- The challenges of lending to Main Street George Selgin, Alt-M
- What Sweden has done right on Coronavirus Joakim Book, AEIR
- The State Has Seized Many New Powers. It Won’t Let Go of Them Easily. Andrei Znamenski, Mises Wire
- When disease comes, rulers grab power Anne Applebaum, Atlantic
The future isn’t written yet
Last week Richard Epstein predicted around 500 fatalities in the United States (I originally misread his estimate to be 50,000 for the US, not the whole world). His estimate was tragically falsified within days and he has now revised his estimate to 5,000. I still think that’s optimistic but I am hopeful for less than 50,000 deaths in the United States given the social distancing measures currently in place.
Today, several US peers have become excited about a Daily Wire article on comments by a British epidemiologist, Neil Ferguson. He has lowered his UK projections from 500,000 to 20,000 Coronavirus fatalities. The article omits the context of the change. The original New Scientist article (from which the Daily Wire is derivative with little original reporting) explains that the new fatality rate is partly due to a shift in our understanding of existing infections, but also a result of the social distancing measures introduced.
The simple point is:
- Never reason from a fatality rate, and
- Never reason from a change in estimated fatalities
Policy interventions will change infection rates, alter future stresses on the health system, and (when they work) lower future projections of fatalities. When projections are lower, it is not necessarily because the Coranavirus is intrinsically less deadly than believed but because appropriate responses have made it less deadly.
No matter how old, frail or vulnerable it may be, a life isn’t something to take or risk at another’s discretion. Nor does it undermine culpability when someone dies as a result of negligence. The common law ‘eggshell skull’ rule reflects this moral principle.
During the Coronavirus pandemic, some erstwhile defenders of the famous Non-Aggression Principle (NAP) appear to have forgotten that natural rights are conceived to protect life as well as liberty and property. They seem to think that the liberties we ordinarily enjoy have priority over the right to life of others. The environment has changed and, for the time being, many activities that we previously knew to be safe for others are not. They are not part of our set of liberties until a reformed set of rules, norms and habits establishes a sufficiently hygienic public environment. To say that bans on public gatherings violate natural rights a priori is as untenable as G.A. Cohen’s claim that a prohibition on walking onto a train without a valid ticket is a violation of one’s freedom.
The clue for anarcho-capitalist state-sceptics that this is a genuine shift in social priorities is that even organized criminal gangs are willing to enforce social distancing. You do not have to believe that the state itself is legitimate to see that the need for social distancing is sufficiently morally compelling that it can be enforced absent free agreement, just as one does not need free agreement to exercise a right to self-defense.
Not every restriction is going to be justified, although erring on the restrictive side makes sense while uncertainty about the spread of infection persists. Ultimately, restrictions have to balance genuine costs with plausible benefits. But rejecting restrictions on a priori grounds does not cohere with libertarian principles. Right now, our absolute liberties extend to the right to be alone. Everything else must be negotiated under uncertainty. Someone else’s life, even two-weeks or so in the future, is a valid side-constraint on liberty. People can rightfully be made to stay at home if they are fortunate enough to have one. When people have to travel out of necessity, they can be temporarily exempted, compensated or offered an alternative reasonable means of satisfying their immediate needs.
- Human crap Gabrielle Hecht, Aeon
- “So, as Lenin asked, what is to be done?” Howard Davies, Literary Review
- What the Democrats can learn from a dead libertarian lawyer Damon Root, Reason
- The Silk Road, the Black Death, and Covid-19 Parag Khanna, Wired
- The role of the libertarian in non-libertarian societies Fabio Rojas, Bleeding Heart Libertarians
- Did I have the coronavirus? Ross Douthat, New York Times
- Hospital bed access across Canada Frances Woolley, Worthwhile Canadian Initiative
- The future of public employee unions Daniel DiSalvo, National Affairs
As COVID-19 continues to spread, fears and extraordinary predictions have also gone viral. While facing a new infectious threat, the unknowns of how new traits of our societies worldwide or of this novel coronavirus impact its spread. Though no two pandemics are equivalent, I thought it best to face this new threat armed with knowledge from past infectious episodes. The best inoculation against a plague of panic is to use evidence gained through billions of deaths, thousands of years, and a few vital breakthroughs to prepare our knowledge of today’s biological crises, social prognosis, and choices.
Below, I address three key questions: First, what precedents do we have for infections with catastrophic potential across societies? Second, what are the greatest killers and how do pandemics compare? Lastly, what are our greatest accomplishments in fighting infectious diseases?
As foundation for understanding how threats like COVID-19 come about and how their hosts fight back, I recommend reading The Red Queen concerning the evolutionary impact and mechanisms of host-disease competition and listening to Sam Harris’ “The Plague Years” podcast with Matt McCarthy from August 2019, which predated COVID-19 but had a strangely prophetic discussion of in-hospital strategies to mitigate drug resistance and their direct relation to evolutionary competition.
- The Biggest Killers:
Infectious diseases plagued humanity throughout prehistory and history, with a dramatic decrease in the number of infectious disease deaths coming in the past 200 years. In 1900, the leading killers of people were (1) Influenza, (2) Tuberculosis, and (3) Intestinal diseases, whereas now we die from (1) Heart disease, (2) Cancer, and (3) Stroke, all chronic conditions. This graph shows not that humans have vanquished infectious disease as a threat, but that in the never-ending war of evolutionary one-upmanship, we have won battles consistently since 1920 forward. When paired with Jonathan Haidt’s Most Important Graph in the World, this vindicates humanity’s methods of scientific and economic progress toward human flourishing.
However, if the CDC had earlier data, it would show a huge range of diseases that dwarf wars and famines and dictators as causes of death in the premodern world. If we look to the history of plagues, we are really looking at the history of humanity’s greatest killers.
The sources on the history of pandemics are astonishingly sparse/non-comprehensive. I created the following graphs only by combining evidence and estimates from the WHO, CDC, Wikipedia, Our World in Data, VisualCapitalist, and others (lowest estimates shown where ranges were presented) for both major historic pandemics and for ongoing communicable disease threats. This is not a complete dataset, and I will continue to add to it, but it shows representative death counts from across major infectious disease episodes, as well as the death rate per year based on world population estimates. See the end of this post for the full underlying data. First, the top 12 “plagues” in history:
Note: blue=min, orange=max across the sources I examined. For ongoing diseases with year-by-year WHO evidence, like tuberculosis, measles, and cholera, I grouped mortality in 5-year spans (except AIDS, which does not have good estimates from the 1980s-90s, so I reported based on total estimated deaths).
Now, let’s look at the plagues that were lowest on my list (number 55-66). Again, my list was not comprehensive, but this should provide context for COVID-19:
As we can see, the 11,400 people who have died from COVID-19 recently passed Ebola to take the 61st (out of 66) place on our list of plagues. Note again that several ongoing diseases were recorded in 5-year increments, and COVID-19 still comes in under the death rates for cholera. Even more notably, it has 0.015% as many victims as the plague in the 14th Century,
- In Context of Current Infectious Diseases:
For recent/ongoing diseases, it is easier to compare year-by-year data. Adding UNAIDS to our sources, we found the following rates of death across some of the leading infectious causes of death. Again, this is not comprehensive, but helps put COVID-19 (the small red dot, so far in the first 3 months of 2020) in context:
Note: darker segments of lines are my own estimates; full data at bottom of the post. I did not include influenza due to the lack of good sources on a year-by-year basis, but a Lancet article found that 291,000-645,000 deaths from influenza in a year is predictable based on data from 1999-2015.
None of this is to say that COVID-19 is not a major threat to human health globally–it is, and precautions could save lives. However, it should show us that there are major threats to human health globally all the time, that we must continue to fight. These trendlines tend to be going the right direction, but our war for survival has many foes, and will have more emerge in the future, and we should expend our resources in fighting them rationally based on the benefits to human health, not panic or headlines.
- The Eradication List:
As we think about the way to address COVID-19, we should keep in mind that this fight against infectious disease builds upon work so amazing that most internet junkies approach new infectious diseases with fear of the unknown, rather than tired acceptance that most humans succumb to them. That is a recent innovation in the human experience, and the strategies used to fight other diseases can inform our work now to reduce human suffering.
While influenzas may be impossible to eradicate (in part due to an evolved strategy of constantly changing antigens), I wanted to direct everyone to an ever-growing monument to human achievement, the Eradication List. While humans have eradicated only a few infectious diseases, the amazing thing is that we can discuss which diseases may in fact disappear as threats through the work of scientists.
On that happy note, I leave you here. More History of Plagues to come, in Volume 2: Vectors, Vaccines, and Virulence!
|Disease||Start Year||End Year||Death Toll (low)||Death Toll (high)||Deaths per 100,000 people per year (global)|
|Plague of Justinian||541||542||25,000,000||100,000,000||6,250.0|
|Japanese Smallpox Epidemic||735||737||1,000,000||1,000,000||158.7|
|Smallpox (Central and South America)||1520||1591||56,000,000||56,000,000||172.8|
|Cocoliztli resurgence (Mexico)||1576||1576||2,000,000||2,000,000||444.4|
|17th Century Plagues||1600||1699||3,000,000||3,000,000||6.0|
|18th Century Plagues||1700||1799||600,000||600,000||1.0|
|New World Measles||1700||1799||2,000,000||2,000,000||3.3|
|Smallpox (North America)||1763||1782||400,000||500,000||2.6|
|Cholera Pandemic (India, 1817-60)||1817||1860||15,000,000||15,000,000||34.1|
|Cholera Pandemic (International, 1824-37)||1824||1837||305,000||305,000||2.2|
|Great Plains Smallpox||1837||1837||17,200||17,200||1.7|
|Cholera Pandemic (International, 1846-60)||1846||1860||1,488,000||1,488,000||8.3|
|The Third Plague (Bubonic)||1855||1855||12,000,000||12,000,000||1,000.0|
|Cholera Pandemic (International, 1863-75)||1863||1875||170,000||170,000||1.1|
|Cholera Pandemic (International, 1881-96)||1881||1896||846,000||846,000||4.4|
|Cholera Pandemic (India and Russia)||1899||1923||1,300,000||1,300,000||3.3|
|Cholera Pandemic (Philippenes)||1902||1904||200,000||200,000||4.2|
|Cholera (International, 1950-54)||1950||1954||316,201||316,201||2.4|
|Cholera (International, 1955-59)||1955||1959||186,055||186,055||1.3|
|Cholera (International, 1960-64)||1960||1964||110,449||110,449||0.7|
|Cholera (International, 1965-69)||1965||1969||22,244||22,244||0.1|
|Hong Kong Flu||1968||1970||1,000,000||1,000,000||9.4|
|Cholera (International, 1970-75)||1970||1974||62,053||62,053||0.3|
|Cholera (International, 1975-79)||1975||1979||20,038||20,038||0.1|
|Cholera (International, 1980-84)||1980||1984||12,714||12,714||0.1|
|Measles (International, 1985)||1985||1989||4,800,000||4,800,000||19.7|
|Cholera (International, 1985-89)||1985||1989||15,655||15,655||0.1|
|Measles (International, 1990-94)||1990||1994||2,900,000||2,900,000||10.9|
|Cholera (International, 1990-94)||1990||1994||47,829||47,829||0.2|
|Malaria (International, 1990-94)||1990||1994||3,549,921||3,549,921||13.3|
|Measles (International, 1995-99)||1995||1999||2,400,000||2,400,000||8.4|
|Cholera (International, 1995-99)||1995||1999||37,887||37,887||0.1|
|Malaria (International, 1995-99)||1995||1999||3,987,145||3,987,145||13.9|
|Measles (International, 2000-04)||2000||2004||2,300,000||2,300,000||7.5|
|Malaria (International, 2000-04)||2000||2004||4,516,664||4,516,664||14.7|
|Tuberculosis (International, 2000-04)||2000||2004||7,890,000||8,890,000||25.7|
|Cholera (International, 2000-04)||2000||2004||16,969||16,969||0.1|
|Measles (International, 2005-09)||2005||2009||1,300,000||1,300,000||4.0|
|Malaria (International, 2005-09)||2005||2009||4,438,106||4,438,106||13.6|
|Tuberculosis (International, 2005-09)||2005||2009||7,210,000||8,010,000||22.0|
|Cholera (International, 2005-09)||2005||2009||22,694||22,694||0.1|
|Measles (International, 2010-14)||2010||2014||700,000||700,000||2.0|
|Malaria (International, 2010-14)||2010||2014||3,674,781||3,674,781||10.6|
|Tuberculosis (International, 2010-14)||2010||2014||6,480,000||7,250,000||18.6|
|Cholera (International, 2010-14)||2010||2014||22,691||22,691||0.1|
|Malaria (International, 2015-17)||2015||2017||1,907,872||1,907,872||8.6|
|Tuberculosis (International, 2015-18)||2015||2018||4,800,000||5,440,000||16.3|
|Cholera (International, 2015-16)||2015||2016||3,724||3,724||0.0|
|Measles (International, 2019)||2019||2019||140,000||140,000||1.8|
John Ioannidis, a professor of medicine at Stanford University, fears that the draconian measures to enforce social distancing across Europe and United States could end up causing more harm than the pandemic itself. He believes that governments are acting on exaggerated claims and incomplete data and that a priority must be getting a more representative sample of populations currently suffering corona infections. I agree additional data would be enormously valuable but, following Saloni Dattani, I think we have more warrant for strong measures than Ioannidis implies.
Like Ioannidis’ Stanford colleague Richard Epstein, I agree that estimates of a relatively small overall fatality rate are plausible projections for most of the developed world and especially the United States. Unlike Epstein, I think those estimates are conditional on the radical social distancing (and self-isolation) measures that are currently being pushed rather than something that can be assumed. I am not in a position to challenge Ioannidis’ understanding of epidemiology. Others have used his piece as an opportunity to test and defend the assumptions of the worst-case scenarios.
Nevertheless, I can highlight the epistemic assumptions underlying Ioannidis’ pessimism about social distancing interventions. Ioannidis is a famous proponent (occasionally critic) of Evidence-based Medicine (EBM). Although open to refinement, at its core EBM argues that strict experimental methods (especially randomized controlled trials) and systematic reviews of published experimental studies with sound protocols are required to provide firm evidence for the success of a medical intervention.
The EBM movement was born out of a deep concern of its founder, Archie Cochrane, that clinicians wasted scarce resources on treatments that were often actively harmful for patients. Cochrane was particularly concerned that doctors could be dazzled or manipulated into using a treatment based on some theorized mechanism that had not been subject to rigorous testing. Only randomized controlled trials supposedly prove that an intervention works because only they minimize the possibility of a biased result (where characteristics of a patient or treatment path other than the intervention itself have influenced the result).
So when Ioannidis looks for evidence that social distancing interventions work, he reaches for a Cochrane Review that emphasizes experimental studies over other research designs. As is often the case for a Cochrane review, many of the results point to uncertainty or relatively small effects from the existing literature. But is this because social distancing doesn’t work, or because RCTs are bad at measuring their effectiveness under pandemic circumstances (the circumstances where they might actually count)? The classic rejoinder to EBM proponents is that we know that parachutes can save lives but we can never subject them to RCT. Effective pandemic interventions could suffer similar problems.
Nancy Cartwright and I have argued that there are flaws in the methodology underlying EBM. A positive result for treatment against control in a randomized controlled trial shows you that an intervention worked in one place, at one time for one set of patients but not why and whether to expect it to work again in a different context. EBM proponents try to solve this problem by synthesizing the results of RCTs from many different contexts, often to derive some average effect size that makes a treatment expected to work overall or typically. The problem is that, without background knowledge of what determined the effect of an intervention, there is little warrant to be confident that this average effect will apply in new circumstances. Without understanding the mechanism of action, or what we call a theory of change, such inferences rely purely on induction.
The opposite problem is also present. An intervention that works for some specific people or in some specific circumstances might look unpromising when it is tested in a variety of cases where it does not work. It might not work ‘on average’. But that does not mean it is ineffective when the mechanism is fit to solve a particular problem such as a pandemic situation. Insistence on a narrow notion of evidence will mean missing these interventions in favor of ones that work marginally in a broad range of cases where the answer is not as important or relevant.
Thus even high-quality experimental evidence needs to be combined with strong background scientific and social scientific knowledge established using a variety of research approaches. Sometimes an RCT is useful to clinch the case for a particular intervention. But sometimes, other sources of information (especially when time is of the essence), can make the case more strongly than a putative RCT can.
In the case of pandemics, there are several reasons to hold back from making RCTs (and study designs that try to imitate them) decisive or required for testing social policy:
- There is no clear boundary between treatment and control groups since, by definition, an infectious disease can spread between and influence groups unless they are artificially segregated (rendering the experiment less useful for making broader inferences).
- The outcome of interest is not for an individual patient but the communal spread of a disease that is fatal to some. The worst-case outcome is not one death, but potentially very many deaths caused by the chain of infection. A marginal intervention at the individual level might be dramatically effective in terms of community outcomes.
- At least some people will behave differently, and be more willing to alter their conduct, during a widely publicized pandemic compared to hygienic interventions during ordinary times. Although this principle might be testable in different circumstances, the actual intervention won’t be known until it is tried in the reality of pandemic.
This means that rather than narrowly focusing on evidence from EBM and behavioral psychologists (or ‘nudge’), policymakers responding to pandemics must look to insights from political economy and social psychology, especially how to shift norms towards greater hygiene and social distancing. Without any bright ideas, traditional public health methods of clear guidance and occasionally enforced sanctions are having some effect.
What evidence do we have at the moment? Right now, there is an increasing body of defeasible knowledge of the mechanisms with which the Coronavirus spreads. Our knowledge of existing viruses with comparable characteristics indicates that effectively implemented social distancing is expected to slow its spread and that things like face masks might slow the spread when physical distancing isn’t possible.
We also have some country and city-level policy studies. We saw an exponential growth of cases in China before extreme measures brought the virus under control. We saw immediate quarantine and contact tracing of cases in Singapore and South Korea that was effective without further draconian measures but required excellent public health infrastructure.
We have now also seen what looks like exponential growth in Italy, followed by a lockdown that appears to have slowed the growth of cases though not yet deaths. Some commentators do not believe that Italy is a relevant case for forecasting other countries. Was exponential growth a normal feature of the virus, or something specific to Italy and its aging population that might not be repeated in other parts of Europe? This seems like an odd claim at this stage given China’s similar experience. The nature of case studies is that we do not know with certainty what all the factors are while they are in progress. We are about to learn more as some countries have chosen a more relaxed policy.
Is there an ‘evidence-based’ approach to fighting the Coronavirus? As it is so new: no. This means policymakers must rely on epistemic practices that are more defeasible than the scientific evidence that we are used to hearing. But that does not mean a default to light-touch intervention is prudent during a pandemic response. Instead, the approaches that use models with reasonable assumptions based on evidence from unfolding case-studies are the best we can do. Right now, I think, given my moral commitments, this suggests policymakers should err on the side of caution, physical distancing, and isolation while medical treatments are tested.
[slightly edited to distinguish my personal position from my epistemic standpoint]