Harvard Demonstrates How Not To Do Science In New Self-Confirming Mask Paper

Now I ask you: If you have a computer and want to do a simulation, you must write the code for it.


It doesn’t matter what form this code is, but it has to be something. It must contain, at some level, instructions like “If X then Y”, and so forth. It must take inputs, work on them by rules you construct, you command, and you direct, and create from the input and rules certain outputs.

So help me here. Suppose you wrote code that specified, “If a kid wears a mask then he has a small chance of becoming infected”, and also specified, “If a kid does not wear a mask then he has a larger chance of becoming infected”. You run your code and it outputs certain numbers. Numbers which are the logical result of the rules you set. Numbers from which we deduce these two statements:

  1. Kids who wear masks have a small chance of becoming infected;
  2. Kids who do not wear masks have a larger chance of becoming infected.

Still with me? Now, do you suppose it is the height of scientificity to scientifically declare that your science study research has scientifically discovered “Kids who wear masks have a small chance of becoming infected” and “Kids who do not wear masks have a larger chance of becoming infected”?

If you say yes, you can find employment at Harvard.

Here’s the tweeted press release from the Harvard T.H. Chan School of Public Health (it’s a thread):

A new study says it is too soon to lift mask mandates in schools, citing a mathematical model based on COVID-19 vaccination rates and case rates…

“This study emphasizes that it is important for schools to set clear objectives for what they want to achieve from their policies around masks and other mitigation measures,” said lead author and Harvard Chan School PhD candidate @jc_giardina (@decisionscience)…

Senior author @aciaranello says the model offers a “systematic, mathematical approach” that can be tailored to decision-makers’ goals — such as preventing all in-school transmissions or minimizing absences to promote in-person learning…

Researchers concluded that, even with varying goals, local COVID-19 case rates should be far below those seen at this point in the pandemic to safely lift mask mandates without risking increased transmission within schools.

The peer-reviewed paper is “Model-Estimated Association Between Simulated US Elementary School–Related SARS-CoV-2 Transmission, Mitigation Interventions, and Vaccine Coverage Across Local Incidence Levels” by Giardina and others, and is in JAMA.

Here’s their Conclusion:

In this study, in-school mitigation measures (eg, masks) and student vaccinations were associated with substantial reductions in transmissions and infections, but the level of reduction varied across local incidence.

They have declared “Kids who wear masks have a small chance of becoming infected”. Let’s see how they came to that conclusion.

In the Introduction they state, “While multiple studies indicate that masks are effective at mitigating the transmission of upper respiratory viruses…Masks are physiologically safe”. Later, they allow the possibility that masks might slow learning. And then ignore it.

They have sentences like this:

With the Delta variant and 0% student vaccination, if removing masks (or other mitigation measures) was associated with a decrease in mitigation effectiveness to 30% (mitigation group A midpoint), decision-makers who seek to keep the monthly probability of in-school transmission less than 50% could remove masks at or below an observed local incidence of approximately 2 cases per 100?000 residents per day (Figure 1A).

Writing ability is not required of scientists.

It’s not clear how they hid their discovery until we reach the “supplementary” material. (I complain frequently more and more papers hide mountains of crud and relevant detail in “supplementary” material. Papers, in effect, often use the Appeal to Authority fallacy.)

Anyway, there it is, bold as e-girl asking for donations, in “eMethods 2. Sources for Mitigation Ranges” (my emphasis below).

Interventions in A, plus universal masking (a policy of masking all students and educators/staff): 60-80% assumed effectiveness.

The interventions in A are “Simple ventilation and handwashing (open windows if present, portable air filters, maintain existing HVAC systems, and regular handwashing): 20-40% assumed effectiveness”.

Did you see the word assumed? Did you see it was used twice?

And did you see the conclusion? “Mitigation measures [such as masking] or vaccinations for students substantially reduced these modeled risks” of “in-school SARS-CoV-2 transmission.”

In other words, our Harvardites began by assuming masks work. They then ran a model which assumed masks work. The model said, as it was told to, masks work.

Their press office, I remind you, then said, “A new study says it is too soon to lift mask mandates in schools, citing a mathematical model based on COVID-19 vaccination rates and case rates.”

This is not science. This is video games.

A reminder: all models only say what they are told to say. Say that out loud!

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Categories: Statistics

18 replies »

  1. “Later, they allow the possibility that masks might slow learning. And then ignore it.”

    Who would’ve guessed that “flattening the curve” would actually mean flatten the learning curve …

    “Writing ability is not required of scientists.”

    Not required of peer reviewers either, it seems.

    “In this study, in-school mitigation measures …”

    Why use the word study? They didn’t study anything, they simulated it. Studies examine experimental data. One of the surprising things that I learned after reading the COVID medical papers for a year or so (not my field), is the use of terms: in vitro, in vivo, and in silico. In silico? Really? Whatever.

    In post graduate engineering school research, we learned that we should not use the term “fudge factor” in our work; instead use the term “closure coefficient”. Seriously! LOL.

  2. There is always a market for such papers.

    Fak-chakers use them all the time.

    Their guillible subscribers and social media censors need them, without which they could not engage in internet arguments as that wouls require them to think critically, but they can’t do that… they need to outsource it to some agency.

    The agency provides, but they do not read the footnotes to see how it all works… they simply need the headline, the meter, at the very most… the opening paragraphs that summarize the assumption – “what we think our enemy said is wrong!”

  3. Heard a bit the other day claiming that peer reviewers typically don’t see the raw data, only the paper and whatever data synopses that are included, which means that they are only actually reviewing whether the paper accurately analyses the provided data, not all the data. True/false? Was this something you wrote about? I think it was a podcast but who knows.

  4. If the paper in question is anything like the crazy one that was held up as ‘important and new’ science, the last time, then it isn’t necessary to see ‘data’. It takes a peer to spot an impostor.

    right tool right task,
    right setting right mask
    Someone should write a rap ‘song’ about it

  5. Thanks Sarge; always interesting to see the ‘method in the madness’ revealed. Amazing that such cheap tricks bamboozle so many. And that cheap tricks plus chutzpah are the flimsy basis of the Overlords’ power. That and sin, which makes people weak and foolish.

    The math:


  6. Someone with far more skills than I possess, should do a meme of George Floyd wearing a surgical mask with the caption *I Can’t Breath*…

  7. Briggs

    Have you had a chance to examine the Ethanol Paper?

    The “True Deniers” claim that the paper assumes the worst case scenarios … dare to weigh in on its Science?

    Of course “we’ve” been saying what the paper says for 30 years ever since the Ethanol Mandate

  8. This still isn’t as bad as the idiots at Cambridge who claim wearing pantyhose over your face diaper makes it work extra super good.

  9. If I were going to do such a paper, I’d do a heck of a lot more measuring work. 1. Sample outside air and use ultracentrifugation, electron microscopy, and culturing to allow sequencing of every bit of microbial matter in the air. 2. Do the same thing to the inside air in various locations, noting air exchange rates, filtration methods (if any), use of UV light (includes sunlight). 3. Split into matched groups of people tested for previous, current or lack of current exposure. 4. Split those into intervention and non-intervention groups. List the interventions done and record *all* results. Avoid *any* statistical analysis until Sarge can be consulted.

  10. “All models only say what they are told to say.”

    They are never “told to say” anything about the dangerous pathogens found in children’s masks:

    ….and they are never “told to say” anything about breathing in toxic synthetic fibers:

    ….and of course, these models are never “told to say” that even if a child becomes infected, they have a 99.99995 (or thereabouts) survival rate.

  11. Ann Cherry, that’s because models only use perfect masks, that exist in the same realm as a perfect circle, where there are no edges. In this case masks are represented by M = Infinity.

    Models such as these are essentially no different than thought experiments, completely imaginary scenarios run under perfect conditions that are completely unverifiable in the real world and could not possibly exist.

    So whenever you encounter an issue with a mask in a practical real world scenario, the fak-checka will ignore the real world, trapeze to the abstract world of the model, and cite this as THE SCIENCE ™, and thus “debunks” the practical reality. There is no war in Ba Sing Se, as the kids say. The divine realm of the model, the Heavens, have given their mandate, if they didn’t then the model obviously wouldn’t exist! Therefore treat it as the will of the gods.

  12. HEPA filters are actually over 99% effective at capturing virions. But Harvard assumes that they are only 20% to 40$ effective at reducing transmission.
    Cloth masks are completely ineffective at capturing virions, But Harvard assumes that they are at least 60% to 80% effective at reducing transmission.

    These Harvard “scientists” are very good at continuing the funding of their jobs, but very lousy at science.

  13. You’ve really knocked it out of the park with this one. I can’t believe how low they’ve allowed standards to slip, but it’s really the perfect example for public consumption. This is exactly why I too am no longer interested in participating in academia.

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