JACKSON — Professional oddsmakers’ over/under is usually close to the actual result. It draws bettors on both sides so bookies can balance their book and make money on the vigorish (betting fee).

Too bad there’s not an over/under for COVID-19 metrics. It would be a useful check against numbers coming from the president’s experts, the Centers for Disease Control and Prevention, the World Health Organization, reporters, and others in the Chicken Little business (i.e., the bad news sells business).

Kelley Williams 2016


On March 29, presidential adviser Dr. Anthony Fauci predicted 100,000 to 200,000 U.S. deaths from COVID-19. He later increased it to 240,000. That’s quite a range. He wouldn’t make it as a professional handicapper. His point spread is too big.

It’s reported that he and Dr. Deborah Birx, another presidential adviser, used the World Health Organization’s model for their early predictions. That’s the abandoned model that predicted the 3.4% death rate that started the COVID-19 pandemic panic. For what it’s worth, experts now seem to agree that the death rate will be 0.1% to 1.0% of cases. That may be close enough for government work. But the 10:1 point spread is too wide for bookies to make book.

It’s reported that many experts are now using the University of Washington’s model based on Farr’s law of epidemics. Computer models are known for the “garbage in, garbage” out phenomenon. The accuracy or value of the output depends on the validity of the inputs.

Farr’s curve of cases, deaths and other measures of epidemics is initially exponential but then declines, peaks and decays in a bell-shaped pattern. The numbers don’t reach the sky. But early trends lead to Chicken Little panics. And to overreactions like shutting down the economy.

We are told it’s better to be safe than sorry. One death is one too many. It’s therefore heartless to note that COVID-19 deaths since February are about 1.5% of total U.S. deaths for the same period, according to the CDC. People die, and life goes on. But not the economy if people die from COVID-19.

One key input for models is infection rates. It’s estimated based on increases in reported cases. Reported cases are confirmed based on tests. The test detects the presence of the virus that causes COVID-19 but not the quantity (viral load). If the viral load is low, you may not get sick. Many cases are asymptomatic. If you don’t have symptoms, you may not get tested. If you test positive and have pneumonia, you probably have COVID-19. Food and Drug Administration guidelines say you can then be treated — in a hospital.

If you test positive and don’t have pneumonia, you may be sent home until you get it and can then be treated under FDA guidelines. You may end up on a ventilator in a hospital where your odds of survival are probably less than 30% if you have other health issues or are old. (The viral load increases rapidly, and early treatment improves outcomes. Delay is deadly.)

Reporters and others for whom bad news is good eagerly report new cases as evidence of bad news. New cases may be increasing because the virus is spreading. They may be increasing because more tests are available and are being given. Probably both. The big questions are how many more deaths will there be? And how many deaths will social distancing prevent — or delay? And how many deaths, if any, will social distancing and shutting down the economy cause? What are the trade-offs?

These are hard questions to think about. The answers are hard to quantify. They affect deaths of COVID-19 victims and lives of compliant citizens who lose their jobs. And their liberties. And maybe the deaths of some of them. They affect careers — of politicians and experts. Doomsayers have a vested interest in doom. They may exaggerate doom to frighten people into social distancing and other well-intentioned responses to mitigate damage from exaggerated doom — which never happens. They may be wrong. It may be hard — or impossible — for them to admit it.

Let’s look at deaths from COVID-19. It’s the best measure of doom. It’s not as easy to determine as you might think. People die with COVID-19 and from COVID-19. The death is not the way I want to go either way. It’s drowning in your own juices as pneumonia kills you despite ventilators, which may just prolong the agony.

Who dies? Mostly the old and the sick who are already dying and those with weak immune systems and diseases they may not know about. Those who die with COVID-19 may be coded as dying from COVID-19 as it becomes the default cause of death. Check the box. So deaths from COVID-19 are probably overstated.

The range of COVID-19 deaths predicted by University of Washington model is 40,000 to 178,000, or about 3-13% of total deaths.

The experts are just guessing. With our lives and well-being. And freedom.

Kelley Williams, a Greenwood native, is chairman of Bigger Pie Forum, a Jackson-based think tank promoting free markets and government efficiency.

(1) comment


I am sure that the citizens of Greenwood are thrilled to get daily reassurances that everything is under control if we just recur to the preternatural wisdom of William Farr. Nothing our current batch of epidemiologists can say will ever match the sterling wisdom of this 19th Century genius. As the worlds top epidemiologist, Kelly Williams tells us again and again, all this social distancing stuff is "well meaning." The disease will list where it will and we can all get back to work.

There are problems with this profound message. Farr's law is not a law. A law, in classical mechanics, is an exceptionless statement of the relationship between initial conditions and a strictly predictable outcome. Farr modeled the statistical distribution of infections in a "normal" outbreak of a contagious pathogen. It proceeds along a Bell curve and eventually bottoms out. This model has exactly zero empirical content. The curve depends entirely on the effects of the pathogen.

Farr studied Cholera. The nature of the outbreak was known, the source of the pathogen was unknown. He spent much dedicated time trying to show that the pathogen was airborne and especially sensitive to elevation. He was wrong, unlike his more lightly disregarded contemporary John Snow, who used Farr's statistical techniques to discover that Cholera was a waterborne pathogen.

Whatever moral we are to draw from a consideration of these events obscured by time, it can't be that we should sit back and let the curve do its work. Both Farr and Snow both contributed to a practical science that aimed to alter the course of diseases, not simply to sigh about their inevitable progression. All of talk about "flattening the curve" is predicated on an obvious awareness that Farr was right about the "normal" statistical progression of most epidemics. It also shows that they don't succumb to the idiotic notion that nothing we do can to contain or mitigate them.

The evidence is overwhelming from the population data that social distancing works. California, South Korea and other places demonstrate that we can slow the progression of the disease buying our hospital system time to prepare as best they can. That is not about death rates, it is about hospitalization rates that are clearly high.

Yes. We have to plan to go back to work. That will require antibody testing, which is unlikely to be available soon enough given our poorest in the world track record on testing. Simply sending people back to work to increase the rate of spread is not the answer. Fever testing, social distancing at work, and other half-measures may have to be employed. Rushing to do that before the various peaks occur is not the right answer.

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