Christians Shouldn’t Be Dismissive of Scientific Modeling
Image
Projections from an IHME model.
Over the last several weeks I’ve encountered a range of negative views toward the models epidemiologists have been using in the struggle against COVID-19. Skepticism is a healthy thing. But rejecting models entirely isn’t skepticism. Latching onto fringe theories isn’t skepticism. Rejecting the flattening-the-curve strategy because it’s allegedly model-based isn’t skepticism either.
These responses are mostly misunderstandings of what models are and of how flattening-the-curve came to be.
I’m not claiming expertise in scientific modeling. Most of this is high school level science class stuff. But for a lot of us, high school science was a long time ago, or wasn’t very good—or we weren’t paying attention.
What do models really do?
Those tasked with explaining science to us non-scientists define and classify scientific models in a variety of ways.
The Stanford Encyclopedia of Philosophy, for example, describes at least 8 varieties of models, along with a good bit of historical and philosophical background. They’ve got about 18,000 words on it.
A much simpler summary comes from the Science Learning Hub, a Science-education project in New Zealand. Helpfully, SLH doesn’t assume readers have a lot of background.
In science, a model is a representation of an idea, an object or even a process or a system that is used to describe and explain phenomena that cannot be experienced directly. Models are central to what scientists do, both in their research as well as when communicating their explanations. (Scientific Modeling)
Noteworthy here: models are primarily descriptive, not predictive. Prediction based on a model is estimating how an observed pattern probably extends into what has not been observed, whether past, present, or future.
Encyclopedia Britannica classifies models as physical, conceptual, or mathematical. It’s the mathematical models that tend to stir up the most distrust and controversy, partly because the math is way beyond most of us. We don’t know what a “parametrized Gaussian error function” is (health service utilization forecasting team, p.4; see also Gaussian, Error and Complementary Error function).
But Christians should be the last people to categorically dismiss models. Any high school science teacher trained in a Christian university can tell you why. I’ve been reminded why most recently in books by Nancy Pearcy, Alvin Plantinga, William Lane Craig, and Samuel Gregg: Whether scientists acknowledge it or not, the work of science is only possible at all because God created an orderly world in which phenomena occur according to patterns in predictable ways. For Christians, scientific study—including the use of models to better understand the created order—is study of the glory of God through what He has made (Psalm 19:1).
Most of us aren’t scientists, but that’s no excuse for scoffing at one of the best tools we have for grasping the orderliness of creation.
Should we wreck our economy based on models?
The “models vs. the economy” take on our current situation doesn’t fit reality very well. Truth? The economy is also managed using models. A few examples:
- Calculating the unemployment rate
- Unemployment forecasting (also this)
- Business forecasting
- Cost Modeling
Beyond economics, modeling is used all the time for everything from air traffic predictions to vehicle fire research, to predictive policing (no, it isn’t like “Minority Report”).
Models are used extensively in all sorts of engineering. We probably don’t even get dressed in the morning without using products that are partly the result of modeling—even predictive modeling—in the design process.
Christians should view models as tools used by countless professionals—many of whom are believers—in order to try to make life better for people. Pastors have books and word processors. Plumbers have propane torches. Engineers and scientists have models. They’re all trying to help people and fulfill their vocations.
(An excellent use of predictive mathematical modeling…)
Why are models often “wrong”?
An aphorism about firearms says, “Guns don’t kill people; people kill people.” Implications aside, it’s a true statement. It’s also true that math is never wrong; people are wrong. Why? Math is just an aspect of reality. In response to mathematical reality, humans can misunderstand, miscalculate, and misuse, but reality continues to be what it is, regardless.
The fact that the area of a circle is always its radius squared times an irrational (unending) number we call “pi” (π) remains true, no matter how many times I misremember the formula, plug the wrong value in for π, botch the multiplication, or incorrectly measure the radius.
The point is that models, as complex representations of how variables relate to each other and to constants, are just math. In that sense, models are also never “wrong”—just badly executed or badly used by humans. That said, a model is usually developed for a particular purpose and can be useless or misleading for the intended purpose, so, in that sense, “wrong.”
When it comes to using models to find patterns and predict future events, much of the trouble comes from unrealistic expectations. It helps to keep these points in mind:
- Using models involves inductive reasoning: data from many individual observations is used in an effort to generalize.
- Inductive reasoning always results in probability, never certainty.
- The more data a model is fed, and the higher the quality of that data, the more probable its projections will be.
- When data is missing for parts of the model, assumptions have to be made.
- Changes in a model’s predictions are not really evidence of “failure.” As the quantity and quality of data changes, and assumptions are replaced with facts, good models change their predictions.
- True professionals, whether scientists or other kinds of analysts, know that models of complex data are only best guesses—and they don’t claim otherwise.
- The professionals that develop and use models in research are far more tentative and restrained in their conclusions than people who popularize the findings (e.g., the media).
In the case of COVID-19, one of the most influential models has been one of IHME’s (Institute for Health Metrics and Evaluation). Regarding that model, an excellent Kaiser Family Foundation article notes:
Models often present “best guess” or median forecasts/projections, along with a range of uncertainty. Sometimes, these uncertainty ranges can be very large. Looking at the IHME model again, on April 13, the model projected that there would be a 1,648 deaths from COVID-19 in the U.S. on April 20, but that the number of deaths could range from 362 to 4,989.
Poor design and misuse have done some damage to modeling’s reputation. Some famous global-warming scandals come to mind. But in the “Climategate” controversy, for example, raw data itself was apparently falsified. The infamous hockey stick graph appears to have involved both manipulated raw data and misrepresentation of what the model showed. Modeling itself was not the problem.
(XKD isn’t completely wrong … there is such a thing as “better garbage”)
Why bother with models?
Given the uncertainty built into predictive mathematical models, why bother to use them? Usually, the answer is “because we don’t have anything better.” Models are about providing decision-makers, who don’t have the luxury of waiting for certainty, with evidence so they don’t have to rely completely on gut instinct. It’s not evidence that stands alone. It’s not incontrovertible evidence. It’s an effort to use real-world data to detect patterns and anticipate what might happen next.
As for COVID-19, the idea that too many sick at once would overwhelm hospitals and ICUs, and that distancing can help slow the infection rate and avoid that disaster, isn’t a matter of inductive-reasoning from advanced statistical models. It’s mostly ordinary deduction (see LiveScience and U of M). If cars enter a parking lot much faster than other cars exit, you eventually get a nasty traffic jam. You don’t need a model to figure that out.
You do need one if you want to anticipate when a traffic jam will happen, how severe it might be, how long it might last, and the timing of steps that might help reduce or avoid it.
Leaders of cities, counties, states, and nations have to manage large quantities of resources and plan for future outcomes. To do that, they have to make educated guesses about what steps to take now to be ready for what might happen next week, next month, and next year. It’s models that make those guesses educated ones rather than random ones.
Highly technical work performed by exceptionally smart fellow human beings is a gift from God. Christians should recognize that. Because we’ve been blessed with these people and their abilities (and their models) COVID-19 isn’t killing us on anywhere near the scale that the Spanish Flu did in 1918 (Gottlieb is interesting on this). That’s divine mercy!
(Note to those hung up on the topic of “the mainstream media”: none of the sources I linked to here for support are “mainstream media.” Top image: IHME.)
Aaron Blumer 2016 Bio
Aaron Blumer is a Michigan native and graduate of Bob Jones University and Central Baptist Theological Seminary (Plymouth, MN). He and his family live in small-town western Wisconsin, not far from where he pastored for thirteen years. In his full time job, he is content manager for a law-enforcement digital library service. (Views expressed are the author's own and not his employer's, church's, etc.)
- 180 views
This is not how models work… and it’s very common in every field of study or practice, for outsiders to not know enough to know what they don’t know. This results in “obvious” conclusions that simply aren’t so. Those of us with a strong theology background see this all the time with people telling us “what a passage means to them” in a Bible study discussion group. Sometimes they’re right, but often they don’t know enough to know what they don’t know.
In the case of models, it relates to the expectations problem I addressed in the article.
- When data is missing for parts of the model, assumptions have to be made.
- Changes in a model’s predictions are not really evidence of “failure.” As the quantity and quality of data changes, and assumptions are replaced with facts, good models change their predictions.
When you’re working with incomplete and low-quality data, you get different outcomes. When you have to make assumptions, then later obtain facts and replace your assumptions with facts, you get different outcomes.
Besides, the models have always produced ranges of possible outcomes. From the Kaiser article…
Looking at the IHME model again, on April 13, the model projected that there would be a 1,648 deaths from COVID-19 in the U.S. on April 20, but that the number of deaths could range from 362 to 4,989.
I’ll bet the number that died yesterday was in that range…. given how large it is, that might not be saying much, but the probability is greatest in the middle of that range. I haven’t checked the count yet, but I’ll bet it was close to the middle of that range.
The purpose of a predictive model is to help you make the best guess you can with the information you have at a moment in time. The information you have is going to change, and if your model is any good, so will your projections.
Now it may eventually turn out that there were models that made better assumptions or better decisions about how the many variables involved relate to eachother. We won’t know that until pretty long after this thing is over and with the usually-far-better data of hindsight. That isn’t going to change the fact that leaders had to make the best guesses they good based on the ranges of possible future levels of infection/levels of hospitalization, etc.
So that raises the question, why have some models been more heavily leaned on than others? In a perfect world, this would be because some models of a longer-established reputation of working better. In the real world, it’s probably more complicated than that, but a model as a reputation for a reason. If it’s widely respected in a field where there are a lot of competing alternatives, that’s not insignificant.
Of course, those with a contrarian drive, or more than a pinch of paranoia, or are passionately anti-establishment or passionately populist (or all of the above!) are going to prefer to say we’ve all been duped by a handful of people with a secret agenda.
Well, again probability of that is > 0. …But not much greater than zero, given the number of independent groups and individuals involved and the competitiveness involved. But this is a position that isn’t rational, and all I have to offer on this is reasoning. So, I don’t think I have anything to say those who strongly prefer unlikely, sinister narratives as their explanatory process.
Edited to add…
Looking at the IHME model again, on April 13, the model projected that there would be a 1,648 deaths from COVID-19 in the U.S. on April 20, but that the number of deaths could range from 362 to 4,989.
Actual deaths in the US on April 20: 1,695. IHME was too low by 47. (well within the margin of error)
Views expressed are always my own and not my employer's, my church's, my family's, my neighbors', or my pets'. The house plants have authorized me to speak for them, however, and they always agree with me.
Yes we should be careful criticizing public servants trying to help us all. I appreciate that.
In the case of COVID 19 we have a serious issue. First, the disease has not run its course yet, so I note that. But, we shut down a $22 trillion economy over projected forecasts of 2.2 million people dying. It stands to good reason to check to see if that number was ever a reasonable number, or whether something else caused that number to be thrown around.
So that raises the question, why have some models been more heavily leaned on than others? In a perfect world, this would be because some models of a longer-established reputation of working better.
Many decisions were made based on Neal Ferguson’s models, projecting, initially 2.2 MILLION deaths in the USA. (See this post from Powerline)
The guy has a long track record. Of being wrong. Way wrong. Yet his model moved people to action all over the world? (He has since ratcheted his numbers way down. Oopsie.)
Maranatha!
Don Johnson
Jer 33.3
[Aaron Blumer]This is not how models work… and it’s very common in every field of study or practice, for outsiders to not know enough to know what they don’t know. This results in “obvious” conclusions that simply aren’t so. Those of us with a strong theology background see this all the time with people telling us “what a passage means to them” in a Bible study discussion group. Sometimes they’re right, but often they don’t know enough to know what they don’t know.
Yes, clearly the teacher at Harvard with a PhD in epidemiology that I quoted deriding the model you’re trying so hard to defend is an outsider who doesn’t know what he doesn’t know about modeling infectious diseases.
Let’s be blunt here. Official measurement of infections is about a million, deaths about 40k. Perhaps 5-10 times more people have been infected and not tested. Now let’s scale that upper bound of infection numbers to assume that everyone gets infected—30 to 60 times more infections and deaths. So absent social distancing, yes, you do get Ferguson’s numbers. With social distancing, you get 60-200k fatalities (assuming that this virus peters out before everyone gets infected), and quite frankly, with 42k fatalities already, 60k is looking fairly optimistic.
Seems to me that the models are working pretty darned well for a disease we’ve only named for a few months.
Aspiring to be a stick in the mud.
The death counts from COVID-19 are being inflated at least partly in an effort to make reality match the models (There’s tremendous financial incentive to inflate the numbers as well—the printing machines are getting a workout creating new money.) They’ve gone from counting those dying from COVID to counting those dying with COVID, and now they’re also adding in those presumed to have died from COVID. This is not a secret. It’s not a conspiracy made up by crazy people. It’s fact. Thousands (no one knows for sure how many) of the people who are being listed as COVID deaths are at best unproven cases and at worst intentionally added to skew the numbers.
About my “outsiders” observation, I wasn’t talking about the Harvard guy, but media folks and some of us here in this thread who appealed to a kind of it’s obvious argument.
Having not dug into that particular story, about all I can say about the Harvard PhD is that of course there strong differences of opinion among the experts. That’s why there are different models. And I’ll repeat this also…
[Me:] So that raises the question, why have some models been more heavily leaned on than others? In a perfect world, this would be because some models [have] a longer-established reputation of working better. In the real world, it’s probably more complicated than that, but a model [has] a reputation for a reason. If it’s widely respected in a field where there are a lot of competing alternatives, that’s not insignificant.
About Neil Ferguson
[Don:] Many decisions were made based on Neal Ferguson’s models, projecting, initially 2.2 MILLION deaths in the USA. (See this post from Powerline)
Ferguson is mainly active in UK and has said IHME model projections for UK were too high. He’s not an IHME guy, and so if we’re going to try to blame the curve flattening strategy on a single source, we’ll have to decide if it’s IHME or Neal Ferguson.
In any case, the famous millions dead projection was an “if we don’t act” prediction, I’m pretty sure. And we have acted. But either way, wether the worst case was millions dead or tens of thousands dead, basically the same strategy was called for, because of all the unkowns with this non-flu, “novel” virus.
Let’s remember please that private sector was shutting down well before governors started issuing decrees.
Worth noting, too: our economy is not actually “shut down.” I was in the MNPLS St. Paul metro area last Friday for Dr appointment, and I still got bogged down for a bit in traffic at 11 AM. … construction going on everywhere. The economy is crippled. It’s not (yet) crushed. I’m sure it’s worse in lots of places, but given the hype, I expected to see empty streets and tumbleweeds… not even close.
Lots and lots of models
Canada is using completely different models than the US, apparently https://thetyee.ca/Analysis/2020/04/14/Canada-COVID19-Response-Fumbles/
Predictably, the narrative there also seems to be “government didn’t do enough fast enough.” Isn’t that likely to be the story everywhere regardless of the facts on the ground?
Anyway, I thought it might be interesting to compile a list of just how many different, independent models have been/are being used in the COVID-response strategies in various countries and states. Not sure I’ll have time to even take a stab at it, but I’ll bet there are dozens, at least.
Most of them apparently pointed/point in roughly the same direction as far as response goes.
Edit to add:
One more model: UK “imperial”…
This [IHME] is a different type of model from that of the Imperial College London group advising the government, because it will constantly evolve. But even the Imperial modellers had to change their predictions some weeks ago. Famously, their changed advice persuaded the government to bring in physical distancing guidance, with towns closed for business and people staying home to reduce what, it had suddenly become apparent, would be an unacceptably high death toll. https://www.theguardian.com/world/2020/apr/07/how-can-coronavirus-model…
Views expressed are always my own and not my employer's, my church's, my family's, my neighbors', or my pets'. The house plants have authorized me to speak for them, however, and they always agree with me.
[Robert Byers]The death counts from COVID-19 are being inflated at least partly in an effort to make reality match the models (There’s tremendous financial incentive to inflate the numbers as well—the printing machines are getting a workout creating new money.) They’ve gone from counting those dying from COVID to counting those dying with COVID, and now they’re also adding in those presumed to have died from COVID. This is not a secret. It’s not a conspiracy made up by crazy people. It’s fact. Thousands (no one knows for sure how many) of the people who are being listed as COVID deaths are at best unproven cases and at worst intentionally added to skew the numbers.
This cannot be understated. I’m assuming everyone here knows that about 1,800 people die in the US every day from heart disease. Heart disease is a risk factor for COVID-19. So if a person with heart disease now dies and has COVID-19, he is a COVID-19 statistic, even though there is a good chance he was already going to die of heart disease. Maybe not today, maybe not tomorrow, but sometime. But now that heart disease death is a COVID-19 death.
This is not a heartless (no pun intended) conversation, it is a plea to everyone reading to IGNORE THE NUMBERS!!!. Leave the numbers analysis to the experts, and trust God, not the government or the experts. The numbers can be made to say whatever one wants them to say, and unless you are a key decision maker in political office or a scientist in the field applying your expertise to try to solve the problem, this is all just a waste of our time. Focus on the eternal, not on the models.
Ashamed of Jesus! of that Friend On whom for heaven my hopes depend! It must not be! be this my shame, That I no more revere His name. -Joseph Grigg (1720-1768)
[Aaron Blumer]Most of them apparently pointed/point in roughly the same direction as far as response goes.
If by “roughly” you mean “this event will have a significant health impact and there will be deaths,” then I agree the models all go roughly the same direction. Most governments would feel it necessary to take some kind of action based on that.
However, I would argue that a model that allows for between 300-some deaths and 4900-some deaths in a day is not very accurate (and thus, not very helpful) at all. Seeing the high number and basing all actions on that (i.e. the > 2,000,000 total number) may seem prudent, but then not modifying the actions when those numbers prove to be way wrong, is the real problem, especially when all the other strongly negative effects of taking those actions are essentially ignored as not important. And as others have pointed out, the inclusion of deaths that occurred while the person had covid, or even (as in New York) “presumed” covid, makes any model trying to track actual covid completely meaningless.
As you have already mentioned, any model is only as good as its data, but when the data are corrupted (whether for political reasons or not), it doesn’t really matter how good the model is, or how expert its creators are.
Dave Barnhart
A tweet by radical leftist Michigan Governor Gretchen Whitmer has surfaced from before her time in the governor’s mansion that shows her commitment to killing unborn babies is a passion that runs deep. Her disdain for conservatives was also on display, given the aped MAGA-style hat she was pictured wearing.
The 2018 tweet shows the Democrat proudly wearing a pink cap that reads, “Planned Parenthood Makes America Great” and “The Future is Bright and Pink!”
Recently, Governor Whitmer–reportedly a candidate to be Joe Biden’s running mate later this year and current co-chair of the Biden campaign–earned every bit of the outrage that followed when she declared on a podcast interview that abortions are “life sustaining.”
During David Axelrod’s Axe Files podcast she proudly noted that under her leadership, Michigan has put a hold on all “elective” surgeries, but has maintained ongoing access to abortions in the state. “We stopped elective surgeries here in Michigan,” she said. “Some people have tried to say that that type of a procedure [abortion] is considered the same and that’s ridiculous. A woman’s healthcare, her whole future, her ability to decide if and when she starts a family is not an election, it is a fundamental to her life,” said Whitmer. “It is life sustaining and it’s something that government should not be getting in the middle of.”
Of course, she had no problem putting major obstacles to the use of Hydroxychloroquine with Z-Packs/Zinc for sick patients in Michigan with Covid-19, even threatening the licenses of Doctors and Pharmacists for prescribing and filling those prescriptions. Likely, many lives could have been saved by the early use of this potentially life-saving medicine before the disease became too advanced in patients. That has changed recently, but too little too late for many. “Doctor” Whitmer has no authority to inject herself between a medical doctor and the patient.
We have had numerous individuals in our school and church suffer the postponement of necessary operations and critical exams on account of the Governor’s takeover of the Health Care operations of Michigan. This has even effected my immediate family directly. Hospitals are closing. Clinics are closing. Hundreds, perhaps thousands, of health care workers are being laid off while the health care needs of Michiganders are being postponed or ignored. Also, many in our church and school are losing their jobs. This, like some (not all) of her unnecessary and draconian mandates, is cruel. We should not be quarantining our US Constitution and Michigan Constitution and the Bill of Rights, even during an epidemic. Her so-called desire to “save lives” in Michigan rings hollow to me in light of the above. Instead, we should make policy to “SAVE LIVES AND SAVE LIVELIHOODS”. Both are critically important and necessary. We need balance and proportion, not dictatorship. Unfortunately, these erroneous models have dictated policies which in some cases will prove to be disastrous.
Pastor Mike Harding
It’s ironic that those who deride “the models” are working from a mental picture which they believe to approximate reality—i.e., they’re working from a model. It’s often a singularly uninformed model, based on a few poorly understood data points—and much more handwaving than mathematics—but it’s a model.
You can choose data-driven models formulated by people who have spent their lives studying this field, or you can choose fuzzy, uninformed mental picture, but you can’t get rid of models.
Silly Andrew. Why trust experts when we have YouTube? In a few clicks, by the time I consume a half-bag of Cheetos, I can become an expert on epidemiology and relevant modeling, and know the TRUTH about Fauci and Brix. #deepstate
Tyler is a pastor in Olympia, WA and works in State government.
[Andrew R.]It’s ironic that those who deride “the models” are working from a mental picture which they believe to approximate reality—i.e., they’re working from a model. It’s often a singularly uninformed model, based on a few poorly understood data points—and much more handwaving than mathematics—but it’s a model.
You can choose data-driven models formulated by people who have spent their lives studying this field, or you can choose fuzzy, uninformed mental picture, but you can’t get rid of models.
I hadn’t looked at it that way. It’s an interesting point. It’s not the same kind of model, but it is a representation.
It’s more of a philosophy question but arguably, we do all our thinking using models… without being aware of it.
On a related note, there seems to be a personality type that leans toward viewing intuitive understanding as superior to conscious/methodical thinking.
For some it’s not a personality thing, though; it’s a selective way of approaching certain topics. So they might see math and other kinds of conscious analysis as great for understanding some things but automatically suspect for other things.
I might be one of those, but I definitely don’t see disease models as one of the “intuitive is better” topics.
About Mike’s comments…
There have definitely been some government overreaches—or at least irrational applications—in some places. It’s worth keeping in mind that wherever an authority draws a line between allowed and not allowed there are going to be some weird individual cases at the boundary. Many states temporarily suspended elective medical procedures, and I’m not sure that’s a bad idea. It certainly causes suffering, and those making those decisions are certainly aware of that. They had to make a judgment call as to which option would cause the most suffering.
But it’s pretty clear that some things that aren’t allowed vs are, in some locations, don’t make a whole lot of sense. Matt Labash wrote a piece the other day about how they had specifically banned fly fishing in his state… which is an activity for which social distancing is pretty much built in. In the same location, fishing for food was allowed, so the guy couldn’t fly fish alone (and throw the fish back) but a boat full of people crowded together could fish if they kept them and ate them.
But I’ve seen it in so many places and times and situations: any time you make a rule, there will be situations that make it look ridiculous, at least hypothetically if not in reality. So I don’t really know how to judge these things fairly. … some of the rules certainly seem pretty obviously dumb or unfair.
There isn’t much that is “unconstitutional” during a plague, though.
Views expressed are always my own and not my employer's, my church's, my family's, my neighbors', or my pets'. The house plants have authorized me to speak for them, however, and they always agree with me.
Yes, experts are needed in the medical field as well as other fields such as economics. Knowledge, however, is a far cry from wisdom. Wisdom takes knowledge and applies it looking at all the factors involved. Experts tend to focus on one thing. We went from estimated 2 million deaths which scared our leaders to death (understandable), to 250 thousand, to 200,000, to 100,000, and now to 60 plus thousand—those numbers from the beginning included mitigation. That’s how far they were off. Same thing is true in the UK. Experts said 500,000 would die in the UK with mitigation. Ended up with ca. 20,000. Cannot trust these models. They tend to overshoot by very wide margins and cause policy makers to react very harshly. Sweden found more of a middle-ground reaction. Still had much death, but not more per million than the USA; however, they didn’t destroy millions of lives through Draconian measures. We need to look back to the Asian flu of 57 and the Hong Kong flu of the late 60’s. We didn’t shut down the whole 22 trillion dollar economy for those epidemics. Estimates now by the UN are that additional hundreds of thousands of children will die from hunger as a result of the Corona economic shutdown. They are projecting 66 million additional children WW to go into “EXTREME POVERTY” because of what has happened in the WEST over the last six weeks. Law of untended consequences. Must see the big picture. This does not include massive increases in suicide, abuse, divorce rates, mental illness, alcohol deaths, drug deaths, and tens of millions of ruined lives. Save lives while saving livelihoods. Learn to work safely, rather than not work at all. Work is life-sustaining. Without it people die. Work is essential for life. Must find a way to allow more people to work safely or millions more will die. Home, work, home model to begin with, then expand from that later on.
Pastor Mike Harding
[Robert Byers]The death counts from COVID-19 are being inflated at least partly in an effort to make reality match the models (There’s tremendous financial incentive to inflate the numbers as well—the printing machines are getting a workout creating new money.) They’ve gone from counting those dying from COVID to counting those dying with COVID, and now they’re also adding in those presumed to have died from COVID. This is not a secret. It’s not a conspiracy made up by crazy people. It’s fact. Thousands (no one knows for sure how many) of the people who are being listed as COVID deaths are at best unproven cases and at worst intentionally added to skew the numbers.
Yes, Dr. Birx herself said this from the White House podium. I am also told Medicare pays a certain amount for a death listed as pneumonia, but a significantly larger sum if the doctor notes COVID-19.
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