On Saturday, “growth hacker” Aaron Ginn published a data-heavy piece called “Evidence over Hysteria“.
Ginn, who received a Bachelor’s of Science degree in Education and co-founded the Lincoln Network (a firm that allows collaboration between technology professions to for “advocacy, disrupting beltway bandits and reducing barriers to entry for new tech”). The analysis he did related to the pandemic sprung from his marketing experience, and his concepts how an idea or innovation “goes viral”.
In a nutshell, Ginn’s article concludes that the 15-day plan and the subsequent multi-state “stay-hat-home” approach will do more harm than good. He concludes that the mortality rates do not justify the enormous potential damage to the economy.
After watching the outbreak of COVID-19 for the past two months, I’ve followed the pace of the infection, its severity, and how our world is tackling the virus. While we should be concerned and diligent, the situation has dramatically elevated to a mob-like fear spreading faster than COVID-19 itself. When 13% of Americans believe they are currently infected with COVID-19 (mathematically impossible), full-on panic is blocking our ability to think clearly and determine how to deploy our resources to stop this virus. Over three-fourths of Americans are scared of what we are doing to our society through law and hysteria, not of infection or spreading COVID-19 to those most vulnerable.The following article is a systematic overview of COVID-19 driven by data from medical professionals and academic articles that will help you understand what is going on (sources include CDC, WHO, NIH, NHS, University of Oxford, John Hopkins, Stanford, Harvard, NEJM, JAMA, and several others). I’m quite experienced at understanding virality, how things grow, and data. In my vocation, I’m most known for popularizing the “growth hacking movement” in Silicon Valley that specializes in driving rapid and viral adoption of technology products. Data is data. Our focus here isn’t treatments but numbers. You don’t need a special degree to understand what the data says and doesn’t say. Numbers are universal.
And there is where I part ways with Ginn. Numbers aren’t universal in this case. The pandemic has different effects on regions, depending on a myriad of factors: The age of the population, genetics, gender, the quality of the nation’s healthcare, the number of smokers, the level of obesity, etc.
Italy’s mortality rate is a good case in point. The nation has a grater number of elderly than most countries, and its combined with a weak national health system that declares anyone with COVID-19 as having died of the disease, even though that may not be the case.
And here is another reason why number’s aren’t universal: Americans value the individual. I suspect most people in this country will be willing to follow the Presidential 15-Day Guideline to save lives, as long as drug treatments arebeing distributed, test kits expedited, ventilators produced, and other means to cure the disease and alleviate its effects are put in place. The fewer the dead, the greater the victory.
That being said, the article was a very thoughtful attempt to distill all the news and assess the productivity of the response. It apparently struck a nerve, too. At one point, the piece had over 2 million views….before Medium took it down.
While I am not sure why Medium took this action, part of the reason may be the result of this analysis by Dr. Carl Bergstrom, a professor of biology who also does mathematical and computational modelling at the University of Washington.
I respect Bergstrom’s credentials. However, his remarks drip with snark, derision, and contempt for Ginn’s piece that distracted from some very fair points being made. But this is Twitter, which is the Thunderdome of social media:
Unfortunately, anyone with a more conservative point of view than Bergstom had to wade though a lot of right-hate through his discussion:
While Ginn’s analysis may be flawed, both discussions should be allowed to stand. The scale of response to the Wuhan Coronavirus is worthy of a robust debate.
Frankly, I am worried that, like in the “global warming” studies, bad data in is leading to erroneous conclusions. The faulty determinations may lead to bad policy choices. I can only hope our policy makers have a plan for returning Americans back to work after this 15-day plan has ended, which I think is the proper action once all the drugs, tests, and medical equipment have been put in place.
“Evidence over Hysteria” shows that Americans are ready for real answers to the crisis, showing that they will digest data-heavy articles and daily press briefings to get them.
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