recommendations and Comments

• To the National Governments and Central Banks: don’t create a lot of policies that you might need to reverse. This crisis is coming to an end faster than most are reporting/proclaiming. If a policy is easily reversible, get ready. Start planning for dealing with the second wave of the pandemic.
• To US State Governments and city/county governments: start figuring out how you will targetedly let up on the restrictions that you have imposed before you realize that you are behind the curve (again). Start planning for dealing with the second wave of the pandemic. It would be better to let younger people go back to work, and shelter those more likely to get a deadly case of COVID-19. (Aside: if this ends early, note the people who told you that it would long and big, and remove them as advisors.)
• To the media: please calm down. This is one of those situations where it gets worse before it gets better. We are through most of the worse, but to the average observer, they don’t see the better, even though the point of maximum pessimism has passed.
• To individuals: if you don’t have a lot, take heart that this first wave likely won’t be here much longer. Use your money carefully. To those who do have money, as a nation moves from 50% to 75% complete in the first wave of the virus, it might be a good time to own a little more stock. I don’t usually encourage speculation, but it might be warranted here. Remember, don’t invest anything you can’t afford to lose.
• Last, my models last week were too optimistic, but not by much. The growth rate of total cases is generally dropping pretty quickly, but you couldn’t tell that from what you are hearing from politicians and the media.

Introduction

Before I start, I want to explain what the tables above and below mean.

• The figures underneath the percentages are dates. The dates are estimates of when the country, state or city will have experienced 10%, 50%, 90%, and 99% of the total COVID-19 cases that they will experience in the first wave.
• The peak day is the day each has the most new claims.
• “Expected Total” is my estimate for the total number of reported COVID-19 claims in the first wave.
• “% pop” is the percentage of each population that will be reported as infected with COVID-19.
• “% complete” is the ratio of estimated current total cases to estimated final total cases fo the first wave.
• Pseudo-R2 is the percentage of the total variation in the total cases explained by my three-parameter nonlinear regression. Because the regression is nonlinear, it is not an F-statistic, and gives us only a spit-in-the-wind sense for how good the regression is. Some have asked if I could add error bands to my models and the answer is no, because the nonlinearity of the equation makes that difficult. I’m only working with Excel, and looking through my old Econometrics texts, they don’t have an answer for this one. Maybe I should start modeling in R.

You’ll note that I added six additional models to this post.

• One country: Turkey (I am modeling any country that gets more total cases than S. Korea.)
• Three states: Maryland, Massachusetts, and New York. I’m modeling my home state, Maryland, Massachusetts for a friend, and New York because it has the most cases of any state.
• Two cities: New York City and the Boston Area, which is the five counties near Boston (Essex, Middlesex, Plymouth, Suffolk and Worcester). New York City, because it has the most cases of any city, and Boston, because of the aforementioned friend.

Data and Resources

Before I go on, I want to point out some useful sites for getting data and resources. If you think you have other useful resources, please post them in the comments.

• Courtesy of the New York Times, you can get state and county total cases and deaths on a day-by-day basis. (Main page) (state data) (county data) Data is in a Comma Separated Values [CSV] file format.
• Worldometer is an excellent website for all manner of COVID-19 data. I particularly use their country data pages. They provide links to most of their data sources. I also think this page that breaks out mortality rates for those who have caught COVID-19 by age, sex, and pre-existing conditions is pretty neat.
• Finally, you can look at my models if you like. Here’s the country model. Here is the states and cities model. Now you can look at what I am doing, and play with it yourselves. Note that you will have to activate the Solver application inside of Excel.

Limitations of what I am Writing About

I am forecasting one variable in each geographic area — reported total claims as of a given date. I am forecasting this for several reasons.

• It is relatively easy to do. If I tried to estimate medical resource usage or even deaths, I would need more data that I don’t have access to in order to do reasonable models in that area. (Now that said, a hidden assumption of the analyses is that there is some regularity to how cases get reported. If that changes, the models will be less accurate.)
• Reported total claims is a leading indicator for other variables of interest. In addition to those mentioned in the first point, total reported claims is a leading indicator for the economy, lifting of government restrictions, and the financial markets.
• It’s not as if there aren’t complexities that could mess with an analysis like this. When testing becomes common, you might see total cases go up a lot from all of the asymptomatic or low symptomatic people who are suddenly found and are no longer infected. That sort of shift would give the appearance of COVID-19 taking off, when we realize that that data belongs to the past, even though it is reported in the present.
• No one wants to say it, but there are tradeoffs involved in having governments be too ham-fisted in their regulations. Those regulations are impoverishing a lot of people, and many of the restrictions are not needed in order to have the same level of societal safety.
• There are also tradeoffs of life and money… and this is not new. Life is precious, no doubt, and money can often be replaced, but where does the money come from? Would it be right to be Robin Hood and push 100 unrelated people out of work in order to save a life? Perhaps it would be better ask for volunteers. It would be more ethical for the government to raise taxes, than to put on restrictions that harm the economy a lot, with few additional lives saved.

This is an economics, investing and finance blog. I focus on those matters. It’s not a healthcare blog. When I think of my average reader, that person is not thinking a lot about the problems from medical resource shortages, except perhaps the lack of ability to test for COVID-19. It’s different if you are in the medical profession or if you are sick. You would care a lot about these issues then, and my heart goes out to you, because you are having a challenging time with short resources.

As an aside, when you think of medical efforts in the US generally, with the emphasis on trying to manage costs, hospitals and inventories of supplies and equipment are light because in normal times, those were easy places to save money. Few would complain much (except closing rural hospitals) because there would be enough resources under 99%+ of all circumstances. This is fine, until you experience the low probability and high severity event. This is common to other disaster scenarios as well — there is often a complaint over lack of redundancy or robustness of some resource. (Not enough policemen, firefighters, ice, electricity, phone connectivity, emergency shelter, etc.)

I am not saying my analysis is the whole enchilada, but it is an important part of it. And with that, on to the graphs:

Past the First Wave, in the Second Wave

China has averaged 55 new cases of the past 10 days, and South Korea that figure is 99. The trend seems to be up, but with a lot of variability. I liken the second wave to what needs to be done after the main battle with a forest fire is done. You still have to put out some minor fires before they turn into something major. Eventually, like say in a month or two, most nations will be dealing with this.

Because of this situation, the models fit less and less well. I could add in a second logistic curve that starts where the first one ends… though it seems like overkill from a modeling standpoint. It wouldn’t be difficult to do.

Approaching the End of the First Wave

Austria, Switzerland and Italy are most likely past the 80% point. By that point reported new cases are declining quickly, and total cases are growing at around a 4% daily rate, and the growth rate is falling quickly.

As an aside, this is a good time to talk of how the media, and sometimes even policy makers who should know better, are practically innumerate in terms of the verbs that they use. They look at the raw increase in cases and say that they are soaring. It varies by geographic area, but the daily percentage growth in total cases and daily percentage growth in new cases is like this:

Percentage Completed	Daily % Growth in Total Cases	Daily % Growth in New Cases
0-10%	18-35% nearly constant daily growth, but absolute numbers are low.	Exceptionally high and erratic, 30-50%/day , but absolute numbers are low.
10-50%	Rate of growth falls into the teens of percentages. If the starting percentage is lower in this interval, so will the ending percentage. Absolute numbers sound large, especially nearing the halfway mark.	Rate of growth falls rapidly to zero by the end of this period
50-90%	Rate of growth continues to fall to the low single digits of percentages, say 2-4%. Absolute numbers sound large but rapidly get smaller toward the end of this interval.	Rate of growth is negative, and gets more negative as the interval gets to the 90% mark.
90-100%	Growth is very low. Absolute numbers are low.	Growth is negative and erratic. Absolute numbers are low.

It’s in the middle two zones where the absolute numbers are high that the rhetoric gets shrill. Compare that to me where at 8PM Eastern Time, I sit down and update my models and comment on how close they came to the modelled estimates. The absolute numbers of total cases, new cases, total deaths and new deaths make great headline fodder, but the real news should be looking at the percentage rates of growth of total cases and new cases. But I suspect that would be a tough thing to see change.

Middle of the Pack

Germany, the Netherlands, Spain, and the US are the next group. New cases are either rising at a low rate or declining. Growth in total cases is in the high single digits of percentage. These countries aren’t out of the woods yet, but are likely past the halfway point.

Some of these had high new case surprises over the last week, but on the whole showed improvement.

Bringing up the Rear

Each of these had significant upward surprises in terms of new cases reported. The growth rate of reported total cases is in the mid-to-high teens.

Too Early to Tell

I did not model Turkey in the last article. It has a really sharp takeoff and deceleration of growth that looks too good to be true. (The US is that way to a lesser extent.) I need more data before I can be definite about this.

Problem Child

Compared to last week, Iran has gone backward. New cases have been growing more rapidly, and the growth in total cases shows no sign of slowing. It will be interesting to see how this develops — it doesn’t fit the model well, unless….

Unless you think of it as several logistic curves in different areas that have taken off and leveled at different points in time. Now that said, from what little I have read, there seems to be a lot of disagreement in Iran over what to do. And to some degree, a populace that doesn’t trust the government much… so it’s not a recipe for constructive collective action.

States and Cities

Massachusetts and Boston Area

New York State and New York CIty

Maryland

The logistic curves for smaller, more homogeneous areas tend to be shorter and sharper than those for broader areas. The data also tends to be more noisy, but that’s what the regression analysis is for — smoothing out the data in a theoretically consistent way, and allowing tracking to be done so that a policymaker could estimate if they are doing better or worse than expected. It would certainly calm some politicians down if they had an idea of how things are likely to develop, and if a deviation happened, they could try to explain why, allowing for the level of uncertainty in analyses like this.

And so at the end, can I offer a happy surprise to New Yorkers, both those in the city, and those that are upstate? There will still be problems for a while, but it really seems like you are getting to the end of the trail. In two weeks, you should be a lot happier. And the same will likely be true in Massachusetts and Boston, and in my adopted home state, Maryland.

But here’s the key question. How ready will the politicians and policymakers be to accept the good news? I fear they will not be happy with it at all, but will remain cautious in the wrong way too long. There is kill, and there is overkill. Kill is enough.

I would encourage the politicians to have us continue to do social distancing, but to reopen businesses, requiring them to follow certain sanitary and distancing procedures. Perhaps those who are infirm, or are over 60, 65, 70, or so should continue remain at home, or only go to necessary places a while longer.

There is a price to everyone staying home. There is a political price to politicians that maintain it too long. Better to modify policy such that it is a sniper rifle, and no longer a blunderbuss.

PIcture credit: Aleph Blog, and the same for all the graphs and charts in this post. All liability for mistakes here is mine.

This post is different than any other I have done at Aleph Blog. I will try to write this in a nice way even though it is a strong and out-of-consensus opinion on a topic that many are edgy about.

I realize I might be wrong here, but I will present to you what I think, along with what I think are the limitations of my analysis. Part of my reason for writing this is that I think that most of the reporting on COVID-19 is subject to a bias common in our culture among politicians, lawyers, bureaucrats, and the media: an extreme bias toward safety because the costs of being wrong on the optimistic side are high than the rewards for being right. (Example: NOAA overpredicts disasters, and so do most hurricane forecasters.)

This post will be structured like this:

• Summary of findings and recommendations
• Limitations of the analysis
• Breaking down the results by groups of countries
• A discussion of the “Second Wave,” with policy recommendations
• Closing comments
• Appendix for math nerds

Summary of findings and recommendations

• The First Wave of the crisis will pass more quickly than most expect. Most countries with a large number of COVID-19 cases will have 99% of their First Wave cases reported by mid-April.
• Of the 13 countries with the most cases of COVID-19, the least of them has reported 41% of their likely First Wave cases. Of those same nations, none are expected to have more than 0.3% infected with COVID-19.
• The real challenge will come in dealing with the Second Wave of the crisis. How do governments deal with a smallish number of new cases, and keep them from growing into a new epidemic?
• In the Second Wave, governments should selectively tell some to stay home, while telling most people to get about their normal work.
• Quarantine those who are sick with COVID-19 and those who have been with them, until they are tested and have a negative result. Continue to disallow international travel, or insist on a two week quarantine upon returning.
• Let healthy people return to their work. All businesses are necessary businesses.
• Avoid bizarre stimulus programs that are harmful in the long run. Tell the Fed that monetary policy can’t solve everything, and not to play favorites.

Limitations of the analysis

• I am not a public health specialist. I am a statistician with a background in econometrics, which has its similarities with biometrics.
• My analysis assumes that processes for finding new cases of COVID-19 are constant, or mostly so. That is not always true — an example is when China announced a large amount of new cases all at once.
• I use an inverse logistic curve for my analysis. All functional forms have their limitations, and for the nations analyzed as of this date, the minimum pseudo-R-squared is 79.4%, and the highest is 98.5%. That said, this is a common functional form for epidemics.
• The model assumes that there is one wave. That will not prove to be true, as can be seen from China and South Korea.
• All sorts of things can go wrong that are not in the data now — mutations, civil disobedience, large bureaucratic errors, large policy errors, etc.

Analysis By Group

Those that are though the First Wave

We have two in this group: South Korea and China. I don’t trust China’s data. In each case, though, you have the First Wave go through their nation and burn out, followed by an excess number of new cases where the public health authorities may not be catching up with what could be the Second Wave. I’ll talk more about the Second wave below.

The unusual case of Iran

Really, I don’t know what is going on in Iran on COVID-19 but it looks like the initial new cases started to slow down, and then they let up on restrictions too soon. New cases hit a new high yesterday, which doesn’t fit the paradigm of a consistent response the the crisis. COVID-19 seem to be out of control in Iran.

Those that are close to done

Italy and Germany are past the halfway mark in the epidemic, and are having lower new cases on average daily.

In general, the policy responses of a nation influence the amount of the population subject to infection, and the ability of the infected to interact with the broader society.

The rest

These are the nations that have not certainly passed the 50% mark as of today as I estimate the infection. As I have watched this develop over the last week, the most difficult aspect of estimation comes when you are near the halfway point. Small changes in actual new cases make a big difference in estimated new cases. An example is the United States, who has had significantly lower new cases than expected for a preponderance of the last week. The US got off to a slow start in its reaction to the crisis, but seemingly has caught up and then some.

WIth these countries, the odds of being wrong is the highest. Thus all conclusions with them must be considered tentative. But with so many of them following nearly the same pattern, despite very different responses to the crisis, gives more certainty to this analysis.

A discussion of the “Second Wave,” with policy recommendations

When you look at the data of CHina and South Korea, you see how the epidemic went through the s-curve, and then has persistently high new cases thereafter. I call this the “Second Wave.” Iran seems to be a case where their society inadequately stops transmission, and so instead of following an s-curve of an exponential, it seems to keep increasing in a way that is almost quadratic — slow but steady.

This will be the grand problem for most countries. How do you eradicate the virus after you have had large success in interrupting its transmission? Looking at the relative success of South Korea in the First Wave I would say that you do the following:

• Test and quarantine aggressively.
• Of those who test positive for COVID-19, quarantine all of their contacts, and test them. Continue quarantine for those who test positive, and quarantine/test their contacts as well. Repeat as needed.
• But don’t quarantine everyone. Let those who are healthy work. Encourage those who are old or have compromised immune systems to stay home for the duration of the crisis, and give some assistance to them.
• Don’t assist all of society because that is way too expensive and not needed — get them back to work. Don’t give into the idea of denying people work and then offering meager assistance. It is an inferior idea for those who are healthy.
• This applies to the actions of the Federal Reserve as well — don’t harm the value of capital by artificially creating more capital that has no earnings capacity.

Closing comments

This analysis shows the the slowest of the nations written about here is passing the middle of the crisis quite rapidly, and the practical end of the crisis is in mid-April, when 99% of all First Wave new cases will have been realized. The real challenge will come in dealing with the cases after that, which will be sporadic and localized. How do we keep that from becoming a semi-permanent bother to the world, because the cost of putting life on hold is high, as is the cost of losing lives.

Quarantining and testing aggressively is the best solution, together with letting the healthy work. This should be the guiding star for all policymakers, because we need to strike the right balance between breaking the social connections that lead to disease transmission, and allowing people to labor to support themselves. We are not trying to save the financial markets; we are trying to protect people who work.

Appendix for math nerds

The above was how I structured my analysis. It followed a logistic curve, which has the following benefit: infections begin exponentially, but get retarded by two factors: one is that even if people do almost nothing as in 1918, the uninfected population shrinks, which blunts further growth. Second, people act to blunt further growth. They separate themselves from each other, and particularly those who are infected. This is is akin to removing fuel from the fire.

The logistic curve has a number of advantages for estimation. It notices the slowing down of the percentage growth in total cases, while media and politicians continue to panic.

Remember that that the media and politicians selfishly like to maximize their influence, and try to create panics — it is good for them to maximize fear. The same is true for many in public health. Truly, we should spend more on public health, but it is one of those things that governments naturally neglect… because they are short-sighted, and will not spend money on something the lowers risk, but does not bring any present good. (Note to Christians: in the Old Testament public health was a function of their government via the priests. It should be a normal function of government to deal with contagion.)

Final note: I did not write this with Donald Trump in mind. I did not vote for him and will not vote for him. That said, he is on the right track when he says the cure should not be worse than the disease.

It is foolish to warp monetary policy and fiscal policy when healthy people are perfectly capable of working. Don’t destroy ordinary incentives and rack up tons of debt by keeping people idle. Test, quarantine, test, quarantine, etc. , but leave the main body of society alone, particularly for a virus that does not harm the healthy working population much.

To that end, I ask that Republicans be real Republicans, and not expand the deficit further. I ask that the Federal Reserve stop trying to be God, and be content with merely having a currency with a consistent value.

Government is best when it is small. We are not facing the Black Death, nor the Spanish Flu. We will get through this, God willing. We don’t need to panic.