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Flatten the Curve of Armchair Epidemiology

Noah Haber
Mar 19 · 3 min read

Vet your sources or more people will be deluded

written by Noah Haber, ScD (@noahhaber), Mollie Wood, PhD (@anecdatally), and James Heathers, PhD (@jamesheathers), none of whom are infectious disease epidemiologists

Everyone has seen messages telling you we must “act today or people will die,” COVID-19 is basically just the flu, and/or that “flattening the curve is a deadly delusion.” These often have numbers, charts, citations, retroactively edited titles (“taksies backsies”), and data “science.”

Unfortunately, all of the above are signs of DKE-19, a highly contagious illness threatening the response against COVID-19. We must act today to flatten the curve of armchair epidemiology, or we will all be in peril.

What is DKE-19?

Dunning-Kruger Effect (DKE) is a phenomenon where people lack the ability to understand their lack of ability. While strains of DKE typically circulate seasonally, a new and more virulent strain called DKE-19 is now reaching pandemic proportions.

When you’re done reading this article, this is what you’ll take away:

  • DKE-19 is coming to you.
  • It’s coming at an exponential speed: gradually, then suddenly, then suddenlier.
  • When it does, your feeds will be overwhelmed.
  • Exhausted fact checkers will break down. Some will die of sadness.
  • The only way to prevent this is social media distancing. Not tomorrow. Today.
  • That means vetting sources BEFORE you share, starting now.

What are the symptoms?

Signs of DKE-19 generally appear 3–5 days after learning that the word “epidemiology” is not the study of skin diseases. Symptoms vary, but include extreme claims, making charts, and publishing on Medium. Although most cases are mild or even entirely asymptomatic, the recent outbreak indicates that severe DKE-19 primarily affects men ages 24–36 working in tech, for reasons unknown to scientists who are unaccountably also men.

How is the infection spread?

DKE-19 is in the same family of misinformation viruses as the one that caused the b00m3R-FB outbreak in 2016. It is transmitted person-to-person through a variety of means, including listening to/repeating bullshit while on the toilet (“feco-aural transmission”), and sharing dirty tweedles.

Transmission most often occurs through casual digital contact from asymptomatic individuals. This strain tends to be hidden in well-intended partial truths, making population detection more difficult. DKE-19 can hide in viral reservoirs throughout the internet. Once infection takes hold, DKE-19 is exceedingly difficult to treat. Several cases of second-hand craniofacial injury have been reported, related to collisions between desks and actual experts’ heads.

It will only get worse

Recent lockdowns to contain COVID-19 have resulted in Bay Area tech employees having vastly more time on their thumbs. We expect that exponential growth of bullshit takes are likely to grow exponentialer until the heat death of the universe and/or last Tuesday.

Are you at risk?

We have combined the collective expertise of three people with PhDs to create a machine learning model which predicts the spread of DKE-19-related misinformation. We believe this to be the best, most accurate infectious disease model published on Medium as of the time of this writing.

Things you can do to flatten the curve

  • Wash your phone for at least 20 seconds fully immersed in soapy water
  • 6 ft of social media distancing
  • If take appears hot/feverish, seek expert help
  • Check the qualifications of authors BEFORE sharing
  • Listen to people who know what they are talking about
  • Push for better social media infrastructure that can slow the spread of DKE-19 and future strains

Only you can help #flattenthecurve of DKE-19

Thanks to Dan Larremore (@DanLarremore) and Megan Sass (@Megan_Sass) for helping edit.

Noah Haber

Written by

Meta-Research Innovation Center at Stanford (METRICS) postdoctoral researcher in meta-science, causal inference, statistics, health econ, epidemiology and HIV

Noah Haber

Written by

Meta-Research Innovation Center at Stanford (METRICS) postdoctoral researcher in meta-science, causal inference, statistics, health econ, epidemiology and HIV

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