The Great COVID Boondoggle
Before spending a lot of money, it is good to do good research, to make sure that what you will be spending money on, is the kind of thing which should have money spent on it. For example, if climbing to the top of Mount Everest, it may be good to invest a lot of money into prepping and climbing gear (because of how dangerous it is).
The task-at-hand would then require that a good chunk of money be applied to it — in order to prevent disaster.
But when COVID hit in 2020, there were reports that it was highly lethal. When validating published reports of infection fatality rates (IFR) though, it helps to know how they could fluctuate. When various estimates come in, you can ask about the possibility of them being “too high” or “too low.”
Good Research
An estimate that is too high might be one where doctors implemented poor medical care, so that patients weren’t really dying from COVID, but dying from substandard care, instead. The probability of a high estimate is, itself, moderately-high, due to all of the suboptimal medical practices during COVID, which are too many to list here.
But what about the probability of obtaining an infection fatality rate estimate that is “too low” — what would that require? As it turns out, it is difficult, if not impossible, to get an estimate which is “too low” (because the inherent lethality of the disease will not allow for it). The probability of obtaining an estimate that is “too low” is nil.
This means that the very best IFR estimate will be the lowest one with a large sample.
The lowest estimate is expected to be more accurate than all of the higher estimates, because there are too many confounding reasons for IFR estimates to creep up above their real value, but there are little-to-no known reasons for IFR estimates to creep down below the true value. Inherent lethality prevents such things.
The Best Estimate
Perhaps the best-ever estimate of the IFR for COVID came from the UK Technical Briefing No. 5, where 117,000 COVID infections were followed through time, resulting in 169 deaths by Day 28. When corrected beyond Day 28, the death estimate rises to 190. Using those 190 deaths from those 117,000 infections, you find this:
At right is the result of a 150-million-round computer simulation of IFR values which were capable of reproducing the UK death data. The vertical dashed lines are the lower and upper boundaries of a 99.99% credible interval. The vertical orange line is the flu IFR from the 2014/15 flu season.
Notice how COVID wasn’t even 50% worse than the flu of 2014/15. Other data from the largest-ever phase 3 trial (Pfizer trial) show that the rate of hospitalization was only 1.7 per 1,000 person-years — i.e., 7 of these 9 “severe placebo cases” were hospitalized in 4,006 person-years of observation time:
But when compared to the flu of 2017/18, there were 1.5 hospitalizations per 1,000 person-years, using a 7-month (0.583 year) time-window for flu hospitalizations (the image below shows the average hospitalization of all of the 2009-to-2019 flu seasons, but the study data supplement, not shown, shows the 2017/18 hospitalization rate):
Once again, COVID is shown to not even be 50% worse than severe flu (e.g., 2014/15 season or 2017/18 season). But that didn’t stop government officials from allocating almost $5 trillion in spending on it:
And beyond the $4.7 trillion spent, there are another $5.9 trillion in added costs from specific legislative actions surrounding COVID:
Here is the question of the day:
If you had an outbreak of a disease which was not even 50% worse than a bad flu, would you spend or divert over $10 trillion toward it?
Reasonable people would not spend so much on something which was not very deadly to begin with, but government officials might have been acting with ulterior motives toward a hidden agenda: they might have been facing very perverse incentives, as Peter Schweizer reports on in the book, Blood Money.
Reference
[phase 3 trial showing 1.7 COVID hospitalizations per 1,000 person-years] — FDA Briefing Document. Pfizer-BioNTech COVID-19 Vaccine. https://www.fda.gov/media/144245/download
[study showing 1.5 flu hospitalizations per 1,000 person-years in 2017/18] — O'Halloran AC, Holstein R, Cummings C, Daily Kirley P, Alden NB, Yousey-Hindes K, Anderson EJ, Ryan P, Kim S, Lynfield R, McMullen C, Bennett NM, Spina N, Billing LM, Sutton M, Schaffner W, Talbot HK, Price A, Fry AM, Reed C, Garg S. Rates of Influenza-Associated Hospitalization, Intensive Care Unit Admission, and In-Hospital Death by Race and Ethnicity in the United States From 2009 to 2019. JAMA Netw Open. 2021 Aug 2;4(8):e2121880. doi: 10.1001/jamanetworkopen.2021.21880. PMID: 34427679; PMCID: PMC8385599. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8385599/
[169 COVID deaths by Day 28 from 117,000 COVID infections] — Page 3. Epidemiological findings. UK Technical Briefing #5. https://www.gov.uk/government/publications/investigation-of-novel-sars-cov-2-variant-variant-of-concern-20201201
[89% of total COVID deaths occur by Day 28, using the 95% upper bounds of both the mean and the SD of a lognormal distribution] — Linton NM, Kobayashi T, Yang Y, et al. Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data. Journal of Clinical Medicine. 2020 Feb;9(2). DOI: 10.3390/jcm9020538. PMID: 32079150; PMCID: PMC7074197. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074197/
[All-ages flu IFR from 2014/15 was .00143 (.143%) after correcting for 16% asymptomatics] — CDC. Burden Estimates for the 2014-2015 Influenza Season. https://archive.cdc.gov/www_cdc_gov/flu/about/burden/2014-2015.html
[16% of all flu infections are asymptomatic] — Leung NH, Xu C, Ip DK, Cowling BJ. Review Article: The Fraction of Influenza Virus Infections That Are Asymptomatic: A Systematic Review and Meta-analysis. Epidemiology. 2015 Nov;26(6):862-72. doi: 10.1097/EDE.0000000000000340. PMID: 26133025; PMCID: PMC4586318. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4586318/