|The predicted deaths is an estimate of weekly deaths based on linear regression of continuous time series and seasonality is derived by assigning each week to one of 13 periods. Actual data from 2015 through 2019 is used to determine the predicted totals. |
The excess deaths in 2020 through 12/5 stands at 310,000 according to this model.
Colorado has been under a mandatory mask order since July 17. The rationale is that masks reduce rates of asymptomatic transmission– never mind there is scant evidence supporting that assertion.
In a nutshell:
Masks reduce rates of infection which in turn…
Reduces the number of cases which…
Reduces the number of hospitalizations which by extension…
Being an inquisitive person, I contacted Colorado’s Ministry of Health (sarcasm mine) in hopes of understanding what the scientific basis was for this mask diktat. I was able to speak to an actual “epidemiologist”, there. When I asked her what evidence there was supporting the mask mandate, she said she would send it to me. When I asked her if she had reviewed the evidence herself, as I presume any inquisitive scientist would, she said, “No. We rely on the CDC guidelines.”
To my surprise, I received a list of links to studies that presumably form the basis for the mask diktat. The most prominent was a study by a Doctor Lyu. What I found interesting was how the conclusion that masks reduce infections was actually derived. The study compiled thousands of time series data on daily county case rates, both before and after mask mandates went into effect, and concluded that the daily growth rate of infections declined by .9% to 2% from 5 to 21 days after a mask order was signed.
Well, there you have it! Science!
But here’s the rub: The study indicates that the sample group (with mandates) started with a fatality rate of 277.8 per million, whereas the control group (no mandates) was at 55.4 deaths per million. The 277.8 per million figure indicates the study evaluated a sample of counties that was already well past inflection and experiencing declining, if not outright dropping daily new case totals.
Classic case of cherry-picking data.
Furthermore, no overall correlation coefficient was provided. It could be 90% or it could be 9%– don’t know.
So, the “science” behind the mask diktats is a statistical correlation exercise that involved no actual observations or randomized trials.
Now, finding patterns and correlations is crucial to scientific discovery. No doubt about that. Ferreting out miniscule correlations between drugs taken and desired outcomes forms the basis of the entire pharmaceutical industrial complex. But causation, on the other hand, is another thing entirely. In regards to masks, the mask diktat must precede the drop in cases, hospitalizations, and deaths, otherwise, it cannot be said to be more than just coincidence.
Luckily, Colorado has good hospitalization data (that I’m sure they will cease publishing once it clearly shows the futility of all these edicts.)
I present two graphs. The first is daily deaths with a line before and after the mask order. Notice any significant change?
The second is a plot of hospitalizations and a line indicating the earliest point at which the mask diktat could have had an impact. A COVID hospitalization absolutely MUST lag initial infection by some number of days. The line here is set at only 5 days after the diktat was signed (which is very, very generous to the mask promoters as the time from infection to hospitalization is likely much longer.) Did masks reduce hospitalizations? According to the graph, they had already peaked and were in decline before the diktat could have had any possible impact.
So much for “science!”
It’s my position that these diktats are just theatrics. The fear has been spun up within the plebes, and fearful people want to feel like there is a way to regain control. What better way than to give them a “tool” to combat the boogeyman. Couple that with the pernicious and pervasive need of bureaucrats to “do something” and… voila! Mask diktat.
Meanwhile, back in the realm of reality…
In the words of Dr. Fauci: Masks are a “symbolic gesture”.
And to paraphrase Carlin: I’ll leave the symbols to the symbol-minded.
P.S. Here are some actual empirical studies that suggest no significant impact on transmission related to mask wearing.
Jacobs, J. L. et al. (2009) “Use of surgical face masks to reduce the incidence of the common cold among health care workers in Japan: A randomized controlled trial,” American Journal of Infection Control, Volume 37, Issue 5, 417 – 419. https://www.ncbi.nlm.nih.gov/pubmed/19216002
N95-masked health-care workers (HCW) were significantly more likely to experience headaches. Face mask use in HCW was not demonstrated to provide benefit in terms of cold symptoms or getting colds.
Cowling, B. et al. (2010) “Face masks to prevent transmission of influenza virus: A systematic review,” Epidemiology and Infection, 138(4), 449-456. https://www.cambridge.org/…/face-masks-to-prevent-transmiss… review/64D368496EBDE0AFCC6639CCC9D8BC05
None of the studies reviewed showed a benefit from wearing a mask, in either HCW or community members in households (H). See summary Tables 1 and 2 therein.
bin-Reza et al. (2012) “The use of masks and respirators to prevent transmission of influenza: a systematic review of the scientific evidence,” Influenza and Other Respiratory Viruses 6(4), 257–267. https://onlinelibrary.wiley.com/…/…/j.1750-2659.2011.00307.x
“There were 17 eligible studies. … None of the studies established a conclusive relationship between mask/respirator use and protection against influenza infection.”
Smith, J.D. et al. (2016) “Effectiveness of N95 respirators versus surgical masks in protecting health care workers from acute respiratory infection: a systematic review and meta-analysis,” CMAJ Mar 2016 https://www.cmaj.ca/content/188/8/567
“We identified six clinical studies … . In the meta-analysis of the clinical studies, we found no significant difference between N95 respirators and surgical masks in associated risk of (a) laboratory-confirmed respiratory infection, (b) influenza-like illness, or (c) reported work-place absenteeism.”
Offeddu, V. et al. (2017) “Effectiveness of Masks and Respirators Against Respiratory Infections in Healthcare Workers: A Systematic Review and Meta-Analysis,” Clinical Infectious Diseases, Volume 65, Issue 11, 1 December 2017, Pages 1934–1942, https://academic.oup.com/cid/article/65/11/1934/4068747
“Self-reported assessment of clinical outcomes was prone to bias. Evidence of a protective effect of masks or respirators against verified respiratory infection (VRI) was not statistically significant”; as per Fig. 2c therein:
Radonovich, L.J. et al. (2019) “N95 Respirators vs Medical Masks for Preventing Influenza Among Health Care Personnel: A Randomized Clinical Trial,” JAMA. 2019; 322(9): 824–833. https://jamanetwork.com/journals/jama/fullarticle/2749214
“Among 2862 randomized participants, 2371 completed the study and accounted for 5180 HCW-seasons. … Among outpatient health care personnel, N95 respirators vs medical masks as worn by participants in this trial resulted in no significant difference in the incidence of laboratory-confirmed influenza.”
Long, Y. et al. (2020) “Effectiveness of N95 respirators versus surgical masks against influenza: A systematic review and meta-analysis,” J Evid Based Med. 2020; 1- 9. https://onlinelibrary.wiley.com/doi/epdf/10.1111/jebm.12381
“A total of six RCTs involving 9,171 participants were included. There were no statistically significant differences in preventing laboratory-confirmed influenza, laboratory-confirmed respiratory viral infections, laboratory-confirmed respiratory infection, and influenza-like illness using N95 respirators and surgical masks. Meta-analysis indicated a protective effect of N95 respirators against laboratory-confirmed bacterial colonization (RR = 0.58, 95% CI 0.43-0.78). The use of N95 respirators compared with surgical masks is not associated with a lower risk of laboratory-confirmed influenza.”
When looking at COVID fatality data, 2 things jumped out at me. 1) correlation between state population density and deaths per capita and 2) the shape of the curve of fatalities. If you build a model that estimates per capita fatalities using pop density and weeks beyond peak fatality day, you get a very strong predictor (89% R^2).
The (linear) equation is:
Est fatalities per million = -56 + 14 * (weeks from peak) + .92 * (Pop Density)
Where there are large differences between estimated and actual, one could hypothesize that they are an indication of where that state is in the “curve” or lifecycle of the outbreak.
States that are the farthest along in the curve (expect declining fatality counts) are as follows:
States that are the furthest behind (expect rising fatality counts) are as follows:
The numbers are gonna get really scary-looking before they get better. But perspective is important.
Annual global deaths attributable to influenza: 650,000
Annual global deaths attributable to tuberculosis: 1,600,000
Case fatality rate of influenza for confirmed cases = 10% (scary)
Here are some very rough GLOBAL COVID 19 Estimates:
In this model…
Total global confirmed cases reach 1 million on 4/2
Total global confirmed cases reach 5 million on 4/22
Total global confirmed cases reach 10 million on 5/16
Peak global fatality day: April 26, 18,000
Total global fatalities through April 9: 100k
Total global fatalities through May 6: 500k
Total global fatalities through June 30: 640k
A really bad flu season, but hardly an apocalypse.
Sanity check: Ignore the reported case numbers. The reported confirmed cases are essentially meaningless in that they are not random samplings of the population and are plagued with selection bias that indicates a grossly exaggerated fatality rate. in other words, the only people getting tested are the ones who are already ill. Mild or asymptomatic cases are almost entirely ignored in the calculations.
This has been shown in a CDC study of the H1N1 outbreak of 2009.
The study predicted “laboratory-confirmed case equaled 166 infections in reality”. In other words, the true infection rate of the H1N1 outbreak was 135 to 212 times the confirmed case rate! And the prediction of that infection factor was proven out by antibody tests of the population. Applying the low end of this factor range to the 3/26/2020 confirmed case total of 531,865 and 24,073 fatalities implies, by extension, a true case fatality rate of .03%, or 3 out of 1000, or 1/135th less than what is reported by our agenda-driven, sensationalist media.
But more testing is a paradox. The more cases, the more media hysteria. The more hysteria, the more panic. The more panic, the more outrageous assaults on civil liberties by bureaucrats. Increased testing will dramatically increase the case counts, but that, in turn, if it becomes more random rather than just testing sick people, will start to reduce the case fatality rate… eventually.
“Journalists” keep touting S. Korea as the model for handling the virus due to their very low fatality rate.
You want to know how they did it?
They created 500 testing locations and tested 250,000 people. Turns out, 3% of that somewhat random sample set was infected. In other words, they radically increased the “confirmed cases” number which caused the fatality rate to plummet. It also implied that the virus is already everywhere in South Korea.
Italy conducted a similar experiment.
Italy tested the entire town of Vo in early March and found 3% of the population was infected.
Notice a pattern, here?
The testing in Vo actually implies what I have suspected and suggested from the start, that 1) COVID 19 has already spread beyond any realistic possibility of containment, meaning the economic destruction we’re suffering is pointless political theater, and 2) the virus is largely benign and asymptomatic.
Good news, BTW: It looks like Italy may have topped.
We’ll see what happens.
I’ll predict that Big Brother will come riding in to take the credit for any “flattening of the curve”, when in reality, the home detention and economic shutdown was probably useless to stop the virus that has already infected 3% of the population.
When this is over, it’s probably going to look like a bad flu season. Trouble is, we will be in a terrible recession, or worse, with an added $3 trillion in U.S. federal debt, 10-15% unemployment, and 401k values cut in half.
Assuming a $6 trillion price tag to combat this virus, I wonder how many ventilators, medical care salaries, supplemental incomes for at risk individuals, and temporary hospitals could have been built for a tiny fraction of that cost?
Politics is not guided by rationality. It’s driven by fear.