By Kent R. Kroeger (Source: NuQum.com, April 15, 2020)

Key Takeaways: At this point in the 2009–2010 coronavirus pandemic (9 April), the country-level variable that most strongly correlates with the incidence rate of coronavirus-related deaths is the number of hospital beds relative to population size.

Curiously, or perhaps not, there is also tentative evidence that countries with the most political freedom are experiencing higher numbers of coronavirus-related deaths per 1 million people, all else equal.

This could be a function of such countries having a harder time implementing restrictive, yet effective, mitigation and suppression policies — such as travel bans, stay-at-home orders, and business closures — due to public resistance, or…

…it could be because countries with political freedom are aided by law, economic means and/or cultural norms to be more forthcoming with accurate mortality figures.

And, of course, both statements could be true.

Accurate record keeping on deaths will be critical to our understanding of the coronavirus pandemic of 2009–2010. It is, somewhat sadly, a record keeping function governments do fairly well.

Governing authorities have collected vital records on births, marriages, and deaths for centuries. Among the earliest was China’s Xia Dynasty (2070 BC — 1600 BC), which instituted household registration as part of a larger effort to pacify the Great Floods along the Yellow River that annually devastated Chinese agriculture.

China’s Han Dynasty has the oldest surviving census data in the world, collected in 2 AD when China’s population numbered 57.7 million in 12.4 million households.

When European monarchs and aristocrats during the Middle Ages found such data collection threatening to their privileged status, Church parishes picked up the slack by keeping records on births, marriages and deaths — and in the case of deaths, it was often no more than a ledger entry acknowledging that the deceased’s family paid the Church for burial fees.

More relevantly, the World Health Organization (WHO) has been periodically measuring the completeness of cause-of-death documentation in 112 countries since 1990, with scores ranging from 0 (no cause-of-death information) to 100 (complete information on cause-of-death).

In the most recent WHO data, a little over half of all countries (including most of Europe) achieved a perfect score, and around 70 percent of countries earned a score of 90 percent or better. The countries with the lowest scores (all scoring under 60 percent) were Kuwait, United Arab Emirates, Dominican Republic, Peru, Jordan, Albania, Malaysia, Qatar, and Saudi Arabia.

While there are good reasons to suspect the numbers for national-level confirmed coronavirus cases are not unbiased measures of the virus’ actual incidence within these populations — as the number is also a function of test availability — there should be slightly more confidence that the number of coronavirus-related deaths is a more reliable outcome measure when comparing countries.

As many government experts and social scientists have already warned, the coronavirus numbers being reported by the WHO (for cases and deaths) are not unbiased and are especially difficult to compare across countries. In fact, impossible to compare across countries when there is wide variation in the implementation of mass testing, says economist Derek Scissors.

Nonetheless, there are questions that need to be answered — such as the role of political freedoms in implementing effective coronavirus mitigation and suppression policies — and we have abundant, though flawed, cross-national data to help answer them.

Statistics professors love to warn their students about the dangers of “garbage-in-garbage-out” statistical models. But I consider the dangers of “nothing-in-nothing-out” statistical models to be equally problematic.

It is possible to work with and learn from messy data, if presented with the proper caveats. And such is my attempt here.

The Research Question

What national-level factors independently correlate with the relative number of coronavirus deaths so far experienced by countries worldwide? Factors tested in this analysis initially included:

• National wealth (2019 GDP per capita)
• Income inequality (GINI coefficient)
• Political rights (Freedom House Political Rights Index — PRI)
• Medical capacity (Hospital beds per 1000 people, Doctors per 1000 people, Nurses per 1000 people, Pulmonary Admissions per 1000 people)
• National Demographic and Health Characteristics (Percent Smokers, Percent Overweight, Percent Diabetic, Percent Over 65 years old)
• Geographic location (Latitude, Longitude)
• Presence of a national universal health care system (1 = Yes, 0 = No)
• Coronavirus Testing Frequency (as of 9 April, according to OurWorldInData.org)
• Day Since First Coronavirus-related Death (based on WHO data)

The Dependent Variable

The number of coronavirus-related deaths per 1 million people (as reported to the WHO) as of 9 April 2020. The total population numbers were obtained through The World Bank Open Data portal. Due to this variable’s skewed, non-normal frequency distribution, its logged value was used in the statistical models.

The Countries Analyzed

As so often is the case, it is hard to find complete data for all countries — and even if we did, is it actually helpful to include the Faroe Islands or Nauru when the central inquiry is more about China’s Cultural Revolution-scale virus suppression and mitigation strategies (e.g., hundreds of thousands of volunteers checking people’s temperature, logging their movements, and overseeing quarantines) than if or why small North Atlantic or Pacific islands are able to avoid the coronavirus pandemic?

Therefore, for my analysis here, I limited the countries to those with at least 3 million people which were also categorized as upper-middle-income or upper-income by The World Bank. The three million population cut-off eliminated many of the countries with large amounts of incomplete data.

Within these 91 countries there is significant variation (as of 9 April) in the number of COVID-19-related deaths per 1 million people.

The 10 countries with the most COVID-19-related deaths per 1 million people (as of 9 April) are:

The 210 countries with the fewest COVID-19-related deaths per 1 million people (as of 9 April) are:

After dropping any remaining countries with too much incomplete data on the key variables, I was left with 91 countries for the linear model estimates. I also repeated the model estimates using only the upper-income countries (n = 48), losing most of the non-democratic countries from my original sample (including China and Iran).

Since the parameter and model fit estimates were only marginally different between the two frames (except for one variable — political rights — which will be discussed in more detail), I will only present the linear model results for the 91 countries. Linear model estimates for the 48-country frame are in the Appendix at the end of this article and diagnostics are available upon request to: kroeger98@yahoo.com.

Estimation Methods and Software

All results presented here were conducted using the software package, JASP, a free and open-source graphical program for statistical analysis supported by the University of Amsterdam. Its easy-to-use graphical interface resembles commercial software like SPSS and includes options for standard (frequentist) and Bayesian linear regression models (see Figure A.3 in the Appendix for some of the Bayesian model output).

The Results

Data on COVID-19 is the foundation on which we understand this pandemic and how it is spreading.

Data equips us to respond effectively to this threat, both as individuals and as a society. More importantly, it allows us to learn the best individual-level and societal-level countermeasures to fight against this threat.

The most important data definitions, therefore, are what defines being a carrier of the coronavirus and its role in a final disease outcome (i.e., surviving versus death).

The World Health Organization defines a confirmed COVID-19 case as “a person with laboratory confirmation of COVID-19 infection.” By definition, therefore, a death resulting from COVID-19 requires a positive test result on the coronavirus. This is the assumption I use when modeling coronavirus-related deaths worldwide.

The first step in understanding the relationship between variables is to look at their bivariate correlation. In the case of coronavirus-related deaths we see some interesting correlations in that regard. Figure 1 through 3 show the bivariate relationship between COVID-19 deaths and the number of hospital beds (per 1,000 people), the number of doctors (per 1,000 people), and the Political Rights Index (as measured by the Freedom House).

Figure 1. Bivariate relationship between COVID-19 deaths and hospital beds per 1000 people.

In the case of hospital beds (per 1,000 people), the relationship has some nuance. Overall, there is no clear relationship. But if we focus a subset of countries based upon economic development, there is an apparent connection. Wealthy, high-developed economies with a relatively low number of hospital beds per 1,000 people (e.g. Spain, Italy, UK, Sweden, Switzerland, Belgium, US, Luxembourg, Ireland and France) seem to have a high incidence of COVID-19-related deaths. Whereas, among highly-developed countries with a relatively high number of hospital beds per 1,000 people (e.g, Japan, South Korea, and Russia), the incidence rate of COVID-19-related deaths is much lower.

Figure 2. Bivariate relationship between COVID-19 deaths and Freedom House’s Political Rights Index.

The relationship between political rights and COVID-19-related deaths is similarly nuanced. Within the highly-developed economies (mostly occupying the far-right quadrant of the graph in Figure 2) there seems to be no relationship; however, when adding the upper-middle-income countries, the relationship becomes decidedly positive: countries with the most political freedom are suffering the highest incidence rates of COVID-19-related deaths.

Is this a function of their greater ability of less-free countries to constrain citizen rights in the pursuit of effective coronavirus containment strategies? Or is it a function of the higher quality (and honesty) of data emanating from freer countries?

Figure 3. Bivariate relationship between COVID-19 deaths and doctors per 1000 people.

Lastly, in Figure 3, we have the relationship between the number of doctors per 1,000 people and COVID-19-related deaths. As the relative numbers of doctors in a country is correlated with the relative number of hospital beds, the bivariate plot with COVID-19-related deaths is similarly positive in form. However, we do not see a similar subset relationship within highly-developed economies. Unlike the hospital beds graph, we do not see a cluster of highly-developed economies in the lower-right-hand quadrant of the doctors-per-1000-people graph. At least in the bivariate context, it does appear the relative number of doctors in a country is positively related to the relative number of COVID-19-related deaths.

Can a country with too many doctors per capita cause COVID-19-related deaths? Of course not. That is not the conclusion anyone should draw from Figure 3.

What is true is that upper-income countries are experiencing significantly more COVID-19 deaths per capita than upper-middle-income countries. Thus, predictor variables strongly correlated with national wealth (such as Doctors per 1,000 people and Freedom House’s Political Rights Index) tend to be positively related to the number of COVID-19-related deaths. Those are not necessarily causal relationships.

My hunch as to why the relative number of doctors is positively correlated with coronavirus deaths is that wealthy countries with a high percentage of citizens over 60 years old will tend to have more doctors (per capita). Those most vulnerable to the coronavirus (the elderly) are also more likely to live in an advanced economy.

That is what I think is going on.

We learn more when we consider all of the predictors jointly in a linear regression context.

Figure 4 shows the linear model estimates for COVID-19-related deaths using the following independent factors for each country: (1) its latitude (at its geographic center), (2) its political rights index score, (3) its incidence of doctors (per 1,000 people), (4) its incidence of hospital beds (per 1,000 people), (5) its GDP per capita, and (6) the number of days since its first COVID-19-related death (log transformed).

Along with GDP per capita, other independent factors that were not significant predictors of COVID-19 morality rates included: the presence of a universal health care system and the relative frequency of coronavirus testing. Additional, more dynamic, models will need to be tested to determine the how those otherwise important variables might impact COVID-19 mortality rates.

Figure 4. Linear model for COVID-19-related deaths per 1 million people (log transformed) for upper- and upper-middle income countries with at least 3 million people (n = 91).

Using the standardized beta coefficients in Figure 4 as an indication of the relative importance of each independent factor, it appears the number of hospital beds (per 1,000 people) is the most importance correlate with COVID-19-related deaths (standardized beta = -0.521), followed by days since first COVID-19-related death (standardized beta = 0.512), political rights (standardized beta = 0.490), latitude (standardized beta = 0.445), and doctors per 1,000 people (standardized beta = 0.445).

The overall linear model offers a decent fit to the data with 72 percent of the variance explained.

[Casewise diagnostics for selected countries are available in Figure A.2 in the Appendix below]

Figure 5 illustrates the impact of standard deviation changes in the predictor variables in which the countries have some control over the factor (i.e., a country can’t changed its geographic location).

Figure 5. How standard deviation changes in the “controllable” predictor variables relate to the number of COVID-19-related deaths.

The most notable finding in Figure 5 is how a relatively modest increase in hospital beds per 1,000 people can have a meaningful impact on the number of COVID-19-related deaths (per 1 million people). An increase of 2.38 hospital beds per 1,000 people would reduce a country’s relative COVID-19 death totals by 2.5 people; a 4.4 hospital bed (per 1,000 people) increase would translate into 6.5 fewer COVID-19-related deaths (per 1 million people).

Keep in mind, this is a static cross-sectional (one-point-in-time) model where each country enters the model at a different stage in the coronavirus process. China and South Korea passed through the peak stages of the crisis over a month ago, while Italy and Spain reached their peaks a few weeks ago, and the U.S. is only now moving past the peak of its crisis. For this reason, the variable — days since the first COVID-19-related death — is employed to control for these stage differences. It is an imperfect control, to be sure.

Ideally, the estimated model would be constructed in a dynamic, time-series context in which predictor variable interactions could also be tested for significance. Quite likely, some regression parameters will vary by country-types, for example.

However, for the sake of simplicity, the static model reported here still reveals the relative importance of the independent factors, even without the full dynamic model.

Figures 6 and 7 compare linear model estimates to the actual values for COVID-19-related deaths. Figure 7, specifically, filters down to the major countries in this analysis.

Figure 6. Actual versus Predicated Values from a Linear Model for COVID-19-related Deaths per 1 million people.

Figure 7. Actual versus Predicated Values from a Linear Model for COVID-19-related Deaths per 1 million people (Selected Countries Only).

One rule-of-thumb in outlier identification is to look for cases with a Cook’s D of greater than 3. As seen in Figure A.2 in the Appendix, China has a Cook’s D value of 38. China is an obvious outlier and a good reason for re-running the linear model using only upper-income countries (which excludes countries like China and Iran and leaves us with only 48 cases).

However, by excluding countries like China and Iran, the statistical significance of Freedom House’s Political Rights Index disappears (probability of t-value = 0.154, two-tailed test), for the likely reason that when eliminating upper-middle-income countries from the analysis we lose much of the variation in the Political Rights Index.

Still, if we split the 48 countries into two groups — Countries with High Political Rights Index scores (PRI > 33) and Countries with Lower Political Rights Index scores (PRI =< 33), a group that includes the U.S., Israel, Hungary, South Korea, Bahrain, Brunei, Qatar, UAE, Saudi Arabia, Singapore, Oman and Kuwait — we can compare the mean number of COVID-19-related deaths (per 1 million people) for the two groups.

The low political freedom countries had an average number of COVID-19-related deaths of 6.9 versus 50.8 for the high political freedom countries. And, as it turns out, the t-statistic for the difference-in-means test (t = 1.718, p = 0.093, df = 46) is still not significant.

Nonetheless, I believe the insignificance of the political rights factor is one of methodology and sample size and not its inherent importance. With a more systematic approach, I expect we will see the significant role a country’s political rights had on how they responded to the coronavirus pandemic.

Final Thoughts

Clearly missing in the linear model results presented here are controls for the types and timing of coronavirus mitigation and suppression policies implemented by each country. Some countries adopted broad, systematic testing protocols early (South Korea, Taiwan, Singapore, Germany) and the end benefit has been that they’ve experienced fewer deaths per capita than countries that came to systematic, mass testing late.

CNN’s recent story on the success of German efforts to control the coronavirus reinforces the findings from the model reported here. Writes CNN’s Angela Dewan, Henrik Pettersson and Natalie Croker:

The difference appears to be the German healthcare system’s huge capacity. Germany is projected to need about 12,000 beds at the peak of this outbreak in the middle of the month, according to projections from the Institute for Health Metrics and Evaluation. It has over 147,000, more than 10 times its need.

In contrast, the US has around 94,000 beds, some 15,000 beds short of its need. Germany has more spare beds in intensive care units that Italy has altogether.

In fact, Germany’s health system has such a large capacity, its hospitals are now treating people for coronavirus from Italy, Spain and France.

Hospital beds are merely a proxy for the real factors that have saved lives during the coronavirus pandemic of 2019–20. With a hospital bed comes the hour-by-hour care of nurses and other health professionals. There is no substitute for a hospital bed. The fact that more New York City residents than usual are dying at home due to the coronavirus should not be a surprise to anyone.

It is all about capacity. Do you — city, state, or country — have enough hospital beds to handle a surge in need? Figure 1 (above) reveals this simple reality. South Korea and Japan (and to a lesser extent, Germany and Austria), have enough hospital beds to handle a crisis like the coronavirus.

You can’t ramp hospital beds late in the game (as China did) and expect to escape a virus’ rampage. There must be planning. As World War II Allied Commander, General Dwight Eisenhower, once said: “In preparing for battle I have always found that plans are useless, but planning is indispensable.”

Such is the lesson of the 2019–20 coronavirus pandemic.

But there are other lessons.

My Russian-born friend, Zhora, an American citizen, recently told me, “Americans do not know how to be sick.”

Americans work when they are sick. They cough on you without consideration or remorse. They are working, after all. Why should they be sorry? Americans wear their illness among workmates like a badge. People at home sick are not working. What good are they? They aren’t getting the big promotion.

A new economic world is forming under our feet. Workaholics aren’t the modern gladiators they pretend to be. Since the coronavirus pandemic, they are genuine threats to the public good.

This is a preliminary analysis. For one, the coronavirus (2019-nCoV) and its associated disease (COVID-19) are still working through the world’s population. And secondly, explaining COVID-19-related deaths requires a dynamic modeling approach (i.e., references to time) to fully understand the process by which this pandemic has played out worldwide so far.

Stay tuned…and stay home, for now.

• K.R.K.

Datasets used in this analysis can be obtained by request to: kroeger98@yahoo.com

APPENDIX

Figure A.1. Linear model for COVID-19-related deaths per 1 million people (log transformed) using only upper-income countries (n = 48).

Figure A.2. Casewise diagnostics for linear model of COVID-19-related deaths per 1 million people (log transformed) for upper- and upper-middle income countries with at least 3 million people (n = 91).

Figure A.3. Posterior distributions and credibility intervals for Bayesian regression coefficients in a linear model of COVID-19-related deaths per 1 million people (log transformed) for upper- and upper-middle income countries with at least 3 million people (n = 91).

By Kent R. Kroeger (Source: NuQum.com, April 12, 2020)

“The government are very keen on amassing statistics. They collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But you must never forget that every one of these figures comes in the first instance from the village watchman, who just puts down what he damn pleases.”

— Josiah Stamp (1880–1941), an English industrialist, economist, civil servant, statistician, writer, and banker

Barely a month into the coronavirus pandemic, the congressional Democrats and Republicans are already viewing this human tragedy through their narrow, partisan lenses.

And it was only two weeks ago they were working so well together, when the Congress passed and President Trump signed the $2 trillion bailout bill — U.S. history’s greatest wealth transfer from the U.S. Treasury to wealthy Americans.

That is what Democrats and Republicans can agree on when the American people are preoccupied with whether to go to the grocery store to re-stock their water and food supply, thereby risking coming down with the scariest virus since the fictional Andromeda Strain.

Now that both political parties have guaranteed that wealth inequality is baked into the American economic system for at least another generation, they can get back to pretending they can’t agree on anything.

And what is the current partisan disagreement de jour? Whether the Americans should go back to business-as-usual, or continue to shelter-in-place until every last known RNA segment of the coronavirus has been wiped from the face of the earth — or, at least, the blue states.

While the bailout bill guarantees the Top 1% will survive the pandemic with their bank accounts intact (plus a hefty Christmas bonus), a significant percentage of Americans are genuinely at risk of going financially bankrupt if this pandemic lockdown extends beyond summer. Even Trump understands those people represent voters who could potentially tip the balance against him in November. The Democrats, likewise, led by Alexandria Ocasio-Cortez and the Cuomo brothers, Governor I’d-Better Cover-My-Backside and Fredo, see a tremendous opportunity to extend the Blue Wave started in the 2018 midterm elections.

The Republicans, for good reason, want the U.S. economy back to normal as quickly as possible. Every day this near-national lockdown extends, the more Republicans, independents, and disaffected Democrats who are adversely affected and potentially more likely to vote against Trump.

On the other side, the Democrats are saying if end this lockdown too soon we risk a second wave of the coronavirus. And what is too soon, according to the Democrats? Anything that ends this economic catastrophe before November is my guess.

Most disturbing about this morbid ballet dance now going on in Washington, D.C. and in the national media is how both sides cherry pick the coronavirus numbers that best support their policy position. There is nothing new about that. Politicians do that on every issue.

But it is particularly damaging in the case of coronavirus pandemic because it indeed puts lives and livelihoods at additional risk. Furthermore, it perpetuates the myth that the statistical numbers governments and independent agencies are producing for decision making about the coronavirus are accurate…when, in fact, they are not.

That is not an assertion on my part. It is a well-documented characteristic of the statistics gathered during previous epidemics and pandemics. The numbers initially collected and tabulated are invariably under-counts of the true spread and mortality rate of a virus.

The 2009 swine flu pandemic is a perfect example. In August 2010, over a year after the pandemic started, the World Health Organization (WHO) had confirmed 1,632,710 cases of the swine flu worldwide and 18,449 resulting deaths. The WHO knew it was an under-count and reported the numbers explicitly with that caveat.

It would not be until August 2011 and June 2012 that the WHO and U.S. Centers for Disease Control (CDC) would release their modeled estimates of the 2009 swine flu’s actual reach worldwide: 700 million to 1.4 billion people infected and between 151,700 and 575,400 dead.

And those modeled estimates too are subject to error and dispute.

So why is the news media telling us the U.S. has had six times more confirmed cases of COVID-19 than China (530,200 to 83,134 at last count), when they know (and we know, and even China knows!) that isn’t close to true.

Some estimates put China’s COVID-19 cases and deaths at up to 10 times the government-sourced numbers, including my own estimate. And it is not just China’s numbers being questioned. The Italian government’s own civil protection authority has estimated Italy’s true case numbers are 10 times higher than officially reported, and for many of the same reasons used to dispute China’s numbers, including: (1) lack of testing kits, (2) unmeasured asymptomatic cases, (3) symptomatic cases who never sought medical help, (4) misclassification by health officials (e.g., failing to properly categorize COVID-19-related pneumonia cases, (5) government changing definitions for COVID-19 classification, and (6) simple human error.

Yet, there is no reason to pick on China and Italy. The U.S. numbers most likely are equally wrong — and for the same reasons.

Of course, Fox News and the conservative blogosphere have found reasons to believe the U.S. is over-counting coronavirus cases and deaths, noting an apparent drop this year in CDC reported pneumonia-related deaths, compared to previous years.

“For the last few weeks, that [pneumonia] number has come in far lower than at the same moment in previous years. How could that be?” Fox News host Tucker Carlson asked on his show recently.

The Washington Post’s Aaron Black rightly noted that the CDC pneumonia-related death numbers were not down by much and historically those CDC-reported numbers tend to be under-counts (that word again!) when the numbers are first reported. The CDC will produce more accurate numbers later in the year (and take their word for it, those numbers will be right!).

While Trump’s own director of the National Institute of Allergy and Infectious Diseases, Dr. Anthony Fauci, also knocked down that Fox News theory, it is a reasonable assumption that some pneumonia-related deaths have been classified as coronavirus-related despite no lab-test confirmation (recall Reason #1 above for why national coronavirus counts are likely wrong).

The question shouldn’t be if this has happened, but, how frequently this has happened. And asking that question is not tacit support for Fox News’ conspiracy theory, it is merely an acknowledgement that these coronavirus statistics contain error.

In my introductory statistics classes I always shared with students what data scientists call The Confusion Matrix:

It shows the potential for error in any measurement process. In this example, the matrix is updated to apply to the COVID-19 pandemic.

If COVID-19 existed in a perfect world, every government would accurately measure every citizen for the coronavirus on a regular basis. Everyone would either fall into the True Positive category or the True Negative category.

That is not happening…not in the U.S….not in China…not in Peru…not in Germany…not even in South Korea (the apparent gold standard on how to measure and contain the coronavirus).

The vast majority of world citizens have not been tested for the coronavirus and probably will never be tested. According to OurWorldInData.org, 8.3 million tests (as of 8 April) have been reported by government authorities worldwide. But we already know that number is low because some countries — particularly outside Europe, North America and East Asia — have not reported their testing numbers (including China).

But even if we double the testing number, only about 0.2 percent of world citizens have been tested for the coronavirus — and that number is squishy because countries don’t always distinguish between the number of tests they’ve administered (some people require more than one test) versus the number of people tested.

Keeping in mind The Confusion Matrix, Chinese scientists report preliminary evidence that the most common type of COVID-19 test — the reverse transcriptase polymerase chain reaction (RT-PCR) test — may give false-negative results around 30 percent of the time.

Dr. Harlan M. Krumholz, a professor of medicine at Yale University and director of the Yale New Haven Hospital Center for Outcomes Research and Evaluation thinks the U.S. false-negative rate is even higher.

”Unfortunately, we have very little public data on the false-negative rate for these tests in clinical practice,” says Dr. Krumholz. And there are false-positive tests cases as well — though our better-safe-than-sorry bias makes us less concerned about those cases.

In sum, of the six disposition cells in The Confusion Matrix, four work against the accuracy of any number being generated about the coronavirus. We can minimize how many people fall into these cells, but can’t empty these cells entirely.

This is why modeled estimates are so critical to understanding the coronavirus and its associated disease, COVID-19. They explicitly acknowledge classification error and try to compensate for it. The models aren’t perfect, God knows can I attest to that, but they are typically better than the raw data when it is initially collected.

Which is why the political debate right now about when the U.S. can return to normal (which I don’t believe will happen until there is a vaccine) is so unproductive; and, indeed, does harm to the general public’s understanding of the coronavirus.

The Trump administration, congressional Republicans, and their loyal media outlets, are starting to minimize the severity of the coronavirus and risk re-igniting its spread if they follow through on plans to return the U.S. to near business-as-usual too soon (in May if Trump gets his way).

Likewise, congressional Democrats have cherry-picked worst-case scenario forecasts (which are fast being proven wrong, particularly predictions about the U.S. death toll) that do more to create panic in the general public than build their knowledge about the coronavirus.

Underwriting the fact-denial and fear-mongering are the coronavirus numbers which offer evidence for both sides. But the reality is that we still don’t know how far the coronavirus has spread within the population (morbidity rate), its contagiousness (R0 — “R naught”), or its mortality rate [with estimates ranging from the “common flu” (0.1%) to SARS-like numbers (10%) in some locations].

As long as we don’t have a better handle on these numbers, our political debates are going to be disproportionately driven by the hourly updates on Johns Hopkins University’s COVID-19 Dashboard website. That is a bad thing because these numbers are not accurate.

[According the COVID-19 Dashboard, the number of U.S. coronavirus cases just rose from 530,000 to 542,000 while I am writing this article.]

Economic research blogger, Christopher Balding, puts it best: “The panic and debate clearly suffers from profound misunderstandings.”

That is probably another under-estimate.

• K.R.K.

Datasets used for this article are available upon request to: kroeger98@yahoo.com

By Kent R. Kroeger (Source: NuQum.com, April 9, 2020)

Key Takeaways: There is no systematic evidence that the overall quality of a country’s health care system has had an impact on the spread (morbidity rate) and lethality (mortality rate) of the coronavirus. Instead, a country’s per capita wealth and exposure to the international economy (particularly international tourism) significantly increases the spread of the virus within a country. This latter finding may be partly a function of wealthier populations being more likely to have their coronavirus-related illnesses diagnosed and treated. But it is also likely that international travel is spreading the virus worldwide.

As for the mortality rate, the story is more complicated: The single biggest driver of the mortality rate, so far, is simply the time since the country’s first coronavirus-related death. Once the virus has found a vulnerable host, the final outcome may be difficult to change (at least for now).

As for the charge by the US intelligence community that China has under-reported the coronavirus’ severity in their country, the model reported here suggests China, given its size and characteristics, should have so far experienced 10 times the coronavirus cases they have reported and a case fatality rate twice their current estimate. If they are under-reporting, as charged by the US, China may have between 33,600 to 70,000 deaths related to the coronavirus, not the 3,339 they are currently claiming.

To the contrary, it is also plausible that their aggressive suppression and mitigation efforts have successfully limited the spread and lethality of the coronavirus. The model reported here cannot determine which conclusion about China is true. Or if both conclusions have truth.

_________________________________________________________________

Its OK to feel some tentative optimism about the coronavirus pandemic. It does appear, finally, that the virus and its associated illness — COVID19 — is peaking in many of the countries hardest hit by the virus (see Figure 1).

Figure 1: New daily COVID-19 cases in Italy, South Korea, Iran and Spain

Almost a month-and-a-half after the coronavirus reached its peak in new daily cases in South Korea (around 900 cases-a-day), the virus has peaked in Italy around March 22nd, and in Spain and Iran around April 1st.

If President Donald Trump’s advisers were correct in Monday’s White House daily coronavirus update, the U.S. may also witness its peak in new daily cases within the week.

This weekend, New York, the current locus of the US outbreak, saw a significant decline in the number of new infections and deaths.

“In the days ahead, America will endure the peak of this pandemic,” Trump said Monday.

In fact, from April 6th to 7th, the aggregate US data showed its first day-to-day drop in the number of new COVID-19 cases since late March (see Figure 2).

Figure 2: Cumulative and new daily COVID-19 cases in the U.S.

In many of US states hardest hit by the coronavirus — such as New York, Washington, and California — the number of new cases each day have leveled off or declined in the past week.

These are genuine reasons for optimism. While Trump’s hope for an economic return to near-normal by Easter was overly optimistic, the possibility it could happen in early May is not.

Europe and the U.S. were caught flat-footed by the coronavirus, but it is looking increasingly like they will escape with far fewer cases and deaths than originally anticipated by many epidemiological models.

[Of course, additional waves of this virus may still occur and we may never see a true return to normal until a coronavirus vaccine is made widely available — and by widely available I mean free to everyone.]

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In this moment of cautious cheer, my questions increasingly focus on how the world measured (and mismeasured) this pandemic and what national-level factors may have suppressed and, conversely, aided the spread of the coronavirus?

Everyone has theories. Some are convinced autocratic countries (i.e., China, Iran, Venezuela, Russia) have hidden the true impact of the coronavirus on their countries. Others have declared the coronavirus proves the importance of universal health care in containing such viruses. Still others have conjectured the number of COVID19-related deaths have been over-reported by anti-Trump forces, most likely to make Trump look bad. Conversely, the national media has unofficially declared (without conclusive evidence, as usual) that the US government has been under-counting COVID19 deaths (presumably to make the Trump administration look more effective in its coronavirus response than justified).

It is speculation at this point. It will be many months — probably years — before we know what actually happened during the 2019–20 Coronavirus Pandemic. The coronavirus pandemic is still on-going, after all, and the reality is: counting the number of people with any disease or virus is genuinely hard and prone to human error.

But we can start to address some of the controversies, if only tentatively.

If we assume that the majority of countries have exercised a fairly high level of due diligence in measuring the presence of the coronavirus within their jurisdiction, we may be able to identify those countries who have been much less than honest.

Moreover, after controlling for suspected dishonest coronavirus measurement, we may also see hints at the impact of national health care systems and containment policies on the spread and lethality of the coronavirus.

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Let us start our inquiry with this premise — there are two fundamental measures of the coronavirus: (1) the number of confirmed coronavirus cases relative to the total population (morbidity rate), and (2) the number of coronavirus-related deaths as a percent of those confirmed to have the virus (mortality rate).

For simplicity’s sake, what I am calling the mortality rate is actually the case fatality rate. In reality, the coronavirus’ mortality rate is much lower than the case fatality rate as its calculation will include undiagnosed cases experiencing only minor or no symptoms.

If universal health care were ever to show its value, now is the time. The logic is simple: Countries where citizens do not need to worry about the cost of a doctor visit, the probability these citizens get tested and treated early for the coronavirus is significantly higher.

Also, countries with universal health care may also be more likely to institute broad-based coronavirus testing, thereby identifying asymptomatic super-spreaders of the virus. Subsequently, when diagnosed with the virus, these citizens will be isolated sooner from the healthy population. Furthermore, early diagnoses of the coronavirus may also improve the chances infected individuals survive the virus.

Can we see this in the data?

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Figure 3 (below) is produced directly from World Health Organization (WHO) data. The chart shows the morbidity rate of COVID-19 (i.e., frequency of COVID-19 cases per 100K people) compared to its mortality rate (i.e., deaths per confirmed case).

I’ve segmented the chart in Figure 3 into four quadrants, each defined by countries’ morbidity and mortality rates. Countries with high morbidity and mortality rates are in the upper right-hand quadrant of Figure 3 (e.g., Italy, France, Spain, Netherlands, UK and Iran.); while countries with low morbidity and mortality rates are in the lower left-hand quadrant (e.g., Russia, Japan, Pakistan, Nigeria, and India).

Figure 3: COVID-19 Cases per 100K persons versus Number of Deaths per Confirmed Case.

What does Figure 3 tell us? In truth, not much.

Ideally, a country would want to be in the lower left-hand quadrant (Low/Low) of Figure 3, right? But a simple inspection of the quadrant reveals it is occupied mainly by countries in eastern Europe, Africa, South America and southern Asia (Russia, Ukraine, Pakistan, India, Nigeria, among others) — few of which find themselves ranked by the WHO among the countries with the best health care systems. One reason for their favorable performance so far may be that the coronavirus hasn’t significantly spread to those countries yet — after all, many are in the southern hemisphere.

Here are two fair questions to ask: Are these countries performing relatively well with the coronavirus due to favorable circumstances (fewer people traveling to and from coronavirus sources like China; climatic context; stronger containment policies — an area where authoritarian governments may have an advantage; and/or better health care systems)?

Or, are some of these countries simply not deploying the resources and expertise necessary to measure the impact of the coronavirus? Do they even have the capacity to do so?

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Figure 3 begs more questions than it answers, but it still may hint at some tentative conclusions. For example, experience tells me countries clustered around the intersection of the average country-level morbidity (34 cases per 100K people) and mortality rates (3.4%) are in the accuracy ballpark. If I am feeling generous, that list includes the US and China, along with countries like South Korea, Poland and Turkey.

The countries that raise my eyebrows are the major outliers from the center cluster: Italy, Spain, UK, France, Bangladesh, Nigeria, Indonesia and India.

The variation in the coronavirus mortality rate ranges from 12 percent in Italy to near zero percent for New Zealand (a country with 1,239 confirmed cases and only one death). What could possibly explain this difference in the coronavirus mortality rate between two advanced economies? Could it be their health care systems? WHO ranks Italy’s health care system 2nd in the world, while New Zealand’s is only 41st. Russia has a reported coronavirus mortality rate of 0.8 percent and has the 130th best health care system in the world, according to the WHO.

More in line with expectations, Germany, a country given significant positive coverage for its coronavirus response — plaudits comparable to perhaps only South Korea’s — has a reported 2.1 percent mortality rate on a base of 113,296 confirmed cases.

Why such discrepancies in reported mortality rates?

Dietrich Rothenbacher, director of the Institute of Epidemiology and Medical Biometry at the University of Ulm in Germany, credits Germany’s broad-based, systematic testing as being the reason his country’s mortality figures are hard to compare to other countries.

“Currently we have a huge bias in the numbers coming from different countries — therefore the data are not directly comparable,” Dr. Rothenbacher recently told the BBC. “What we need to really have valid and comparable numbers would be a defined and systematic way to choose a representative sampling frame.”

This is where statistics — my profession — becomes critical. As Dr. Rothenbacher asserts, Germany would not have understood the extent of the coronavirus crisis without testing both symptomatic and asymptomatic cases, just as South Korea and, sadly, only a few other countries have done.

Systematic random sampling needed to be a component of every nation’s coronavirus testing program.

It wasn’t.

In New Jersey, where I live, the office of the state’s Health Commissioner told me I couldn’t get tested for the coronavirus without meeting one of the following qualifications (…it felt like a job application):

• Already being hospitalized and showing symptoms of COVID-19.
• A health care worker showing symptoms and having who been exposed to others known to have the virus
• Anyone known to be part of a cluster outbreak (one example being a recent Princeton, NJ dinner party where multiple attendees were diagnosed with the coronavirus)
• And vulnerable populations (e.g., nursing home residents).

Someone like me, a 55-year-old male with no underlying health problems but showing mild flu symptoms — low-grade fever, persistent cough, and chest congestion — cannot get tested in New Jersey.

The New Jersey testing protocol is common across the U.S. given the relative scarcity of testing kits.

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Anytime the anecdotal evidence is contradictory or unclear, I turn to data modeling — even if crude — to test some of the initial hypotheses surrounding a controversy.

The challenge with the coronavirus is the availability and data quality of the key causal factors we’d like to test in a coronavirus model for morbidity and mortality rates. In the following linear models, I tested these independent variables:

(1) GDP per capita,

(2) Health care system overall quality (according to the 2019 edition of the CEOWORLD magazine Health Care Index),

(3) Quality of health care professionals (also measured by CEOWORLD magazine),

(4) Exposure to foreign tourism (as measured by World Bank’s measure of international tourist arrivals and international tourism receipts as a percent of total exports),

(5) Exposure to East Asian trade (measured as the percent of total merchandise exports),

(6) Percentage of population over 65 years old (as measured by the World Bank),

(7) Hospital beds per 1,000 people (as measured by the World Bank),

(8) Total COVID-19 tests per 1,000 people (as measured by OurWorldInData.org), and

(9) an indicator variable for autocratic countries the U.S. government or U.S. news media have accused of under-reporting COVID-19 cases and deaths (i.e., China, Russia, Iran, Vietnam, Turkey and Venezuela).

Since countries come into the coronavirus pandemic at different points in time and are in different stages at any point in time, I also controlled for (10) the days since the first coronavirus case was confirmed, and (11) the days since the first coronavirus death occurred.

Out of necessity, I limited the data analysis to countries with reliable data on all key independent measures and with populations over 3 million people, leaving the analysis with 76 countries.

[Note: The linear models, however, were not weighted by country population size. For example, China weighted the same as Serbia in the following models.]

The estimated linear models for morbidity and mortality rates are reported in the Appendix below.

Figures 4 and 5 show the model predictions for each country versus the actual morbidity and mortality rates. In the morbidity model graphic (Figure 4), I only show a selection of key countries in order to simplify the data presentation.

Figure 4: Predicted versus Actual COVID-19 Cases per 100K Persons for Selected Countries (as of 4 APR 2020).

Figure 5: Predicted versus Actual COVID-19 Deaths per Confirmed Cases (as of 4 APR 2020).

On the issue of autocratic countries (who are also U.S. adversaries), there is circumstantial evidence that Venezuela, China and Russia have fewer COVID-19 cases than we would expect given their key characteristics, even while their deviance as a group is not statistically significant.

For example, China may have 10 times the coronavirus cases they have officially reported and a mortality rate twice their current estimate. If true, China may have between 33,600 to 70,000 deaths related to the coronavirus, not the 3,339 they are currently claiming.

Likewise, Russia may have 19,500 coronavirus cases, not the 10,031 they have reported to the WHO and Venezuela may have 1,625 cases, not 167 cases.

Even if, according to the model, the reported numbers for China, Venezuela and Russia are low, we can’t rule out the possibility they are low because these countries have done a superior job containing the virus.

Perhaps the most puzzling (and saddest) case is Iran. Our model suggests Iran has experienced far more COVID-19 cases than we would expect given its characteristics. The most recent WHO numbers for Iran are 66,220 confirmed cases and 4,110 deaths.

Has Iran done an especially poor job of containing the virus or are they measuring more comprehensively than other countries? Unfortunately, my model can’t settle that point.

Final thoughts

I anticipated when I started looking at the coronavirus in 76 countries that the quality of their health care system s— starting with affordable, universal health care — would show up as a significant factor in distinguishing between countries that successfully took on the coronavirus pandemic (e.g., South Korea, Germany, Singapore, and Japan) and those less successful (e.g., Italy, Spain, France, UK and Iran).

While the number of hospital beds per 1,000 people does correlate significantly with lower mortality rates (see Appendix, Figure A.2), the overall quality of a country’s health care system did not. In fact, countries with the best compensated medical professionals actually have higher coronavirus mortality rates.

The coronavirus has hit Europe (and China) the hardest. In Italy, the high percentage of elderly helps explain its high volume of cases, but that can’t be the only explanation. And isn’t just that advanced economies have put more effort into measuring the occurrence of the virus in their communities that explains this fact. The coronavirus has found disproportionately more friendly hosts in these societies. We may have to accept that the coronavirus is one of the evolving risks associated with high disposable incomes and deeps global connections through trade and tourism.

I know this: I will never go on a cruise ship ever again.

Theories on why some countries handled the pandemic better than others are also plentiful. The most compelling analysis may have occurred while the pandemic was just starting.

Writing in early March, Chandran Nair, founder and CEO of the Global Institute for Tomorrow, may have come up with the best explanation still. “Strict and centralized enforcement of lockdowns, quarantines, and closures are the most effective way to contain the virus,” wrote Nair. “What’s emerged from the coronavirus crisis is the fact that some states are equipped to handle this type of action, and some are not — and it has little to do with development status.”

Or, more cynically, could we conclude that one of the costs of emphasizing individual freedom is that when collective action is necessary — including a strong, central state response — Europeans and Americans answer the call by hoarding toilet paper and Jim Beam?

I’m not quite there yet. For one, I don’t believe Nair fully appreciates how the modern state and elites consolidate their power during these uncertain times, and how this can leave even more people vulnerable economically and physically to the next pandemic — and there will be another one. Second, for every example of state power getting this done quickly and efficiently, there are dozens more where greed, incompetence, and arrogance lead the state to do more damage than good. Before we give the modern state more power, let us think this through some more first.

Here is what our governments and scientific community should be doing…

If this global pandemic ends relatively soon — as it appears it might — our governments and health researchers must immediately resolve themselves to understand how many people really did get infected by the coronavirus and how many actually died from its consequences.

Currently, we have a global mish-mash of epidemiological data of unknown quality or generalizability. Only probability-based sample studies can give us the real numbers and it is only with those numbers that we can really sit down and decide: What worked and what was a total waste of time and resources?

• K.R.K.

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

APPENDIX: The Linear Models

Figure A.1: Linear Model for Confirmed COVID-19 Case per 100K Persons (Morbidity)

Figure A.2: Linear Model for COVID-19 Deaths per Confirmed Cases (Mortality)

 

By Kent R. Kroeger (Source: NuQum.com, April 2, 2020)

According to recent a Pew Research Poll, 59 percent of Democrats (and those Democrat-leaning) said the coronavirus outbreak is a major national threat, but only one-third of Republicans (and those Republican-leaning) had the same view.

Partisans in the U.S can’t even leave politics out of a worldwide pandemic. Perhaps German writer Thomas Mann was right when he told a German audience in 1930 that “politics is everything.”

Still, we can’t all agree the coronavirus pandemic is a national crisis?

Nope, apparently not.

And our hyper-partisan political culture is a having a tangible impact on coronavirus suppression and mitigation strategies at the state-level.

For example, 37 governors have issued statewide stay-at-home orders, according to CNN. Notable, however, are those states that not issued such a statewide order: Alabama, Arkansas, Iowa, Missouri, Nebraska, North Dakota, Oklahoma, South Carolina, South Dakota, Texas, Utah and Wyoming.

Every one of the stay-at-home laggard states was won by Donald Trump in the 2016 election. Furthermore, among governors in Trump-states that have implemented stay-at-home orders, many have Democratic governors: Kansas, Michigan, North Carolina, Pennsylvania and Wisconsin.

The state that surprises me the most is Iowa — my home state. While the Hawkeye State is not immune to partisan rancor, the state has a long history of leading on important socially progressive policies (public schools, racial integration, Head Start programs, marriage equality, etc.).

Not anymore.

“I can’t lock the state down,” Iowa Governor Kim Reynolds told reporters last Tuesday. “I can’t lock everybody at home.”

But, actually, Iowa law says she can — she just chooses not to.

According to the Iowa governor, the “internal data” she is looking at concludes that a stay-at-home order is not necessary.

What “internal data” is she talking about? She isn’t clear on that. Is it a super secret data modeling method that forecasts coronavirus transmission? Who knows?

And, while I’m picking on Iowa, a big ‘Boo’ on the Iowa press corps for not pressing Reynolds on exactly what “internal data” she is using to make this life-and-death decision. By the time Reynold’s secret data source signals a stay-at-home order is necessary, it will be too late for hundreds of Iowans who will die because her short-sighted response to the crisis.

Says Reynolds about her reluctance to issue a stay-at-home order, such a policy would put undue strain on the supply chain and essential workforce.

Is Iowa’s economy more important than its retiree population? Reynolds might not put it exactly that way, but by delaying the stay-at-home order (and she will issue such an order at some point), that is precisely what she is saying.

With one of the oldest populations in the U.S., Iowa is particularly susceptible to a coronavirus outbreak in nursing home and long-term care facilities. In fact, Reynolds shared some of her coronavirus data when she admitted a Cedar Rapids (Linn County) long-term care facility — Heritage Specialty Care — is already experiencing an “outbreak” with 30 confirmed cases.

As the tragic experience at the Life Care Center in Kirkland, Washington attests, once an outbreak starts within a vulnerable population living in close quarters, the virus is impossible to stop and the results are deadly.

If Reynolds’ internal data isn’t telling her that “Iowa is a ticking time-bomb,” then it is junk data.

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Now that I’ve vented my frustration about Iowa, here is the really distressing thing I’ve discovered in the U.S. data regarding the coronavirus: There is clear evidence that the extent to which states are testing for the coronavirus is significantly impacted by partisan politics.

I get it that some people believe the ‘coronavirus panic’ is more hype than reality. Why didn’t the world lock-down during the 2009 Swine Flu pandemic that killed 150,000–575,000 worldwide, including approximately 12,000 Americans, according to a Centers for Disease Control (CDC) estimate?

It is a legitimate question, though Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases, aptly responds with three reasons the coronavirus is different from the 2009 Swine Flu: (1) The 2009 swine flu pandemic was spread out over many months and didn’t place nearly the level of strain on the world’s health care system that the coronavirus has done in just a matter of a weeks; (2) the medical community has extensive experience developing vaccines for influenza viruses and, in the 2009 pandemic, a vaccine was developed within a year of the outbreak (There is no such assurance that a vaccine will be developed as quickly for the 2019 coronavirus); and, finally, (3) we don’t know the contagiousness and lethality of the coronavirus yet — only research and time will settle that question. In the meantime, prudent levels of action (such as lockdowns) are well-advised, according to Dr. Fauci.

Yet, there will always be people suspicious of the news media and politicians anytime drastic measures such as lockdowns and stay-at-home orders become common, particularly when it has a dramatic effect on the economy. At times during this pandemic, I have included myself in that skeptical crowd.

But not when it comes to testing.

Broad-based, systematic testing is a critical element of any national effort to contain a highly contagious and deadly virus, according to health organizations like the CDC and the World Health Organization (WHO). A country can’t address a problem effectively if it doesn’t understand the breadth of the problem.

As former NASA Flight Director Gene Kranz might say: ”Let’s work the problem people. Let’s not make things worse by guessing.”

Broad-based, systematic testing for the coronavirus removes some of the guesswork out of policy making during a pandemic.

Unfortunately, coronavirus testing is not being consistently implemented by U.S. states, as of 30 March. As Figure 1 (below) shows, the states that have conducted the fewest coronavirus tests per 100,000 people tend to be states that Trump won in 2016 — red states like Oklahoma, South Carolina, Texas, Alabama, Mississippi, Kentucky, Georgia and, of course, Iowa.

Figure 1: State-level coronavirus testing per capita (Data source: The COVID Tracking Project)

Republicans will rightfully point out that many of the states doing the most testing are also red states: Louisiana, Utah, North Dakota, Alaska, South Dakota and Montana. All have tested, so far, more than the national average of 359 coronavirus tests per 100,000 people.

Before concluding that there is a genuine political bias in the application of coronavirus testing, I tested a simple regression model to explain levels of coronavirus testing at the state-level. Included in my model were the following control variables: (1) the number of people hospitalized in the state for the coronavirus, (2) percentage of tests coming back negative (a proxy for states with broader testing procedures that include people without symptoms), and (3) an indicator variable for states where large (>50) infection clusters occurred (NY, WA, LA).

Additionally, I tested numerous political variables, two of which were significant predictors of state-level testing: (1) Trump’s statewide share of the 2016 vote, and (2) an indicator variable for states with a Republican governor but a Democrat-controlled state legislature.

According to the model, the relative importance of each variable in explaining state-level coronavirus testing are as follows (in ranked order; numbers in parentheses indicated standardized coefficients and the direction of the relationship):

• States with large-cluster infections (+0.65)
• Number of coronavirus patients hospitalized (-0.56)
• Percent of tests coming back negative (0.33)
• Trump’s share of 2016 statewide vote (-0.29)
• A Republican governor in a Democrat-controlled state (0.21).

The finding that states with the most coronavirus hospitalizations tend to have lower rates of testing, all else equal, is not as surprising as it sounds. Its significance is driven by the fact that the states with the most hospitalizations tend to be bigger states, which will also tend to have lower per capita levels of testing given their large populations.

More intriguing is the finding that Trump states, all else equal, are testing for the coronavirus at lower per capital levels. For every 10 percent of Trump’s 2016 vote share, the state tests 41 fewer people (per 100,000 people), all else equal.

Likewise, in Democrat-controlled states and holding other factors constant, the presence of a Republican governor adds 148 tests per 100,000 people.

See, not all Republicans are bad.

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This may be the most important statistic that has so far emerged during the coronavirus pandemic: Twenty-five percent of those infected with the coronavirus may be asymptomatic, according to the CDC. These are people who carry the virus, show none of its COVID-19 symptoms, yet may still be contagious.

Super-spreaders are a likely source of many cases of the coronavirus worldwide.

To my mind, the super-spreader is the scariest aspect of the coronavirus. Given this virus kills between 0.1 and 3 percent of those who have it, identifying asymptomatic coronavirus carriers — or, at least, the prevalence of these people in the general population — should be among the highest priorities for government agencies charged with controlling the coronavirus.

Without large-scale levels of testing, including the systematic testing of people without any COVID-19 symptoms, it is impossible to know the extent of the super-spreader problem.

The time is long over for Republican governors in red states to operate under the fiction that the coronavirus pandemic is a phantasm of the liberal (Trump-hating) media. The time is now to lock-down and test.

• K.R.K.

To Iowa Governor Kim Reynolds: If your “internal data” is not telling you to lock-down, stay-at-home and do far more testing of Iowans, you need new “internal data.”

Requests for the data used in this analysis can be sent to: kroeger98@yahoo.com (It is also available at www.COVIDtracking.org)

APPENDIX:

A Linear Model of State-level Coronavirus Testing