Open Thread

This is a page for your use, to try out html tags and see what they look like, to post new ideas, to pass along interesting information, to suggest future topics for discussion, to reach out and contact me directly, the floor is yours.



507 thoughts on “Open Thread

  1. Willis —
    Your recent Covid-19-related graphs [1] are excellent.
    With regard to your recent “US COVID-19 Associated Deaths … Compared to … Flu Epidemics” graph [2]:

    #1. Am I correct that the intended meaning of the left y-axis legend is “Deaths As A Percentage Of US Population [At Time of Graphed Pandemic]?
    #2. I am unclear as to how the right y-axis legend (“Deaths per 2020 Population”) applies to the displayed pre-2020 pandemic fatalities.
    #3. If there any chance of persuading you to re-issue the graph showing: updated US deaths through the current date and; updated IHME fatalities projections and; adding a horizontal line for the 61,099 2017-18 flu season fatalities [3]?

    Thank you.



  2. Dear Willis,
    Unless I am mistake, there is a change in curve on the recent period for the UK, leading to much higher death figures, the UK overpass if France.
    Did you investigate this point ? Possibly taken into account fatalities outside hospital.
    Best. Daniel


  3. Willis,
    Just in case you missed this story, Prof Ferguson – the great modeller whose boots we are not fit to lick – has had to resign, at least from his governmental advisory duties. Despite being one of the principal movers toward the economically catastrophic UK lockdown, he didn’t think distancing applied to him – he and his girlfriend have been visiting each other.
    It seems that those makers of the green air travel passes have moved on to Covid-19 with distancing passes for the great and good. Great idea but just don’t get caught if you are a public figure.


  4. Willis,

    Thanks for your excellent work.

    I know you are not fond of video interviews, too slow, however you may find these worthwhile. Dr Mikovits has a powerful, frightening and believable story about corruption at the highest level of medicial research. With the trillions of dollars involved corruption is always possible.

    1) “Plandemic The Movie” What your not being told about Dr. Fauci.

    2) Former AIDS Scientist Judy Mikovits PhD EXPOSES Dr Fauci, Dr Birx & UNCOVERS Medical Corruption.

    Here is one of her books:


    You may already know of this news conference; 2 Kern County MD’s make the statistical/medical case that the shutdown should end. This conference was filmed by “23ABC Bakersfield” – An ABC affiliate – with multiple reporters asking questions and, after 10’s of thousands of views, YouTube removed it for ‘violating community standards’. Another fine example of either ‘1984’ or ‘Fahrenheit 451’ or both. Fortunately the TV station has a Facebook page. I frequently find that which is censored to be the most interesting.



  5. Dear Willis,
    Regarding your frustration as to IHME higher forecast, it is interesting to have a look on the Following Singapore based forecast of the pandemics end

    It looks like pandemics may be forecasted to end mid Mid October in the US, vs mid August in most Western Europe. At least this is what current data and a Gauss law assumption would tell.
    Best. Daniel


  6. Willis, the Minnesota model as updated will be discussed in a webex at 11 am central, there are instructions below to request access to the webex. they are also releasing the code for the model. It would be great for someone like you to review the work.

    The Minnesota Department of Health will hold a WebEx briefing to present members of the media with the latest version of modeling that is used to inform public health and state policy decisions in response to COVID-19. Reporters will have time for questions as part of the briefing.


    Wednesday, May 13, 11 a.m. Please note: There will not be a 2 p.m. media call.

    To RSVP for the media briefing, send an email to by 10 a.m.

    Topic: Minnesota COVID-19 Modeling 3.0


  7. I just ran across this headline in the Daily Mail.

    “Reopened Texas sees a surge in COVID-19 cases with 1,000 new daily infections over five consecutive days after state lifted lockdown measures two weeks ago”

    I looked up the current status here – – -
    and saw nothing justifying using the description of “surg”.


  8. Willis, here is the link to the Minnesota modeling material, at the bottom of the page, including the actual code. I think it would be great for you to look through the material, run the model and see what you think. I wrote an extensive critique of the original model and am writing one of this one, but I am not the math person.

    Minnesota has actually been very transparent and I think this is one of the few examples where you can get the code for the full model along with a lot of description of the thinking behind it


  9. W.E., just wanted to drop you note about your Wuhan Virus graphs. When this thing hit I had exactly two panic attacks. Never had them before so it goes without saying that I bought into the media scared shitless mantra. I’m a regular to WUWT and noticed your update. FIrst time I read it I was still a nonbeliever. But I’m a numbers guy and after a while it started becoming clear that we should NOT have shut down. I grab a graph of yours about ever day to share to others so they can see the light. So, just wanted to say I appreciate what you’ve done here and I go out now all the time and the fear is gone.


    • Thanks, Darren. The amount of hype about this has been astounding. I’ve lived through two worse pandemics, the Hong Kong Flu with 150,000 US dead, and the Asian Flu with 225,000 US deaths. No hype. No panic. No lockdowns. Life just went on.

      Glad that my work was of value to you, your kind words are appreciated.



      • Following on from Darren’s comment Willis, it is clear that – and I can only speak about the UK – the biggest fear now is fear itself. Because we took the ludicrous actions that we did, the nightmare hole that we have dug ourselves is that the question for government ministers has moved from “should we lockdown?” to “can you prove to the public that it is 100% safe before you ease lockdown?” Of course, nobody can answer “yes” to the second question so:
        1) no government official will sanction the removal of lockdown; and
        2) countless “progressive” people and groups are refusing requests, for instance, to go back to work or school their children.
        The other rods for our back that we have created include:
        a) slagging off Trump and anyone that suggests we might take drugs like HCQ (apparently some groups have side-effects but let’s ignore all those for whom there has been benefit);
        b) discussing the cataclysmic “second wave” as if it’s very likely and using sparse data from over 100 years ago that, in all probability, is barely relevant;
        c) refusing to analyse countries like Sweden and Belarus (which the British MSM never even mention) where lockdowns have been limited compared to Britain; and last but not least
        d) total governmental faith in the R number to define the potential for lockdown easing or reinstatement. I’m really sorry but I believe that the creation of this figure to be a purely academic modelled statistical exercise of no practical use. How the hell do they know where and how people contracted the virus and who they gave it to? The odd individual, yes, the whole nation over the past six months – you have to be kidding. The R number’s validity looks to me about as compelling as the global average surface temperature or indeed global anthropogenic carbon dioxide emissions (and the historical graphs created for both
        What a self-inflicted mess.


  10. Hi Willis, This morning before I was fully awake (apparently) I stumbled on a chart showing about 12 to 15 (maybe more) models and how they have performed over the course of this madness. As one might guess the IHME model is the #1 worst performer among all of them.
    I think I was following a link in one of your posts when I found this. I can no longer find it. I’m not sure but I think it might have been a post somewhere on Github.
    If you run across this could you please post a link to it?
    It is VERY telling.



  11. Willis,

    A couple of things,

    1. The IHME model quit adding new projections to the states on May 16th. Any idea why?

    2. In the U.S., the average daily deaths in the US went from 1,220 the 7 day period beginning 3/29, up to 1,999 the week beginning 4/5, up to 2,159 the week beginning 4/12, then has proceeded steadily downward, to 2,073 the week beginning 4/19, 1,884 the week beginning 4/26, 1,799 the week beginning 5/3, 1,439 the week beginning 5/10, to 1,224 the week beginning 5/17.

    3. The average daily deaths have dropped 43.29% from the week beginning 4/12 to the week beginning 5/17. If the rate keeps dropping at the same rate, the average daily deaths will near zero the week beginning 6/22.

    4. The last four weeks, each successive Sunday, Monday, Tuesday, Thursday and Friday has seen a decline. Overall, Wednesdays have trended down, but weirdly, every Wednesday from 4/1 to 5/6 saw each Wednesday increase, with the last two dropping. Saturdays have trended down too, until this week, which saw an increase from the previous Saturday, after drops in the three previous weeks.

    5. For some reason, probably a quirk of how the states report deaths, Sundays are typically 70% or so as compared to Tuesdays. If this week follows suit, then daily deaths tomorrow in the U.S. will be around 884. The biggest discrepancy was 5/3 compared to 5/5, which was at 59%, which could mean tomorrow could be as high as 1,045.

    6. Of course, who knows how good or even timely the data is…

    Thanks for everything you do.



    • Willis,

      After I saw your thread on WUWT which referenced Our World in Data, I’ve been tracking daily deaths in the United States there, in Worldometer and in IHME. For the weeks beginning on Sunday May 3rd through June 7th, both data sources have shown weekly drops in deaths.

      For each week, Our World in Data shows total weekly deaths, the decrease from the previous week, and the average daily deaths as:

      Week Total deaths Increase/(Decrease) Average daily deaths

      5/3-5/9 12,112 (1,939) 1,730
      5/10-5/16 10,388 (1,724) 1,484
      5/17-5/23 8,439 (1,949) 1,206
      5/24-5/30 6,289 (1,610) 976
      5/31-6/6 6,307 (522) 901
      6/7-6/13 5,526 (781) 789

      For each week, Worldometer shows total weekly deaths, the decrease from the previous week, and the average daily deaths as:

      Week Total deaths Increase/(Decrease) Average daily deaths

      5/3-5/9 12,593 (595) 1,799
      5/10-5/16 10,076 (2,517) 1,439
      5/17-5/23 12,593 (1,511) 1,224
      5/24-5/30 8,565 (1,686) 983
      5/31-6/6 6,879 (576) 900
      6/7-6/13 5,431 (872) 776

      For each week, the IHME smoothed data (only up-to-date through 6/6) shows weekly total deaths, the decrease from the previous week, and the average daily deaths as:

      Week Total deaths Increase/(Decrease) Average daily deaths

      5/3-5/9 11,473 (1,695) 1,639
      5/10-5/16 9,868 (1,605) 1,410
      5/17-5/23 8,295 (1,573) 1,185
      5/24-5/30 6,861 (1,434) 980
      5/31-6/6 5,787 (1,074) 827

      Daily deaths in Worldometer and Our World in Data have both been decreasing, on average, about 14% per week. At that rate, the average daily deaths would be about 670 this week, and would approach 200 per week the week of August 15th.

      IHME forecasts today that daily deaths will decline from 778 on 6/6 to 366.49 on 8/12, at which point average daily deaths will begin to increase, presumably with the dreaded second wave.

      As an aside, I haven’t drilled down into any of their ultimate sources, exactly how their timing works, or how IHME has smoothed their data. Having deaths on 6/7 of 764.59 hearkens back “I’m not dead yet” from Monty Python and mostly dead from The Princess Bride. Both Worldometer’s and IHME’s historical data changes over time, which is annoying.

      Also annoying are the wild swings in IHME’s model. On April 21st they forecast Texas to have 1,241 deaths by August 1, 2020. On April 26th, the forecast jumped up by 415 to 1,656. On April 27th, the forecast went down by (209) to 1,447. On April 28th, it went down again by (159) to 1,288. On May 1st, it went up by 2,344 to 3,632. On May 8th, it went down by (1,065) to 2,567. On May 16th, it went up by 1,143 to 3,710. On May 23rd, it went down by (725) to 2,985. On May 28th, it went down by (960) to 2,025. On June 3rd, it went up by 729 to 2,754. On June 6th, it went up by 694 to 3,448. On June 8th, when they extended the forecast out to October 1, 2020, the forecast for August 1, 2020 went down (814) to 2,674. What kind of crazy rollercoaster ride would the Texas governor have put his residents through if he made day-to-day decisions based on their model? By the way, actual total Texas deaths per IHME went from 533 on April 21st to 1,828 on June 8th.

      I can’t discern a pattern in the IHME forecasts for this crazy hopping around. Other states exhibit swings, but not at the same time or in the same degree. From April 21st through June 3rd, the August 1, 2020 total death figure for California was forecast to go up in every single update, from 1,719 up to 12,951, then on June 3rd the forecast went down (5,999) to 6,952. On June 6th, it went up 1,770 to 8,722, then on June 8th, it went down (1,348) to 7,374.

      time will tell. Stay safe.



  12. Just a thought….correlation to weekly international air flights to state airports….maybe ? since your correlation to population density was unexpectedly near zero, yet NY has many cases…..


  13. Further…..There was close correlation, but not a big sample, to international travel shown here: until international flights were shut down…..Alberta is interesting, 1/3 of the mortality of Canada as a whole, most cases in Calgary which has a busy international airport and flights to China, Iran, Europe…Eastern Canada especially Quebec hit heavily, and is blamed on bad luck of spring break being the two weeks before lockdown, and very large elder care facilities where most deaths occurred.


  14. Hi Willis — doing some research on covid, I ran across Inglesby, et al. (2006) Disease Mitigation Measures in the Control of Pandemic Influenza Biosecurity And Bioterrorism: Biodefense Strategy, Practice, And Science 4(4), 366-375.

    Under Large-Scale Quarantine Measures it says,
    There are no historical observations or scientific studies that support the confinement by quarantine of groups of possibly infected people for extended periods in order to slow the spread of influenza. A World Health Organization (WHO) Writing Group, after reviewing the literature and considering contemporary international experience, concluded that “forced isolation and quarantine are ineffective and impractical.””

    It goes on to discuss home quarantine, restricted social gatherings, school closures, and is very cautious about implementation of any and all of them.

    It looks to me that both the CDC and the WHO ignored their own best-practices knowledge.

    I figured if you don’t have the paper, you’d like a copy. But the email address I have for you bounces.

    If you’d like the paper, let me know.




    • “Inglesby, et al. (2006) Disease Mitigation Measures in the Control of Pandemic Influenza Biosecurity And Bioterrorism: Biodefense Strategy, Practice, And Science 4(4), 366-375.”

      Pat, I would appreciate a copy of the paper if you can send it to me: Thanks!


  15. Willis,
    I like your plots on the CV–19. You have a lot of plots on death. Can you also plot total cases per 10 million population and do it by state for the US? Looking at JHU data, it is interesting that number of new cases in the previous hot-spot states have really fallen off and the other states are flat or falling slowly. I wonder if they will all trend toward the same case incident rate.

    I find the correlations interesting. The lack of correlation on population density is counter-intuitive. This could also be done with US counties and may give a better answer. Defining population density may be difficult. Note that some countries in Europe have large land mass, but most people live along the coast (Sweden is an example).

    Again, I like the plots and I apologize for offering this unwanted advice or more to do but am curious (like many are).


    • Thanks, Keith. I’ve stayed away from cases because they are totally dependent on testing. So when any government say doubles the testing, they double the number of cases … which is less than meaningless, it’s totally misleading as to the spread of the disease..

      The only case related graphs I’ve done are the percentage of positive tests to total tests. When that goes up it means another peak may be on the way.



  16. Hello Willis, Wondering what some of your illness / hospitalizations / deaths / data / stats / graphs would look like, one year ago, before the Wuhan Flu, side by side (I realize there is a lot of ‘definition “slop”‘.



  17. Hi Willis,

    On your update of 2020-06-01.
    excess deaths won’t help us because the numbers are all over the map

    I.m.o. Excess death might say more about corona death then the ‘official’ count. Take for instance The Netherlands, where I live, and Belgium. In The Netherlands we count as corona death only the people who are tested positive before they die, no post-mortum, and most likely not tested when you die in a care home. So we are under counting what is confirmed by the chart. Belgium count as corona death if there is the slightest possibility that the deceased had corona, so way over counting the corona death. That is why Belgium is at the top of the chart with death per capita but low in the chart of excess death vs corona death.

    Jan Fluitsma


  18. Hi Willis – I’ve only recently started following your site (my loss in waiting so long), and wanted to thank you for your dedicated work. I also wanted to make a suggestion that you might want to explore another aspect of the spread of the pandemic that may not have been given much scrutiny yet. I don’t have your skill with the necessary analysis to see if there are any strong correlations between public transportation utilization by area – in particular by MSA. I don’t think anyone is breaking it down by MSA. From the wiki article I’ll link below:

    “The OMB defines a Metropolitan Statistical Area as one or more adjacent counties, or county equivalents, that have at least one urban core area of at least 50,000 population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by the commuting tie.”

    I’m wondering if the shapes of the curves in the early stages vary between MSAs with high transit ridership versus those with a low percentage, or anything else you might tease out of this. If you’re interested, these links may serve as a useful start:


  19. Hi Willis,
    I have been following your plots on WUWT for a while now. And there is still no sign of a second wave.
    What do you think about this twitter thread: He is using the Wapo as a source … and they are presenting an increase in positive cases

    I am getting so tired of the fearmongering but I am even more tired to see so many people following for it: intelligent people who are supposed to be educated in modelling and biology.

    For example they looked at the now retracted Lancet article (Mehra et al 2020) and found the stats convincing and were surprise by the very conservative conclusions, despite the apparent clear results they obtained.
    When I read this article, I couldn’t say anything about the stats because it’s not my field, but oh boy I had so many questions about the control and treatments groups, so many things were not making sense. So I start looking at observational studies and learn about confounding by indication and the problem with the propensity score matching method.

    Then the article was retracted and they were wondering if the retraction was related with pressure from the pharmaceutical industry … insert palm face here …
    I suppose that if you are not following anything other that the MSM you don’t know about the problem about the database used for the study and the suspected conflict of interest with the authors.
    Ok, I am stopping my ranting now, I suppose I was needing that: I am feeling quite isolated among people who don’t question the officiel narrative.

    Thank you for your good work.



  20. I’ve enjoyed your posts at WUWT and finally wandered over to your site. Thought I would provide you with some actual under Arctic sea ice video taken with a low light black/white camera located in the sail of nuclear submarine. Dark is generally deeper sea ice (less light transmission) and light is thinner sea ice; very few “skating” spots to be had. Let me know if you would like to see more.
    PS: I hope this link works.


  21. Am a humble CPA who follows your COVID-19 graph pages… Could you please comment/share your thoughts on this article:
    The Case Is Building That COVID-19 Had a Lab Origin
    This appears to be a valid article but have no background real backgroud in Science.

    FWIW, I really like this COVID-19 graph page showing daily tests and % positive:


  22. Willis, your last graph on the trend of Texas deaths dated June 14 showed things past a peak, slowing down, and somewhat leveled out. As of today the Texas Health and Human Services people are showing what looks like an uptick in that trend since that date.

    Your graph corresponds to their’s with little exception, but that little makes me think that your data source is different. Not surprising, locally we are reporting nineteen county cases while the state is saying sixteen. Information is scarce for providing understanding of whys and wherefores. A lot of testing is going on, so what is being turned up as case numbers is saying little about case circumstances.

    Whether sixteen or nineteen locally, these numbers represent about a doubling of what we had until just the last couple of days. In this number range doubling is a much easier thing to achieve than say with our statewide cases. Most of the increase is reported to be in a nursing home, a rich territory for infections. But, an evaluation of whether this represents something of increased current or future consequence is impossible for me to evaluate with some possible factors involved not necessarily having dark implications. For instance, nothing is said whether the cases are symptomatic. Two were reported by the hospital which suggests a possibility of being current and symptomatic. However, the state reports no current cases. Dribbling information? Perhaps time will tell.

    After racking up eighty two years of interesting incarceration on planet Earth, the many implications of current events strikes me as suggesting that people grow bored with an excess of successful nationhood feeling a need for reversing the pattern to something less comfortable- especially, if the discomforted are someone else. I appreciate your sharing your view of this existence from your place in it. It has been a truly valued enhancement of my own viewing.


  23. Hi Willis,

    I could understand the early focus on controlling the infection rate, or the number of infections especially the infection per million population because of the initial high mortality rates. After all a person could not die from the COVID if the person is not infected. Looking at the statistics, morbidity per infection has gone down. Looking at the the number of critical patients relative number of outstanding cases is only 1 per cent, it was 2 per cent a few days back and if I recall back in March it was almost 12 per cent with Italy and Spain reporting almost 20 per cent. I look at the present situation like a traveler planning his trip. If the airline has a bad record, he would look at the number of accidents per trip in the last few months but if the accident has come a record low, he just board the plane. The accident rate or accident per trip does not matter anymore is no longer of any relevance. In fact, it might even be good for getting the herd immunity if the infection rate or number of infections skyrocket to hundreds of millions or even a few billions if the mortality rate per infection is very low.

    Lock down seems to be a strategy when the mortality to infection is very high. Isolate the infection until a vaccine is available. On less draconian measure, identify the weak and the highly vulnerable, isolate them until a vaccine is available but not the whole population that not only weaken the immunity of the strong through mental stress, poor diets and even exercise but destroy the economy and future of the whole society. Complete lock down may work in the short term but as the destruction of the economy becomes unbearable, the infection will just take place. Unknown is the extra death due to the reduced immunity induced by the lock down.


  24. I live in Utah and have been monitoring the COVID test result.

    Recently there daily tested positive rate essentially doubled (150 to 300). There is all the hype about more exposure and need for more controls.

    I have an alternate theory. When I look at the data, it looks more like a step change, not an exponential change. My theory is that step change is caused by testing method change. FDA has over 31 emergency COVID test methods. It isn’t clear to me there isn’t a testing bias.

    I work on lab acceptance methods and to me the change looks like a test method bias change. Although the daily rate has doubled, the hospitalization, ICU rate, death rate hasn’t correspondently increased.

    Just wondering your opinion


  25. willis: If this is a question I should simply research myself, tell me to F-off and I’ll do it. (I have searched at your article and all comments.)
    Even I, as a dumb injuneer, noticed the daily death numbers vary weekly from a low that suddenly jumps to a high and then declines again only to be repeated. Is this cycle an artifact of something about our medical system? I can’t imagine it’s related to the disease.


  26. Hi, Mr. Eschenbach;

    Thank you very much for your frequent Covid-19 contribution at WUWT ( You now say that it has become even more difficult to extract true information from the noise, and I perfectly understand.

    You say that CDC now asks to register deaths WITH Covid-19 instead of deaths OF Covid-19. I have two questions about that:
    a) Is that why we seem to see an uptick in Covid deaths, since one or two weeks, in the US?
    b) May you provide a reference for that change of mind of the CDC? It looks very, very important to me.

    Thanks again,



  27. Hi Willis, masterly summaries of the COVID-19 virus and its impact.

    Australia is a basket case on steroids. We are now an island of isolation and no end in sight because our politicians are so scared. We have had few cases, in a few locations but the whole place is only just beginning to allow businesses to operate.

    With no record of how many may have had symptom-less cases – and that may still not be very many – we are sitting ducks in a way. To survive people are now talking of becoming a self-sufficient economy again, as we probably were pre WWI. All is madness.



  28. Hi Willis. Quick question. Do those countries which routinely wear masks all year have a lower incidence of the “seasonal flu”? Thanks


  29. Hi Willis,

    First of all, thanks for the podcast w/ Anthony a few days ago RE models. It’s nice to associate a voice with your WUWT blogs and data analysis. Secondly, I’ve observed that the US COVID-19 daily deaths stopped declining and rapidly started increasing exactly 18 days after the FDA revoked the HCQ Emergency Use Authorization. I have a write-up w/ graphed data through 7/15 that tells the story which I would like to share w/ you. Here’s the text that accompanies the graph:

    US COVID-19 cases began showing an uptick in mid-June as testing was increasing. The daily reported deaths began increasing July 3rd (see my graph below). I’ve added timeline notes to the graph including phased state reopenings, street protests and riots, and the date the FDA revoked the Emergency Use Authorization (EUA) for Hydroxychloroquine (HCQ) from the Strategic National Stockpile. The average time between first symptoms and death is 18.5 days. The uptick in deaths began 18 days following the HCQ EUA revocation by the FDA. As a scientist/engineer, correlation does not mean causation, but this is a correlation that needs to be checked by somebody. I have not seen data showing HCQ has been used less since the FDA action but have read that an FDA revocation can have a chilling effect on doctors, pharmacists, and insurance companies regarding use of a drug. In support of HCQ effectiveness, a new study was published July 1st in the International Journal of Infectious Diseases that concluded HCQ cuts COVID-19 mortality in half ( For the scientists out there, here is a quote “The Cox regression result for the two propensity matched groups (Table 4) indicates that treatment with hydroxychloroquine resulted in a mortality hazard ratio decrease of 51% (p = 0.009).”
    The source data for my graph below is from:

    Please let me know how I can share the graph on this blog. I have ppt, pdf, & jpg formats. I also have a YouTube channel but don’t think I can upload a still graphic.

    regards – Ken


    • Willis – it was nice to associate a your voice with the blogs & data analysis (from your interview w/ Anthony on models). Here’s the text that goes w/ graph. I thought this was already posted but haven’t seen it.

      US COVID-19 cases began showing an uptick in mid-June as testing was increasing. The daily reported deaths began increasing July 3rd (see my graph). I’ve added timeline notes to the graph including phased state reopenings, street protests and riots, and the date the FDA revoked the Emergency Use Authorization (EUA) for Hydroxychloroquine (HCQ) from the Strategic National Stockpile. The average time between first symptoms and death is 18.5 days. The uptick in deaths began 18 days following the HCQ EUA revocation by the FDA. As a scientist/engineer, correlation does not mean causation, but this is a correlation that needs to be checked by somebody. I have not seen data showing HCQ has been used less since the FDA action but have read that an FDA revocation can have a chilling effect on doctors, pharmacists, and insurance companies regarding use of a drug. In support of HCQ effectiveness, a new study was published July 1st in the International Journal of Infectious Diseases that concluded HCQ cuts COVID-19 mortality in half ( For the scientists out there, here is a quote “The Cox regression result for the two propensity matched groups (Table 4) indicates that treatment with hydroxychloroquine resulted in a mortality hazard ratio decrease of 51% (p = 0.009).”
      The source data for my graph below is from:


      • To preserve this WUWT comment from open thread weekend, I’m reposting here:

        7/20/20 Update on HQC revocation deaths:

        Dr. Harvey Risch (epidemiology prof at Yale) estimates 75,000 to 100,000 excess US deaths due to FDA revocation of HCQ EUA. I estimate over 3,000 excess deaths in first two weeks since uptick began (July 4 – 17th).

        US combat deaths for comparison:
        2,335 – Pearl Harbor
        33,686 – Korea ’50-’53
        47,424 – Vietnam ’55-’75

        It is plausible that deaths due to FDA revocation of the HCQ EUA could result in the deaths of more US residents than were killed in combat during the Korean and Vietnam Wars combined in a fraction of the time. . . . and no media fanfare . . .

        Please share this analysis (graph above and applicable references) with your State Medical Licensing Boards, who are targeting some doctors for using HCQ according to Dr. Risch. The front-line Drs know whats up on this.


  30. Hi, Any chance of updating your Covid death rate by country graphs. England has just changed the accounting so you don’t get included if you test positive and get run down a month later. It’s dropped the total by about 5400 and I’d be curious to see what it looks like now.


  31. Hi Willis–three quick points. First, a belated ‘thank you’ for all the fun/informative writing you’ve provided at WUWT and elsewhere. I especially enjoy your interesting stories involving your ‘former fiance’, travel, and the thunderstorm thermostat concept–great stuff. Second, on Feb. 8th I started the ‘pandemic prepping’, incl. buying/planting crops around the property, stockpiling canned goods, stockpiling firewood, buying N95 masks, and more, out of concerns re supply chain and utilities; fortunately the only truly bad thing that came out of this was that my tomatoes were planted too early and thus were under-productive (and some money was wasted, but now I have ‘stuff’ to donate to charity). Third, early on I put together a little worksheet that showed what percent of the US population had died in the pandemics of 1918/19, 1957, 1969, and the current C-19. We recently exceeded the percentage from 1969, and we still have 33K more deaths until we equal 1957 (when nothing was shut down). We’re still over 2M deaths away from the severity of 1918/19. My prediction is we will slowly exceed that of 1957, but not by much, and likely end up around 230K deaths. Obviously, all the data should be questioned. Anywho, wishing you the best, and if you ever get up Sacramento way, please give a shout. Would love to enjoy a glass of wine w/you around the fire. Rgs, DY


  32. Willis,
    The link below is from the ever-reliable BBC website and an ever-reliable anthropogenic climate change fanatic “Environment Correspondent” called Matt McGrath.
    The article is about research indicating that a clear oceanic heating footprint can be found from analysing the recorded sound wave data from earthquake studies. Apparently, sound waves travel faster in warmer water.
    If true and robust, this idea could have real importance in analysing oceanic temperature trends.
    Now, as you will have guessed from my occasional previous correspondences, I am no luke-warmer. In fact, prior to a recent post of yours I thought I was a sceptic. Now, however, I realise that I am a fully paid-up heretic (and indeed expect to be burned at the – sustainable – stake in due course). Thank you for that.
    Back to the reported science. Can this be for real? Is it that simple? Are they sure there are no other factors (other than temperature) were involved in changing the soundwave velocities and/or could variations in recording technologies and processes explain things? What about water depths and salinity? What are the error bars and do they – as so often in climate science – go beyond the supposed trends? Colour me a sceptic, I mean heretic but it looks as if the researchers could have been specifically seeking a warming footprint here, rather than seeking to discover an underlying truth in an open-minded and unbiased fashion.
    As I trained originally as an Earth scientist, I am intrigued by this but my knowledge did not extent to the “sound waves speed up in warmer water” meme and I can’t find reference to it in my old text books. I have however, shown this to one of my student peers (who did have a career in the field) and he is looking into it (it intrigued him too). What do you reckon?
    PS Please feel free to pass this to others – like David Middleton – if you think their input would add value.


    • Willis,
      I think my Earth sciences peer as referred to above has rather nailed this with the reply below. The take-away line is at the end but I copy it here:
      “To summarise, I think that the probability that changes in deep ocean temperatures over the last few decades can be measured with confidence by comparing earthquake arrival times is almost vanishingly small. I would have loved to have been a referee on the paper which gave rise to this all too predictable BBC article.”

      The science described in the reply perfectly illustrates why, when questioned by others quoting the usual misleading “media climate science” memes, I repeatedly need to use the catch-phrase “it’s more complicated than that”.

      I shall end by noting that this person is still employed in the oil & gas industry but feels that he must remain anonymous in the present, politically constrained environment. He wishes to state, however, that – like me – he is a huge fan of your work and really hopes you can continue with it for a great many years to come. I fully understand his position – which I am sure is mirrored by vast swathes of people world-wide. It is a terrible indictment of modern society that those “coming out” as deniers risk severe reputational damage and/or the loss of employment opportunities.


      Reply from an Earth scientist:
      Earthquakes produce p-waves (“primary”), s-waves (“shear”) and, at the earth’s solid surface, l-waves (it stands for “Love” would you believe). Because fluids have zero shear strength s-waves do not travel through fluids such as sea water, neither do l-waves though by shaking the sea bed the latter can generate tidal waves. In short though, because we are dealing with the speed of seismic waves in water, p-waves is what we are taking about.

      The distance from Sumatra to Diego Garcia is 3,290 km according to Google and a p-wave travels in water at approximately 1,480 m/s or 5,328 km/h so the travel time of a p-wave from Sumatra to Diego Garcia is 3,290 / 5,328 * 60 = 37 minutes. So if the researchers are looking at differences in travel times of “a few tenths of a second” then a travel time difference between two separate events of (say) 5/10ths of a second, half a second, equates to a distance difference between two different earthquake focal points of 3,290 / 3,600 (number of seconds in one hour) * 5/10ths = about 740 metres (or to put it another way, 0.022% of the distance). Note that the equivalent distance difference in gabbro, basalt or ultramafic mantle rocks will be a lot smaller, because p-waves in those media travel much faster.

      (Note also as an aside that the BBC article calls these 5328 km/h seismic waves “slow moving signals”. I am assuming they are talking about p-waves not ocean waves? Surely yes, in the context of the arrival time difference of “a few tenths of a second” and the reference to “about half an hour”).

      Scientists can make a pretty good estimate of the distance between a seismometer and an earthquake by comparing the difference between the arrival times of p-waves and s-waves (the so-called “first arrivals”), though that involves making assumptions about the seismic velocities of the media through which these two different types of waves travel. They could do this on Diego Garcia as well, in respect of an earthquake in Sumatra. HOWEVER the first arrivals that the seismometer on Diego Garcia will be “seeing” will be a function of the “seismic velocity” (meaning the speed at which p-waves and s-waves travel through a medium) of rock, specifically the rock of the mainly oceanic crust between the two locations. This is (i) because p-waves travel much faster through rock than through water, as stated above, and more importantly (ii) because s-waves can’t travel through water at all, see above.

      Given an adequate coverage and distribution of seismometers, the location of an earthquake can be more or less accurately calculated by triangulation, though again some assumptions about crustal rock seismic velocity are necessary. And I am certain there are plenty of seismometers in Indonesia for obvious reasons. However this method typically uses p-wave (and s-wave) first arrivals through crustal rock, for reasons alluded to above. Looking for p-wave first arrivals through ocean water I would have thought – though I don’t know – would potentially be complicated by the earlier arrivals through rock and associated reverberations, and by aftershocks arriving through rock. And even if you find the p-wave first arrivals through sea water, in order to derive sea water temperature from sea water p-wave velocities you would need to know very accurately where the p-wave seismic energy entered the medium of the water – see below.

      The other concern I would have is that the media through which the seismic waves might have travelled to get to Diego Garcia might have rather different velocities for spatial reasons rather than for temporal reasons. This I am sure is why the researchers are looking to use “repeaters”, as they are called in the article … if the travel path through the ocean is the same then it may be reasonable to ignore spatial differences. But let’s be clear about this – a difference in 740 metres between the foci of the two different earthquakes would be enough to in effect eliminate the 5/10ths of a second difference in travel times between their respective p-waves in water. (See also my comment above about faster seismic velocities of gabbro, basalt and ultramafic rocks – the greater the proportion of the travel path in these media, the smaller is the water-caused variation in travel time, proportionally speaking.) Now typically a large earthquake generated in a subduction zone would I suspect have a slip area whose radius would be far larger than 740 metres. Or to look at it another way, the effect of a trivial and almost certainly unmeasurable lateral distance between the foci of “repeaters” in the Sumatra subduction zone (which is a global scale fault, in effect) would be sufficient to negate the p-wave velocity effect of a change of a degree or two in deep ocean water temperature. (Note the difference between focus and epicentre – the former is the 3D location in the earth’s crust or mantle at which the earthquake occurs, the latter is the location at the earth’s surface vertically above it.) Using “repeaters” is a good idea but frankly the focus of a recent earthquake is almost the last place where another earthquake will happen in the near future … the pressure has been released there, and will continue to build and subsequently be released elsewhere along the same fault plane … I believe this phenomenon is quite well known from studies of the San Andreas and other faults in California.

      Finally as you know subduction zones may extend up to 700 km vertical depth, well into the mantle … the p-waves may need to travel from the focus through a lot of rock before they move into the medium of ocean water … the effect therefore will be that over a short period of time the p-wave front will propagate into the water along the coastline of Sumatra in a way which will be complicated by the structure of the rocks through which it has passed, the shape of Sumatra’s coastline, the properties of both shallow and deep ocean waters with varying salinity as well as temperature I would suspect, and also the fore-arc islands located offshore Sumatra and between it and Diego Garcia (Nias, Pulau etc).

      To summarise I think that the probability that changes in deep ocean temperatures over the last few decades can be measured with confidence by comparing earthquake arrival times is almost vanishingly small. I would have loved to have been a referee on the paper which gave rise to this all too predictable BBC article.

      Liked by 1 person

  33. Willis,

    Major discussion in UK now (finally) about the False Positive Rate in COVID-19 statistics. Clearly the gov’t is making major life-affecting changes based on flawed (deliberate or accidental) statistics. This is right up your street to dig into and perhaps see how that might affect what States in USA are doing … Best introduction is today’s “Lockdown Skeptics” led by reputable journalists Toby Young and Will Jones. FYI, I’m one of the moderators. Toby is @toadmeister on Twitter (a blue check person).



  34. Willis,
    Re Covid-19, you will have heard that many parts of Europe, and especially the UK, are now in the grip of what some informed observers are calling a “casedemic” – for which read a mass, over-sensitive testing regime that is “confirming” vast numbers of Covid “cases” but very few are actually getting ill, leave alone dying. Well, today, the British government has gone into full panic mode and appears on the brink of another serious, nation-wide lockdown. Where, you may ask, is the “data” to support this economy-changing action? Please see this link: (apologies, I don’t know how to copy-in the chart).
    In particular, look at the bar chart. Ostensibly, this is only a “projection” IF “cases” doubled each week (let’s forget about people actually dying). The media and all parties keen on another lockdown paid for by the magic money tree are, however, inevitably treating this as a firm forecast.
    I’m sorry but you have GOT to be fracking kidding me! How do they keep a straight face presenting this fine example of computer modellers’ onanism as “science”? The chart hasn’t even been going up in the last couple of days but they project exponential growth completely out of line with recent months. OK so let’s just close the economy down again and finish off all those businesses and people that were barely surviving – based on that fabricated chart.
    You couldn’t make this up.


  35. Hi Willis,
    I really enjoy your articles at WUWT. I try to follow up with the data and the calculations to understand better.
    In one of your articles published in 2014 there are links to the CERES data, and the R code for calculations.
    However, those links are broken 😦

    Would it be possible to get access to the data and the code?

    Thanks a lot!
    you can email me at
    gabriel at LEADER.SE


      • Hi Willis, thanks a lot!
        I’ve been tip-toeing around the CERES data for some time, without coming to grips with it. When I recently found your articles at WUWT I was thrilled, and even more so when I found those articles back in 2014 where you shared your R code. I have learned a lot of really essential things about our climate from your analyses, and I’m eager to dig deeper. My focus now is to have a closer look at selected areas of the tropics, and I’m also hoping to be able to make some critical comparisons with some of the new CMIP6 models..

        I’ve now got the files up in Rstudio, but there seems to be smth missing still, as I get a (first) error message about a missing LandSea, it’s a mask file.

        Before digging deeper into this, I had another look at WUWT, and found another of your articles where you link to R files, but with a bit different set of files, including a CERES setup file, and another datafile for the surface data:
        “There are three code files: CERES Setup.R, CERES Functions.R, and the code for this post, CO2 and CERES.R. In addition, there are two datafiles, one for the CERES TOA files, and the other for the CERES surface files, entitled CERES 13 year (230 Mbytes), and CERES 13 year surface (112 Mbytes).”
        That article is one week later, so from the same period, but its links to the R files are also broken. Would be great if you could share those files as well! Maybe the missing mask file is there.

        Further, we have your latest contribution at WUWT, “Watts available”, where you also analyze the CERES data.
        Would you mind sharing files for that article as well?

        Thanks a lot in advance.


  36. Willis
    I’m a big fan of your practical, common sense approach to thinking about and analyzing lots of different subjects.
    Are you aware of the Great Barrington Declaration?
    The “Focused Protection” plan sounds a lot like what I remember you and a few others espousing.


      • “Our findings indicate the potential for increased human mortality with warmer winter air temperatures.” It took 16 scientists to write a paper concluding that?
        Also note the word “potential” – are we sure it’s even happening? Apocalypse not quite yet.


  37. Willis,

    You may already be familiar with this.

    Johns Hopkins Study Saying COVID-19 Has ‘Relatively No Effect on Deaths’ in U.S. —- Deleted After Publication

    “The study found that “This trend is completely contrary to the pattern observed in all previous years.” In fact, “the total decrease in deaths by other causes almost exactly equals the increase in deaths by COVID-19.”





  38. Dear Willis,
    Firstly, apologies for the length of this post. Like you I start studying something within the broad sphere of Earth sciences and climate and somehow get side-tracked. Such was the case here.
    The BBC has released another of its never-ending and weepy climate change videos, this time show-casing the worries of an Inupiat woman. The link is here: It was an interesting little film and you’d have thought from it that her part of the world was becoming rather warm, perhaps with sea ice originally all year round but now their peaceful existence being shattered constantly by ships. As ever, the propaganda film raised more questions than it answered so I decided to look into it.
    Now, I have been to AK a few times and knew that the Inupiats lived in the bitterly cold northern coastal region on the shores of the Beaufort Sea. As an aside, having flown over this area in the summer and having the belief that sea ice hasn’t, in my lifetime, covered all parts of the Beaufort Sea all year round, I started to question the sentiments in the film. Anyhow, I guessed that long term climate and sea ice data would be available for Utqiaġvik (formerly Barrow) so I decided I’d look into it. I quickly found this NOAA site and article: Alaskan North Slope climate change just outran one of our tools to measure it | NOAA The article contains several links to other NOAA information for Utqiaġvik going back 100 years or so.
    This is where I got side-tracked and I would appreciate your comments on the following notes and questions. Feel free to refer this to Anthony Watts if his input would be useful.

    Notes on the article:
    1) In December 2017 it was discovered that weather data from the Utqiaġvik weather station had been missing since late-2016.
    2) The official reason was stated as “the average temperature observed at the weather station at Utqiaġvik has now changed so rapidly that it triggered an algorithm designed to detect artificial changes in a station’s instrumentation or environment and disqualified itself from the NCEI Alaskan temperature analysis, leaving northern Alaska analyzed a little cooler than it really was.”
    3) The article details some interesting (if you like that sort of thing) and informative stuff on regional temperatures, sea ice extent and so forth.
    4) Getting back to the thrust of the piece, the article describes the “pairwise homogeneity algorithm” – the “PHA test” and the reasons for it, for example moving weather stations.
    5) Under “What happens to stations behaving badly” the author states the following: “the algorithm running the test is hungry for more data points. Generally speaking the more neighboring stations, the better and more confident the algorithm is. When there is enough information from neighboring stations to build high confidence, the station record is adjusted (my bolding). If not, an estimate is made, and the data are flagged as an estimate.”
    6) The author goes on to say: “As a relatively isolated station, experiencing profound and unique change, Utqiaġvik was destined to get flagged. Having built confidence that a disruption to the station was afoot, the PHA test retroactively flagged the last 16 months and removed them from the monthly analysis. But in this case, instead of a station move, or urban sprawl, or an equipment change, it was actually very real climate change that changed the environment, by erasing a lot of the sea ice that used to hang out nearby.”
    7) Under “A silver lining” the author states: “On a longer time horizon, those same folks that designed the PHA test are constantly working to improve it. The next version of the Global Historical Climatology Network (GHCN-Monthly), upon which the monthly temperature analyses are based, is coming in early 2018. You know what’s already built into that version? A certain latitude (65°N, currently), poleward of which the PHA test becomes even more forgiving, considering the rapid Arctic changes, and the station scarcity in the region. That’s a posture of continuous improvement, and it’s just one way that even old stalwart datasets like GHCN-Monthly get better over time”.
    8) After the end of the article there follows a lengthy set of questions submitted by observers, many of which are answered. UHI is questioned as an issue and the siting of the weather station is also queried. At one point (and I’m not completely sure that this refers to the whole of the NOAA datasets in question) the author states that the station is at Barrow Airport.

    My questions inter alia include:
    1) How common is it for weather stations to stop reporting raw data because an algorithm kicks in?
    2) In this case the data was removed completely, flagging a major issue and initiating enquiry. On other occasions, however, do such algorithms simply adjust rather than remove datasets? If so, what is ultimately used by NOAA for reporting or further adjustment – the raw data or such data already adjusted at source?
    3) Why would an algorithm be applied at source (to coin a phrase) onto raw data anyway? Surely, raw data is just that and algorithms (or indeed any other form of adjustocene fun) should only be applied separately and later to the original recording (as can clearly happen on top as well).
    4) I am intrigued by the statement above “leaving northern Alaska analyzed a little cooler than it really was”. How do they know this if the data is missing? Are warming anomalies the only ones permissible at NOAA?
    5) What does “A certain latitude (65°N, currently), poleward of which the PHA test becomes even more forgiving” mean or indeed tell us?
    6) Why was it necessary in such a vast area of uninhabited wilderness to use a dataset accumulated at an airport?

    Overall, it strikes me that the totality of the issues described in this article and subsequent questions and answers epitomise just what an unreliable minefield long term weather data – as we public are allowed to see it – is. There is clearly huge scope for manipulating the data in the way in which the analysts wish – logging datasets that fit the narrative and adjusting those that don’t until they do. For the avoidance of doubt, I absolutely understand why temperature datasets do need to be adjusted on occasion to become comparative and meaningful but what’s happened here seems a step too far to me. It’s not even as if the PHA adjusting mechanism is consistent – we are led to believe that the PHA itself can be adjusted to become “even more forgiving” in places like Alaska. Oh what a tangled web we weave…
    We sceptics/heretics here in Britain (yes Boris, BBC etc we really do exist) have grown to distrust strongly what our Met Office says. Even from this – clearly biased – study alone it is easy to see why our American sceptic/heretic friends distrust NOAA. My original quest was to look at long term climate data for Utqiaġvik to understand what the Inupiat woman was unhappy about. I’m really not sure that I can achieve that understanding comprehensively with NOAA datasets.


  39. Willis
    Have enjoyed reading your site, though we are on different worlds. Have seen a site which needs publicity, from your Northern Brethren or Sistren, but due to their presently being in the prime of their career, and with dependents, cannot risk an Internet Smear Campaign:
    Hope you can include the link in one of your stories!
    … Brian


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