Imagine you’re moving to a new town that you don’t know much about. Where do you start?
You don’t even know what you don’t know about the place. You know the name of the town and generally where it is. So you start your discovery process. You Google it. You search around to find out if there are properties you like, whether any are in your price range or not, and where they are. You look up the crime statistics. If you have kids, you check out the ratings of the schools. You hit up Yelp! and other resources to find out about the restaurants, the parks, the churches and whatever else is available. If you know anyone who lives in the town, you call them up and ask about it. If you can, you visit the town to see what it’s like and if you can imagine yourself living there. Soon enough, you’ve gone from not knowing a lot to knowing enough to make some informed decisions about the move.
Your preconceived notions about the town have probably turned out to be wrong about something. Maybe you thought crime was high there, but it turns out that it’s lower than where you currently live. Maybe you heard the schools there were great, but it turns out they’re pretty average. That great restaurant you’ve heard a lot about closed six months ago. There’s a great church with a well-respected pastor and it has programs your family will like. You get the picture.
Now imagine yourself in charge of a task force that’s dealing with an entirely new pathogen that may (or may not!) kill a lot of people. Where do you start? You can’t Google or Yelp! this thing.
You have little choice but to rely on what others who are experiencing it are saying about it. What they say matters a lot. That’s going to inform your first take and probably lead you down certain research paths and away from others. Meanwhile, people are dying. You also study it to find out if it’s similar to anything else that has been out there. If you can get a sample of it. And you start to build a model both of the pathogen itself and how it may behave so you can make more informed predictions about it, and so you can make better models as you learn more. Meanwhile, people are dying.
That first model is not going to be very accurate, is it? You simply don’t know that much about the thing, and you don’t know what you don’t know about it. It’s a poisonous black box that you’re trying to pick open. And then it turns out that the first data you received about it — the statements of those who were dealing with it first — were wrong. Meanwhile, people are dying.
That’s where the coronavirus modelers found themselves in January and where they find themselves now. The first reports about the novel coronavirus that had sprung up in China late in 2019 were wrong. China and the World Health Organization issued misleading, even dishonest, information about it. So the modelers in the rest of the world started out trying to open up the black box when China had given them the wrong combination to the lock. What the modelers thought they did know was very disturbing. The new virus was a lot like the one that caused the Sudden Acute Respiratory Syndrome (SARS) a few years ago, only this one appeared to be far more contagious and deadlier than that one.
What they needed more of, but did not yet have, was real data. It was always going to take time to gather that data. The virus has an infection cycle of about two weeks. It doesn’t even present symptoms in many of those it infects. How many? Meanwhile, people are panicking. Politicians don’t know a lot but they know they have to act or they will be blamed. Tribal politics start taking over. Trial lawyers are just waiting to assign blame, file lawsuits and get very rich feeding off the misery. And people are becoming infected and dying.
As we’ve gone from knowing very little about the coronavirus to knowing quite a bit more over the past couple of months, we’ve seen the models revised down. To some, this is evidence that the models are wrong! Can’t believe ‘em or anything the modelers say!
Yeah, that’s wrong. The models started out with very little actual data and some assumptions based on previous outbreaks, known aspects of virology and epidemiology, human behavior, weather, and lots of other variables. The human piece of that includes cultural aspects, economics, geography, population density, mobility and other factors that change a lot. The circle of possible outcomes was very wide — millions of deaths projected! — and it’s shrinking as we learn more and as we see the effects of measures taken to halt the spread.
That’s just how science works. We have more data now. A new German study says the coronavirus has about a .4% death rate. That doesn’t sound like a lot, but it’s still four times as lethal as the common flu, which kills thousands of Americans every year. The flu killed 80,000 in the US in 2017. Supposing the German study is accurate — which we don’t know without peer review and other studies and more data — COVID-19 could kill 320,000 Americans this year. But wait! This virus is not like heart disease or other things that kill a whole lot more people every year. It appears to be much more contagious than the common flu. So it could spread faster and therefore kill even more. It could overwhelm the healthcare system. We do know it has killed a lot in Italy and it’s killing a lot in New York. There’s still a lot we don’t really know.
And that’s the point. The models should get better and more accurate over time as researchers gather more data. That the major models have been revised from the earliest versions, projecting fewer deaths, is great news. And it’s a reflection of how science actually works. The mistrust also reflects just how dangerous and stupid it is to politicize science, as the left is fond of doing to short-circuit debate, which delegitimizes and even stigmatizes research as just another political weapon.
Now, does this say anything about the models used to project “climate change”? Yes, it says a lot. They’re only as good as the assumptions and data input into them. We’ve only had about a century of consistent temperature measurement across a fraction of the earth. We don’t know why climate changed radically in the past, eons before the industrial age, why there were ice ages and such. We don’t know much about the Sun’s impact on climate change. We have only known that it’s a variable star since 1969 or so. That’s a flash in the historic timeline, practically no time at all. And tribal politics have infected the climate debate to the point that it’s more of an argument than an actual debate. Strip away the agendas and rhetoric, and the fact is, we really don’t know a lot about how the climate works and are certainly in no position to base policy on what we do know. Screaming “We only have 12 years left or the world ends!” is idiotic and irresponsible. The world has been here for 4 billion years.
We did have to base drastic policy, right away, on what little the best researchers and modelers in the world knew about coronavirus. Did they get things wrong? Yeah, they did. But they’re homing in on answers as they gather more data and will eventually know enough to be “right” about it.
Bryan Preston is the author of Hubble’s Revelations: The Amazing Time Machine and Its Most Important Discoveries.