Over the weekend, conversative pundit and talk host Steve Deace posited a theory on Facebook that mirrors one I’ve settled on myself. Deace noted related data to the Wuhan coronavirus infection and mortality numbers and wondered if we haven’t been saddled with this outbreak for a lot longer than anyone thinks.
He referenced a piece he’d written at the Blaze in which the Centers for Disease Control had reported 7.1 percent of all deaths in America were from flu and pneumonia the week of March 7, which was the week before the country began shutting its economy down to deal with the Wuhan virus. Those rates have since plummeted, while Wuhan coronavirus deaths have risen. Typically, according to numbers compiled by the National Center for Health Statistics, flu and pneumonia numbers for January through March will float from just above 4,000 per week in January to just below 4,000 by the end of March. This year, they started near 4,000 in the first week of the year and dived below 3,000 at the end of March.
Deace then asks the obvious question:
Could it be because #coronavirus was wreaking havoc the entire winter? We just weren’t aware, panicked, or testing for it? So people with pneumonia-like symptoms were counted as having pneumonia then. Which could also be why we were above the CDC’s epidemic line for pneumonia several times maybe?
And gives the obvious answer:
That could also explain why the models continue to be so off. The horse had already left the barn, so to speak. The virus had already gotten in, so we have the wrong baseline to begin with. And if that’s the case, why are we risking a Great Depression?
We’re told that Patient Zero was identified in Seattle in January coming from China. But why would we necessarily believe that?
As I’ve written here before, in New Orleans, which is a major hotspot for Wuhan virus infection, starting in late November and carrying on through the winter much of the city had been dealing with a persistent cough that wouldn’t go away and carried very similar symptoms to those described by Wuhan virus patients. Talk to people there and they’ll swear they’ve had this thing and gotten over it. If and when people start to be tested for antibodies, there’s a good bet their theories will be proven correct.
And if that turns out to be the case, it would certainly explain why every data model attempting to predict the spread of this virus has crashed and burned. There was the Imperial College London study that predicted 2.2 million deaths in the United States and a half million in the UK, only for its author to abandon those numbers and then predict less than 20,000 deaths in Great Britain (as of this writing, the total UK Wuhan coronavirus deaths are 6,159).
And more recently there is the Institute for Health Metrics and Evaluation (IHME) data model emanating out of a Bill Gates-funded think tank at the University of Washington, whose numbers have informed public policymaking in the United States. IHME’s numbers attempt to predict the number of hospitalizations and amount of health-care system resources necessary to deal with the virus, and it has been woefully, embarrassingly wrong from the beginning. The Federalist’s Sean Davis clobbered IHME for across-the-board misfires on hospital bed, ICU, and ventilator use last week, and in some states, including Louisiana, where at one point IHME predicted more than 5,800 hospital beds would be needed, IHME has had to pull an Emily Litella-esque “Never mind” and essentially throw out its numbers. It now projects the Bayou State will only need 1,386 hospital beds and that Louisiana’s peak hospital resource use actually took place on April 1.
Other states still appear to be on the upsurge of the coronavirus curve, but very few appear to be hitting any of the dire projections of these models.
Deace’s theory, which I share, explains that. We are further along this curve than anyone thought, and there is a reason for it.
Remember that in a typical year, some 3.4 million Chinese travelers will visit the United States. That’s just under 600,000 per month. And the Chinese government has lied relentlessly about the Wuhan coronavirus since it popped onto the scene at the beginning of winter; the Chinese communists disciplined doctors in Wuhan who first began diagnosing cases and warning that something new had come on the scene, then denied human-to-human transmission was possible (which they had their stooges at the World Health Organization back them up on), and finally locked down the city of Wuhan and admitted the problem after it had gone worldwide. A large Chinese community in northern Italy, and Lombardy in particular, spread the virus there, while countless vectors spread it here in the U.S. before President Trump imposed a travel ban in late January. But the travel ban still allowed for some 40,000 Chinese travelers to come here, including lots of Chinese college kids (there are some 300,000 Chinese college students in America now, not that there are currently colleges for them to go to).
How hard is it to believe that the virus was here in November and December given those numbers?
And how hard is it to believe that its early fatalities were misdiagnosed as flu and pneumonia patients?
It’s still a theory, though an increasingly plausible one. It isn’t proven yet. But as science begins to understand the Wuhan virus and how it works (it attacks red blood cells and keeps them from carrying oxygen from the lungs), we’re beginning to understand why it has such a specific demographic profile — affecting elderly, obese, diabetic, and other less-healthy people so much more than others. And that’s providing an understanding of why hydroxychloroquine and azithromycin, drugs that affect the pH of the blood and strengthen red blood cells’ ability to move oxygen, continue to show promise in treating it.
Those are good things. They’re advancing our ability to properly treat people coming down with severe cases of this virus.
But maybe it’s time to begin understanding why those models are less useful than they should be if we’re going to trash our economy over them.
Data models are only as good as the inputs fed into them. A good data model is still garbage if it’s given garbage inputs. And an invalid Patient Zero would be a garbage input.
Perhaps we’re closer to the back half of this curve than we’ve been led to believe. Perhaps we’ve been rising along this curve a lot longer than anyone thought, but because so many of the earlier infections were written off as common colds or influenza, and the worst cases diagnosed as pneumonia (it would be worth checking to see if the death rate this winter from pneumonia cases was higher than normal, because that could be a telltale sign), we never knew.
And perhaps, if the theory bears out, we’ll know what the real cost of those Chinese prevarications about the early spread of this virus has been.
Thousands of lives. Millions of livelihoods. Trillions of dollars.
Before he was placed in intensive care with his own case of the Wuhan virus, British prime minister Boris Johnson spoke of a “reckoning” coming with China over this outbreak and its role in making it worse. That reckoning will necessarily be substantial.
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