How COVID Vaccines and Fake Data Drove People Insane: Justin Hart | Clip | Crossroads

The world was lied to at nearly every step in the COVID-19 pandemic and in the rollout of the vaccines. The lies then caused a type of madness, as people battled over the facts and over their own versions of reality. This is according to Justin Hart, data analyst and author of Gone Viral, How COVID Drove the World Insane. We speak with Justin Hart about what the data really shows us on COVID-19, infections, vaccine effectiveness, and the realities that were kept from the public. Hey, Justin Hart, thanks for being on Crossroads. Josh, great to be with you today. Hey, Justin, you know, you're an interesting figure on kind of the forefront, challenging a lot of the data around the vaccines, the lockdowns, the mandates, because you're not really on the journalism side, you're not really on the science side, you're on the data side. And really, it seems like the data is what's being messed with. I guess just broadly, you know, what have you seen in terms of data, the data we're using to inform all of our decisions on COVID-19? What have you seen with this, kind of on a broad brush? Well, you're right, Josh. I think one of my colleagues called me a data contrarian, which is an apt title for that. MIT dubbed me the head of the anti-mask brigade, right? I'll take that moniker, too. But, you know, it started out early in March 2020, when I was looking at the data, when Dr. Fauci got before Congress and said that one out of 100 people would die of the disease. And I said, that's not what I'm seeing when I do the math. My background is actually in some statistics, some data, some machine learning, but also in marketing funnels. Now, that applies pretty quickly because you have, like, people that get sick and then it goes down the level to people that get hospitalized and people that die. This is how they track, for example, the burden of influenza on a yearly basis. So that started out, but I remember late March thinking to myself, I'm really sticking my neck out here, right? What if I am wrong? What if this is like the apocalypse, as the other side of the equation likes to say, team apocalypse, we call them. What if this really is as bad as the 1918 pandemic? Well, on that night, I remember I woke up to an article by John Ioannidis from Stanford. He is the most cited living scientist today. And he called out what he said, we might be living through a once in a century data fiasco. And that's what we saw. We saw that the data that they're putting out was primed for fear mongering. And it really did little to reflect the truth of what was happening. Just a perfect example. A lot of people consider their risks so high. But John Ioannidis broke it down very quickly within his first study. Within the first two months, he said, the risk of COVID if you're under the age of 65, the risk of dying of COVID is about the same as dying on your commute to work. And the risk of anyone over the age of 65 is about the risk that a professional truck driver faces. So these, you know, when you take that in consideration, people pause and say, well, why did we do a one size fits all quarantine, social distancing, school closures, business closures, lockdowns? It made no sense, Josh. So the overarching data on the danger of the virus is what kind of, you know, kind of started you on this. You talked about it as data, data fiasco we're facing. What is the fiasco we're facing with this? Well, the motivating factor, I'll be clear, was I had a bunch of great clients at the beginning of 2020. And I'll tell you real quickly, one was high-end vacations for baby boomers. Another one was for high-end family vacation club. And then another one was a client I was working with who is building a digital dashboard for parents wanting to send their kids to college. All of those were dead, dead, dead by the end of April. You can imagine golf excursions for baby boomers were not terribly popular in the spring of 2020. And so they may have been verboten. So that was the motivating factor, which is, wow, you know, I usually I'm not an epidemiologist. I'm not a health care expert, but I'm a darn good data guy. And normally I wouldn't insert myself into someone else's domain. Josh, they had no problem inserting themselves into my domain, my kids' education, my church, my gym, my grocery store. And that root sort of element of the American experience, right, the pursuit of happiness was just quelled. And so that was the motivating factor. When we came out, the results, you know, we looked at, I mentioned the the actual risk of fatalities. But the average age of death, for example, was 80 years old compared to the 1918 pandemic, where the average age of death was 29 year olds. I think we would have a much different experience. I'd probably have a much different reaction if a bunch of millennials, I have kids that are aged and I have young kids, too, were felled all around us. But in truth, the impact of society is much different for someone who dies at the average age of death that they usually would at 80 years old versus someone who's in their 20s and 30s. And then the way that the data was set up, it's very laggy. Now, this is a problem that we have in our democracy, in our capitalist society. If you go over to the UK, everyone's health care records are kept in the same system. You can do some very good drill downs. But here in the United States, we have municipal hospitals, religious hospitals, V.A. hospitals, county hospitals, state hospitals. Right. And all of them have their own products and procedures and services. All of them feed their data up to the county in a specific way. And then that data is fed up to the state. And then that data is fed to The New York Times dashboard. And by the time it gets there, it's lagged so much that people get freaked out. So I remember one weekend early in April when Colorado just was scared to death because they came in Monday morning and found 200 people had died of covid over the weekend. But 200 people didn't die of covid over the weekend. It was just the lag of records that we've been collecting and collecting up until Monday. The best analogy I had is if I if I went to my kids room and I found a whole bunch of Starburst wrappers like 70, 80, 200 Starburst wrappers, I'd be going, I'm going to take this kid to the ER. He just completely OD'd on Starburst. But let's say he was, you know, dutiful autist and he dated each one of the wrappers on when he ate them and it went back a month. Now, I'm not so concerned. That's what happened with our data.