A History of Plagues

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.Death rates

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:

Capture disease top 12

 

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:

Capture covid

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:

Capture diseases by year

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)
Antonine Plague 165 180 5,000,000 5,000,000 164.5
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
Bubonic Plague 1347 1351 75,000,000 200,000,000 4,166.7
Smallpox (Central and South America) 1520 1591 56,000,000 56,000,000 172.8
Cocoliztli (Mexico) 1545 1545 12,000,000 15,000,000 2,666.7
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
Hawaiian Plagues 1848 1849 40,000 40,000 1.7
Yellow Fever 1850 1899 100,000 150,000 0.2
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
Indian Smallpox 1868 1907 4,700,000 4,700,000 9.8
Franco-Prussian Smallpox 1870 1875 500,000 500,000 6.9
Cholera Pandemic (International, 1881-96) 1881 1896 846,000 846,000 4.4
Russian Flu 1889 1890 1,000,000 1,000,000 41.7
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
Spanish Flu 1918 1919 40,000,000 100,000,000 1,250.0
Cholera (International, 1950-54) 1950 1954 316,201 316,201 2.4
Cholera (International, 1955-59) 1955 1959 186,055 186,055 1.3
Asian Flu 1957 1958 1,100,000 1,100,000 19.1
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
AIDS 1981 2020 25,000,000 35,000,000 13.8
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
SARS 2002 2003 770 770 0.0
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
Swine Flu 2009 2010 200,000 500,000 1.5
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
MERS 2012 2020 850 850 0.0
Ebola 2014 2016 11,300 11,300 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
COVID-19 2019 2020 11,400 11,400 0.1

 

Year Malaria Cholera Measles Tuberculosis Meningitis HIV/AIDS COVID-19
1990 672,518 2,487 670,000 1,903 310,000
1991 692,990 19,302 550,000 1,777 360,000
1992 711,535 8,214 700,000 2,482 440,000
1993 729,735 6,761 540,000 1,986 540,000
1994 743,143 10,750 540,000 3,335 620,000
1995 761,617 5,045 400,000 4,787 720,000
1996 777,012 6,418 510,000 3,325 870,000
1997 797,091 6,371 420,000 5,254 1,060,000
1998 816,733 10,832 560,000 4,929 1,210,000
1999 834,692 9,221 550,000 2,705 1,390,000
2000 851,785 5,269 555,000 1,700,000 4,298 1,540,000
2001 885,057 2,897 550,000 1,680,000 6,398 1,680,000
2002 911,230 4,564 415,000 1,710,000 6,122 1,820,000
2003 934,048 1,894 490,000 1,670,000 7,441 1,965,000
2004 934,544 2,345 370,000 1,610,000 6,428 2,003,000
2005 927,109 2,272 375,000 1,590,000 6,671 2,000,000
2006 909,899 6,300 240,000 1,550,000 4,720 1,880,000
2007 895,528 4,033 170,000 1,520,000 7,028 1,740,000
2008 874,087 5,143 180,000 1,480,000 4,363 1,630,000
2009 831,483 4,946 190,000 1,450,000 3,187 1,530,000
2010 788,442 7,543 170,000 1,420,000 2,198 1,460,000
2011 755,544 7,781 200,000 1,400,000 3,726 1,400,000
2012 725,676 3,034 150,000 1,370,000 3,926 1,340,000
2013 710,114 2,102 160,000 1,350,000 3,453 1,290,000
2014 695,005 2,231 120,000 1,340,000 2,992 1,240,000
2015 662,164 1,304 150,000 1,310,000 1,190,000
2016 625,883 2,420 90,000 1,290,000 1,170,000
2017 619,825 100,000 1,270,000 1,150,000
2018 1,240,000
2019
2020 16,514

Nightcap

  1. Bernie, Cuba, literary, and ill-gotten gains Irfan Khawaja, Policy of Truth
  2. The weird global coronavirus data Scott Sumner, EconLog
  3. Why the Fed shouldn’t “Do Nothing” George Selgin, Alt-M
  4. Corporatism (“anarchy”) on the Indian subcontinent Priya Satia, LARB

There is no Bloomberg for medicine

When I began working in medical research, I was shocked to find that no one in the medical industry has actually collected and compared all of the clinical outcomes data that has been published. With Big Data in Healthcare as such a major initiative, it was incomprehensible to me that the highest-value data–the data that is directly used to clear therapies, recommend them to the medical community, and assess their efficacy–were being managed in the following way:

  1. Physician completes study, and then spends up to a year writing it up and submitting it,
  2. Journal sits on the study for months, then publishes (in some cases), but without ensuring that it matches similar studies in the data it reports.
  3. Oh, by the way, the journal does not make the data available in a structured format!
  4. Then, if you want to see how that one study compares to related studies, you have to either find a recent, comprehensive, on-point meta-analysis (which is a very low chance in my experience), or comb the literature and extract the data by hand.
  5. That’s it.

This strikes me as mismanagement of data that are relevant to lifechanging healthcare decisions. Effectively, no one in the medical field has anything like what the financial industry has had for decades–the Bloomberg terminal, which presents comprehensive information on an updatable basis by pulling data from centralized repositories. If we can do it for stocks, we can do it for medical studies, and in fact that is what I am trying to do. I recently wrote an article on the topic for the Minneapolis-St Paul Business Journal, calling for the medical community to support a centralized, constantly-updated, data-centric platform to enable not only physicians but also insurers, policymakers, and even patients examine the actual scientific consensus, and the data that support it, in a single interface.

Read the full article at https://www.bizjournals.com/twincities/news/2019/12/27/there-is-no-bloomberg-for-medicine.html!

Changing the way doctors see data

Over the past four years, my brother and I have grown a business that helps doctors publish data-driven articles from the two of us to over 30 experienced researchers. However, along the way, we noticed that data management in medical publication was decades behind other fields–in fact, the vital clinical outcomes from major trials are generally published as singular PDFs with no structured data, and are analyzed in comparison to existing studies only in nonsystematic, nonupdatable publications. Effectively, medicine has no central method for sharing or comparing patient outcomes across therapies, and I think that it is our responsibility as researchers to present these data to the medical community.

Based on our internal estimates, there are >3 million published clinical outcomes studies (with over 200 million individual datapoints) that need to be abstracted, structured, and compared through a central database. We recognized that this is a monumental task, and we therefore have focused on automating and scaling research processes that have been, through today, entirely manual. Only after a year of intensive work have we found a path toward creating a central database for all published patient outcomes, and we are excited to debut our technology publicly!

Keith recently presented our venture at a Mayo Clinic-hosted event, Walleye Tank (a Shark Tank-style competition of medical ventures), and I think that it is an excellent fast-paced introduction to a complex issue. Thanks also to the Mayo Clinic researchers for their interesting questions! You can see his two-minute presentation and the Q&A here. We would love to get more questions from the economic/data science/medical communities, and will continue putting our ideas out there for feedback!

Some more borderline fraud from the higher education industry.

From the Wall Street Journal: For Sale: SAT-Takers’ Names. Colleges Buy Student Data and Boost Exclusivity

The title pretty much says it all: the College Board is selling data about test-takers (i.e. high school students) to colleges who use that to market to a wider pool of applicants. That wider pool often includes students who don’t stand a chance of getting in to the schools that are now marketing to them, but the marketing gives the false impression that the school wants them.

Joe Six-pack Jr. takes the SAT, fills out a survey, and that survey goes into a database. Some school that normally ranks near the middle of the pack buys a piece of that database, including Joe’s data. They send him a brochure and a letter that looks like it was written specifically for him (and he doesn’t know any better) so Joe, figures he’s being recruited. Instead of just applying to his local state schools, now he shells out an extra $50 to apply to Middling University. They summarily reject his application because his SAT scores were 1100 and they’re only accepting students who scored above 1300. MU now looks a little bit more prestigious in the rankings (which means their current administration can take credit before jumping ship to take a higher paying job at a school looking to also increase in the rankings). The College Board gets paid. The administrators get paid. The U.S. News rankings get a little less useful for incoming students, but they don’t know that. On the other hand the rankings get a little more important for decision makers at schools. And Joe Jr. is funding this whole mess despite being a) the least informed, and b) the least well funded player in this whole mess.

My Startup Experience

Over the past 4 years, I have had a huge transition in my life–from history student to law student to serial medical entrepreneur. Essentially, I have learned a great deal from my academic work that taught me the value that we can create if we find an unmet need in the world, create an idea that fills that need, and then use technology, personal networks, and hard work to create novelties. While startups obviously tackle any new problem under the sun, to me, they are the mechanism to bring about a positive change–and, along the way, get the resources to scale that change across the globe.

I am still very far from reaching that goal, but my family and cofounders have several visions of how to improve not only how patients are treated but also how we build the knowledge base that physicians, patients, and researchers can use to inform care and innovation. My brother/cofounder and I were recently on an entrepreneurship-focused podcast, and we got the chance to discuss our experience, our vision, and our companies. I hope this can be a springboard for more discussions about how companies are a unique agent of advancing human flourishing, and about the history and philosophy of entrepreneurship, technology, and knowledge.

You can listen here: http://rochesterrising.org/podcast/episode-151-talking-medical-startups-with-keith-and-kevin-kallmes. Heartfelt thanks to Amanda Leightner and Rochester Rising for a great conversation!

Thank you!

Kevin Kallmes

Let’s Find Out – or: the Power of Reference

The core message of a number of books I’ve recently had the great pleasure to read has been fairly simple. Have a look. Check it out. Put your numbers in perspective. In a world awash with statistics and cognitive biases imploring us to cheer mindlessly for our own team, having the skill and wherewithal to step back and carefully ask: “can this really be so?” is golden.

One of recently passed celebrity professor and YouTube phenomenon Hans Rosling’s most profound advice for countering misinformation about the state of the world is precisely this: put all numbers in perspective. Never accept unaccompanied numbers – never believe the numerator without checking the denominator. What matters, as Bryan Caplan never ceases to emphasize as the GMU Economics creed, “are statistics, not emotions – and arguments, not stories.”

But, a statistic may never be left alone, Rosling maintains, but always compared to other relevant numbers. What share of its total category does this statistic represent? What was it last year, 5 or 10 or 20 years ago? Is there some self-evident change in associated behavior that is relevant or ought to explain it? A century ago street cars used to kill and injure hundreds of people every year, but since very few American cities make use of street cars today, the casualty is fortunately much lower. If we keep in mind that miles travelled by cars far outnumber miles travelled by street cars, reporting the number of street car deaths – while probably correct – entirely miss the point when discussing traffic safety. In How Not To Be Wrong, Mathematics professor Jordan Ellenberg quipped

Dividing one number by another is mere computation ; knowing what to divide by what is mathematics.

Here’s another example. If I told you about 23 000 individual deaths and spent a brief 10 second on each of them, going through the list would take me almost three days. On a personal level like that, 23 000 deaths is an absurd, insane, catastrophe-style event that few people are emotionally equipped to handle – essentially the size of my hometown, wiped out in a single year. If I told you those 23 000 deaths were due to antibiotic resistant diseases in the U.S. last year, the pandemic scenarios working through your mind quickly escalate. That many! Let’s find the nearest bunker!

If I then told you that cancer and heart diseases (each!) claim the lives of about 20x that, the fear of lethal apocalyptic germs consuming the world ought to quickly recede. Oh.

Here’s another example. It is entirely correct to point out that the number of people killed in worldwide airplane accidents in 2018 (556 people) was much higher than the year before (44 people) and the year before that (325 people). Would one be excused for believing that air travel is getting more risky and dangerous? Forbes, for instance, ran a roughly accurate story claiming that airline fatalities increased by 900%.

Not in the slightest. The number of fatalities from air travel has been falling for decades, all while the number of flights and miles travelled have increased exponentially, meaning that the per-flight, per-mile or per-passenger risk of death has kept dropping. Not to mention that alternative modes of travelling like driving is magnitudes more dangerous.

While Rosling teaches us to figure out what the base rate is, i.e. putting our statistic into appropriate perspective, one of Philip Tetlock’s tricks for becoming a ‘Superforecaster’ is to use Bayesian updating of one’s beliefs. This picks up precisely where Rosling’s idea left off. Once we know where to start, we have to amass more information, numbers and observations from other points of view – Bayesian updating is a popular method to incorporate and synthesize new information with the old.

In short “Calculation, like logic, is your friend” (Landsburg 2018: 44). Statistics matter and numbers can deceive. In order to better understand our realities and see through mistakes that others make – either intentionally to deceive or persuade, or unintentionally through ignorance – we must embrace the core message of people like Ellenberg, Tetlock, Duffy, Rosling or Pinker.

Always Be Comparing Thy Numbers. Never accept an unaccompanied statistic. Never trust numerators without denominators.