A Stanford professor of medicine who also has a doctorate in economics, Prof Jay Bhattacharya has questioned the conventional wisdom on the death rates from Covid-19 that governments all over the world are using to model their responses. He said it is likely to be orders of magnitude lower than the estimates of 3.4% of the World Health Organization. The death rates, he believes, are much closer to that of flu. He has subsequently concluded a study in Santa Clara Country, California that he released at the end of last week that confirms his claims. In a Youtube podcast, he explained to Hoover Institution fellow, Peter Robinson that the study used an antibody blood test to estimate how many people had been infected. Prof Bhattacharaya said his study meant that governments need to rethink modelling and their policies on Covid-19. – Linda van Tilburg
We drew a sample of people from Santa Clara County – basically using a Facebook targeted ad strategy. We looked at about 3,200 people in these drive -through testing facilities that we set up on the fly. They took the fingerprints and then we just looked to see if the fingerprint test showed evidence of antibodies to Covid-19. Why is that important? Because those antibodies imply very strongly that you had Covid-19 previously – some sort of infection with SARS -CoV-2 virus (which is the technical way to put it). We’ve figured out that somewhere between 2.8% – 4% of Santa Clara County has had evidence of Covid infection. OK, so what does that mean? So first thing; right around the time when we were doing this study there had been about a thousand cases of Covid infections – active SARS-CoV-2 infections found within the county. There’s about 2 million people in the county. If 4% have it (have evidence of the infection) – that means that there’s about 85 times more people who’ve had it per person than the amount actually identified as having it.
___STEADY_PAYWALL___
That’s the critical finding.
Yes, and if it’s on the low end – it’d be 2.8% – it would be 50 times. So, for every single person that the healthcare system in Santa Clara County has identified as having the virus actively in them – there are 50 people out there who had it and never showed up with a positive test. The Covid infection is substantially more common in the population than we’d realised prior to this study. So, it’s not as deadly as we might have thought. If 50 people had it – for every person that we’ve identified as having had the infection previously – we’re counting the number of deaths we divide by the number of people we think have it. We say, ‘OK gosh – somewhere between 1 and 2 percent of people who have it die’. But if there’s 50 people who have had it and cleared it – then you get a much lower fraction (death rate). Yes, there’s some more nuance to that calculation (it’s not worth going too much into). The bottom line is that once you do that nuance, it’s probably about as deadly as the flu or a little bit worse. Instead of having a death rate – like the World Health Organization said – three in a hundred (so you get Covid and three out of a hundred people die) – our estimates suggest about somewhere between one and two in a thousand die. A lot of the people that have it probably never knew – they had it and cleared it – and they could have infected other people very easily. It is very infectious, as you say.
Even when you’re totally asymptomatic?
Yes. I think it’s less likely that you’ll spread it if you’re asymptomatic, but it’s possible. I think part of the issue is that, as we talked about last month, the tests themselves are relatively new and I’ve learned a lot about the characteristics of these tests and the errors they have in the course of doing this research. It’s stuff that people didn’t know – I didn’t know a month ago. The worry about the error rates of these tests have slowed down some of the spread of these kinds of studies. Obviously, it hasn’t slowed everybody down. Maybe I’m foolish to have gone ahead, but I think it was the right thing to do because I believe this is critically important information. Even though I know the tests are going to get better (as better research gets done on them by immunologists all over the country) – I’ve rushed ahead because I think this number will help make policy. An early version of this number (even if it’s a little more error prone), rather than a late version of this number with better tests is incredibly important, in my view. We’ll see. I hope that people take this number and start to put into their models a better and more accurate picture of how extensive this epidemic actually is, and I think they will.
Your advice for a few officials – if we could sit each of these down in your office and you could give them a sentence or two of advice. What does that imply?
One – run these studies everywhere and keep running them until the epidemic is done. Otherwise, we’re making policy on the basis of no real information or very little real information. The second thing is – redo the models. You’ve seen these models about flattening the curve – redo them once we have these studies, take a very close look at the available resources of the hospital and ask, ‘If I lift the caps – will I really stress the hospital systems or not?’. We could follow the same structure, except now – with real numbers, It could be that in many, many places around the country, including California, it’s safe to lift the caps. So, the next step for me is – run the studies everywhere, redo the models, and take a hard look and ask, ‘Is it really worth it to suppress the economy if I’m not going to stress the hospital systems and have Covid-19 patients die as a result of it?’. Nearly every country on earth has implemented these kinds of economic caps – the global macroeconomic numbers are incredibly scary. It looks like a Great Depression. The health effects of that in the United States are going to be bad. In poor countries and in poor people in poor countries – it’s going to be absolutely catastrophic. Those lives count for something and they should count for something in the calculus. They’re not counted right now in the calculus – we’re just counting Covid deaths. Don’t worry about the fact that the tests aren’t perfect yet, because they’re good enough to get numbers that will guide policy right now and we desperately need to do that so that we can actually start to do the right thing. I don’t mean to say that they were doing the wrong thing. I mean to say that we’ll now start to have enough information so that we can figure out what the right thing is. These studies are really, really critical. I think weighing the effects of this shutdown policy on other non-Covid deaths should also weigh on President Trump’s mind. Those lives matter too, the health and well-being of those people matter as well – all those that are thrown out of work, that die of depression (all of those really bad things) – those lives count. President Trump, I say, should count both the lives of the people that are dying from Covid and the people that are going to be dying from the depressions. It’s not going to go away and suppressing the economy forever so that it goes away seems like it’s too costly – it is too costly in lives. So the question is what do we do about it – we’re going to have to learn to live with it, in some sense. If it’s a one in a thousand risk of dying (from getting it) – we could learn to live with it, right? If it’s three in one hundred – maybe not.
But the ultimate aim is to reduce this – to contain it the way we have contained what we think of as the ordinary flu. Is that the correct hope?
Yes, that’s my hope. And also, that better treatments could become available. All of that is fine – but the question is – ‘do we shut everything down?’. The only reason you would ever do that is if it was a three in a hundred death rate – then you might do that. If it’s one in a thousand and you know there’s deaths on the other side of the policy – then you wouldn’t do that.
