Accen-tuate the Negative, Eli-minate the Positive

If you get that reference, you're old. Welcome to the club.

It's a play on an old song written by Johnny Mercer in 1944. It has been covered by numerous artists over the years: the Lennon Sisters, Bing Crosby, Ella Fitzgerald, Sam Cooke, even Paul McCartney, as recently as 2012, for you young whipper-snappers.

The song actually goes, "Accentuate the positive, eliminate the negative." And it cites doing so as the key to happiness. So why are we all so unhappy right now, besides some of us being scared out of their wits that if they go outside, they may become gravely ill, or worse; and some of us tired of what they perceive as their rights being trampled on, and ready to just go back to work and get back to life?

The media.

The media - I can't even use the word "news" anymore, it doesn't exist - has been trying to scare people into the first camp all along, and incite others into the second camp, through an intentional campaign of misinformation. On either side of the health vs. economic impact argument, they consistently highlight, exaggerate and mis-state the negative, and bury the positive.

My alter ego posted a picture on Facebook of a TV set with CNN's Wolf Blitzer on the screen. Next to his head was the ubiquitous case and death count in the U.S. from COVID-19, that by now have probably been burned-in to all our screens. It showed nearly 3 million cases in the U.S. And the banner at the bottom of the screen screamed, "U.S. DEATH COUNT APPROACHES 3 MILLION."

Incompetent? Perhaps. The media talking heads are all, by and large, under-educated buffoons. But more likely it was intentional. Get one person, one math-challenged person (and they're everywhere, folks) to believe the banner, and they'll tell their friends, who'll tell their friends, who'll post it on Facebook and Instagram and even Nextdoor, for crying out loud. And then you get them all coming back for more, huddled in front of the TV in abject terror or uncontrolled rage, riveted to the screen, waiting for more doom and gloom.

What's even more interesting about the picture I posted?

Facebook took it down.

Social media is complicit in fomenting panic. They don't want people to see the hypocrisy, incompetence and/or intentional misinformation displayed by the media.

They want it shared.

The other night, I was watching one of the major cable networks. The anchor first interviewed Gov. Burgum of North Dakota. She said, "You're re-opening your state, but the number of cases is increasing. Why are you re-opening?" Would you have needed to wait for the governor to respond? I sure didn't. Reported cases are increasing everywhere, because of MORE TESTING. North Dakota ranks 47th among U.S. states in population. Care to venture a guess as to its rank based on testing per capita?

Fourth. FOURTH. Behind Rhode Island, New York and Massachusetts. (My home state of Kansas is dead-last, in spite of ranking 35th in population, which is proof that for some governors, it's easier to shut down a state than it is to run it.)

So of course more cases are being reported if there's more testing. That has happened, and is happening, everywhere.

Gov. Burgum responded by saying, "We're doing more testing." Duh. "North Dakota ranks fourth among all states in testing per capita," he went on to boast. Which anyone can find out with a simple web search. The anchor went on to say, a few sentences later, "Of course cases are increasing because of testing, that's obvious."

So why ask the stupid question in the first place? It could be that she was saving face for not knowing the reason, not getting the obvious correlation, not having done her homework, like competent journalists used to do before the breed went extinct. More likely she knew all along that testing was the reason for the increase, but was hoping some uninformed viewer would hear her question, freak out, go get some Ho-Hos to fear-binge on, and miss the rest of the interview.

But wait, there's more. Her next guest was Ohio Gov. DeWine. She kicked off her interview by citing the hopelessly flawed IHME model, which I have debunked numerous times. She noted that the model showed that it had been six days since cases in Ohio peaked, and the model indicated that it wouldn't be safe to re-open the state until May 14, yet Ohio was opening some businesses as early as April 29. "Aren't you jumping the gate?" she asked.

Anybody with even a minimal amount of intelligence is looking at the data, not the model - from Drs. Birx and Fauci, to the governors who have done a good job throughout this mess. And the "gate," referring to the gating criteria for re-opening, is based on data, not on the model. So what does the data say?

Well, the data shows that new deaths in Ohio peaked on April 22, three days after the date the model shows they peaked.So one might surmise that the model is pretty close.

Except the model was last revised on April 22, three days after its forecast peak. So at that point it reported the peak had already been reached, even though deaths continued to climb for a few days. The previous revision, made April 17, "forecast" the peak would be reached on April 16. Oops. The April 10 revision said April 10. Oops again. The initial public release said April 21, which was actually more accurate than any of the subsequent revisions.

Does that mean the initial release of the model was "right?" No, it proves the old adage that even a blind squirrel finds a nut once in a while.

This anchor's next guest was the creator of the IHME model himself. She asked him why the model's numbers were still going up. (Let's conveniently ignore the fact that the actual numbers of new daily cases and deaths are going down. That would be way too positive. The media's job is to crush hope, not inspire it, and foment panic, not reason.)

The modeler cited several factors, including more presumptive cases (in other words, not tested and proven, but hey, even though they might just have the flu, we'll say they have COVID); protracted flat peaks in some states (he specifically cited New York, but neither the actual data nor the man's own model show a protracted flat peak in that state - look it up); and -

The model uses actual cases as a leading indicator! And we already established that actual cases are going up, right? And we already established that the reason is more testing, correct? So the more we test, the more the model is going to predict will die. That's probably true, but the actual data will continue to show the mortality rate falling significantly as testing is increased, while the model will continue to assume the same inflated mortality rate that has rendered it useless all along.

Did the anchor draw any conclusions on behalf of viewers, such as pointing out that since testing results in increased reported cases, and the model uses cases as a leading indicator, it's always going to show increasing numbers? Did she question the model's veracity?

She did not. She left viewers with the impression that the model is accurate. But she had no problem challenging the governors who were re-opening their states, even though they're doing that based on data, which is what the gating requirements dictate. The media are well aware that viewers are unlikely to actually look at the model or the data to determine whether New York's peak is actually flat and protracted. Nor are they likely to understand the model's flaws, or review the actual data. They're banking on being people's only source of (mis)information, so they can create either fear of the virus, or anger over the response. Or both - often within the same anchor's time slot.

(A quick note about the model's projected "safe" re-opening dates: that's a new wrinkle the modelers added after the re-opening guidelines were released. I've looked at it state-by-state, and it has no basis in the actual data. It should be based on that data and tied to the gating requirements, but it is not. It's not even looking at testing numbers. It also still shows many states exhausting health care resources like ICU beds and facing shortages, when in fact no state has. I'm not sure why they bother keeping the model running, other than to get on TV every other day.)

The local outlets are equally complicit, for the most part. On April 29, Kansas City, Missouri Mayor Quinton Lucas announced the most onerous re-opening requirements of any jurisdiction I've seen to date. Businesses can only re-open after May 15, and then at only 10% capacity - not the 25-50% or more established by most jurisdictions. When the announcement was made, I questioned why a restaurant would bother if it's already doing decent carry-out business.

After the press conference, a local TV station posted on Facebook that it had talked to business owners in attendance, and all of them supported the requirements. The post quoted two business owners. I was skeptical; what are the chances that 100% of business owners in attendance support such draconian restrictions? And can we extrapolate that sample to all Kansas City businesses?

Sure enough, in a radio interview the following morning, a prominent local chef and restaurateur stated that he was very much opposed to the plan, and was very upset about it. He said that it wasn't worth it to re-open at 10%, as I expected. He had tried to reach out to the Mayor's office, which was not responsive.

(The Mayor complained that other contiguous jurisdictions' health officials would not return his health commissioner's calls, yet he himself won't return his constituents' communications?)

Another local radio host said that he had heard from hundreds of local businesses, and not one of them was happy with the plan.

And yet the TV station, with a much larger audience, reports that no business owners are against it. Based on a sample size of two. Did they follow up with a larger sample? Talk to any other business owners? No - because that's not the narrative they're trying to shape in unsuspecting viewers' minds.

Besides local and national TV and print media, social media is out of control as well. I was watching the Kansas governor's press conference a couple of days ago. I was at my desk, so I was watching on Facebook, and could see the comments scrolling by. One commenter said that he had just learned that the state was going to extend the lockdown through October, and that they were deploying the National Guard the next day to enforce a mandatory 24-hour a day curfew. (The Kansas order expires May 3; the KC metro counties on the Kansas side are extending that by one week.)

People actually believed this person. They were terrified. This was the equivalent of me and my high school friends calling stores and asking if they had Prince Albert in a can (do a web search on the question if you're too young to get it), only far more cruel.

So you see? Individuals are preying on people's fear - and yes, gullibility - on social media, for sport. The TV media are doing the same, for ratings. Facebook is helping them all along - leaving the false comments like the one I noted above, but removing anything that exposes the panic-mongering.

All of which leads us to a simple rule: turn the TV off. Don't pay attention. Tune in to something else. Unfollow the fear-mongers and conspiracy theorists on your Facebook friends list. And, as always, go to the source.

I mentioned reason earlier. When it comes to the media, I'm led to paraphrase a very misogynistic statement made by Jack Nicholson's character in the film, "As Good as it Gets." In it, he plays a novelist who writes romance fiction. The character is bigoted, obnoxious and rude. When asked how he can write female characters so well, he replies, "It's easy. I start with a man, and I take away reason and accountability."

And that's the media - devoid of both traits, whether male or female.

I'll close with some good advice from the lyrics of another song, this one by John Prine, titled "Spanish Pipedream:"

"Blow up your TV, throw away your paper,
Go to the country, build you a home
Plant a little garden, eat a lot of peaches,
Try and find Jesus on your own."

Excuse me, while I go have a peach.

Back to the Future

Of the economy, that is. But first, a quick note about the IHME model.

The model was updated as of April 17, and the projected deaths came down - way down, for some states. The overall projected toll for the U.S. was down more than 8,000, to about 60,000, a drop of more than 10%. For Kansas, the projection fell from 555 to 187. The current total for Kansas is 84, and we're pretty much at the peak of our curve here in the Sunflower State. So the model's latest forecast may be close to being accurate, at least in some states and for the nation: if you're at the peak, you'd expect cases to roughly double by the time the curve gets back down to zero new fatalities.

However, the model is still way off in several states. You can look at various metrics and see which states those are. So as more data comes in, the numbers will continue to come down, and the time to flatten will continue to shorten.

But the most significant thing about the latest update?

The assumption that full social distancing would be maintained through May was relaxed in light of the phased plan to re-open America. The current assumption reads:

Current social distancing assumed until infections minimized and containment implemented

What's significant about that, you ask?

The modelers relaxed the social distancing assumptions, and projected deaths came down.

What? How is this possible? Simple. Remember that data informs models. And at the point of the latest update, the model had four more days of data since the most recent previous iteration. A larger amount of data than in any prior period between updates, because the numbers became staggering as we approached the apex of the curve in most states, including those with the highest numbers. So all of that data informed the model that -

We can relax the mitigation protocols, if we're smart about it, and it won't lead to a widespread horror-movie scenario in which every American dies.

(Note: if you're among those who believe we need three times the number of tests, or a proven vaccine, before we can safely re-open, reading further would probably be a waste of your time. None of the real experts - Drs. Fauci, Birx, Giroir and Adams - agree with that. And, if you're one of those who believe that they are all just puppets controlled by Bad Orange Man, don't bother with a mask. Your tin-foil hat will save you. When the rest of America re-opens gradually, and in phases, feel free to stay at home until the vaccine is available. But your fear, in the face of contrary evidence from the experts, doesn't give you the right to impose that sentence on the rest of us.)

I've believed for a while now that the model projections would become better aligned with the actual data only after the curve peaked in every state, and that appears to be correct. In other words, the model will only become accurate when we reach the point where a model is no longer needed. Hindsight is 20/20. And 2020 will be hindsight.

Now, on to the economy. For a while, I was fearful that a V-shaped recovery would be impossible. I'm a bit more confident now. Before I explain why, let me discuss some of what I've heard from other sources, which is the reason behind this post, to clear the air.

Some are saying that this will be the worst economic catastrophe in the history of the U.S., and that recovery will take years, if it ever happens. They're saying they base this on "the data." I'd love to see the data they're looking at, because I can find no indicator that supports this view. If you can, please send it my way.

Yes, it's bad - more than 21 million Americans have filed first-time claims for unemployment insurance in the last five weeks. (During the Great Recession, the total was more than three times that number.) Ongoing claims will exceed 10 million claimants when the next number prints on April 23, as of April 11. The unemployment rate for April, which will be released on May 1, is going to be ugly. There will be a contraction in GDP for the first quarter, followed by an extremely sharp contraction in Q2, and it may also be negative in Q3, and possibly Q4, but Q2 will likely be the worst. That would mean a 12-month recession - about average in duration by historical standards.

One observer said that this would be worse than 9/11. So let's look at that dark time in our history, and use the stock market as a gauge of how fast things went down, how far they went, and how long it took to recover.

September 11, 2001 came five months after the dot-com recession had ended, and it resulted in another decline in GDP for the quarter during which it occurred, effectively extending the recession. And then GDP growth rebounded in the last quarter of 2001, and was up nearly 4% in the first quarter of 2002. It dropped again, to almost zero by the third quarter of that year, before recovering fully.

So yes, this is worse than 9/11. While 9/11 was catastrophic for our nation, its impact on our economy was far from catastrophic, and was brief.

Let's look at the stock market for the whole of the 2000-01 recession. The S&P 500 fell by about 40%, and that decline took 22 months. It was four years before the index recovered to 80% of its value at the pre-recession peak.

Now, let's look at the even deeper, and longer, Great Recession. The S&P fell by 50%, and it took 15 months to fall from peak to trough, and 18 months to recover 80% of its pre-decline value. (That recovery was accelerated by the most unprecedented fiscal and monetary stimulus in U.S. history.)

As a result of the government response to COVID-19, the S&P fell by about 34%, from a record high to its trough.

Folks, that decline took less than five weeks. And it has already recovered 80% of its pre-decline value - in less than four weeks. (And we have again seen unprecedented fiscal and monetary stimulus, with more likely to come. How we pay for it is another conversation.)

Five weeks, peak to trough. Four weeks, trough to 80% recovered. Can you say V-shaped?

We're not out of the woods yet, but I'm extremely confident that we've seen the bottom of the market in this crisis, barring some cataclysmic Hollywood-esque spike after re-opening that kills millions. Which I don't believe will happen, and neither do the real experts.

We could keep looking at data, but readers of this blog know that I'm a big fan of anecdotal economics - that we can perceive more from observing the world around us than we can forecast using models. So let's look at some anecdotal evidence.

What sectors of the economy have been hit the hardest? Let's start with restaurants. As of a couple of weeks ago, a reported 30,000 restaurants had closed permanently. A projection I saw was that, by the end of May, that total would jump to 110,000, roughly 10% of America's restaurant locations. And 70% of U.S. restaurants are single-location operations - mom and pop places.

My lovely wife and I have been picking up food from local restaurants every few days. And what we've experienced is having to redial the restaurant's phone number for several minutes before we get through. Wait times of an hour or more before the meal will be ready. Restaurants running out of some popular items before peak dinner time. Having to wait more than a half hour from the scheduled pickup time before the food is ready. Lines of cars at pickup time.

Why? Demand. Restaurants are reporting that sales are about 50% of normal, just from carry-out and delivery. That won't sustain them long-term. But it's pretty darn good considering the circumstances, and it's a clear indication that people want to eat out again. When those restaurants re-open for dine-in service (on a limited basis due to social distancing), they'll be as packed as they're allowed to be. And they'll have to hire some wait staff back. When they're able to fully open again, they will be packed, with longer than normal wait times. And they'll have to fully staff back up. Based on current projections (which are still too far out), and the phased re-opening guidelines, I expect that to happen sooner than the doomsayers think.

Another indicator is the stocks of restaurant holding companies. I bought several of them at the market bottom. They're all up between 40% and 52%.

In two weeks.

Let's look at cruise lines. Carnival's stock (which I also bought at the bottom) is up 55% in two weeks. Royal Caribbean (which I already owned, but bought more of) is up 45%. Even though the CDC recently extended its stranglehold on the industry through early July (which only applies to ships sailing in American waters, so there will still be some sailings in other locations before the CDC order expires).

Why? The largest cruise-specific travel agents are reporting that bookings for 2021 are up by double digits over 2019. Part of the reason is the generous credits the cruise lines are offering cruisers who've had to cancel due to the shutdown. They're offering those credits in lieu of refunds in order to maintain cash. An industry analyst has said they can remain afloat (pun intended) even if cruising were to not resume until January 2021, which is highly unlikely.

Cruisers are loyal. They love to cruise. They're anxious to be able to cruise again. I should know - my lovely wife and I have cruised more than 20 times, and we will certainly cruise again. We've been on cruises with people who have taken well over 100 cruises. We met one guy from Australia who said he spends three months a year cruising.

What about airlines and hotels? Delta's stock is up 27%. Hilton is up 70%. Park Hotels, which spun off from Hilton to manage some of its higher-end properties, like the Hilton Midtown in Manhattan and the Hilton Hawaiian Village on Waikiki Beach, has doubled from its low. So there's clearly future demand for travel, including staying in high-end hotels. (In most recessions, luxury spending is the last thing that recovers.)

People want to travel again. I travel extensively for work, and I have a trip scheduled in late May - to Jacksonville, where the beaches have already been re-opened, but with limited access and social distancing requirements. I'll need to book a plane ticket. I'll need a hotel room and a rental car. I'll need to eat out, if possible, or at least pick up restaurant food. I'm also going to St. Augustine on business in mid-June. And I expect to make my usual round of four client visits in August, as well as several on-site engagements that were postponed and need to be scheduled. It's not like this won't happen until 2021 or 2022.

Note that the media headlines screamed, "Jacksonville Opens Beaches the Same Day Florida Deaths Peak." Not only is that not true - deaths in Florida peaked on April 14, and the Jacksonville beaches re-opened four days later - but Duval County, where Jacksonville and its beaches are located, has experienced only two deaths in the last seven days. They've had 15 overall, in spite of a population of about one million. And, they closed some public spaces, like malls, later than most cities. Of course, the media omitted those facts.

Another anecdotal indicator is the wave of protests in various states, clamoring to open things up. Are the protesters engaging in risky behavior in some cases? Yes. But that isn't the point. The point is that the protests themselves are evidence that large numbers of people are ready to see things open back up. We need to follow the gating and phasing guidance to do it safely (probably - we don't really know for sure whether it's necessary, so we err on the side of caution). But it's clear that most Americans are concerned that we risk burning down the village to save it, and they don't want the political football game to continue.

Also worthy of consideration is the aforementioned stimulus. Government spending is one component of GDP. So not only is the stimulus helping various sectors of the economy to survive, it will put a floor under GDP declines.

For a final indicator that combines anecdotal and data evidence, let's look at the banking sector. They're anticipating increased loan losses from individuals who default on credit cards, auto loans and mortgages, and from businesses that default on commercial loans. How ugly do they believe it's going to get?

Banks and credit unions set aside reserves as a buffer against the credit risk that they all assume - the risk that borrowers will default and the bank will have to charge off the unpaid balances. These reserves are called the allowance for loan and lease losses, or ALLL. To fund the ALLL, they set aside a portion of their income. That portion is called the provision for loan losses, or PLL. (Don't worry about the distinction - it's a function of accounting.)

So lenders are going to be increasing their PLLs to fund the reserve that they'll likely need to use to cover the cost of loan defaults - the ALLL. In effect, they'll be pre-funding their credit losses out of current income.

My clients are credit unions. Let's take a look at one of them, as an example. This client is located in a sand state that was hard-hit during the housing collapse that precipitated the Great Recession. Its housing market dropped 40% from from mid-2007 to mid-2012, and only last year recovered that full decline. 

In 2007, as home prices began to fall, my client's PLL as a percent of total loans was about 1%. In 2008, they more than doubled it, to 2.2%. They increased it again in 2009 by another percentage point, maintained it in 2010, then were able to reduce it by half in 2011 as credit losses subsided.

This year, they plan to again double their PLL from last year. However, last year it was less than half of one percent of total loans, so this year they'll be taking it to just under 1% - about the same level as in 2007, before the housing collapse began.

In other words, while they expect conditions to worsen on the order of the change from 2007 to 2008, they don't anticipate credit losses at the level of what they were in 2008 - not by a long shot.

At my client's request, I analyzed their assessments of recession and related risks, and my analysis validated their projected increase in the PLL. A colleague conducted a similar analysis across all of our clients, which produced similar results to my analysis. So expected credit losses are pretty consistent across lenders, and a heck of a lot less than during the Great Recession.

The upshot of all of this is that this is likely to be a very sharp, but not longer than average, recession. It is affecting, and will affect, certain sectors of the economy, and leave others relatively unscathed. Millions have lost their jobs, but not permanently. Millions more remain employed, and their pay has not declined. As I've noted before, there is no sector of the economy that will not come back. That isn't the case in every recession. And the businesses that do close permanently, such as the mom-and-pop restaurants, may re-open. (Locally, GoFundMe pages have popped up to help some popular restaurants re-open. If that's happening here in flyover country, it's happening everywhere.) If they don't, others will crop up to take their place, because the demand is there.

America will be fine.

Upon Further Review ...

The patient is dying.

The patient in question is the Institute for Health Metrics and Evaluation (IHME) COVID-19 model, which I discussed at length in my last post. And what is the model dying from?

Starvation. And malnutrition.

You may recall the theme of my last post: Data informs models. And I noted that, as more data becomes available, it is fed into the model, and the model gains predictive value. As with we humans, if you don't feed a model sufficiently, it may eventually starve to death. At a minimum, it will become so malnourished that it can't do its job effectively, just as with a human.

And with the IHME model, that's where we are today.

Before I explain why the model is starving, let me explain how I came to the realization that it is. As I said in the previous post, I analyzed the projections from the first run of the model that was made available for public consumption. That was on April 1. I updated the analysis on April 5, when IHME released the next revision. I updated it again on April 8, when a subsequent revision was released. And in looking at how the data changed, how it moved, from one release to another, just a few days apart, I saw behavior that you'd never see from a "healthy patient" in the modeling world. In other words, I saw symptoms of starvation, and of malnutrition on a major scale.

You've probably seen in the news how projected total deaths have dropped significantly with each update of the model. The initial count was over 95,000. After being fed a few days of additional data, the count dropped by 14,000, to about 81,000. And just a few days later, it dropped to about 60,000. (The media has tried to mislead you regarding why, but more on that later.)

But it's what happened state-by-state that really revealed just how malnourished the model is. Some states are seeing their projected death totals drop by half or more with each update. Dates for the curves to peak and flatten have also changed markedly. The projected date for the curve to flatten in Virginia was originally July 15; in the latest update, it's June 1. Nearly all states are now projected to peak and flatten earlier than in the original release, and nearly all within the April-May timeframe. However, a few states inexplicably have seen their projected deaths and time to peak/flatten increase. And there's little rhyme or reason as to why.

Or is there? The states that appear to be displaying the most counter-intuitive movements in the projections tend to be lower in population than the ones whose trends appear logical. Thus the model appears to be suffering from the law of large numbers.

Let's look at my home state of Kansas, which ranks 35th among all 50 states in terms of its population (about 2.9 million people, or about 15% of the population of the New York City Metropolitan Statistical Area, spread out over 82,277 square miles). The original release of the model projected 640 deaths in Kansas by Aug. 4 (the end date for the model's projections, by which time all curves have long since completely flattened). That's about 220 deaths per 1 million (1M) residents. The curve was projected to peak on May 3 and flatten on June 10.

At the time, Kansas had experienced 10 deaths as reported, so the model was projecting another 630 reported deaths by the time the curve was projected to flatten on June 10. The first death in Kansas was recorded on March 12, so by the first model release, the state had experienced an average of 0.5 deaths per day. The model projected that average to increase to about 10 per day - an increase of nearly 20-fold - through June 10. (Through April 9, the highest number of daily deaths in Kansas has been 5, so that average would be pretty hard to attain.)

The next update projected 265 deaths in Kansas. That's a reduction of 375 projected deaths, or almost 60%, from the original projection. The new date for the curve to peak was April 25 - 8 days earlier than originally projected - and the new date for the curve to flatten was May 23 - 18 days earlier than originally projected. (Some states saw those dates come in by more than a month, like Virginia.) So now, the model was projecting an average of about 5 deaths per day - half the original projection - in a state that to that point had still seen an average of less than one per day.

Those dramatic changes were the result of the model being fed four more days of data. During those four days, Kansas experienced just 264 new reported cases, and 12 new reported deaths. That's a big change in the projections produced by a very small number of data points.

Are you beginning to feel the model's hunger pangs?

In the next release, the model forecast an increase in total deaths in Kansas, to 299 from 265, an increase of about 13%. And it projected that the curves would peak and flatten one day later than in the previous update. Yet Kansas was still averaging just 1.26 deaths per day. This time, the change was driven by 12 new deaths and less than 300 new cases. Again, not a lot of data to produce reliable results. Those projections appear to have been skewed by the fact that, on the last day of data that was fed into the model at that point, Kansas saw a peak in daily cases at 123 (.004% of the state's population) and 5 deaths.

Wyoming is also interesting: There have been zero deaths to date in the state, out of 230 cases at this writing (about the same number of cases per 1M as Kansas or Iowa), but the model projected 67 deaths between the April 8 update and the May 22 flattening of the curve. On what basis? As Wyoming's governor said, "We've been social distancing for the entire 130 years we've been a state."

**Note: the model has been updated again as of April 10. The latest projections are as nonsensical as the earlier ones. I'm not even going to bother updating my analysis anymore. Suffice it to say that projected deaths in Kansas are now back up to 426, an increase of 127, or 42%. Why? Because there were 8 deaths on April 9.**

More broadly, here's the first thing that's wrong with the IHME model, and any other model making projections about COVID-19 cases or deaths: the data is simply insufficient in quantity to produce statistically significant results.

Let's put it into perspective. To date, there have been about 490,000 cases of the virus in the U.S., and about 18,000 deaths. That's an incredibly small number relative to the U.S. population. In the last flu season, the CDC reported more than 35 million cases, nearly 500,000 hospitalizations, and over 34,000 deaths. So last flu season as many people were hospitalized from the flu than have been reported to have COVID-19 (and remember, 96% of cases are mild), and about twice as many people died of the flu than have been reported to have died from COVID. The flu data is a lot richer dataset to feed into a model.

The average number of auto accidents in the U.S. each year is about six million. Average injuries are about three million, and average deaths are about 33,000. Again, a far richer dataset that would produce a more robust model.

Remember my discussion of mortgage prepayment models in the last post? In a year when rates are falling and more people are refinancing their mortgages, millions of mortgage loans will prepay, resulting in billions of dollars of prepayments. In a year. And during the Great Recession, billions of dollars in mortgages defaulted, resulting in full prepayment. So the mortgage prepayment models are far more robust than a model predicting auto accidents or influenza would be. And those models are twice as robust as any of the COVID models at this point in time.

But the problem with the IHME and other COVID models runs far, far deeper. It is not an insufficient amount of data alone that is producing such inaccurate results and wild swings in results based on tiny amounts of additional data.

The quality of the data is horrible.

When a mortgage loan prepays, we know for certain that it has prepaid. And we know exactly why, whether it is from refinancing, default, death, or any of the other factors that drive prepayments.

When there's an auto accident, we know it (unless it isn't reported and nobody gets hurt or dies). We know exactly how many people are injured in auto accidents (again, unless the injury is very minor and it's not reported), and we know how many people die from auto accidents. (We also don't state cause of death as "auto accident" if the decedent had a fatal heart attack that then resulted in the car veering off the road.)

We know less about the flu, because some people probably get mild cases and don't go to the doctor, and thus are not tested or diagnosed. Doctors can't report data they don't have. But there are a lot more data points, so the data and the modeling are more reliable.

It's even worse with COVID. What are the data points needed to feed the model? Number of cases, number of deaths, and number of recoveries (daily and total for each).

We have no earthly clue how many cases there have been, or how many active cases there are, for two reasons. One, since 96% of cases are mild, there are probably a huge number of people who have had COVID-19 during this cold and flu season who didn't know it. Their symptoms were mild. They might have thought they had a cold or the flu. I know a number of people who believe they may have had it in December or January, though I am always cautious about self-diagnosis, especially with something like this.

My not at all curmudgeonly wife and I went on a cruise in late January. We flew to Tampa, spent the night in a hotel, went out for dinner, then went to the cruise port to board the ship. We cruised to several Western Caribbean ports: Belize, Cozumel, Costa Maya and Roatan. We got off the ship at each port. We ate lunch in Costa Maya and Belize, and had a day pass to a resort on Cozumel, where there were numerous other vacationers from all over the world. We shopped. We touched things. We washed our hands, as we always do, and used hand sanitizer when entering the ship's dining room. Of course, we saw the usual random foul louts who walked out of a bathroom stall without washing their hands, didn't use tongs in the buffet line, etc.

We disembarked and flew home from Tampa on Feb. 1. I flew to San Francisco for business on Feb. 4. The next day, I developed a dry cough. I may have had a fever; I didn't check it until after I returned home. On Feb. 10 I went to the doctor, and the PA I saw said that it looked like I had "this virus we've been seeing going around." She asked about all the symptoms that are now associated with COVID-19, including shortness of breath. I didn't have all of them, and I also tested positive for Influenza A (despite getting the vaccine last October). On that basis, I do not believe that I had COVID. But I may have.

So there are people who likely had it that we don't know about, but even if they self-reported, a significant number of them would probably be wrong.

The other reason we don't know the number of cases or active cases is that reported cases may be overstated due to cases being reported on the basis of a diagnosis of symptoms, without a positive test. Since there aren't enough tests yet for every suspected case, health care providers are reserving the tests for those with the most severe symptoms. (You may recall my story from the last post about my friend who was diagnosed based on symptoms, and sent home to self-quarantine.) Some of those diagnosed but untested cases may have been flu, a cold or some other virus.

So some cases are probably being over-reported, and vast numbers are under-reported. We won't really know the number of cases until every man, woman and child in this country has had the antibody test to see whether they've ever had the virus. That won't happen this year. So if and when there is a second season, we won't know if the people who've had it contracted it this year or next. We will never have reliable case-count data for this season.

We do know that a group of researchers from MIT have been testing sewage from 10 U.S. cities for traces of the virus. And the amounts they've found suggest far more cases than have been reported. Far more, as in a multiple of about 258 times. That could mean that more than a third of the U.S. population has had the virus, which would further indicate a recovery rate of more than 99.99%, making the mortality rate a fraction of what's been estimated.

What about deaths? Well, Dr. Birx admitted that they are erring on the side of citing COVID-19 as primary cause of death (PCOD), even when there are one or more co-morbidities, and regardless of the patient's age. We know that 83% of coronavirus deaths in Italy are among patients over the age of 70, and the majority of them had three co-morbidity factors. Three.

If you're over 70 and have three co-morbidity factors already, your chances of surviving anything - COVID-19, the flu, or a common cold - are dicey, I would think.

So if somebody has heart disease, and they die of a heart attack, but test positive for COVID-19, PCOD is COVID-19. Heart disease may or may not be listed as an underlying cause of death (UCOD).

That first death in Kansas on March 12? It was a gentleman in his 70s living in a long-term care facility who had a "heart condition."

The COVID diagnosis on that patient was made post-mortem. The patient had already died from the heart condition, then COVID was diagnosed and listed as PCOD.

The deaths reported by generally reliable sites such as worldometers.info, and reported in the news media and in public health briefings, are far higher than the number of cases reported on the CDC's own website - about four times as high. According to the CDC site, the reason is that the more widely reported death count includes cases that are "presumptive positives," meaning that a lab has recorded a positive test but the CDC has not confirmed it by submitting the completed death certificate to the National Center for Health Statistics (NCHS) and processing it for reporting purposes. In other words, there's a lag due to government bureaucracy.

The CDC's statistics by age clearly show that the risk of death is infinitesimally small for anyone under the age of 55 - and I am rather generously defining "infinitesimally" as less than .001% of the population of Americans under the age of 55. If you're 55-65, it's still only .0012%. Even for those over the age of 84, the death rate is .0176%. Those numbers will, of course, increase, as there will be more deaths. But even if we account for the fact that the more widely reported deaths are four times what the CDC reports as "confirmed," the death rate for those over the age of 85 is less than .07%. The overall mortality rate for people in that age group, from all causes, is over 38%. Suffice it to say that, if I'm lucky enough to celebrate my 85th birthday, I won't be buying unripe bananas.

Here's the link to the CDC site I'm referring to, for your own perusal. Note in particular the footnotes, the table broken down by age, and pay special attention to the Technical Notes at the bottom related to cause of death reporting: https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm.

Now, back to the model. What if we hit the 60,000 or so deaths it currently projects? That would put the death rate for those 85 and older at .26%, if the distribution by age holds statistically. If we hit the 95,000 cases the model originally projected? It would be .41%. To even get to a 1% rate, there would have to be more than 231,000 total deaths in the U.S., a 14-fold increase.

The final data point we need for accurate modeling is recoveries. And the reporting there is laughable. I've written previously about the lag in confirming a recovery (at least 14 days) and confirming a death (as little as two days, typically no more than five). That's not the issue.

Recoveries are, by and large, simply not being reported. Originally they were reported by state for the U.S., but on the worldometers.info site, but after several days, they took that column out of their table altogether. (I don't attribute this to some conspiracy theory. I'll explain below.)

Recoveries are, however, reported at the country level. In most countries, the ratio of recoveries to deaths is steadily climbing. In Italy and Spain, it was up from April 8 to April 9 by 0.1 (to 1.6 and 3.4, respectively). In Germany, it was up from 16.5 to 18.9. The most reliable "mature" data is probably from South Korea, where the outbreak hit early and now appears to be largely contained. There, the ratio of recoveries to deaths is 34.2. In the U.S., the ratio isn't advancing at all. The UK updates cases and deaths daily, but hasn't updated recoveries for two weeks.

This isn't because there aren't recoveries in the U.S. the UK and the states. It's because they aren't being reported.

Why aren't recoveries being reported? Again, I don't see a conspiracy here. I see a lack of data, and a lack of reliability of any data there is. Since we have no idea how many cases are out there, any reported recoveries will be vastly understated. All of those people who likely had it in December and January and didn't know it have recovered, but they'll never be counted until we've all had that antibody test.

Let's recap: we have no idea how many cases there have been, or how many active cases there are now. We have no idea how many deaths are directly and solely attributable to COVID-19. And we have no idea how many people have recovered. If we ever have reliable case and death data, recoveries are easy: cases minus deaths. That won't happen soon.

So the model is starving from a lack of sufficient data, and it's malnourished from poor quality data. Imagine trying to survive on one moldy Ho-Ho a day, and you understand the model's extreme weakness.

A final point about the model: many media outlets and people in general are pointing to the sharp decline in projected deaths as evidence that social distancing is working. To be polite, those people are being stupid. They believe what they hear and read, without going to the source. They haven't looked at the model's website. If they had, they would see, at the top of the page, in large letters, these words:

COVID-19 projections assuming full social distancing through May 2020

Assuming full social distancing through May. No re-opening of businesses and churches. No eating out. Limits on the number of shoppers in a store at any time. Grocery store aisles marked one-way. One customer per cart. "Non-essential" items like electronics being removed from store shelves. People ticketed for being too close to each other. For another seven weeks.

(For what it's worth, I don't see that happening. Nor, if it doesn't happen, do I see the death toll rising to the horror movie levels that the models were projecting "if we do nothing." I expect some reasonable happy medium that carries no more risk than the risk of the flu. We'll see.)

So it's clear that the decline in the model's projected deaths is not at all related to some notion that social distancing is "working." (I'm sure it is - if we followed these guidelines every flu season, we'd hardly see any flu cases or deaths either. But a hell of a lot of Americans aren't working, which also bears a cost.) In any event, the decline in projected deaths is solely attributable to new data, which is still insufficient and not reliable enough to produce realistic projections. It has nothing to do with social distancing's effectiveness. So stop kidding yourselves regarding that myth.

**Note: in the April 10 model update, projected total U.S. deaths went up by about 1,000, further debunking the myth that the projections are influenced by the "success" of social distancing.**

The upshot of all of this is that any decisions made on the basis of these models are foolhardy. Much of the American economy has been destroyed, hopefully not permanently. The stock market has been moving higher this week. Other countries are beginning to ease mitigation measures, including allowing some "non-essential" businesses to re-open, with some social distancing requirements. I've even seen a couple of local restaurant locations that initially closed, re-open for curbside and delivery service. As Red said near the end of The Shawshank Redemption, "I hope."

However, here are some real numbers for you: 30,000 U.S. restaurants have closed permanently. That number is projected to hit 110,000 by the end of May if this continues. That's about 10% of all U.S. restaurants - a much higher "mortality rate" than COVID-19's. U.S. restaurants employed more than 15 million people before this shutdown. And 70% of U.S. restaurants are single-unit operations. I won't even mention the downstream effects on farmers. Or the similar decimation of the hotel, leisure and airline industries. And the government can't spend our way out of this, because it's our money that is ultimately being spent.

The fact is that ALL businesses are essential to the people who own them and work for them.

A couple of final points. The panic that has, in part, led us to where we are today was fueled in no small part by the news media's reprehensibly sensationalist and inaccurate reporting. Sadly, they will never be held to account. They never are.

But another group used a different medium - social media - to foment panic. They posted wildly inaccurate articles based on bad math. When called on it, they tried to sanctimoniously scold those who tried to be a voice of calm reason. That group can, and should, be held to account, by their friends. They and the news media should apologize to everyone who lost a job, experienced investment losses, and had to go to several different stores just to find items that were heretofore commonly available. The overreaction was born of panic that led local governments - cities, counties, and states, not the federal government - to issue mandatory shut-down orders.

I'm all for saving lives, but there may have been better ways to go about this. There will be plenty of time for recriminations, Monday-morning quarterbacking, and situation analysis later. But perhaps the concept behind a familiar phrase should be broadened, and applied to local government, the news media, and the panic-mongers:

First, do no harm.

Are You Part of the Solution, or Part of the Problem?

I was walking my two mini Schnauzers yesterday, on their leashes. Why on leashes? Well, first, they're flight risks. I haven't trained them to walk with me. So it's for their own safety, so they don't run out into the street, and so they aren't a nuisance to someone else.

And why haven't I trained them to just stay with me when we walk? I haven't needed to, because the city in which I live has a leash law. So they only go off-leash in our fenced backyard or at the dog park.

When we see other dogs on our walks, my guys go nuts. Charlie and Topher are our fourth and fifth mini Schnauzers, and if we've learned anything about the breed, it is this: you can't train the Schnauzer out of them. They are yappy. They bark at other dogs (but they love to play with them). They bark at people (but they love people too - complete strangers are family to them). They bark at cars driving by. They bark at parked cars. Every situation is Threatcon Orange with a mini Schnauzer.

So on our walk yesterday, Charlie did his business, and I turned around to pull out a bag to pick it up. I looked down the street and saw a couple walking in the street, with what appeared to be a Great Dane on a leash, and what looked like a large Lab mix. The Great Dane was very well-behaved, but the Lab, which was off-leash (the woman was holding its leash, folded up), was scampering around.

My dogs barked, of course, and the Lab took off - it charged us. Now, this may have been a perfectly docile, playful dog that just wanted to come check out some new buddies. But I had no way of knowing whether that was true. I have a friend whose dog was killed by another dog that attacked it, and a relative's dog was nearly killed by another dog. My own Topher needed staples to close a wound inflicted by another dog at a local dog park (and the tough little guy didn't even act like he felt it).

I stepped in front of my dogs, between them and the Lab, and yelled to the people, "HE NEEDS TO BE ON A LEASH!" The lab ran back to them, and the woman clipped the leash on. I proceeded to clean up after Charlie while restraining him and Topher, who were now frantic. People don't understand that dogs behave very differently when they are restrained, but are in proximity to another dog that is not. They go into protection mode and can become aggressive, even if normally they are not.

As the couple passed, the man offered a feeble "Sorry." I didn't respond - what was I going to say, "That's okay"? Because it isn't.

They walked around the corner, past a cul-de-sac, and to the end of the block. They crossed the street, and then -

The woman took the leash off the Lab again. And it went scampering into people's front yards and out into the street.

Now I had intended to go to the corner, turn left, go up the street and then around another block, and then head home. But I didn't know whether they'd be going that direction when we got around the block. So instead, I turned the opposite direction and went back down the street. I didn't just want to take my dogs around our block, because it was a pretty nice day, so I walked on past the street that leads back to our house, and to the corner by the entrance to our neighborhood. Then we turned around to come back.

We got to the street that leads to our house, and turned to go home - and there they were again, coming our way. And the Lab was off-leash. I just looked at them, shook my head, and went back around the block to go home another way.

There was another situation recently in our neighborhood. Our HOA has a Facebook page. A woman posted that she had had a basketball goal permanently installed, set into concrete, in the island of their cul-de-sac (the island is city property, not the HOA's or any homeowner's). Someone called to complain, and some city workers came and said they were going to take it out. The workers also went around the neighborhood and saw several free-standing basketball goals in the cul-de-sacs - in the streets, not on the islands, where they're in the way of trash and recycling trucks and snow plows in the winter. The workers said those had to go, too. The woman actually said that the workers should have better things to do during a crisis, and that the complainant - not she - had put those workers at risk by making them come out to her block.

You'd have thought they had come and seized people's houses, judging from the hue and cry that went up on Facebook. (This is what happens when people are under a stay-at-home order and have time on their hands.) The woman threatened to "out" the person she suspected of calling in the complaint. She refused to believe that it was against a city ordinance to install a goal on city property, even after someone posted a link to the ordinance. One guy demanded to know who the complainant was, and tried to see whether the city would give him the name. I envisioned a scene like the one in Young Frankenstein, in which the villagers are hunting for the monster with torches and pitchforks. Another guy said they should get all the local news stations to come out and film the city workers taking down the goal (which would only make public what idiots these people are).

Someone else lamented that the poor kids' childhoods would be ruined because they didn't have a basketball goal, which begs the question: if you don't want your kids scarred for life, why not put up a goal next to your own driveway, like your responsible neighbors do? Oh, that would be be inconvenient for you? Any more inconvenient than those of us who live in the cul-de-sacs having to dodge all the kids who use it as their personal playground, bike track and skate park, while their parents are inside watching TV?

After the episode with the dogs yesterday, I began to think about the relationship between these two incidents, and to see them in a broader context. And I realized why the coronavirus is not being contained more rapidly, and why our economy is being decimated by mandated stay-at-home orders:

Too many of our fellow Americans are selfish. They think the laws don't exist for them. They don't understand that their non-compliance with the laws only works if the rest of us comply - in fact, they are relying on us to comply so that chaos doesn't ensue.

Leash law? To heck with it. I'll walk my dog off-leash if I want to - but I can really only do that if everyone else has their dog on a leash, so the whole street isn't filled with a pack of dogs chasing each other about, and so that my dog doesn't run off after another dog and get lost, or hit by a car.

An ordinance against installing a basketball goal on city property? I'll install one if I bloody well please, ordinance be damned. But that only works if everyone else in the cul-de-sac obeys the ordinance. Otherwise, there would be trampolines, jungle gyms, skate ramps and who knows what else in the middle of the street.

So if you're one of those who believe the rules don't apply to you, understand that your non-compliance depends on my, and everyone else's, compliance.

How does that apply to the present situation? Simple. As the virus began to spread within communities, the doctors that lead the Task Force to fight it implored people to stay at home as much as possible, and to maintain social distance and practice good hygiene.

Some of us did. But too many others decided the rules didn't apply to them. High school kids hung out together at parks. College kids went on Spring break and partied on the beaches in large crowds. Adults had their friends over for coronavirus parties, or went to bars. We were asked to use our judgment, but too many of us proved themselves incapable.

Stay-at-home order? Social distancing guidelines? I can only violate those if nearly everyone else doesn't.

And people got sick. And they made other people sick. And some people along that chain died.

So the government had to force compliance. They closed the beaches. They closed the bars. They closed the restaurant dining rooms. They closed most retail shops. All to save us from the stupidity of the minority.

And hundreds of thousands of people lost their jobs. That number will soon be in the millions. Businesses that closed temporarily may ultimately fail. People's retirement savings have been decimated.

It's time for an attitude check. We are Americans. We won't hand ourselves over to a totalitarian regime like today's China, or Iran, without a fight. But that cannot translate into an attitude that we don't need to follow rules or laws. We may be a Democratic Republic, but we are still a nation of laws. You can't drive 60 mph through a neighborhood where kids are playing outside. You can't just walk into someone's house and take their toilet paper.

This attitude of, "Aw, heck, I can go ahead and do that, it's not gonna hurt anything," is in effect a parsing of the rules into those that we know need to be followed, and those we think it's okay for us to ignore (again, counting on others not ignoring them so that we can get away with it). The problem with that is that you might think ignoring a leash law or a city property ordinance is okay to ignore. Somebody else might think that, with fewer cars on the road right now, the speed limit doesn't matter. Well, the Kansas City, Missouri Police Department has issued tickets for speeds as high as 125 mph recently. From March 16-30, injury accidents were up 43% vs. the same period a year ago.

Laws, rules, and even guidelines exist for a reason. Some of them may seem stupid, and we may not like them. But if we ignore one, then we're headed down a slippery slope. You may think a leash law is stupid. So your kid sees you walking your dog off-leash, and might think, "If mom can do that, why can't I go to the park and hang out with my friends?" or "Why can't I go on Spring break?" Your neighbor might see the basketball goal you put up on city property and say, "What the heck - I'm having my buddies over for a party."

If you believe the rules don't apply to you, know this: that attitude has resulted in more people getting sick and dying than would otherwise be the case. It has resulted in significant parts of our economy shutting down, in some cases unnecessarily if not for you. It has cost people their jobs and their retirement savings. It has forced all of us to eventually pay the piper, for the trillions of dollars of government spending that your attitude, your selfishness, has required.

So check that attitude. And make sure you're part of the solution, and not part of the problem.

They're Trying to Scare You. This Time, Let Them.

Okay, calm down - I'm not talking about the media, and I'm not talking about economic or financial data. I'll explain a little later in this post who I'm talking about, how they're trying to scare you, and why you should let them.

But first, let's introduce the topic of this post: models.

Not runway or swimsuit models. (I know, you're disappointed.) But mathematical models. Don't worry, I'm not going to get technical. And - long post alert. So if you know how mathematical models work, or don't care, scroll down to the dashed line and read from there.

Now first, a caveat: I am not an epidemiologist, or an infectious diseases specialist. I'm not a doctor, and I don't play one on TV.

However, I do know mathematical models, and I know data. I look at numbers in a spreadsheet and see them graphically. Data is my drug. I've never built a pandemic model, but I've built econometric models, and I've built stochastic mortgage prepayment models. So I know a bit about modeling data. (This explains why I'm not popular.)

Here's a truth: all models are wrong. Let's consider a model that predicts the price of a bond. Not to bore you, but such a model will be based on the projected interest and principal cash flows of that bond, discounted at current and projected interest rates. The theoretical, or modeled, price of the bond is the sum of the discounted principal and interest cash flows. Don't worry if you don't understand that; it's not important for this discussion.

When clients used to ask me about the modeled price of a bond, I would reply, "No model ever bought a bond." The real market value of a bond is what the next buyer will pay you for it. Thus the modeled price isn't the market price. So what value do models bring, and how are they built?

The value proposition of models is that they may give us some idea of what is likely to come. The key words are italicized. How well do they do that?

The first thing to consider is that data informs models. Let me say that again: data informs models. By the same token, a model is only as good as the data that informs it.

Let's look at mortgage prepayment models as an example, because I know them intimately (don't worry, I'm not going to bore you with a lot of math). Bear with me through this: it will help you understand how the COVID models work.

Mortgage prepayment models forecast how likely a group of mortgage loans is to pay off early, based on the mortgage interest rate, prevailing interest rates, how long the mortgage has been outstanding, geography, and other factors. They do this using historical data that captures those variables. The data informs the model.

This is important to mortgage lenders and investors in pools of mortgages in forecasting cash flows. If rates fall, more people will refinance (i.e., prepay the entire mortgage), and I'll get my money back sooner than expected - then I'll have to re-lend or re-invest it at now-lower rates.

Back in 1990, we thought the mortgage prepayment models were pretty darn reliable. Then, in 1993 the Fed cut interest rates to what was then an all-time low, and in the following year, they raised rates by about 3%, effectively doubling them. We called that a "whipsaw." As a result, a lot of people refinanced their mortgages at record-low rates in 1993, so prepayments exceeded what the models - informed by historical data - projected. (Prepayments happen for a number of reasons, but the biggest driver is refinancing when rates have fallen.)

Then, when rates rose sharply the next year, people prepaid their mortgages more slowly than the models - again informed by historical data - projected. So cash flows slowed as people just paid the minimum monthly mortgage payment, instead of further reducing principal. Why should I pay early on a 5% (at that time) mortgage, when the prevailing rate is 8%, especially when I get to write off my mortgage interest on my taxes, so that my after-tax mortgage rate is even less? Now, lenders and investors had to wait longer to receive the amount of prepaid principal they expected, when they'd rather have had it right away to reinvest at higher rates.

Thus the prepayment models totally missed the mark in the 1993-94 whipsaw. The data from previous periods that had been incorporated into the models to inform them didn't capture how readily people would refinance a mortgage if rates fell by a certain amount. That's because people used to want more of an incentive to refinance than they want now. And that's because the process of getting a mortgage is more streamlined today, and the fees are lower. Thus mortgages that we thought wouldn't prepay in 1993 did refinance, and mortgages that we thought would prepay in 1994 didn't. Investors took a beating by betting wrong based on the models, which were based on historical data.

The silver lining is that after that whipsaw, all of the actual prepayment data from 1993-94 was fed into the models, making them more robust. Data informs models. With more data capturing more unique circumstances, including changes in borrower behavior, the model is made more robust.

So, in 2005, we thought the mortgage prepayment models were really good. But we'd never seen subprime mortgage loans before. Housing bubbles had always been local; by 2007 they were widespread. Credit ratings on packaged mortgage loan pools were being gamed.

And the housing market came crashing down. Mortgage defaults reached unprecedented levels. Borrower behavior was different, too. My Dad, a Depression kid and a WWII vet, taught me that the last thing you ever miss a payment on is your mortgage, because that's your family's home. By 2008, the family home was seen as an investment, and if the value of that investment had fallen to less than the mortgage balance, you initiated a "strategic default." In other words, you walked away from the property and defaulted, even if that meant you wrecked your credit in the process. Unwise, yes. But it became common.

Those shifts destroyed the efficacy of the prepayment models. Drastic changes in borrower behavior, interest rates, mortgage structures, default protections, and other factors resulted in the actual prepayment experience being vastly different than what the models projected. A lot of people just walked away from their homes, as described above. A lot of other people had to default because they lost their jobs. (A default and subsequent foreclosure counts as a prepayment, because the loan goes away at that point - due to charge-off by the lender, not payoff by the borrower).

Once again, the models, informed by historical data, did not capture how high prepayments would go on a mortgage with a given interest rate. But as before, after the carnage, all of that data was plugged into the prepayment models to inform them, and thus today they are more robust than ever before (though they're still not prepared for the next thing we haven't seen yet). We have more data, covering a far wider range of scenarios. Data informs models.

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Okay, so what does all of this have to do with the current situation? Well, you've probably heard Drs. Fauci and Birx refer to a coronavirus model that predicts when the curve will peak and flatten, and how many deaths are likely. That model was developed by researchers at the University of Washington's Institute for Health Metrics and Evaluation (IHME), which was funded by Bill Gates (thank you, Bill).

You may have also heard that, in a matter of just a few days, total U.S. deaths projected by the model jumped by about 10,000. You may have wondered why the big change - is the virus more deadly than previously thought? More contagious? Are containment measures failing?

None of those are reasons for the change in the model. The reason is that more data was fed into the model, and that changed the model's forecasts. What kind of data does the model incorporate? We'll get to that in a minute.

Since the COVID-19 outbreak got serious, I've been following the actual data (cases, deaths, recoveries) daily. I track it by each state, and by a number of countries (China, S. Korea, Iran, several European countries, and the U.S.). I'm looking at cases and deaths per capita, because denominators matter. I'm looking at maturity of the outbreak in those locations (time since first death, because time since first reported case isn't always available). I'm looking at the ratio of recoveries to deaths, which is encouraging. It improves with maturity. The problem is that it's clear from the data that in some locations (including the U.S. and the U.K.) recoveries are under-reported, and they aren't reported at all for the individual states.

Sidebar: lest you think this is morbid, please know that, as with the unemployment numbers, I recognize that each data point is a human life, someone who is sick, or a loved one lost. In fact, that's why I'm writing this post. Read on.

Also, since the IHME model data was made public, I have been tracking that as well, and comparing it to the actual data. I update it every few days, because it changes. As noted above, total deaths jumped from the first time the model's projection of them was mentioned to just a few days later. Since then, they are down by a few hundred. I'm tracking the model's projections for total deaths, when the curve peaks, when it flattens (no more new deaths, or several days with just one per day), per capita data (you know why), days to curve peak, days to curve flattening, and actual to projected deaths. I'm looking at this for the U.S. and state by state. And for the days to curve peak or flattening, I'm looking at averages, minimums, and maximums.

Let's first get to the question of what kind of data the model uses, then we'll look at some examples from the model. It doesn't use some scientific chemical formula related to a possible treatment or vaccine. It just uses math - complex math, but math. The variables it considers are number of cases to date, number of deaths to date, whether there are mandated containment measures, and how stringent they are. (In states that have strong stay-at-home orders, the curve is expected to peak and flatten sooner. That's what Drs. Fauci and Birx have been telling us.) At the state level it is also influenced by the population of the individual state. Finally, the model appears to be considering the assumption that this virus is seasonal.

Some quick examples. For the U.S., using April 1 model data and an April 2 date, projected days to the curve peaking are 13 - therefore the projected peak for the country as a whole is April 15. The minimum is 8 days, in NY and NJ, where the outbreak and containment measures started early. The maximum is 56 days, in MO, where the governor has yet to issue a stay-at-home order. In my home state of KS, the curve peaks in 30 days. And the curve flattens for the U.S. as a whole in 60 days (June 1). The earliest state to flatten is Delaware, in 27 days. The latest are MO and VA, 104 days (this may be why the VA governor recently issued a stay-at-home order that doesn't expire until June 10). In KS, it's 64 days (June 5).

So, how and why have the model's projections been changing? I first started analyzing the model's projections on Monday, March 30. I updated the data on Wednesday, April 1. In those two days, the projected total U.S. deaths jumped by about 10,000. In some states they doubled. In others, they fell by half. Also, in some states, the time to the curve peaking or flattening extended by as much as two weeks. In other states, the time shortened by as much as two weeks.

Why the big changes? Data. Data informs models. The model was initially way off, because the data was sparse. As more data comes in - more cases, more deaths, more mandated containment measures - the model's predictive value will increase. So it will continue to change. My guess is that the time to peak/flattening will remain more or less constant for the U.S. as a whole - about April 15 to peak, and late May to flatten. This is consistent with the season for influenza and other coronaviruses such as the common cold.

However, I expect the projected total deaths will come down, and I don't think the actual data will reach the 93,000 or so currently projected. Before I explain why, I'll repeat my disclaimer: I'm not an epidemiologist, an infectious disease specialist, or a doctor. I'm just a data guy.

Why are we seeing big increases in the number of cases? More testing. There are probably a lot of people who've been sick this winter that had COVID-19 and didn't know it, because their symptoms were mild. Francis Suarez, the mayor of Miami, tested positive, and posted daily videos on his Twitter account describing his symptoms, which were mild. The CDC reports that 95% of cases are mild. In Iceland, which has tested 5% of its total population, 50% of cases had no symptoms at all. I know someone who presented with symptoms, was diagnosed without a test, and sent home to self-quarantine. Maybe he had COVID, maybe he had something else with similar mild symptoms. We're not going to know this until we have a test to see if people have ever had it - which is coming.

As the U.S. is now testing more than 100,000 people every day, we're going to see more cases, so the case total will go up. The IHME model doesn't project total cases, at least not that I've been able to find, but it probably incorporates them. So why are projected deaths going up?

Let's go back to my friend who presented with symptoms. Why did they diagnose him without testing him? Because his symptoms were mild, he's under 60, and in good health. And even though we're testing very large numbers of people daily, there still aren't enough tests to test everyone who has symptoms. The U.S. has tested 1.3 million people, but that's less than half a percent of the population (because people are failing to follow the guidelines, not because the government response is failing). So they're reserving the tests for the most at-risk population - the elderly, those with complicating health issues, and those who present with severe symptoms.

The sad reality is that a larger proportion of those people will die after they're diagnosed, usually due to those complicating health issues, but brought on by the virus. Remember the CDC said that 95% of cases are mild? Of closed cases, 80% have recovered.

So just as more testing leads to more diagnosed cases, which feeds into the model, more testing of critical or at-risk cases will lead to more deaths, which will also be fed into the model. And that's why, based purely on math and data alone, I don't think we'll see 93,000 deaths in the U.S. I hope and pray we don't.

Now, here's the point of this post, beyond helping you understand how the model works, what feeds it, and why it changes daily as more data informs it.

Drs. Birx and Fauci cite the model projections to keep people following the guidelines and social distancing. It's critical. If we stop doing it because we think the model numbers are too high, then we will indeed reach the model numbers - remember, part of the data informing the model is the presence of mandated mitigation strategies.

So even though I think that, based on math and data, the projected numbers may be on the high side, I'm not hanging out with friends and family. I'm still washing my hands thoroughly and frequently. We have a routine for handling groceries and take-out food and we're following it to the letter. I wipe down everything I touch in my car every time I come home from the grocery store. I use a credit card at Target instead of their Red Card or another debit card so I don't have to touch the PIN pad. We all need to do that, to do our part.

Looking to the future, here's my fear. Let's say total deaths in the U.S. come in much, much lower than 90,000. (I'm not saying they will - I think they'll be lower; I can't even guess by how much.) You know how every time there's a hurricane approaching the U.S., and officials issue evacuation orders, and lots of people ignore them, thinking, "They said Hurricane XYZ was gonna be really bad, and it wasn't, so I'm not going to let them scare me into leaving my house"? Then, when it is bad, first responders get overwhelmed having to evacuate those people using boats or helicopters, when the people could have just driven to safety had they heeded the warnings?

You guessed it. If the total casualties from this seasonal round of the coronavirus are far below the modeled projections, the next season - and there will be one, whether it's in the fall or next spring or both - people will take the social distancing guidelines lightly. They'll ignore the hygiene protocols. They'll sneeze into the air, or on their hands and then touch public surfaces like airplane seatbacks and handrails and shopping carts. And when there's a vaccine available, they won't get it because they don't think the risk warrants getting jabbed in the arm.

And they may be okay. But they'll pass the virus to me. To you. To your child, or your grandparent. And the health care system will be overwhelmed, unless the government shuts down the economy again because people are too selfish to do what they need to do to protect other people.

The model isn't bad. It's well-constructed by people who know what they're doing. But any model is only as good as the data, and even though the numbers reported sound tragically staggering, it's not enough data as a percent of the U.S. population for the model to be considered robust yet. Its predictive value improves every day as new data is added, and the projections are going to continue to change.

So the doctors on the task force aren't misrepresenting the model or the projections. They are pointing to those numbers because they want to make sure we continue to comply with the guidelines, to avoid ever reaching those numbers. I don't know whether they believe the numbers will reach the model projections if we all do our part. But they are trying to scare you, and for good reason.

Let them. We need to be scared - scared of what would happen if we drop our guard. If not for ourselves, for our grandparents, our parents, our kids, our nurses and doctors, our police and firefighters, our grocery store clerks, our restaurant take-out workers - all of the people who are on the front line, every day, putting themselves at risk so people's health is cared for, we're safe, we have food. Be scared for them. They're counting on you.