By Kent R. Kroeger (Source: NuQum.com, March 27, 2020)

Every so often, when events seem to demand, political scientists crawl out of their cubby-holes and lecture the American people on how no Republican president can be popular without some combination of luck, war, deception, and a chronically lazy American public.

Political scientists have had a dim view of Americans ever since Angus Campbell, Philip Converse, Warren Miller, and Donald Stokes concluded in their book, The American Voter (1960), that the typical American voter has little more than a superficial, periodic interest in politics, over-estimate their knowledge about politics, and generally are incapable of organizing their political attitudes in a coherent manner that would resemble anything intellectually sophisticated or ideological.

This view has served political scientists much the same way the Big Bang’s cosmic background noise reminds astrophysicists that everything they measure and explain shares the same origin story.

In fairness, the research has evolved beyond the dark conclusions found in The American Voter, with some political scientists even recognizing the average American voter is remarkably adept at choosing political candidates that best match their opinions and attitudes.

Nonetheless, it seems like every time an American president — particularly one of the GOP variety — surges in job approval or popularity, academics and the news media have to set us straight.

The latest example is Donald Trump’s job approval rise since the start of the coronavirus (COVID-19) pandemic. RealClearPolitics.com’s aggregate poll summary of Trump’s job approval shows this clearly (see Figure 1):

Figure 1: Trump Presidential Job Approval (RealClearPolitics.com poll aggregate)

Like the grasping pincers of an earwig, the gap in Trump’s approval and disapproval has dramatically narrowed since mid-March when it became apparent that the coronavirus was not just a news story about China and Europe, but something that was going to effect every American’s daily life.

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Mortified at the sudden relative popularity of a man who most political scientists and journalists probably despise, the cognoscenti have gone into high gear trying to explain why Trump’s rise in popularity is fleeting and won’t matter in November.

Harvard professor of public policy Matthew Baum does his best to sooth the fears of anxious Democrats staring into the abyss as their party prepares to nominate a man recognizably on the downward slope of his cognitive peak:

“Improvements in public approval ratings are notoriously ephemeral — usually lasting only a month or so. The COVID-19 rally seems unlikely to last long enough to help Trump’s reelection effort.

Indeed, as the campaign season approaches, the bipartisan support he has thus far received will almost certainly diminish. It’s impossible to predict how the current crisis will unfold. But unless the pandemic and all its attendant consequences are successfully resolved, the most likely scenario is that Trump has now reached his approval ceiling. If so, it may be downhill from there.”

NBC News, the most anti-Trump news organization — and its hard to pick just one for that title — coughed up this “hot take” on the sudden swell in Trump job approval:

“…Trump’s approval ratings — an important gauge of his re-election chances a bit over seven months away — are staying strong, by some counts even reaching highs for his tenure. In part, that’s a sign that no matter how much criticism Democrats lob at Trump, and whatever amount of negative media coverage he endures, his base supporters are resolute and unflinching. But the surge in support that goes beyond his base is a reflection of a familiar phenomenon whereby presidents become more popular in times of crisis, and according to history, his surge is puny — and likely to be short-lived.”

Puny is NBC News’ level of curiosity about why Trump, whose job approval has been far underwater since the start of his administration and has experienced the least variation of any president in the modern polling era, should suddenly climb at a rate unseen during his presidency (except for, perhaps, during the Senate’s Kavanaugh hearings in Fall 2018 and, in early 2019, when the Mueller investigation exonerated Trump of any Russiagate-related conspiratorial acts).

Since hitting rock-bottom job approval in December 2017, Trump’s job approval has increased at a tortoise’s pace of 0.36 points per month.

In the midst of a pandemic, Trump’s job approval has gained three percentage points in barely half a month. A three percentage point rise may seem “puny” relative to George H. W. Bush’s 31 percentage point gain prior to the First Gulf War or George W. Bush’s 39 percentage point surge after 9/11, but it must be viewed in the current context.

The dynamics behind presidential job approval have fundamentally changed in the past 30 years as intensifying hyper-partisanship within the U.S. Congress, the news media, and the general public has become the fundamental force in American politics. As a result, most Americans get their political news from the news source most aligned with their personal political views; and, in turn, makes aggregate presidential job approval much harder to move.

MSNBC and CNN basically run 24/7 with the same story: Trump is a lousy president. Fox News counter-programs with breaking news that Trump is still winning. And this news broadcasting model works for all the cable news networks as it produces fairly stable and profitable audiences.

Unfortunately, it also produces an overabundance of ill-informed, narrow-minded citizens.

[I will blow an aneurysm the next time I hear another Democrat defend Joe Biden’s babbling donnybrook of verbal nonsense as a function of his childhood stutter. Stuttering is not evidence of a cognitive deficiency. What at an ugly stereotype Democrats nonetheless are now pushing out about stutterers.]

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To deride Trump’s approval gain as a temporary “rally-around-the-flag” effect is to miss the forest for the trees.

The most impressive feature of Trump’s approval is its consistent rise since December 2017, despite a national news media — Fox News and The Wall Street Journal notwithstanding— that, in his first 100 days, gave Trump the most negative news coverage of any recent president and has since maintained that negative tone.

If the national news media hammered on you the way it does on Donald Trump, do you think you would be as popular among your casual friends and acquaintances? Most certainly not.

How could the New York-Washington, D.C. media axis allow Trump’s approval to rise as it has in the past two plus years? The answer is simple: They win either way. The Trump presidency is the national media’s golden goose. And should the media axis powers defeat Trump in November, they win then too, as they will put into power a chief executive who won’t challenge their credibility and privileged status. That is what they call a win-win.

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So how why has the coronavirus pandemic worked in Trump’s short-term favor?

In contrast to the pundits dismissing Trump’s recent approval rise, as a public opinion researcher I talk in-depth to people who describe themselves as “undecided” or “independent.” They aren’t reflexive Trump supporters, but they aren’t anti-Trump either. And they may represent little more than 10 percent of the voting populace. But they matter electorally, should they overwhelmingly move for or against Trump in November.

Like all of us, swing voters directly experience the consequences of the coronavirus pandemic, but they are less influenced by national media narratives. They aren’t looking to blame the Trump administration for this crisis; but, rather, are seeking comfort in these times.

In my research with swing voters, Trump’s daily news conferences on the coronavirus pandemic has given them a sense of optimism, in contrast to the incessant gloominess of the national media.

The most recent World Health Organization (WHO) data on the coronavirus epidemic supports at least a modest level of optimism (see Figure 2). Italy and Spain, the two hardest hit European countries, are discernibly past the peak growth of the virus — both experiencing a decline in new daily cases about 20 days after the initial surge in coronavirus cases.

Figure 2: New Daily COVID-19 Cases for Selected Countries

This empirical evidence supports Trump’s optimism that the U.S. will get past the worst of the coronavirus pandemic by mid-April. Forecasts by the University of Washington’s Institute for Health Metrics and Evaluation (IHME) further buttress Trump’s confidence when they project that the number of deaths-per-day and the stress on hospital resources will peak around April 15th.

Still, according to the IHME, the U.S could see 80,000 deaths due to COVID-19, a significant increase over the usual number of communicable disease related deaths the U.S. normally experiences annually.

Should the Trump administration bear some responsibility for the apparent inadequacy of its initial response to the coronavirus pandemic? Yes, of course — just as most European countries bear some responsibility for the same failure.

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There is a reason why MSNBC is not covering the coronavirus pandemic to the same extent as Fox News, and even CNN. They realize it helps Donald Trump (see Figure 3).

Figure 3: Percentage of News Network Airtime Devoted to the Coronavirus by MSNBC, CNN and Fox News (Source: The GDELT Project)

The coronavirus pandemic is humanizing Trump. His proclivity for bombast and unearned confidence actually serves the public good in these times.

People want optimism when they are feeling uncertain and stressed. Trump oozes such sentiments; in contrast, the Democrats wallow in gloom and doom.

This pandemic is going to loom over the general election in November, whether or not it is completely vanquished by then. In this time, Trump holds a tremendous advantage over the Democrats. He is the Optimist-in-Chief.

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Donald Trump is the tortoise and the Democrats are the hare in the Aesop’s Fables classic story. Trump is slowly building support among swing voters and disenchanted Democrats, most recently through his very human attempt to sooth our fears in the midst of the coronavirus pandemic. The Democrats, meanwhile, revealed their indifference to average Americans by voting unanimously with the Republicans for the Coronavirus bailout program, the biggest wealth transfer to wealthy Americans in history.

Two (only marginally different) Republican parties dominate the U.S. political system. The Democrats’ Vote Blue No Matter Who mantra doesn’t sell to intellectually honest people.

Yes, Trump is not a good person, but he is a transparent one. He shows us what he is everyday and doesn’t hide it.

I can’t say the same for most Democrats.

• K.R.K.

Offended Democrats can send their complaints to: hello@tomperez.org

By Kent R. Kroeger (Source: NuQum.com, March 26, 2020)

Nobody truly knows when the coronavirus pandemic will end and its final human toll.

That doesn’t stop some of us from trying to predict how this crisis will resolve.

I predicted in late January that the coronavirus (2019-nCoV) — the virus that causes the COVID-19 disease — would peak in China in mid February around 20,000 cases, effectively joining the pantheon of pandemic scares that never materialized in a big way on a global scale (e.g., 1976 Swine Flu, 2002 SARS, 2012-present MERS, etc.).

After that bad call, I’m officially out of the coronavirus prediction business.

I know I don’t know what is ultimately going to happen with COVID-19.

But the real experts don’t know either.

Most of us have visited the Johns Hopkins University (CSSE) website that provides “near real-time” counts of coronavirus COVID-19 cases and deaths worldwide.

Their website has numbers — lots of them — from reliable government sources, nonetheless. Those numbers must be fairly accurate, to the extent possible under such difficult circumstances, right?

Well, no. We don’t know the accuracy of the WHO/JHU(CSSE) numbers are yet.

Scientists who think most about viruses and pandemics are only now beginning the process of understanding the true numbers surrounding this pandemic and how it will end — and we know it will end, someday.

When viewed across countries, the numbers reported by the World Health Organization (WHO) and the JHU (CSSE) website should be taken with a grain of salt. These numbers are probably significant under-counts of the true numbers of COVID-19 cases and related-deaths — numbers that we will know with some confidence perhaps many months (or even years) from now.

At some point, probability samples within those countries most affected by the coronavirus will reveal the percentage of their populations with the virus’ antibodies, indicating how many people were actually infected.

Those types of epidemiological studies take time and aren’t amendable to the 24-hour news cycle that drives our current dialogue around the coronavirus.

Yet, we need good information about the coronavirus now, not eight months from now. Effective public policy requires as accurate, timely information. How contagious is the coronavirus? What is its mortality rate? How does its mortality rate vary by demographic categories (i.e., who is most vulnerable)? What percentage of those infected with the virus failed to ever show symptoms?

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The problem is that there is no single simulation model for assessing the trajectory of a pathogen like the coronavirus. Different models, requiring a different sets of assumptions, can render different conclusions. May the best model win.

Presently, a disagreement between two academic groups in the United Kingdom studying the coronavirus highlights this analytic mélange.

In one corner are researchers from Imperial College (London, UK) who, after exhaustive research on individual-level coronavirus transmission dynamics, ran simulations on how the coronavirus would put pressure on the Great Britain (GB) and US health care systems depending on the non-pharmaceutical interventions (NPIs) adopted by the two countries.

Should either country fail to adopt any control measures, according to the Imperial College study, GB and the US can “expect a peak in mortality (daily deaths) to occur after approximately 3 month.”

“In such scenarios, given an estimated R0 of 2.4, we predict 81 percent of the GB and US populations would be infected over the course of the epidemic,” the study concludes. “For an uncontrolled epidemic, we predict critical care bed capacity would be exceeded as early as the second week in April, with an eventual peak in ICU or critical care bed demand that is over 30 times greater than the maximum supply in both countries.”

[R0 represents the expected number of infection cases directly generated by one infected person in a population where all individuals are susceptible to infection.]

That’s the worst-case scenario.

In contrast, the study finds that the most effective combination of interventions is predicted to be “a combination of case isolation, home quarantine and social distancing of those most at risk (the over 70s).”

The Imperial College study is credited with changing GB policy on the coronavirus almost immediately upon its release.

In another corner, researchers from Oxford University (UK), employing a different simulation method, come to a drastically different conclusion about the coronavirus.

“The results we present (in this study) suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries,” write the authors of this, as yet, unpublished study.

Herd immunity is critical in dampening or stopping the spread of a virus like the coronavirus. The higher the population percentage already carrying the virus antibodies, the less likely the virus can spread within those still vulnerable to it.

The Oxford study suggests that the overwhelming majority of those infected by the coronavirus never become sick enough to need hospital care. The public policy implications are profound: The coronavirus was spreading rapidly within the population at least a month before the first case was confirmed. In other words, it may be too late for any suppression and mitigation strategies to have a major impact on the trajectory of the coronavirus. The cake is already baked, as they say.

Despite the study’s key findings, the study’s lead researcher Professor Sunetra Gupta, still believes behavioral changes, such as social distancing and self-quarantines, will still be effective at reducing the number of people who become seriously ill due to the virus.

Even if the Oxford study is found to be substantively wrong, Professor Gupta understands the magnitude of the research task needed to render that conclusion: “We need immediately to begin large-scale serological surveys — antibody testing — to assess what stage of the epidemic we are in now.”

[I should note that the Oxford study has come under intense criticism from some prominent epidemiologists since it was released to the media. A sense of that criticism can be found here.]

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It is popular in some circles now to dismiss the coronavirus pandemic as more media hype than a globe-threatening health crisis. And, superficially, the numbers support that cynicism.

As of 8:15am today (March 27, 2020), the coronavirus has killed a reported 24,361 people worldwide. There have been five other pandemics in the past 125 years with death tolls far greater than the 2019 coronavirus.

Figure 1: Previous pandemics (Name, Time Period, Source, Death Toll)

HIV/AIDS has killed over 25 million people since its first case in 1981. That is about 625,000 people a year. The 2009–10 Swine Flu killed 200,000 people worldwide (12,000 in the U.S.). The first pandemic I vaguely recall — the 1968–70 Hong Kong Flu — killed 1 million people worldwide, including 100,000 in the U.S.

I don’t recall large chunks of the U.S. going into a stay-at-home lockdown in 2009 (…because we didn’t). There weren’t daily presidential news conferences about the Hong Kong Flu in 1969.

Why is the coronavirus being treated differently?

Keeping in mind that the statistics for the new coronavirus (COVID19/2019-nCoV) are moving targets (for example, estimates on its mortality rate range from 1 to 3 percent), the panic over this latest pandemic most likely stems from the combination of its contagiousness and mortality rate.

The 2014–16 (West African) Ebolavirus killed almost half of the 29,000 people that contracted the virus. It had a high mortality rate, but was geographically concentrated. While it was highly contagious, its symptoms were quite visible and the ability to identify and isolate its carriers much easier than the coronavirus.

The two other coronavirus outbreaks (SARS, MERS) were qualitatively different from the current coronavirus outbreak as well. While SARS had a mortality rate around 10 percent, it was less contagious than 2019-nCoV and, in the end, only infected 8,500 people (mostly in China).

The ongoing MERS outbreak has killed 912 people out of the 2,494 who have contracted the virus (a more than 30% mortality rate). But, again, MERS has been geographically isolated and does not appear to be nearly as contagious as 2019-nCoV.

The 2009 Swine Flu is perhaps the most instructive comparison to 2019-nCoV. Like the current coronavirus, the 2009 pandemic received considerable media attention when the WHO announced the potential for a global pandemic from this new H1N1 flu virus in April 2009. However, in the end, the 2009 Swine Flu’s mortality rate of roughly 0.1 percent was not substantially different from the common flu; and, while 12,000 people did die in the U.S., perceptually it felt like just a “bad” flu season.

The new coronavirus (2019-nCoV) is fundamentally different from these other pandemics. Where the common flu typically has a mortality rate around 0.1 percent (1 in every 1,000 cases), the current coronavirus most likely has a mortality rate at least 10 times more lethal.

Lastly, the numbers commonly cited for pandemics such as the 2009 Swine Flu are epidemiological estimates derived after those outbreaks have ended. Epidemiologists go back into the aggregate diagnostic and mortality data and look for evidence of what impact the pandemic virus may have actually had (beyond simply what was reported contemporaneously). They make sophisticated estimates with known (and unknown) amounts of error. \

The numbers for COVID-19 are reported to the WHO by national health ministries. At a minimum, we know by anecdote that not everyone who contracts a virus goes to the hospital. Many (maybe most) resort to home-care and never seek medical attention. Some will contract a virus and never exhibit symptoms. Evidence of this latter fact comes from the NBA where hundreds of players and coaches exposed to carriers of the coronavirus were tested.

“Following two players testing positive last week, others were tested and five additional players tested positive,” the NBA issued in a statement last week. Since March 11, when Utah Jazz player Rudy Gobert was the first NBA to be diagnosed with the coronavirus, thirteen other NBA players have tested positive. From the most recent information made available to the public, not one of these players has experienced significant symptoms from the coronavirus — which reinforces what may be the most significant feature of the new coronavirus: most infected by it will have no (or only minor) symptoms related to it.

The occurrence of asymptomatic coronavirus carriers has also been revealed by the Germans who have done significantly more coronavirus testing (per capita) than the U.S.

As of March 19, the U.S. had conducted in total only 104,000 tests, according to The COVID Tracking Project. In contrast, according to German health officials, they are equipped to conduct 160,000 tests per week and, unlike the U.S., systematically test people who are not symptomatic. Germany’s broader testing effort is designed to understand the true extent of the coronavirus infection rate and has revealed thousands of Germans who carry the virus but are not symptomatic.

As of March 27, the Germans have reported 304 deaths against 49,344 confirmed cases, for a mortality rate of 0.6 percent — among the lowest rates in those countries most affected by the coronavirus. Some epidemiologists are citing Germany’s effort to identify asymptomatic carriers early as one of the reasons for Germany’s low mortality rate. Identify those infected before they are sick and you increase their chance of a full recovery.

But more than asymptomatic carriers and those who treat their symptoms exclusively at home can fail to get counted by health officials. Research has shown that medical databases are not always accurate. For example, people can die from complications associated with a virus (organ failure, hemorrhaging, etc.) and yet never get classified as having died from the virus if they had not been tested for it.

The reality is that data collection on a scale as large as the coronavirus requires is going to have significant amounts of error through selection bias, incomplete population coverage, coding mistakes and simple random chance.

I will make this one prediction about COVID19: The numbers were are seeing now (as reported to the WHO) are nowhere near the true impact of COVID-19 — particularly in countries with less-developed health care systems and limited resources for health testing and monitoring.

When this is all over, it will take many months before we know what COVID-19 really did.

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Even so, I believe there is evidence in the current COVID-19 data from the WHO that this pandemic will peak soon.

We have already seen countries get control of their first wave of the coronavirus — China, South Korea — and Italy, U.S. and France may be making this critical turn soon (see Figure 2).

Figure 1: Coronavirus trajectories of daily new cases for selected countries

The current coronavirus pandemic will end. It is not a question of if, but one of when.

But I’m not making any predictions.

• K.R.K.

Datasets used in this article are available by request to: kroeger98@yahoo.com

By Kent R. Kroeger (Source: NuQum.com, March 23, 2020)

Health experts continue to warn us that the confirmed coronavirus cases reported by the World Health Organization (WHO) and national health ministries are significantly under-counting the actual number of cases in the U.S. and worldwide.

Earlier this month Dr. Marty Makary, a surgeon and health policy expert at Johns Hopkins University, told Yahoo Finance’s “On the Move” that when the U.S. Centers for Disease Control and Prevention (CDC) was reporting 1,600 confirmed cases in the first week of March, the actual number of infected people could be “between 50,000 and half a million.”

“Don’t believe the numbers when you see, even on our Johns Hopkins website, that 1,600 Americans have the virus,” Makary said. “No, that means 1,600 got the test, tested positive. There are probably 25 to 50 people who have the virus for every one person who is confirmed.”

Dr. Makary’s estimated range of coronavirus cases in the U.S. mirrors my own cross-sectional analyses of the WHO and Johns Hopkins databases.

As of March 21st, I estimate that the actual number of Americans infected with the coronavirus (including those who have died or recovered) numbered between 62,000 and 215,000.

[The methodology I employed for these cumulative case estimates is described here.]

Building upon a country-level coronavirus case estimation model I developed in an earlier article, I added a control variable (NUM_TEST) that measures the number of coronavirus tests conducted by each country through March 18th (53 countries). This variable is being collected ourworldindata.org and, as of today, does not have reliable testing data for a number of important countries, including China, Canada and most South American and African countries.

Writes ourworldindata.org about this coronavirus testing variable:

“To understand the statistics on cases, we would want to know how many people in the world are being tested for COVID-19 every day, and how the available tests are being allocated. Unfortunately there is no centralized WHO data on COVID-19 testing and most countries do not provide official reports on tests performed.

Some countries do provide aggregate estimates on the total number of tests performed up to a specific date, but these are published across individual websites, statistical reports and press releases — often in multiple languages and updated with different periodicity.

Given the large problems with data availability, we did a manual review of data across national reports, and included the most recent estimates that we could find as of 20 March 2020, 18:00 GMT.”

Given the uncertainties with how countries are reporting their coronavirus testing efforts, I report two estimation models in this article: Model 1 (which does not include a control for the number of coronavirus tests) and Model 2 (which does include a control for the number of coronavirus tests).

The Results

With each new day that the WHO reports confirmed coronavirus cases, my original model (Model 1) has increased its ability to explain country-level variation in coronavirus cases. The original March 14th model explained 73 percent of the variance in country-level case numbers and found the following variables to be significant predictors: National income per capita, total population, number of international tourism arrivals per year, percent of annual deaths due to communicable diseases, and an indicator variable for China.

Estimating Model 1 using WHO data through March 21st found that the model now explains 81 percent of the variance in country-level case numbers (see Appendix below for the regression output for Model 1).

An analysis of the residuals for Model 1 found the model significantly over-estimated the actual number of coronavirus cases in the U.S., Japan, Myanmar, New Zealand, and the United Arab Emirates (see Figure 1).

Conversely, the model under-estimated the actual number of cases found in Iran, Italy, and South Korea.

Figure 1: Residual analysis (Actual vs. Predicted) for Model 1 (data through 21 March 2020; 167 countries)

Why are we seeing these significant over- and under-estimates?

Using the U.S. as an example, Model 1 estimates that the U.S. — given its national wealth, population size, size of its tourism industry, and experience with communicable diseases — should have reported 215,000 coronavirus cases on March 21st. Instead, the U.S. was reporting 25,000 cumulative cases on that date.

Why the big difference?

There can only be one of two answers: (1) The U.S. has made an heroic effort to keep the coronavirus from spreading (e.g., widespread testing, social distancing, self-quarantines, travel bans, local and statewide lockdowns, etc.), or (2) the U.S. has systematically under-counted the actual number of coronavirus cases in the country and is now playing catch-up with its ramped up testing regimen.

I suspect the smart money is on the latter explanation.

But that explanation may not work for a country like UAE — which reported 154 cases (as of March 21st), though Model 1 expected 1,250 cases.

According to ourworldindata.org, the U.S. has conducted 103,945 coronavirus tests as of March 19th (or about 32 tests for every 100,000 people). Contrast the U.S. number to UAE where there has been 1,298 tests per 100,000 people.

Other countries with a high number of coronavirus tests relative to their population size include: Australia (454 per 100,000), Bahrain (1,188 per 100,000), Norway (823 per 100,000), and South Korea (613 per 100,000).

One possible hypothesis: Whereas the U.S. may have missed the opportunity to prevent a large-scale spread of the virus and is now playing catch-up with its recent increases in testing, the UAE, through its aggressive testing regimen early in the outbreak, may have stunted the spread of the virus to the point where the apparent under-count (according to Model 1) is actually the true value.

In other words, Model 1 isn’t properly specified— we need to add a variable measuring a country’s coronavirus testing efforts.

Model 2, therefore, includes the ourworldindata.org collected data for the number of coronavirus tests at the country-level (Model 2 regression output can be found in the Appendix).

Regrettably, inclusion of the number of coronavirus tests variable eliminated over 100 countries — mostly from Southeast Asia, Africa and South/Central America — from the analysis in Model 2. Nonetheless, Model 2’s parameter estimates were fairly consistent with the Model 1 results (see Appendix for model output and diagnostics), though the model fit fell substantially (from 81 percent of the variance explained, down to 61 percent).

Given the loss of data from the southern hemisphere (and China), the Model 1 variable for communicable disease deaths and the indicator variable for China were dropped.

The remaining variables in Model 2 were significant (numbers in parentheses are standardized coefficients indicating their relative importance): National per capita income ( 0.475), population size (0.240), annual number of tourism arrivals (0.344), and cumulative number of coronavirus tests (0.257).

Figure 2 shows that the outlier nations in Model 2 and the direction of that error. Superficially, Model 2 outlier nations look similar to Model 1. The predicated values for U.S., New Zealand, and UAE remain higher than actual numbers; while Iran, Spain and Italy are experiencing numbers higher than expected based upon their population size, wealth, and annual tourism volume.

Figure 2: Residual analysis (Actual vs. Predicted) for Model 2 (data through 21 March 2020; 53 countries)

For now, at least, we can’t explain UAE’s lower-than-expected count of coronavirus cases to their aggressive testing program. Something else explains their low reported numbers.

Instead, Model 2 offers a glimpse into how the WHO reported numbers differ from expectations based on established covariates with the spread of the coronavirus:

Figure 3: Model 2 predictions versus reported coronavirus case numbers

As of March 21st, at a minimum, the U.S. has almost 62,000 cumulative cases of the coronavirus instead of the 25,468 reported to the WHO.

At a maximum, according to the more inclusive Model 1, the U.S. has over 215,000 cumulative cases of the coronavirus.

Either way, the U.S. is seriously under-estimating the extent of the spread of the coronavirus within its borders.

• K.R.K.

Datasets and statistical software used in this article are available upon request to: kroeger98@yahoo.com

APPENDIX:

Variables

Dependent Variable (Source: WHO): Number of confirmed COVID-19 cases as of 21 March 2020 (log transformed)

Independent Variables (Source: World Bank):

NY_ADJ_NNTY_PC_CD = Adjusted net nat’l income per capita (current US$)

LOG_POP = Country’s total populaton (log transformed)

CHINA = Indicator variable (1 = China; 0 = all other countries)

SI_INT_ARVL = International tourism, number of arrivals

SH_DTH_COMM_ZS = Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of all deaths)

NUM_TESTS = The number of coronavirus tests reported through March 18th by each country’s health ministry or national authority (Data provided by ourworldindata.org and can be found here)

More detail on these independent variables (except NUM_TESTS) can be found in the World Bank’s data catalog at: https://datacatalog.worldbank.org/dataset/world-development-indicators

Linear regression models

Model 1 (Does not include a control for the number of coronavirus tests; 167 cases)

Model 2 (Includes a control for the number of coronavirus tests; 53 cases)

Residual plots

Model 1 (Does not include a control for the number of coronavirus tests; 167 cases)

Model 2 (Includes a control for the number of coronavirus tests; 53 cases)

By Kent R. Kroeger (Source: NuQum.com; March 21, 2020)

For three years the U.S. news media wasted our time promulgating a conspiracy theory that candidate Donald Trump and his campaign conspired with the Russians to steal the 2016 presidential election.

It was never true. Not even a little bit true. It was a conspiracy theory built on half-truths, coincidences, profit motives and malicious, partisan intentions.

And now, with the coronavirus pandemic, we are witnessing a new bull market for conspiracy theories:

• In the midst of a contentious trade war with China, the U.S. military planted the coronavirus in China to weaken their position,
• The Chinese military used the coronavrius outbreak to end the democracy protests in Hong Kong,
• The coronavirus was released due to a lab accident in Wuhan, China,
• The pharmaceutical companies created the coronavirus knowing they already had the drugs necessary to treat it (thereby enriching themselves),

There are many more conspiracy theories about the coronavirus. Too many. All predicated on nothing more than circumstantial evidence — mere coincidences — and implanted into the world’s information bloodstream where they prosper, morph, and instill a pernicious type of distrust in a vulnerable segment the general population desperately looking for explanations for why they are now terrified to leave their homes.

Perhaps actress Kirstie Alley describes many of us are attracted to conspiracy theories regarding the coronavirus (no matter absurd their internal logic):

Many of us have a preternatural need to explain events and phenomena that shake our security and sense of stability.

Sometimes we just can’t accept that random coincidences happen without some larger purpose or meaning. Who wants to live under the constant threat of random chance?

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One of the first challenges greenhorn intelligence analysts face in the U.S. Intelligence Community — as they analyze data and information related to their area of expertise —is learning how to distinguish random coincidence from significance.

Two or more events can appear through their ordering or simultaneity to be significantly related — when, in fact, they are not.

We’ve all said this at one point in our lives: What are the chances?

Two years ago, on an unremarkable Saturday morning, I realized I had not done laundry for a while. I despise doing laundry. In my 50s now, I have never separated whites from colors during the washing process. Never. Not once.

On that day, I couldn’t find even one clean shirt to throw on before getting my morning coffee and donut at the local supermarket. In desperation, I pulled a garbage bag full of old shirts out of my closet that I had never unpacked since moving from Iowa to New Jersey three years ago.

I knew if I dug down too deep in the bag, grody smells might escape, so I just yanked out what was on top: a crisp white t-shirt I had purchased in commemoration of Chinese leader Xi Jinping’s February 2012 visit to Iowa.

The t-shirt had never been worn. It was meant to be a souvenir. But, in a desperate moment, I threw it on — along with some jeans — and drove to the market.

While waiting for my double-shot espresso, I decided to pick up some recliner chair food for watching the day’s schedule of college basketball: some deli meats, sliced cheese, fresh bread and Nabisco animal crackers. As I took a tight turn into the cracker aisle, I was forced to make a Euro step move to avoid a man walking out of the same aisle.

We apologized to each other and instantly realized something bizarre had just happened: we were both wearing a Xi Jingping (Welcome Back to Iowa) t-shirt.

What are the chances?!

We both spit out a laugh and chatted long enough to discover we had lived in Des Moines around the same time and were professionally interested in China.

But what were the chances that we’d wear those identical t-shirts on the same morning, in the same supermarket, in the cracker aisle, in Pennington, New Jersey?

No need to do the math. The probability was not substantively different from zero.

Yet it happened. It was a random coincidence — nothing more significant than that.

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Other coincidences are not as improbable, even if we think they are.

The classic case, often used in introductory college statistics courses, is the Birthday Problem: What is the probability that at least two students in a classroom of 30 share the same day of birth?

The typical first-year statistics student thinks the chances are slim; in my experience, they will guess something well under 50 percent. There are 365 days in a year, after all. Two randomly selected people have a 0.27 percent chance of sharing a day of birth. How much better are the chances in a room of 30 students?

It turns out, a lot. Throwing a simple probability formula against the problem, the chances of at least two students in a class of 30 sharing the same day of birth is 71 percent.

The lesson in these two coincidence examples is that people are prone to making judgment errors in presence of natural randomness.

The Xi Jinping t-shirt story is an illustration of Littlewood’s Law which states that a person can expect to experience events with an odds of one-in-a-million at the rate of one per month.

It’s not a formal scientific law, but it brings attention to the omnipresence of improbability in our daily lives. The improbable is everywhere.

For example, what are the chances that as I write this essay wearing only one sock, my son cries out about not being able to find his science class notebook, at the same time my dog starts barking at the deer in our backyard, at the precise moment my wife walks into the kitchen and complains that I didn’t wash the dishes from last night’s dinner in which I used a new Indian curry recipe she was given while in India last January?

In combination with how generally poor we are at assigning accurate probabilities to even common events — an intrinsic cognitive flaw in how our brains are wired — natural randomness can bring significant stress to our lives.

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Science writer Michael Shermer calls this cognitive flaw “folk numeracy,” which he defines as “our natural tendency to misperceive and miscalculate probabilities, to think anecdotally instead of statistically, and to focus on and remember short-term trends and small-number runs.”

As a result, we frequently exaggerate the probabilities of some threats (e.g., being a victim of terrorism) and wrongly minimize others (e.g., getting in a car accident while we are driving).

In his book, How We Believe, Shermer describes the human brain as evolving out of necessity into a pattern recognition machine that creates structure and meaning so we can navigate our surroundings with minimal stress. Our brain is an efficient uncertainty reducer that aids us in survival and reproduction, says Shermer.

Psychologist R. Nicholas Carleton, an expert in anxiety disorders, ties the human need to recognize patterns to a deep-seated fear-of-the-unknown — what he calls, our fundamental fear.

Not only is fear-of-the-unknown inherent to the human species, according to Dr. Carleton, it is “distributed and normally in the population.” Some people have more of that fear than others — and some less. As such, someone with a strong fear-of-the-unknown but relatively poor at bringing order to their world (e.g., assigning accurate probabilities to events) will potentially suffer high levels of anxiety.

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Coincidences are manifestations of chance and our strategies for interpreting these improbabilities can vary significantly.

Most often, the statistician in me sees the joint occurrence of improbable events (coincidence) as unremarkable. Unless you bring a defensible theory and independent data to the explanation, don’t waste my time.

Yet, there are times when coincidences do reveal something more causally significant. As mystery writer Simon Van Booy once put it, “Coincidences often mean you’re on the right path.”

The current controversy swirling around Tennessee Senator Robert Burr comes to mind. According to a March 19 ProPublica story, “soon after he offered public assurances that the government was ready to battle the coronavirus, the powerful chairman of the Senate Intelligence Committee, Richard Burr, sold off a significant percentage of his stocks, unloading between $628,000 and $1.72 million of his holdings on Feb. 13 in 33 separate transactions.”

Days later, worldwide stocks markets dropped precipitously. Now that is some coincidence. Actions by at least one other U.S. politician is under similar suspicion.

Time will tell if Senator Burr’s actions were criminal, but the story highlights an example where coincidence cannot be dismissed out-of-hand as random chance.

Occasionally, even if rarely, conspiracy theories are right. The trick is knowing when.

• K.R.K.

Send comments to: kroeger98@yahoo.com

By Kent R. Kroeger (NuQum.com, March 16, 2020)

Two news stories this morning (March 16, 2020) caught my attention.

The first story concerned Utah Jazz basketball player Donovan Mitchell who was diagnosed to have the coronavirus (2019-nCoV) and apparently became infected through his close contact with fellow player Rudy Gobert, who also tested positive for the virus.

“I’m asymptomatic,” Mitchell told ABC’s “Good Morning America” on Monday. “I don’t have any symptoms. I could walk down the street (and) if it wasn’t public knowledge that I was sick, you wouldn’t know it. I think that’s the scariest part about this virus. You may seem fine, be fine. And you never know who you may be talking to, who they’re going home to.”

We should assume there are thousands of Donovan Mitchell’s roaming our country feeling healthy and promoting the spread of COVID-19.

The second story I heard on public radio station WNYC this morning. While being interviewed on the The Brian Lehrer Show, Dr. Irwin Redlener, professor of pediatrics and director of the National Center for Disaster Preparedness at Columbia University, told the NPR audience that the emphasis for containing COVID-19 must focus on those already sick and not trying to test as many people as possible for the virus.

I am not a medical doctor or epidemiologist, but as a statistician, those two stories raise a number of red flags.

First, addressing Dr. Redlener’s comments, to suggest our nation’s efforts must be focused on those already sick is a mea culpa suggesting our hope of limiting the spread of this virus is a misuse of limited resources.

According to many health care experts, our health care infrastructure is under too much stress taking care of those infected with 2019-nCoV to take on the responsibility of measuring its incidence.

If he is right, trying to measure the prevalence of COVID-19 in the population is purely an academic exercise that distracts from saving lives.

But then came the Donovan Mitchell story. Infectious disease experts and epidemiologists readily admit they don’t know the percentage of COVID-19 carriers are asymptomatic (i.e., they show no symptoms of the virus while they are contagious).

How could they know? This is a new virus. They call it ‘novel’ for reason.

It seems reasonable to assume the number Americans currently infected with the coronavirus is greater than the 4,464 confirmed cases reported to the WHO (as of 16 March).

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In a previous essay I estimated there were almost 20,000 “unmeasured” Americans infected with the coronavirus (as of 14 March). However, my effort was exploratory, not conclusive, if only because I assume the reported U.S. numbers up to now are an underestimate caused by a flawed government response to the virus. However, an equally valid conclusion could be that the relative low number of U.S. confirmed cases is the result of successful state and federal government efforts to contain the virus.

Frankly, how can we single out the Trump administration when so many European and economically-advanced countries have made the same mistakes? If anything, this crisis exposes the inadequacy of the advanced economies, in general, to manage global pandemics.

One exception is South Korea which saw its number of new confirmed cases start to decline over a week ago (see the orange line in the chart below).

According to experts, South Korea’s aggressive testing effort deserves credit for this coronavirus success story. From the moment the coronavirus starting appearing in South Korea the country has fielded almost 190,000 tests — approximately 3,690 tests per 1 million people.

In contrast, as of 8 March, the U.S. has conducted only 8,400 tests resulting in 213 positive cases (keep in mind, some patients have been tested more than once). If my conclusion is correct that 19,000 Americans currently have coronavirus but have not been tested, the U.S. will need 790,000 tests kits just to test these “unmeasured” people. But even that number is an underestimate as it does not include the Americans who are still to get COVID-19.

Conservatively, if the test’s 2.5 percent yield holds, the U.S. needs millions of test kits to identify the majority of Americans infected by the coronavirus. In that light, Florida Governor Ron DeSantis’ recent announcement that his state will be distributing 500 more test kits to hospitals and private labs feels grossly inadequate.

Even so, Dr. Redlener’s concern has merit that too much emphasis on testing will take resources from hospitals and doctors tasked with treating those already sick. Health care professionals are already reporting shortages due to this pandemic.

Therefore, should the U.S. even try to diagnose every coronavirus carrier?

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Dr. Redlener’s answer appears to be ‘No.’ And he may be right, but my question is: Why should the wealthiest country in the world need to choose between treating its citizens for a deadly virus and accurately measuring the prevalence of this virus in the general population?

I believe its a trade-off we don’t need to make and will only cost us more in the long run as “unmeasured” coronavirus carriers will extend this outbreak even farther into the future.

“Asymptomatic and mildly symptomatic transmission are a major factor in transmission for Covid-19,” Dr. William Schaffner, a professor at Vanderbilt University School of Medicine, told CNN. “They’re going to be the drivers of spread in the community.”

I was reminded of Dr. Schaffner’s warning when I read the story of Nebraska basketball coach Fred Hoiberg being taken to the hospital after he showed flu-like symptoms during his team’s Big 10 basketball tournament game against Indiana. Hospitalized after the game, it was determined Coach Hoiberg had the normal flu (Influenza-A) and not COVID-19. All’s well that ends well, right?

No, not right.

Coach Hoiberg didn’t fall ill during the game, he felt ill before the game. In fact, he felt bad enough to visit the Big 10 tournament doctor an hour before the game started. And what did the tournament doctor conclude? Despite showing flu-like symptoms — in the middle of the coronavirus crisis — the doctor decided Hoiberg was OK to coach from the bench.

If Hoiberg and that Big 10 doctor still have jobs today, the University of Nebraska and the Big 10 should be ashamed of themselves.

Being a self-obsessed college basketball coach is not an excuse for being so dismally unaware of the world around you that you think its OK to potentially expose hundreds of players and fans to a pandemic virus. What was Hoiberg thinking?

His words:

“Please let it be known that I would never do anything that would put my team, family or anyone else in harm’s way. I was feeling under the weather on Wednesday and we felt the right thing to do was to get checked by a tournament doctor prior to our game in the Big Ten Tournament against Indiana. Once that medical official cleared me, I made a decision to coach my team. I would like to thank event staff for their care and professionalism. Also, thank you to everyone who has reached out for your support. This is a scary time for all of us. Let’s offer our thoughts and prayers directly to those affected with Coronavirus.”

Hoiberg proves you don’t have to be smart to be a NCAA Division I basketball coach, but the tournament doctor? What was his excuse for letting Coach Hoiberg into the arena? There is no possible way this doctor could have tested Hoiberg for the coronavirus by examining him right before the game’s tip-off. Clearly, the doctor was more afraid of affecting the outcome of a Big 10 basketball game between two of the conference’s worst teams than he was about potentially spreading a deadly virus to hundreds of people.

This Big 10 tournament doctor needs to appear before a medical review board to defend why he still has a medical license.

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If my March 14th analysis is accurate, there may be nearly 20,000 Americans walking around with the coronavirus but undiagnosed. How do we find them?

From a statistical sampling perspective, the task is daunting. According to my analysis on 14 March, the incidence of undiagnosed coronavirus carriers was 20,000 ÷ 327 million U.S. citizens (or about 1-in-16,000).

Any effort to find these 20,000 people is helped by the fact that these cases will tend to be geographically clustered. We already see this clustering among diagnosed coronavirus cases in the U.S. (e.g., New Rochelle, NY; Biogen conference in western Massachusetts, Life Care Center of Kirkland, near Seattle, WA). This clustering is a function of the coronavirus’ R0— an estimate of the number of people an average infected person spreads the virus to — which the current evidence indicates is between two and three, making it substantially more contagious than the typical flu.

For sampling purposes, a higher R0 assists in detection. Find one infected person and you are likely to find many more.

A back-of-the-envelope estimate of the testing sample sizes needed to detect and isolate areas containing infected persons goes as follows:

First, let us assume the probability distribution of those infected follows a binomial form:

where f(x) is the probability x is the number of undetected infected persons in a sample of size of n. The value of p is the hypothesized proportion of undetected infected persons in the population.

From the above equation we can begin to estimate the sample sizes needed to find coronavirus carriers.

Let us start with a simple case: Assume you live in a city of 16,000 people. Based on our hypothesized incidence rate of 1-in-16,000, there may be one person in your city with the coronavirus. How do we find this person? We test a random sample of the city population, but since tests are expensive to administer, we want to minimize the size of our sample. Therefore, how big does the sample need to be to have a reasonable chance of detecting this one infected person?

Acceptance sampling methods allow us to find a happy middle ground between a 100-percent-census (which is cost prohibitive) and no measurement at all.

Since we consider a population “infected” (and, presumably, quarantined) if we find even one infected person, acceptance sampling (at a General II inspection level) suggests we need a sample size of 315 randomly selected people.

Scaling up to a larger population, a city of 100,000 would be stratified into 6 distinct geographic areas with approximately 16,000 people in each geographic strata. Again, we’d take a sample of 315 people in each strata for a total sample of 1,890 people.

Scaling up further to the entire U.S. population (approx. 327 million people), we would need to test 6.4 million people to have a reasonable assurance of detecting where the most infection cases are present.

Since it takes more than one test kit in some cases to test one person, we could be looking at 10 million test kits or more.

Not going to happen.

Even if we select a lower testing standard (i.e., a greater tolerance for missing some of the lesser infected areas), we’d still be looking at almost 5 million test kits.

A country that produces 1 billion cans of wet dog food every year can certainly muster up the resources to produce 5 million coronavirus test kits on short notice.

Furthermore, even if this country could produce them quickly, the resources necessary to distribute and administer these test kits would overwhelm our already fragile health care system.

Thus, this is why social distancing and self-quarantines are indispensable in containing the coronavirus and all future pathogens that will threaten us.

As Dr. Redlener noted on WNYC, we can’t detect and treat everyone.

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Still, sampling methods such as acceptance sampling need to become standard practice among our front line health professionals, particularly at the beginning of a potential pandemic when its final social costs are still unknown.

The U.S. cannot afford to passively identify infected persons as we mostly have so far with COVID-19. Instead, the U.S. must proactively identify infected persons as South Korea and China are systematically doing now. We need to flatten the infection curve as best we can and sampling can help us efficiently do that.

Unfortunately, during the 2020 coronavirus pandemic, our state and federal governments appear unequipped and untrained to do that level of proactive detection.

As South Korea has proven, measurement is key to fighting pandemics like COVID-19.

Bob Galvin, a former Motorola executive and co-creator of the Lean Six Sigma quality improvement method, once said, “Companies must measure or die.” In the case of the COVID-19 pandemic, that warning is literally true.

• K.R.K.

Data sets and statistical codes referenced in this essay are available upon request to: kroeger98@yahoo.com

By Kent R. Kroeger (NuQum.com, March 14, 2020)

The importance of the vice presidential nominee has never been as great as it will be in the 2020 presidential election. The likely Democratic Party nominee — Joe Biden — is 77 years old and the probability his VP pick could be president before the next presidential election is (to put it gently) non-zero.

As for the incumbent, Donald Trump, the current consensus is that Vice President Mike Pence will be his running mate again in 2020. But if there is a time when an incumbent president should consider changing vice presidents, this is that time.

Pence offers no home state advantage. Trump would win Indiana if his running mate was a ham sandwich. Pence’s executive experience as a former Indiana governor is no longer necessary to compensate for Trump’s inexperience in a high-elected office. And don’t try to convince me that Pence is a charismatic campaigner who generates excitement within the Republican electoral base — he is duller than a wet paper towel.

But the real reason Trump should dump Pence has more to do with Trump’s electoral vulnerability than anything wrong with Pence. First, the coronavirus pandemic is starting to wear down Trump’s job approval — it now stands at 44.6 percent in the RealClearPolitics polling average, down in just a few weeks from 46 percent (the minimum level of approval Trump needs to have a decent chance at re-election, according to my recent analysis) — and without a significant shock in Trump’s favor before November, his chance of re-election is under grave threat.

Second, let us assume Biden will pick a woman as his running mate — according to one prediction market, PredictIt.com, she will be either Kamala Harris (29% chance), Amy Klobuchar (23% chance) or Stacey Abrams (15% chance). The media narrative coming out of the party conventions invariably will include the historic importance of the Biden’s VP selection and its appeal to a lot of swing voters, particularly women.

Trump’s argumentum ad captandum (‘appeal to the crowd’) would be significantly enhanced with his own trailblazing VP choice — which, I believe, will need to be either a woman, an African-American, or an Hispanic (or, even better, some combination of those identities).

The possible VP choices for Trump are fairly obvious (listed here in order of their odds based on my best hunch):

• Former US Ambassador to the UN Nikki Haley — my bet on Trump’s pick
• U.S. Senator Martha McSally (AZ)
• U.S. Representative Elise Stefanik (NY) — my personal favorite
• U.S. Senator Joni Ernst (IA)
• U.S. Senator Ted Cruz (TX)
• U.S. Senator Marco Rubio (FL)
• U.S. Senator Tim Scott (SC)

McSally, Ernst, Cruz and Rubio also happen to come from battleground states (though, technically, Texas is not one…yet) — which should enhance their attractiveness to Trump (more on that down below).

Stefanik, though from a noncompetitive blue state, might be the most intriguing person on the list. She’s young (age 35), telegenic, an energetic campaigner, and in possession of a growing national profile since her prominent role in the House impeachment hearings last fall.

I am far from the only person betting (or hoping) Pence is not going to see a second VP term. According to PredictIt.com, Pence’s probability of being re-nominated has been plummeting since he was selected to lead the Trump administration’s task force on the coronavirus — though he has regained a some support since Trump declared a national state of emergency (see chart below):

If Trump ever wanted to set up his Vice President to fail, making responsible for trying to stop the coronavirus would be at the top of the list. On the other hand, if this country were to somehow avoid the worst consequences of this pandemic (and that looks increasingly unlikely), Pence would not only enhance his value as Trump’s VP in the 2020 election, he’d be set up as the clear favorite for the 2024 Republican nomination.

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This discussion about presidential running mates may all be for naught.

Political scientists and election forecasters have long contented that, in general, vice presidential picks don’t affect U.S. presidential election outcomes.

“Few myths of American politics remain as deeply entrenched and blindly accepted as that of the electoral power of the vice presidential pick,” political scientists Kyle Kopko and Christopher Devine wrote in Politico in 2016.

In their research on whether a presidential candidate is more likely to win their running mate’s home-state, they found that no evidence to support such a conclusion: “Over the course of a little more than the past century, a vice presidential candidate’s state of residence generally has no effect on how a presidential candidate performs in that state. The vice presidential home state advantage is, essentially, zero.”

The same researchers found that VP choices don’t help much with specific demographic groups either.

Further evidence against the significance of VPs is the fact that none of the major econometric models used to forecast presidential elections include a variable for vice presidential picks; yet, the models still successfully forecast presidential election outcomes (most of the time). When the models do make mistakes, it is not because they failed to account for the VP selections.

Nonetheless, the belief in the importance of a VP pick continues in today’s election reporting, if only because a vice president can rise to the highest office due to the death or resignation of a president.

Vice presidential picks can furthermore help in other ways beyond a possible home state advantage. They can represent a critical constituency (Mike Pence in 2000 with evangelical voters), a ground-breaking candidacy (Geraldine Ferraro in 1984, Joe Lieberman in 2000), an augmentation to the qualifications and experience of the presidential candidate (Dick Cheney in 2000, Mike Pence 2016, Joe Biden 2008), or act as the campaign’s attack dog so the top of the ticket doesn’t need to descend into the mud (Richard Nixon 1952).

The political research against notwithstanding, in reality, there is concrete evidence that a VP selection can matter, and the case I cite most often is the 2000 presidential race between Al Gore and George W. Bush.

Most of us remember the 2000 election for how close it was and the ‘hanging chads’ in Florida. Few remember the vice presidential debate between Joe Lieberman and Dick Cheney that took place on October 5, 2000 at Centre College in Danville, Kentucky.

The hot take in the news media immediately after the VP debate was that it was civil, highly substantive, and probably boring to the average voter — hence, wouldn’t affect the final election outcome. The VP debate merely highlighted and amplified differences between Bush and Gore already known within the electorate, pundits said.

But something interesting and profound happened about 24 hours after the VP debate — tracking polls began to show a softening in Gore’s support (and a strengthening in Bush’s). The largest political futures market at the time — The Iowa Electronic Markets — showed an enduring, almost monotonic decline in the probability of Gore winning the election after the VP debate (see graph below).

What was the cause? There was no significant news event in that first week of October 2000 except for the VP debate.

Focus groups I conducted at the time while a graduate student at Columbia University offered an intriguing insight: Undecided voters found Lieberman’s raspy, Elmer Fudd-like voice unpleasant and distracting, while Cheney’s demeanor was perceived as comforting.

“He’s like a wise grandfather,” one female focus group participant said of Cheney. “I want to curl up in his lap and have him read me a story.”

That is probably not the image most of us have of Cheney today (if you do, I would keep it a secret); but, in the 2000 election, the Republicans had nominated a once-hard-drinking frat boy, George W. Bush, and many swing voters were uncomfortable with his anti-intellectual tendencies. Cheney wrapped these voters in a security blanket, giving them assurance that a junior Bush presidency would be run by adults, not just Delta Kappa Epsilon frat bothers.

The rest is history. You can decide if it was for better or worse.

My point is simply that VP choices can a have deep impact on the presidential vote. No research has convinced me otherwise.

If Donald Trump is as savvy as he tells us he is, he will understand how critical it is for his re-election chances to consider picking a new running mate.

Sorry, Mike. No time to worry about getting dumped from the GOP ticket. You have a far more important challenge facing you right now.

• K.R.K.

Comments and hot takes can be sent to: kroeger98@yahoo.com

By Kent R. Kroeger (NuQum.com, March 12, 2020)

Wealth, tourism and inexperience with communicable diseases are significant correlates with the current nation-level distribution of confirmed cases of COVID-19 (the disease caused by the coronavirus).

Using nation-level COVID-19 data from the World Health Organization (WHO) and socio-economic data from the World Bank, I developed a cross-sectional linear model to explain why, as of 10 March, some countries are seeing more confirmed cases of COVID-19 than others.

My initial findings are, on the one hand, unsurprising:

• All else equal, countries with larger populations have more confirmed cases of COVID-19;
• Countries with a high percentage of annual deaths from communicable diseases are seeing relatively fewer COVID-19 cases (mostly African, SE Asian, and Latin American countries);
• China has a disproportionate number of COVID-19 cases, even given its large population size;

And, in other cases, more thought-provoking:

• Countries with higher national incomes per capita are experiencing higher numbers of COVID-19 cases;
• Countries with higher numbers of tourists entering the country are experiencing higher numbers of COVID-19 cases.

[The linear regression model results can be found in the Appendix below]

The linear model accounts for 71 percent of the variance in confirmed COVID-19 cases on a national level.

Residual Analysis

Perhaps more interesting than those factors found to be significant correlates with COVID-19 cases were those countries where the model did not fit the data very well (see chart below).

Two countries in particular stand out in this chart: Turkey and the United States. Both have predicted values for the number of COVID-19 cases significantly higher than their reported numbers. As of 10 March, the U.S. reported 472 cases; whereas, the model suggests — based on the previously discussed variables — that the U.S. should have around 8,350 confirmed cases. Likewise, Turkey had not reported any cases as of 10 March, though the model predicts Turkey should have around 75 cases by now.

Given Turkey’s population size, economic wealth and sizable tourism industry, it is hard to believe there are no COVID-19 cases in the country. My presumption is that they are not testing for the coronavirus in any substantive way.

In the case of the U.S. we are faced with choosing one of two explanations: (1) Either the U.S. has done an unparalleled job of keeping the coronavirus from spreading, or (2) the U.S. has failed to adequately test for the coronavirus and thousands of Americans (possibly as many as 8,350 as of 10 March) have contracted this virus but have not been identified by the health care community and the CDC.

I want to believe the former explanation, but I’m afraid the answer is the latter.

With Vice President Mike Pence’s announcement last week that 1.1 million coronavirus tests would be distributed to qualified labs throughout the country, some optimism that the U.S. government is catching up to the gravity of the crisis. Unfortunately, the estimated U.S. infection numbers presented here tell a different story (see Table 1 below).

Using coronavirus test result numbers collected by British journalist Charlotte Gracias, the 1.1 million tests ordered by the U.S. government may not be near enough to handle the scale of this virus outbreak. If, in fact, there were 8,350 Americans with the coronavirus on 10 March, random testing at a national level would require over 500,000 Americans would need to be tested to find the positive cases. However, most subjects require two tests to determine if they carry the virus, meaning the U.S. will need over 1 million test kits to handle the magnitude of the outbreak on 10 March.

That was three days ago. Since then, the number of confirmed cases in the U.S. has risen from 472 to 1,268 (an over 160% increase). The U.S. government’s 1.1 million test kits won’t be completely distributed until next week (at the earliest). Yet, to handle this crisis, the U.S. already needs twice that number to handle the virus’ spread since VP Pence’s announcement.

This is a public health crisis unlike anything we’ve experienced since the 1918 Spanish Flu.

Implications

As this health crisis unfolds, I continue to test additional risk factor models using measures for volume of international trade, hospital beds/physicians per capita, quality of health care system, availability of affordable health care, demographic characteristics, income inequality, average climate, as well other variables. But so far, in every model I’ve tested, national wealth (per capita), tourism levels, and a nation’s previous experience with communicable diseases consistently maintained their statistical significance.

Of course, COVID-19 is an evolving crisis. These numbers will change over time and many of the countries not currently experiencing high numbers of COVID-19 cases (such as African and South American countries) may see their numbers rise dramatically — thereby, changing the model estimates.

In the meantime, it does appear national levels of wealth, tourism and inexperience with communicable diseases are significant upward drivers in the spread of COVID-19 worldwide.

Could it be that economic neoliberalism and globalization render us increasingly prone to these types of pandemics? Will viruses like the coronavirus become one of the recurring costs associated with the current international economic order?

The most likely answer is Yes.

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Personal Thoughts on COVID-19

I once thought epidemics and pandemics were generally restricted to African countries and places where people eat monkeys and bats.

“I’ll just get a flu shot,” I would say to myself.

Its a callousness built by time and experience.

I lived through the Hong Kong flu of 1968–69, the Swine Flu of 1976, and the H1N1 Flu of 2009 — which is to say, I have no real memory of those epidemics. None of them impacted me personally. I vaguely recall getting in line at a nearby elementary school to get the Swine Flu vaccine in 1976 and being mad at my parents because I was missing an episode of Welcome Back, Kotter.

First world problems.

That has all changed with COVID-19 (which is caused by the virus 2019-nCoV, otherwise known as the coronavirus).

As of 12 March, there are 127,749 confirmed cases of COVID-19, resulting in 4,717 deaths worldwide. Those numbers will go up a lot before this pandemic ends.

According to infectious disease expert Dr. Michael Osterholm, director of the Center for Infectious Disease Research and Policy (CIDRAP) at the University of Minnesota, told podcaster Joe Rogan that COVID-19 could be “at least 10 to 15 times worse than the worst seasonal flu (in terms of fatalities).”

“We can conservatively estimate that this could require 48 million hospitalizations, 96 million cases actually occurring, and over 480 thousand deaths (worldwide),” says Dr. Osterholm.

A conservative estimate.

For me, the overwhelming emotion right now is that of powerlessness. Beyond the obvious but impractical response — not leaving the house until this is over — I don’t know what else to do to protect my family and myself. It feels like a game of Russian roulette on a global scale. We could do everything possible — wear a mask and gloves, avoid crowds, etc. — and still get the virus.

As a statistician, I bring a clinical, dispassionate understanding of random chance and relative risk to this crisis. As a husband and parent, I am staggered by a slow-burning anxiety these days.

I just want this to be over.

• K.R.K.

Comments and suggestions can be sent to: kroeger98@yahoo.com

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Appendix: A Regression Model of Confirmed COVID-19 Cases Worldwide (as of 10 March 2020)

Variables

Dependent Variable (Source: WHO): Number of confirmed COVID-19 cases as of 10 March 2020 (log transformed)

Independent Variables (Source: World Bank):

NY_ADJ_NNTY_PC_CD = Adjusted net nat’l income per capita (current US$)

LOG_POP = Country’s total populaton (log transformed)

CHINA = Indicator variable (1 = China; 0 = all other countries)

SI_INT_ARVL = International tourism, number of arrivals

SH_DTH_COMM_ZS = Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of all deaths)

More detail on these independent variables can be found in the World Bank’s data catalog at: https://datacatalog.worldbank.org/dataset/world-development-indicators

Model Fit

Overall, the model explained 71 percent of the variance in confirmed COVID-19 cases on a national level.

Other Methodological Issues:

I should note that I used a linear regression model in this analysis. However, a Poisson model is the appropriate technique to use with count-data (such as the number of COVID-19 cases). I chose to report the linear regression model, in part, because linear regression is generally more familiar to a larger segment of readers. Nonetheless, I did run the same model using Poisson regression in SPSS and found no substantive differences in the results.

By Kent R. Kroeger (NuQum.com, March 11, 2020)

Try to avoid news coverage of the coronavirus (2019-nCoV). It is everywhere, and rightfully so. This is a serious worldwide health crisis.

And it is far from over.

In the midst of this global turmoil, an unmistakable narrative has dominated the mainstream news media in the U.S. — President Donald Trump and his administration (but mostly Trump himself) are mismanaging the containment of the coronavirus.

Politico’s Dan Diamond sums up the charge against Trump:

“For six weeks behind the scenes, and now increasingly in public, Trump has undermined his administration’s own efforts to fight the coronavirus outbreak — resisting attempts to plan for worst-case scenarios, overturning a public-health plan upon request from political allies and repeating only the warnings that he chose to hear. Members of Congress have grilled top officials like Health and Human Services Secretary Alex Azar and Centers for Disease Control Director Robert Redfield over the government’s biggest mistake: failing to secure enough testing to head off a coronavirus outbreak in the United States. But many current and former Trump administration officials say the true management failure was Trump’s.”

Assuming these allegations are true, did this mismanagement impact the U.S. response to the coronavirus? How would we know if it did?

The current worldwide numbers (as of 11 March) for the coronavirus are dispiriting:

121,564 confirmed cases.

4,373 deaths.

In contrast, the U.S. the numbers (as of 11 March) are more subdued:

1,050 confirmed cases.

29 deaths.

While all deaths are tragic, at a 2.8 percent mortality among confirmed cases, the U.S. looks somewhat better than the world-at-large (3.6% mortality rate).

But this highly-aggregated evidence does not address Politico’s accusations about Trump.

Are the confirmed cases in the U.S. a significant undercount of the true number?

If, in fact, there has been a shortage of coronavirus test kits for the limited number of labs capable of testing for the virus, how can we trust the reported number of confirmed cases?

We can’t.

The worldwide reported numbers are known to be an undercount as people with mild (or no) symptoms may not necessarily have gone to a health clinic to get tested for the coronavirus. It will be months, maybe years, after the crisis is over when epidemiologists will be able to estimate the “true” number of people infected by the coronavirus.

As of now, we know the current confirmed numbers are an undercount.

Even so, we do have a more reliable number that is less susceptible to under-counting: Coronavirus deaths.

The U.S. has 29 such deaths.

In the U.S. context, it is unlikely someone in the past two months could die from a respiratory disease and not at least be checked for the presence of the coronavirus. And while the chances are good there have been respiratory-related deaths in the U.S. since January that were not tested for the coronavirus, it is a reasonable hypothesis that this number is relatively small.

If we accept this assumption, we have the basis for assessing the performance of the Trump administration, also assuming we now the true mortality rate (risk) of the coronavirus.

“Folks want to be able to understand what the true risk is. They want to know just how deadly is it, how deadly is it to me? The challenge is, we don’t totally know,” Dr. Rebecca Katz, director of the Center for Global Health Science and Security at Georgetown University, told The Hill this week.

While that number is also unknown, epidemiologists are able to make estimates.

“The best estimates now of the overall mortality rate for COVID-19 is somewhere between 0.1 percent and 1 percent,” Assistant Secretary of Health Brett Giroir said last week during a U.S. congressional hearing.

If Giroir’s estimate range is accurate, we can derive an estimate of the range of U.S. citizens currently infected with the coronavirus.

The U.S. has 29 coronavirus deaths and 1,050 confirmed cases. At a 0.1 percent mortality rate, the number of infected Americans would be 29,000. Whereas, at a 1 percent mortality rate, that number would be 2,900.

Given there are only 1,050 confirmed coronavirus cases in the U.S., according to the World Health Organization (WHO), the reported number is, at best, a third of the actual number of cases.

Compare the U.S. numbers to Spain, where the coronavirus is particularly prevalent. Spain reports 49 deaths against 2,174 confirmed cases. Assuming a mortality rate ranging from 0.1 to 1 percent, Spain probably has between 4,900 and 49,000 actual cases of the coronavirus. At best, Spain’s reported number of cases is just under half of the true number.

Likewise, in Italy, another European country hard hit by the coronavirus, there are a reported 631 deaths and 10,149 confirmed cases. With a mortality rate between 0.1 and 1 percent, there must be between 63,100 and 631,100 actual cases.

Contrast the U.S., Italy and Spain to South Korea, where they have 54 deaths against 7,755 confirmed cases. Assuming a mortality rate between 0.1 and 1 percent, South Korea probably has between 5,400 and 54,000 actual cases. Their confirmed cases, therefore, fall within the estimated range of actual cases.

Even more distinctive is Germany, with only 3 deaths and 1,629 confirmed cases. In Germany’s case, the true range of coronavirus cases is most likely between 300 and 3,000, with the reported number of cases landing well within the likely true range of cases.

Germany, of course, may stand in contradistinction to other countries as it has a comprehensive universal health care where citizens are more likely to get early diagnoses of the coronavirus and therefore increase their chances of survival.

Nonetheless, if we accept the WHO’s reported case numbers and the scientific communities’ estimate of the true mortality rate for COVID-19, some countries are clearly doing better than others at identifying those with this virus.

The U.S. is not the worst at this task, but the country is far from the best.

As more test kits have been distributed in the U.S. and more non-CDC labs are doing the actual testing, the number of confirmed cases has spiked accordingly.

It is clear the U.S. was behind the curve in being prepared to identify and contain the coronavirus. At the same time, it is also clear that situation is improving.

Whether or not the U.S. news media will acknowledge this improvement is to be seen. That this improvement is occurring is not. That there is far more work to do be done should be equally indisputable.

• K.R.K.

Send comments and suggestions to: kroeger98@yahoo.com

By Kent R. Kroeger (NuQum.com, March 10, 2020)

President Harry Truman said of the vice presidency, “Look at all the Vice Presidents in history. Where are they? They were about as useful as a cow’s fifth teat.”

Perhaps no one has summarized the vice president’s job as succinctly as Joe Biden: “It’s easy to be vice president — you don’t have to do anything.”

Of course, that is not exactly true. There are lots of funerals to attend. And if you are a really unlucky vice president, you get stuck leading a task force charged with stopping a seemingly unstoppable infectious disease.

In other words, vice presidents, when they do get to do something, get the sh*t work.

Nonetheless, ambitious politicians still want the job, and for good reason: 14 out of 48 vice presidents have gone on to become president (almost 30 percent).

It can be the stepping stone to political immortality, or, you’re Dan Quayle.

As I sit here on the morning of the Democratic Party’s Michigan primary, the betting consensus is that former Vice President Joe Biden will be the Democratic nominee in the 2020 presidential race.

Working on that assumption, the next task for Biden is to pick his running mate. Various lists have already been offered, but I will give you the name of the person most likely to get the VP nod.

But, first, let us look at the pros and cons of the likely choices, as well as a couple of long shot candidates.

Using the PredictIt.com futures market to rank order this list, we begin with…

California U.S. Senator Kamala Harris

Pros: The junior senator from California offers a number qualities ideal for a Democratic Party vice presidential nominee: She is a mixed-race woman already tested on the national stage during the Democratic nomination race (though she backed out long before the first caucus and primary contests). When prepared, she is also a formidable debater — which is important should there be VP debates during the general election. Harris’ recent campaign appearance with Biden in Michigan also suggests Harris wants the VP slot and helping Biden get over the nomination finish line can only raise her stock in that regard.

Harris has also demonstrated her intellectual heft and charisma during U.S. Senate hearings on the nomination of Gina Haspel to Director of the CIA. Her verbal body slam of then CIA Director nominee, Gina Haspel, over the use of torture remains one of my favorite political moments of the last 10 years:

When prepared, Harris, a former prosecutor, is as intimidating as any prominent politician today.

Cons: Unfortunately, Harris has a number of flaws — the biggest being her shameless attempt to smear Biden as a racist (or, at least, for being complicit with racists). In last year’s second Democratic Party nomination debate, she accused Biden of being at worst a latent racist, and at best an enabler to racists. Sadly, Democrats use personal smears in their political arguments as often as the rest of us use adjectives. To some extent, that fact might mitigate any damage Harris’ smear will have on Biden’s VP decision. The past offers mounds of evidence that things said in the heat of battle do not affect the VP selection process. George H.W. Bush calling Ronald Reagan’s economic policies ‘voodoo economics,’ a trope that followed Reagan throughout his presidency, did not stop Reagan from picking the Bush as his running mate.

Harris also revealed another deep flaw in her political game during the nomination race. She was not nearly as charismatic on the campaign trail as she has been during U.S. Senate hearings. Like most politicians, she can’t do comedy and that became brutally apparent at campaign events in Iowa and New Hampshire.

But Harris’ biggest negative is that she offers no help to Biden in the Electoral College. California is already a solid blue state and Harris has shown no ability to attract constituencies outside of her home state. I can’t see one state Biden wins because Harris was on the ticket.

PredictIt probability of being Biden VP choice: 27% (My guess: 20%)

Minnesota U.S. Senator Amy Klobuchar

Pros: The Democratic Party establishment loves Klobuchar. Why? The obvious answer is that she’s a woman that looks like the daughter Hillary Clinton would have designed had she the power to do so.

The lifelong Minnesotan also doesn’t tip over any of the Democratic Party’s sacred cows (except among those Democrats who think the U.S. should have a health care system as good as any other in the developed world). The Democratic Party’s mega-donors have nothing to fear in Klobuchar, who proudly supports the interests of the banking, health insurance, military industrial complex, big tech and pharmaceuticals companies — all while claiming this makes her attractive to voters from both parties.

She may be right about her “centrist” credentials, but that is not why Biden might select her. Her stock is high in the Biden campaign given her demonstrable appeal to Midwest voters in Minnesota and Iowa (she finished 5th in the 2020 Iowa Caucuses at 12.3%, but higher than many expected) and likely attractiveness to voters in Wisconsin and Michigan. Three of those four states went to Donald Trump in 2016 (and Minnesota was close). If Biden carries those four states, he just needs six more electoral votes for the presidency.

Yes, Klobuchar is painfully bland, but that may be an additional asset to Biden — who is frequently (though unintentionally) too entertaining on the campaign stump. Add that quality to her possible help with the Electoral College and Klobuchar can’t be ignored.

Cons: It cannot be emphasized enough — Klobuchar is dull and tedious when she speaks, and while she doesn’t beat you down with word volume the way Mayor Pete Buttigieg does, she makes up for it with the deafening banality of her thoughts.

From her candidacy announcement speech:

“We start in this place where about a mile downriver, on a beautiful summer day, a big bridge collapsed into this river. I said on that day, that a bridge just shouldn’t fall down in the middle of America. Not one of the busiest bridges in our state. Not a bridge just a few blocks from our house that John and Abigail and I drove over nearly every day. But it happened.”

Who’s not inspired by a candidate that wants to stop big bridges from falling on beautiful summer days? She must have gone to the See Spot Run School of Political Speechwriting.

During her stint in the Democratic nomination race, I can’t remember one thing Klobuchar as ever said, policy-related or otherwise. Her Midwest Protestantism is not likely to be inspiring to undecided voters outside of her regional base. She’s just forgettable — which might be acceptable to Biden, as long as she can help tilt the balance in favor of the Democrats in Minnesota, Iowa and Wisconsin.

PredictIt probability of being Biden VP choice: 24% (My guess: 30%)

Former Georgia Gubernatorial candidate Stacey Abrams

Pros: Abrams is a Black woman who almost won a statewide race in a red state. At the risk of being too glib, that is pretty much her qualification as Biden’s running mate.

To be fair, she has a good television presence — far better than other potential VPs such as Klobuchar and Elizabeth Warren — and doesn’t seem intimidated by the national spotlight thrust upon her after he close (and arguably controversial) lose to Republican Brian Kemp in the 2018 Georgia gubernatorial race.

Still, her main qualification for VP is her identity, not her concrete policy achievements— which is not unusual in the era of hyper-partisanship, divided government, and meager legislative accomplishments.

Cons: While Georgia is politically competitive, the Republicans still control the governor’s seat and both houses of the General Assembly. Furthermore, Trump holds an eight point lead over Biden in a University of Georgia poll conducted in the state this month and there is no reason to think that will change in the next few months before the Democratic National Convention.

Finally, Abrams appeal as a VP candidate is further muted by having never held a national political office.

PredictIt probability of being Biden VP choice: 11% (My guess: 20%)

Massachusetts U.S. Senator Elizabeth Warren

Pros: Do you remember when Warren was the front runner among the 2020 Democratic presidential hopefuls? It happened, sort of. In the first weeks of October 2019, Warren was neck-and-neck with Biden as the most popular Democratic candidate. She was the candidate with ‘a plan’ and the media loved her for that brief moment in time. However, the Warren-surge ended around mid-November when she released her plan on financing Medicare-for-All, which was either a testament to sound economic rationality or clever subterfuge. Regardless of the facts, her support among progressives started to crater after that and she never recovered.

Yet, I can’t help but wonder if the woman many consider to be the smartest person running for president isn’t the perfect counter-balance to Biden, a man not generally regarded for his intellectual prowess. It is not likely that Bernie Sanders-progressives will ever embrace Warren; but for the average Democrat progressive, Warren still has some appeal and picking her as the VP is not the craziest idea. To the contrary, she is the best hope Biden has for appealing to progressives.

Biden will lose to Trump if a meaningful percentage of Sanders-progressives decide not to vote or go for a third-party candidate. To ignore them is certain defeat. Warren offers at least some attempt, no matter how imperfect, to keep them in the fold.

Cons: Warren generated some of the worst visuals I’ve ever witnessed in a presidential campaign. Her pandering to LGBTQ interests in a rainbow feather boa during the 2018 Boston Pride Parade stands out:

To see this former Republican prance around in such an unpresidential manner does not instill confidence in me that she will help Biden win in states like Ohio, Pennsylvania, Michigan and Wisconsin.

Warren is smarter than all of us. I don’t doubt that. But as my father taught me many years ago, being right is not enough. You must engage your skeptics on common ground, void of prejudice. Warren demonstrates no ability to do that.

PredictIt probability of being Biden VP choice: 8% (My guess: 15%)

The best of the rest (Hillary Clinton, Tammy Duckworth, Pete Buttigieg)

These remaining VP possibilities are interesting but mostly propelled by media-generated wishfulness. The dictionary definition of damaged goods is Hillary Clinton. Pete Buttigieg couldn’t win a statewide race in his home state. And Tammy Duckworth, the most interesting of the rest-of-field VP candidates, shows no interest in the VP position.

If I had to pick a long-shot for the Democratic VP nod, I’d take a close look at popular Michigan Governor Gretchen Whitmer. She’s an energetic campaigner and former prosecutor who doesn’t wilt under media scrutiny. Her selection would guarantee Michigan for Biden, which is not a small consideration.

PredictIt probability of Rest-of-Field: 5% or less (My guess: 5% or less)

In the final analysis, however, I believe the Electoral College calculus will dominate Biden’s VP decision. If that is the case, Amy Klobuchar will be Biden’s VP selection.

• K.R.K.

Send comments and ill-tempered partisan tirades to: kroeger98@yahoo.com

By Kent R. Kroeger (NuQum.com, March 3, 2020)

The first thing you learn when you work in an Office of the Inspector General (OIG) is that a whistleblower’s grievances should be addressed at the lowest level possible.

Why? Two reasons: For one, in most cases, the supervisors closest to the whistleblower’s complaint are generally willing to solve it. Like most people, they want to do the right thing. Secondly, some OIG offices (and their cousins, Offices of the Special Counsel) will receive thousands of whistleblower complaints each year. With investigatory staffs usually numbering less than 30, it is simply impossible to address every whistleblower complaint. Even a thoughtful triage process used to filter down to the most serious whistleblower issues will still leave a case load too large for the average IG office.

If IG and Special Counsel (SC) offices were staffed to handle all whistleblower complaints, the majority of federal government activities would be reduced to investigating itself.

Problems are best solved at the lowest level possible. [In general, that’s a good rule in life.]

In my experience at the Defense Intelligence Agency’s Office of the Inspector General (DIA-OIG), in only a small minority of cases did a Federal employee abuse his or her power in a deliberate and corrosive manner.

Yes, we have the case of a Federal employee using his government credit card to buy breast implants for his girlfriend. That is the wrong use of the government credit card. That shouldn’t require any additional explanation.

But, in most cases, Federal employees who circumvent established rules and regulations, do so either out of ignorance or with honorable intentions — in the latter case, often with a desire to grease (or avoid) the imposing, excessively bureaucratic, gears of government. That is not an excuse, but it does explain most of the law, rule and regulation violations I witnessed.

So when whistleblowers formally lodge complaints against their supervisors or chain of command, it can never be taken lightly, even as it must be placed in a broader perspective. Good people sometimes break the rules.

But my IG office also had to deal with some federal employees misusing the whistleblower laws to lodge personal complaints about supervisors and other employees.

A now-retired Deputy Inspector General in the U.S. intelligence community (USIC) would frequently remind us that whistleblower laws are not designed to address every single employee-supervisor grievance.

They are intended to protect employees from retaliation for calling out substantive violations of law, rules, and regulations. The Whistleblower Protection Act of 1998 (WPA) protects Federal employees and applicants for employment who lawfully disclose information they reasonably believe evidences:

• a violation of law, rule, or regulation;
• gross mismanagement;
• a gross waste of funds;
• an abuse of authority;
• or a substantial and specific danger to public health or safety.

Under the WPA, certain Federal employees may not take or fail to take, or threaten to take or fail to take; any personnel action against an employee or applicant for employment because of the employee or applicant’s protected whistleblowing. The Whistleblower Protection Enhancement Act (WPEA) of 2012 further strengthened those protections for Federal whistleblowers.

The WPA/WPEA legal protections have entered the public spotlight recently, first with a whistleblower complaint regarding a phone call between President Donald Trump and Ukrainian President Volodymyr Zelensky, and more recently with a whistleblower complaint regarding unsafe work conditions within the U.S. Department of Health and Human Services (HHS) as it dealt with the ongoing coronavirus crisis.

According to the February 27th Washington Post story:

“Officials at the Department of Health and Human Services sent more than a dozen workers to receive the first Americans evacuated from Wuhan, China, the epicenter of the coronavirus outbreak, without proper training for infection control or appropriate protective gear, according to a whistleblower complaint.

About 14 personnel from the Administration for Children and Families, or ACF, were sent to March Air Force base in Riverside County, Calif., and another team of about 13 ACF personnel were sent to Travis Air Force in Solano County, Calif., according to the complaint and the whistleblower’s lawyer, Ari Wilkenfeld.

The (ACF) deployments took place Jan. 28 to 31, around the time when the first planeload of evacuees arrived at March, and Feb. 2 to Feb. 7, during the time when additional flights were arriving at Travis. The planes each carried about 200 Americans who were repatriated from Wuhan.

The (ACF) workers did not show symptoms of infection and were not tested for the virus, according to lawyers for the whistleblower, a senior HHS official based in Washington who oversees workers at the Administration for Children and Families, a unit within HHS.

In some instances, the (ACF) teams were working alongside personnel from the Centers for Disease Control and Prevention in “full gown, gloves and hazmat attire,” the complaint said.

The whistleblower is seeking federal protection, alleging she was unfairly and improperly reassigned after raising concerns about the safety of these workers to HHS officials, including those within the office of Health and Human Services Secretary Alex Azar. She was told Feb. 19 that if she does not accept the new position in 15 days, which is March 5, she would be terminated.”

The whistleblower’s complaint was filed February 26th with the Office of the Special Counsel (OSC), an independent federal investigative and prosecutorial agency whose “primary mission is to protect federal employees and applicants from prohibited personnel practices, especially for whistleblowing.”

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There is no reason to doubt the charges made by the HHS whistleblower. In fact, the idea that a federal government agency would send its own personnel into a dangerous situation without proper training or equipment sounds way too familiar.

But the whistleblower complaint raises a number of red flags for me.

Firstly, there were only seven days between the HHS whistleblower being told they would be reassigned or terminated and the filing of the complaint with the OSC. Just seven days.

Having been the OIG statistician assigned with tracking whistleblower complaints, my experience was that such actions worked on timelines measured in months, not days.

It is likely powerful forces pushed this complaint up the flag pole.

Secondly, the day after filing the complaint with the OSC, the whistleblower’s lawyers provide the Washington Post with a redacted version of the 24-page complaint. Another red flag for an OIG or OSC adjudicating a whistleblower complaint. Why would a whistleblower release their complaint to the national news media a day after they filed the same complaint with an OSC?

As with the first red flag, this accelerated timeline also suggests a level of political coordination far beyond anything a typical federal employee would have at their disposal. Add the frenzy the HHS whistleblower put the anti-Trump media into and it begins to cast doubt on the motivation behind her complaint.

Thirdly, the whistleblower’s core complaint centered on employee safety; such that, it should have been handled through Occupational Safety and Health Administration (OSHA) channels, not an OSC or OIG. And while there is a definite public safety element to the HHS whistleblower complaint — which the WPA specifically cites as grounds for whistleblower disclosures — OSHA experts would have been more qualified than OSC lawyers to address the imminent risks posed by poorly-trained, unprotected HHS employees processing Americans arriving from Wuhan, China.

Finally, the Washington Post story begs a more troubling question: How is it possible Center for Disease Control (CDC) personnel, in full hazmat gear, could work side-by-side with improperly equipped HHS personnel and not correct the situation on the spot? Under Federal guidelines for infectious disease control, the CDC personnel should have been in operational control of the situation at March Air Force Base and Travis Air Force Base.

Somebody dropped the ball, but it wasn’t just HHS/ACF leadership.

The HHS/ACF staff never should have been in situations like those they faced at March and Travis Air Force Bases in late-January and early-February. The ‘all-hands-on-deck’ directive — which many civil servants use to justify deployments to dangerous hot zones — is not a free ticket to ignore federally-mandated safety protocols, which is apparently what HHS/ACF personnel did.

The circumstantial evidence suggests the HHS whistleblower was powered by forces beyond her simple professional interests.

And what were those forces? Most likely, ill-intended partisan forces.

Why does this matter?

If hyper-partisanship is allowed to drive whistleblower grievances, the legal protections provided to whistleblowers — and the IG/SC budgets needed to enforce them — could be irreparably threatened. A party out of power will inevitably seek retribution when they regain power — also known as the Third Law of Politics: For every partisan action in government, at the next turnover in party control of the government, there is an equal and opposite partisan reaction.

This tit-for-tat political dynamic never seems to impact corporate tax law, banking regulations and defense spending, but on other government matters, it can stop the march of progress like a block in the pit-entrance to a theater.

In no way am I suggesting the HHS whistleblower’s complaint is illegitimate. Quite the opposite, the complaint appears to be of such an urgent public health matter that it needed to be handled as impartially as possible — particularly in the context of Washington, D.C.’s highly-partisan political environment where the power of motivated bureaucrats and politicians to gum up the works is well-known.

As it was handled, the apparent politicization of the HHS whistleblower’s complaint significantly jeopardizes its successful and timely resolution.

• K.R.K.

Please send comments and suggestions to: kroeger98@yahoo.com