Somehow that dessert—something about fudge and cherries—reminded me of all the math calculations and projections being thrown around as we swim in the deep, dark floodwaters of coronavirus pandemic forecasting.1
Repeatedly my wife has warned me, when I prepare to speak at an ICR event, “Don’t try to do math in public!” So this article is not intended to advocate one statistical analysis, epidemiology model, or pandemic projection over another.
Rather, this consideration of recent coronavirus-related reports is intended only as a caution as we follow the latest news.1,2 Recall that statistics reports can be vulnerable to “cherry-picked” data and analytical “fudge.”2 But the challenge of separating “wheat” (accurate information) from “chaff” (false, misleading, or confusing information) is nothing new. Consider the following illustrations and the questions they raise.
Government statistics are only as sound as their underlying data collection and analytical reporting processes. In a prior Acts & Facts article,2 the example was given of EEOC litigation. At trial the EEOC advocated “cherry-picked” statistics to falsely accuse a private corporation of employment discrimination.2,3 The federal trial judge shamed the EEOC for bearing false witness against the company and portraying the quantitative facts in a deliberately dishonest and skewed analysis.3
Sometimes, however, the shoe is on the other foot. In another Acts & Facts article,4 an example was given of private sector businesses lying to government inspectors. They were reporting only half the Alaska pollock fish they actually caught in order to evade international treaty-mandated catch limits.4,5 Further complicating the math used for government statistics, the pollock population reports were used in environmental politics to fuel the billion-dollar “global warming” industry.5
In short, if data needs to be harnessed to build or defend billion-dollar fortunes, don’t expect purist math.6 But how is this relevant to quantifying and forecasting the coronavirus pandemic’s demographics?
First, as in all forensic evidence contexts, consider the reliability (and potentially influential motives) of the sources who are reporting the facts.6 For example, should all statistical data reported by communist governments be naïvely trusted?
Coronavirus expert Ralph Baric, from the University of North Carolina, is uneasy about the numbers coming out of China. “I’m very suspicious about anything they’re saying,” Baric said, pointing to the low numbers China is reporting from other provinces in the country. “The math says there should be a lot more cases.”7
Is it relevant that a hospital received more federal money for reporting a coronavirus-caused in-patient service than for treating a patient who is not designated as a coronavirus victim?8
What if a patient has a coronavirus infection, recovers, then dies of a heart attack or traffic accident? If the autopsy indicates a recent coronavirus infection, then is that alone sufficient to label coronavirus as the cause of death, etiologically speaking?
This is not to suggest that statisticians need to overly scrutinize coronavirus cause-of-death reports, with cynical “follow-the-money-trail” distrust. However, if government-funded billions of dollars are at stake, then it is at least worth some peer review to confirm what norms were used for reporting and sorting data.8
Likewise, is it prudent to consider who makes a fortune, if one remedy is selected over another? For example, if hydroxychloroquine (perhaps in combination with azithromycin and/or zinc) is a simple, quick, safe, and accessible remedy, then we must immediately ask: Who would profit (or fail to profit) by its wholesale use in treating coronavirus victims?9 Or, if hydroxychloroquine it is the best overall solution and is already available on the market as a generic medicine and safely used for decades to treat disease (so patent royalties are not an economic issue), then who stands to lose a pharmaceutical fortune if it is now used?9,10,11
The statistics have another challenge. What about people who acquire the coronavirus, get horribly sick for days or weeks, then recover—but are afraid to report what they experienced due to fear of job loss or governmental intrusions that may complicate things?12
The statistical lethality of coronavirus is a quotient derived from dividing the numerator (number of coronavirus deaths within a specific population) by the denominator (total number of coronavirus infections in that same population combining the number of survivors with those who died).13
But a fear-motivated failure to report a successful recovery from coronavirus infection leads to an inaccurately smaller denominator, producing a lethality quotient that erringly suggests that coronavirus infection are statistically more deadly than they really are.12,13
In other words, the coronavirus statistics are not a simple matter of merely counting who gets sick and recovers versus who gets sick and does not recover. No wonder I must avoid doing math in public.
So, next time you watch the evening news report, on coronavirus statistics and projections from those statistics—don’t panic. Certainly, the pandemic deserves prayer and care, but not panic.14 Maybe it’s not as bad as the numbers appear to suggest. Maybe some of the experts represent industries that make more money, or gain more power, if the pandemic is worse (or harder to cure) than it really is. It is storming outside, no doubt, but maybe the sky is not falling. For now, being careful and prayerful can’t be a bad idea, but panicking helps no one.15
Think about it. Maybe enjoy a hot fudge sundae with picked cherries on top.
1. Forecasting, based on scientific models is a tricky business. Johnson, J. J. S. 2020. Signs of the Times: Glacier Meltdown. Acts & Facts. 49(4):21. Likewise, recognizing the legitimacy and limitations of experts is tricky business, well served by forensic evidence norms and perspectives. See Johnson, J. J. S. 2012. Acts & Facts. 41(11):8-10.
2. Incorporating “fudge” factors invalidates research data, as well as conclusions relying upon such data. Coppedge, D. F. 2008. Cosmology’s Error Bars. Acts & Facts. 37(7)15. Likewise, cherry-picking research data to skew statistics is fake math. Johnson, J. J. S. 2015. Cherry Picking Data is the Pits. Acts & Facts. 44(7):19.
3. “In an egregious example of scientific dishonesty, Murphy cherry-picked certain individuals…in an attempt to pump up the number of ‘fails’ in his database…conveniently increas[ing] the fail percentage by over twenty percent, rendering it a meaningless, skewed statistic.” EEOC v. Freeman, 961 F.Supp2d 783, 795 (D. Md. 2013), affirmed, 778 F.3d 463, 471 (4th Cir. 2015) (decrying the EEOC’s cherry-picked data presentation as “slipshod work, faulty analysis, and statistical sleight of hand”).
4. Johnson, J. J. S. 2018. Something Fishy About Global Warming Claims. Acts & Facts. 47(3):21.
5. Bailey, K. M. 2013. Billion-Dollar Fish: The Untold Story of Alaska Pollock. Chicago: University of Chicago Press, 2-44, 46-88, 199-215. See also Miles, E. et al. 1982. The Management of Marine Regions: The North Pacific. Berkeley, CA: University of California Press, 160-165, 172, 184-193, 220-223; Kasahara, H. 1972. Japanese Distant-Water Fisheries: A Review. Fishery Bulletin. 70(2):227-282.
6. See Proverbs 20:10, 23; Micah 6:11. Improper influences are known to transmogrify peer review into veneer review. In re Hurricane Sandy Cases (Raimey & Raisfeld v. Wright National Flood Insurance Company), 2014 WL 5801540, *1, *3 (E.D.N.Y. 2014) (Gary R. Brown, U.S. District Judge), analyzed in Johnson, J. J. S. 2015. Forensic Science Frustrated by “Peer Review”. Acts & Facts. 44(2):18. In America, Hurricane Sandy caused 147 direct deaths, at least 75 indirect deaths, and about $70 billion in property damages (as estimated in March 2014). Sandy’s diameter reached 1,100 miles, with storm surges that caused tidewater flooding up to 7.9 feet above normally dry ground. See U.S. NOAA, National Weather Service, “Hurricane/Post-Tropical Cyclone Sandy, October 22-29, 2012” and the National Hurricane Center’s Tropical Cyclone Report, posted on nhc.noaa.gov, accessed April 14, 2020.
7. Branswell, H. 2020. Experts say confusion over coronavirus case count in China is muddying picture of spread. Posted on statnews.com February 20, 2020, accessed April 9, 2020. Totalitarian regimes, like communist China, mandate (and thus exemplify) state-approved “consensus science.” See Guliuzza, R. J. 2009. Consensus Science: The Rise of a Scientific Elite. Acts & Facts. 38(5):4.
8. “Consumer groups and public health experts said paying hospitals for uncompensated care would not help the millions of Americans who are now without coverage. … [those who are ill without insurance are a] burden on emergency rooms and hospital staff.” So, treating coronavirus patients who are uninsured are financially burdensome to hospitals. Abelson, R., and M. Sanger-Katz. 2020. Trump Says Hospitals Will Be Paid for Treating Uninsured Coronavirus Patients. New York Times. Posted on nytimes.com April 3, 2020, accessed April 9, 2020.
9. “The White House coronavirus task force had its biggest fight yet on Saturday [March 28, 2020], pitting economic adviser Peter Navarro against infectious disease expert Anthony Fauci. At issue: How enthusiastically should the White House tout the prospects of an antimalarial drug to fight COVID-19? … Navarro pointed to the pile of folders on the desk, which included printouts of studies on hydroxychloroquine from around the world. Navarro said to Fauci, ‘That's science, not anecdote,’ said another of the sources. Navarro started raising his voice, and at one point accused Fauci of objecting to Trump's travel restrictions, saying, ‘You were the one who early on objected to the travel restrictions with China,’ saying that travel restrictions don't work.” Swan, J. 2020. Scoop: Inside the Epic White House Fight Over Hydroxychloroquine. Axios Health. Posted on axios.com, updated April 5, 2020, accessed April 9, 2020. Updated April 5, 2020; accessed April 9, 2020.
10. One coronavirus-infected Michigan legislator (Karen Whitsett) is grateful to be an “anecdotal” evidence of hydroxychloroquine’s effectiveness. “A Democratic state representative from Detroit is crediting hydroxychloroquine — and Republican President Donald Trump who touted the drug — for saving her in her battle with the coronavirus. State Rep. Karen Whitsett, who learned Monday [March 16, 2020] she has tested positive for COVID-19, said she started taking hydroxychloroquine on March 31, prescribed by her [medical] doctor, after both she and her husband sought treatment for a range of symptoms on March 18. ‘It was less than two hours’ before she started to feel relief, said Whitsett, who had experienced shortness of breath, swollen lymph nodes, and what felt like a sinus infection.” Egan, P. 2020. Detroit Rep Says Hydroxychloroquine, Trump Helped Save her Life amid COVID-19 Fight. Detroit Free Press. Posted on freep.com, updated April 6, 2020, accessed April 9, 2020.
11. Likewise, cartographic comparisons of malaria demographics, globally, with Coronavirus demographics, are worth serious investigation. Spencer, R. 2020. Some COVID-19 vs. Malaria Numbers: Countries with Malaria have Virtually No Coronavirus Cases Reported. Posted on drroyspencer.com March 18, 2020, accessed April 9, 2020.
12. This author, being a licensed attorney, communicates often with other attorneys. In short, there are folks who report (in confidence) that they are afraid of disadvantages if they disclose symptoms of recent illness (form which they are now fully recovered), that appear to match the symptoms of the coronavirus.
13. Centers for Disease Control. 2020. Principles of Epidemiology in Public Health Practice: Lesson 3, section 3: Mortality Frequency Measures. Posted on cdc.gov, accessed April 9, 2020.
14. Care, of course, means efficient delivery of healthcare as needed—please pray for everyone who is involved in fighting the coronavirus pandemic. Actually, this is a Genesis Mandate-relevant crisis. And this is not the first time we have needed heroes in an epidemic or pandemic. Johnson, J. J. S. 2013. Siberian Huskies and the Dominion Mandate. Acts & Facts. 42(6):18-19.
15. See 2 Timothy 1:7.
*Dr. Johnson is Associate Professor of Apologetics and Chief Academic Officer at the Institute for Creation Research.