Coronavirus Update XII — Who Has The Right Numbers?

All the good stuff, caveats, code, data sources and explanations are linked, some in Update III, and the most important in Update II, Update IV, Update V, Update VI, Update VII, Update VIII, Update IX, Update X, Update XI, Bayes Theorem & Coronavirus, and the Sanity Check Perspective, so go to them first before asking what-about-this-and-that. Skip to the bottom for the latest model. Thanks to everybody emailing me sources, including Ted Poppke, Jeff Jorgensen, Jim Fedako, Joe Bastardi, Philip Pilkington, John Buckner, Harry Goff, John Goetz, Warren McGee, Robert Kinney III. Sorry I’m slow answering emails.

“You can discover what your enemy fears most by observing the means he uses to frighten you.”
— Eric Hoffer

Juicing Numbers & Fear

Incidentally, the doc video we told you to watch last week before it was purged—was purged. YouTube must have CDC zampolit on staff to decide which videos are “legitimate” and which must be memory-holed. I’m too lazy to search for the video elsewhere, but I’d guess it’s on BitChute by now.

“1/ 61,000 and counting. And counting. Colorado is now overruling attending physicians to classify nursing home deaths as #COVID.”

It wasn’t only death models that were off. It was hospital resources, too.

Naturally, some will claim the lockdowns account for this. But they say this by assuming it is true. I mean, they begin by assuming lockdowns must work, and then conclude that lockdowns worked. When, in fact, lockdowns cause harm, and the models were just wrong. The only other possible claim is to say lockdowns worked “much better” than predicted, which is absurd. The models assumed lockdowns. Since they assumed it, and they were wrong, it must mean the lockdown assumption was wrong, or other parts of the model were.

In any case, something was wrong, meaning we should not have trusted the models.

Later this week, or maybe next, I’ll show you that WHO agrees with this.

From FoxRiverFlood comes this quip: “Flatten the curve” is good marketing. If they just called it “Extend the Epidemic Indefinitely” people might not have bought in.

Most people would take this as good news, seeing the majority who get it feel little or nothing.

Not our stalwart media, though. They managed to find a way to turn it into FEAR.

Whose Right Numbers

We’ve been using the COVID Tracking Project for our reports. As I’ve told you from day one, other sources differ. As of 8 PM Monday EST, our source says 62,806 reported deaths.

The CDC, on one of its sites, says, as of 4 May, 38,576.

Later this week, we’ll have a guest post on all this. For now we can note this is a bit of a difference, no?

The CDC also reports deaths with pneumonia and COVID at 17,122. Now I’d think this is a proper “and”, meaning you can’t add these to the COVID total, but I don’t know. It may be that their report of COVID alone is without pneumonia. I can’t make out from their description what they mean. But if that’s right, then there would 55,698 total reported deaths. Still 7 thousand less than our source.

Kind of odd, too, there are only 5,886 reported flu deaths since 1 February. There were 92,615 reports deaths “with” (their word) pneumonia , flu, “or” COVID.

As I have been saying, to sort all this, we’re really going to have to look to all cause-deaths. CDC says 739,600. I guess from 1 February, since that’s when the chart starts. But see this post.

We’ll be real careful about “excess deaths”, though, as this other official plot from the CDC indicates.

In other data, the 2017-2018 flu season looks worse, but that’s neither here no there for now. It’s the idea of “excess deaths” we have to be careful about. Deaths not above the model are counted as “excess”. But the threshold undulates, acknowledging the seasonal winter peak. The “excesses” are usually ascribed to flu, now coronavirus.

But what’s causing those other deaths in the peak? Not flu? Not corona? By assumption, yes: those deaths are caused by other things, even in the peak. How we do know the assumption is correct, given how variable death certificates are with flu? We don’t. It’s always an estimate.

In other words, and as I have been saying, it’s a mess.

Here, anyway, are the per 100,000 weekly deaths going back to 2009, with the three official sources. The latest numbers, as CDC says, will be delayed somewhat. Dashed line all deaths minus COVID deaths. Details and sources here.

With under-reporting, there is still a chance the dreaded coronavirus will beat 2017-2018 flu season. Getting harder and harder, though. Be careful how you place your bets.

Models of Doom

Top coronavirus model predicts 100,000 Americans dead by the end of this summer’s first wave

The MOBS model from the Network Science Institute at Northeastern University also estimates that there will be about 89,000 deaths by mid-May if stay-at-home orders remain in place.

That death toll would increase to over one million in an unmitigated scenario, according to the projections that are among those used by the CDC to forecast the pandemic.

Nice source for various predictions. Compare these to the actual totals below. The colors are slightly confusing (to my eye), but it appears UT Austin might be closest. These are all snapshots from maybe a week or two ago, as all models change continuously.

How about this breathless headline (from a “conservative” site)? Leaked CDC Model Projects: Next Month Daily Deaths Will Reach 3,000.

The Times somehow got hold of a FEMA chart based on numbers put together by the CDC. (How they got hold of it is itself an interesting question.) Most of us are already grimly inured to daily reports of 1,500-2,000 dead and 30,000 more infected. Imagine how much more inured we’ll be on June 1 if this data pans out. One death is a tragedy; 3,000 deaths every 24 hours is a statistic.

So this is a model from Department of Homeland Groping, apparently, run on 1 May.

Wowza! Bring out your dead! What I find funny is the nervous guy writing about this (at that link and the AI bot at NYT) focuses on the forecast of daily deaths increasing increasing increasing, with no end in sight!, while not noticing the model has been absolute shit up to this point.

Somehow that the model has predicted so badly so far is proof IT’S WORSE THAN WE THOUGHT. You can make similar claims about our naive model, since it consistently under-predicts, but at least we’re not projecting forever growth, and we got the shape right. This FEMA model makes it look like this virus, unique among all others in history, will accelerate, not in a second wave, but right now. As many as, what, 30,000 deaths a day by 1 June? That smell right to you?

You can read all the political hand-wringing blah-blah-blah at either side about how this model was “leaked”. I refuse to think about it.

Global Reports

I repeat, for the nth time, that these are naive models of reports, not actual totals.

Reported totals:

New totals forecasted: 3.9 million cases, 275 thousand deaths. Last week “3.3 million; new total reported deaths 242 thousand.” About the same under-prediction on reported deaths as the week before. Not too bad for a naive model.

As I’ve been saying week after week after…(how many weeks now?)…the true cases must be much larger than the reported cases. I will repeat that word-for-word for the US. In any case, here’s how we know. This is the reported deaths divided by reported cases.

There is no way the virus is killing north of 7% of those it infects. This graph, using official numbers, proves that (1) reported cases are too low, or (2) reported deaths are too high, or (3) both.

Using the same trick as last time, and using the total reported deaths (so far), assuming the virus actually kills 1% or 0.5% of those it infects, which are on the high end, then there must be 25 to 51 million actual cases in the world. More if kill rate is lower. Assuming the reports deaths match the actual totals. If they’re too high, then so are these estimates.

Again, watch for the media to tout reported cases over deaths in the next couple of weeks. Deaths (as we’ll see) doesn’t look like it will help them keep the panic going.

Reported daily cases:

The increased testing it clear enough. It also means, as with US below, this model is inadequate for reported cases. See US discussion for more on this.

Reported daily cases:

The up-and-down we’ve discussed many times, but it’s perhaps clearer with the US. I’ve explained it below.

Anyway, ignore the model and look only at the data. The trend is clear enough, though noisy.


Reported totals:

New forecasted totals: 1.23 million reported cases, 67 thousand reported deaths. Last week: “New total reported cases 1.1 million; new total reported deaths 57 thousand.”

Obviously with testing ramping up, this simple model is inadequate at capturing reported cases. Watch the news media, though. If they haven’t deaths to report, they will instead emphasize cases, hoping to show how rapidly the bug is spreading. When, of course, at least a good portion of tests reveal cases that were already there.

As I’ve been saying week after week after…(how many weeks now?)…the true cases must be much larger than the reported cases. We’ll prove this again for the US after the daily reported cases.

Reported daily cases:

You can see the weekly signal in the dailies: these are, after all, reports, and even under lockdown, we’re still operating on a seven-day basis. Even with increased testing, the trend is down.

It’s also clear the model will be way off in the total reported cases. That total depends on our dear leaders and how much testing they will require. I’m open to suggestions about that.

Reported deaths divided by reported cases:

This virus is not killing more than 5% of the people it infects in the US, as the evidence above, and everywhere else, indicates. This graph, using official numbers, proves that (1) reported cases are too low, or (2) reported deaths are too high, or (3) both.

Using the same trick as last time, and using the total reported deaths (so far), assuming the virus actually kills 1% or 0.5% of those it infects, which are on the high end, then there must be 6.2 to 12.4 million actual cases in the US now. There could and will be more later.

This assumes the reported deaths match the actual totals. If they’re too high, then so are these estimates.

Reported daily deaths (reminder of source for those who will gasp).

In a way, I had bad luck picking Tuesdays as update day. It just happened the first time, and then it made sense to stick to a weekly schedule for fair comparisons. But because reports are also weekly, and tend to be lower for deaths on weekends, the reports are always lowest Monday nights. So we’re always hitting low points, which paint a brighter picture.

Meaning I am not predicting that new reported deaths will drop to 0 in a few days time. In fact, reports will almost certainly spike back up—but not way back up. The general trend is correct. And in any case, the black line is the data itself.

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33 Thoughts

  1. Briggs, your articles have helped bring me some measure of calm amidst all this. No one I know has gotten Coronavirus, and no one I know has recently died. So in my little bubble, it’s business as usual except my company (a hospital) had to layoff some 400 people, which DID affect people I know. And it was harder to buy certain things at the grocery store. And I couldn’t get my haircut at my barbership. So thats been about the extent of my suffering.

    As far as I can tell, this is the first ever Statistical pandemic. The actual illness was far worse, but the contagion infected our models and caused their readers and interpreters to think models are data.

    It seems reasonable until you realize that anyone can draw a logistic curve. I drew a straight line going up at a steep angle from January to June on a post-it note, and coronavirus has far underperformed my model, so I have felt at ease from the very beginning.

  2. “Not our stalwart media, though. They managed to find a way to turn it into FEAR.” As the old Don Henley song “Dirty Laundry” said, “She can tell you about the plane crash with a gleam in her eye. It’s interesting when people die,” Seems so apropos these days. Even our local news people are bubbleheaded idiots that can lie with a straight face day after day and cheer the increase in Covid nightly. Evil, all of them.

    Other madeup numbers: Unemployment comes from Current Population Survey. It is NOT a real number and does not represent the actual number of people unemployed. 60,000 households per month (I believe) are surveyed, the magic number dance is done and viola! The magic lie of a number produced.

    The modelers are actually unemployed psychics. I know because one of the ways psychics work and complete fools paying them is to keep slightly altering a prediction until it comes true. If we don’t hit 100,000 in the next wave, then the number will be slightly increased for over the winter, or greatly increased, depending on the outcome of the election. Even psychics are politically correct and employed by the government in this case. One NEVER backs out a prediction, one just modifies it over and over until something close happens or a major event takes the spotlight off your prediciotn. If Little Kim starts up with missiles and gets close to the US, Covid will die a quiet death as the MSM goes off the deep end over missiles. Their attention span is less than that of a gerbil, and their IQ lower still.

    The CDC is a lying propaganda piece of garbage. At this point, I believe NOTHING any government agency puts out for numbers. They are ALL liars. (Plus, the CDC employes STUPID psychics who should know better than to increase numbers that far—though I suspect Newson and Gretchen have a gun to the head of the person reporting the model (or their bodyguard, because they “never” would carry a gun)).


    I knew the only purpose of testing was further house jailing and prolonging the crisis for months. TESTING IS POLITICAL, not scientific. Check the false positive and negative rates if you doubt that.

    Considering Covid 19 deaths are just words on a death certificate (dead is dead, no matter how you achieved that condition) and in no way scientific since we make them up as we go. The entire situation is simply Alice in Wonderland and the whole country jumped right down the rabbit hole. Head first. We are idiots.

  3. The great Peter Schweizer shares “media insanity” on models:

    “More media insanity.
    Bold headlines about a spike in deaths coming with reopenings, based on a Johns Hopkins University model!
    “Justin Lessler, an associate professor of epidemiology at Johns Hopkins and the creator of the model, reinforced the government’s claim that the data was taken out of context. He explained that the report was a draft and delivered as a work-in-progress to officials within the Federal Emergency Management Agency.
    “I had no role in the process by which that was presented and shown,” Lessler told the Washington Post. “It was not in any way intended to be a forecast.”

  4. My son in the People’s Democratic Republic of Maryland recently recovered from the Kung Flu. He was sick for two and a half weeks. He doesn’t count as a statistic, though, because the doctors refused to test him to verify COVID-19. Why? Because approved test kits were hard to come by, and he was young and not sick enough.

    If we develop immunity to this disease, then the only way to stop it is to have herd immunity through letting almost everyone get sick and get over it. Everyone will have their chance to catch it over the next three years or so. And the isolation orders will have been pointless and counterproductive.

    If we do not build immunity to this disease, then there is no way to stop it, and everyone will have their chance to catch it over the next three years or so. Repeatedly, in some cases. And the isolation orders will have been pointless and counterproductive.

    Impoverishing 500 working class families (mostly young and healthy) for the death of each person (mostly elderly and infirm) is not merely foolish, but evil. We must not sacrifice the young on the alter of the old. That way lies civilizational suicide.

  5. The CDC numbers are a week to two weeks behind – average it out to ten days. Admitted on their site.

    Let’s add 1000 deaths a day for 10 days in a row. I know, lower than normal – but deaths have been dropping for six days in a row, so we’ll try that. Even then we’re below 50,000. Add between 22 to 25 thousand deaths – as many have confidently told me to do – and they roughly match.

    But that doesn’t seem right considering the deaths are steadily dropping.

  6. I had “excess deaths” explained to me:

    “…excess deaths are the number of deaths in a given week that exceed the average number of deaths in that week over the previous five (or sometimes ten) years. If, as there has been in many European countries, there is a sudden spike in excess deaths at a time of year not usually associated with such spikes, and if the spike is bigger than any other spike, and if the spike follows the arrival of a highly infectious and highly deadly pandemic, then in the absence of any other explanation the cautious and responsible government will bear in mind the probability – though not the certainty – that the pandemic caused the increase in excess deaths.

    The value of studying the excess-death figures is that it overcomes the futile debate about whether people are dying of or with the Chinese virus. For they are dying in larger excess numbers than the reported Chinese-virus cases, which means that governments are undercounting those cases, possibly (and definitely in the UK) by a very large margin.”

    I think I understand that “excess deaths” can be a useful tool when bound by the constraints indicated. But there is the risk of circular reasoning or using excess deaths to “fill in the gaps” in one’s favorite model.

  7. “Daily New Hospitalizations” as a time series would be handy, either confirmed or suspected.
    “Daily Deaths” is too far in the rear view mirror.
    But of course reliable “Daily New Hospitalizations” is unavailable.
    “…it is impossible to assemble anything resembling the real statistics for hospitalizations, ICU admissions, or ventilator usage across the United States….”
    Source :

  8. Here’s what I don’t understand. Why do we need models and statistical graphs etc. to compare year over year, month over month, and week over week actual deaths? Is there some difficulty in determining who is actually dead? In my state of Illinois, yearly actual deaths averaged 104k per year from 2000 – 2018. The high (in 2018) was 110k and the lows in 2009 and 2010 were just under 100k. It’s gone up steadily since 2010 (Illinois population has not increased during that time). Not sure why we can’t just compare total deaths in 2020 YTD vs other years. If it’s significantly higher, COVID might be the pandemic it is claimed to be. If not (which I suspect as Illinois admitted to juicing numbers on the daily Fred Flintstone, I mean, JB Pritzker press conference), then this whole thing was a farce. Since they seem to be able to report the daily death toll, I’m assuming from death certificates, then they should have the all death numbers, even if it’s a week or two behind. No need to do any modeling at all. I feel like I’m missing something.

  9. PaulH,

    It doesn’t prove what is claimed, though. It can easily prove – and I think it’s fairly clear it does – that the lockdown is killing people. It’s a classic case of assuming your conclusion then trying to mold the data to fit it.

  10. Hello William,
    Do you have a sense of whether all states (or the CDC) are tracking “people” with positive COVID-19 tests, or the number of positive “tests.” I read somewhere (now lost) that every positive test in one jurisdiction was counted as a “case.” Example: Arrive at ER with symptoms. Positive “test” in ER makes “you” a “case.” After admission to hospital, test on day 10 is still positive, thus counted again. Test prior to discharge due to lack of need for skilled care is still positive, but counted again. One person, three positive tests counted, three cases counted. Is this possible, and if so, enough to skew test and/or case numbers?

  11. Never has it been more proven that “there are lies, damn lies, and statistics.”

  12. WmBriggs,
    I greatly appreciate your updates. There is another plot available via the CDC dashboard that produced the Excess Deaths plot you show above ( ). That is “excess deaths with and without weighting” which purports to show the effects of “under-reporting” of COVID-19. That plot shows nearly 6500 “under-reported” deaths prior to January 18, including over 400 in the week ending November 9, 2019! Hard to trust any model suggesting an under-reporting of 6500 deaths prior to the first documented COVID-19 case in the United States.
    John Baglien

  13. Re:”Juicing Numbers”

    Pop quiz!
    Q: About what fraction of flu deaths are confirmed flu cases?
    A: About 1/6th.

    “But there are little data to support the CDC’s assumption that the number of people who die of flu each year is on average six times greater than the number of flu deaths that are actually confirmed. In fact, in the fine print, the CDC’s flu numbers also include pneumonia deaths.”

    Fine print on CDC flu counts:

    A far greater portion of covid deaths are actually confirmed, and we know we have even missed some, b/c they have been able to dig up bodies and confirm afterward.

    Another weird thing is the increase in strokes among younger infected people:

    “On average, the covid-19 stroke patients were 15 years younger than stroke patients without the virus.”

    I wonder to what extent this is occurring. There’s some speculation that this accounts for some of the increased deaths that are not in the official covid numbers, but hard to say.

    “Many doctors expressed worry that as the New York City Fire Department was picking up four times as many people who died at home as normal during the peak of infection that some of the dead had suffered sudden strokes. The truth may never be known because few autopsies were conducted.”

    The 0.5-1% CFR assumption used here seems like a reasonable range, from my reading. Certainly not as high as 2%, which was commonly reported, at least for awhile.

    The 3,000 deaths per day in the US is definitely not credible. Pretty sure you’d have to assume that no one would take any voluntary precautions, even if all shut downs were lifted.

    One reason why the Briggs model continually underestimates is because the reported cases are constrained by testing, so it gives a more linear than logistic shape, while the model keeps thinking it should take a sharp right turn in the cumulative infection count.

  14. All,

    Look at Dave’s comment, especially his last paragraph.

    Even after I prove that the true infection must be much higher than reported, and predict people will use increased testing to gin up the fear the virus is spreading, even with the number of juiced deaths dropping, even in the very post, he does just this.

    This is how strong fear is.

  15. 134,475 deaths by August 4 (total cumulative count, with uncertainty range from 95,092 to 242,890 deaths) now projected in the USA by Dr. Murray and researchers in Washington state in the COVID-19 forecasting model by the Institute for Health Metrics and Evaluation (IHME) used by the US Government.

    We’ve obviously flattened the curve according to this graph

  16. Briggs,

    Regarding your comment, I think you misunderstood my intent on that one.

    I agree the true infection numbers are much higher than the testing shows. I would estimate about ~10X higher than confirmed (and more in some locations than others).

    I’m not disagreeing that testing will find currently infected people who were first infected in the past; indeed, all testing does this. I’m just pointing out that testing hit constraints, and that affected the confirmed cases. US tests ramped up to 100K per day, then stayed around ~100-150K for weeks, and recently went up to 200-250K. Because testing has been constrained, you see a pattern in the data that has looked linear for an unusually long time, but you are fitting it with a logistic curve. The curve of true infections probably *is* much closer to logistic, but you can only model the reported (and that’s fine). I’m just commenting on why the predictions have continuously underestimated the reported data.

    I’m not suggesting that true cumulative cases will be linear or anything of the sort. True new daily cases are almost certainly declining. Sorry for the confusion on that one.

    Here is a graph of US testing over time.

  17. Nobody outside of the media with the help of the WHO, has implied that the infection rate is not much higher. Early estimates from Patrick Valance of the actual number was up to 1000 times higher than recorded number of positives. It is still a figure unknown and would have/will be transformative. Also madce clear without caveat right from the beginning. The media doesn’t listen and doesn’t understand.

    He has stated from the beginning that infection mortality rate was not higher than one percent, most likely far south of that. He said it is too early to tell though,.
    The infection mortality rate and the case fatality rate are two separate measures and people are often speaking in cross terms.
    It’s important but quite a basic mistake.

    The problem has always been the clinical symptoms and pathology of the disease which pose management and treatment problems. That contagious individuals romp around for two or three days prior to onset of symptoms if any, makes it very different from flu. THAT plus the R nought being 3. Those two things are what add to the stress and overwhelming of the health care systems.
    Nobody I’ve heard has mistaken the model for the data. Perhaps the media again…
    The language used is painfully plain and slow. Necessary in these times of lying and misrepresentation.
    Couple that with the fact that patients are more likely to require ventilation than for ordinary flu. The problem with clotting causing pulmonary emboli is similar to SARS rather than regular influenza.
    The other indirect deaths re stroke will be due to people not going to hospital and or not being discovered by family and friends in time to attend hospital for non covid strokes and heart attacks. Then add the increased number of pulmonary emboli and cardiac arrests due to thrombosis from all the sitting about that people are no doubt doing. That is a very real risk for certain groups of the population.

    Not sure why there’s an accusation that people are fearful. It seems like normal human nature to me in the absence of correct information.

  18. Dr. Briggs, i have a comment/question regarding the potential impact of false positives on the reported Covid-19 case totals. I’d appreciate your feedback on whether i’m on the right track or not.
    The swab (PCR) tests that are used to determine whether a person has COVid-19 are new and have not been clinically trialed to assess their diagnostic ranges. Therefore, we are left relying on whatever diagnostic data the manufactures provide on the accuracy of their tests. Only a few have apparently made this information public. I gave up looking on my own after a few hours and relied on report by the folks at CEBM (Center for Evidence Based Medicine) which they did on April 7 (
    In Table 3 of the report they produced sensitivity and specificity values along with the 95% CI for 4 PCR test kits. The mean values of the CIs ranged from 93-98% and were similar for both sensitivity and specificity. So my understanding would be that the false positive and false negative rates are similarly in the range of 5%. If this is true, then the PCR testing regime would systematically over count positive test results because the number of negative tests vastly outweigh the number of positive tests. Consequently, you would expect the number of reported confirmed cases to be too high, and by extension, the number of reported deaths to also be too high.
    Does this reasoning seem correct to you? If not, could you explain where it is wrong?

  19. “Not sure why there’s an accusation that people are fearful.”

    Snitching on your neighbors because they have friends over is a fearful reaction, “normal human behavior” or not.

  20. I don’t care enough to argue with you but if my neighbour snitched on me I wouldn’t think they were afraid. I’d just think them mean.
    You win though, it doesn’t matter does it?

  21. “Most people would take this as good news, seeing the majority who get it feel little or nothing.”

    You understand that these people will show symptoms later. And that the vast majority of them will be sick for weeks to follow.

    And now they discovered that kids are affected seriously by the virus though it effect them differently.

    There are also other people that now have failing kidneys after they have recuperated.

  22. I have it on good authority that Prof Ferguson’s unseen model code is currently being ‘debugged’ by a team of postdoc particle physicists in Edinburgh.

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