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Peter Robinson: A month ago I interviewed my friend, Dr. Jay Bhattacharya, a Professor of Medicine here at Stanford who holds both an MD and a Doctorate in Economics. That interview went viral. More than a million people have viewed it on the internet, clips appeared on news shows of every kind, and Jay tells me that even his mother down in Los Angeles called to say she had seen it. In that interview you may recall, in that interview, Dr. Bhattacharya said that he would be testing on COVID-19 here in Santa Clara county and back with us now to talk about the results, Dr. Jay Bhattacharya. Jay, thanks for making the time and welcome everybody to this special plague time addition of Uncommon Knowledge with Peter Robinson.

Jay Bhattacharya: Thanks, Peter. Nice to be here.

Peter Robinson: Jay, the results. I'm quoting from your study which is available today through :. Have I got that right? All right. It's available through: and I'm quoting. Quote "the population-weighted prevalence of antibodies" "among those who tested here in Santa Clara County, "was 2.81%. "This implies that the infection is much more widespread "than indicated by the number of confirmed cases." Close quote. And I read this study carefully for the bit that a layman had the best chance of understanding. You've got to explain this to us, Jay. What briefly the results and then tell us how you conducted this study.

Jay Bhattacharya: Why don't we start with that first just so everybody has the idea of what we did. We drew a sample of people from Santa Clara county. Basically using a Facebook targeted ad strategy. So if you got a Facebook ad from us we were basically inviting you to come do a quick finger-prick blood test to see if you have antibodies present. We looked at basically 3,200 people in this drive-through testing facilities that we set up on the fly.

Peter Robinson: We're recording this on Friday. This is earlier this week or late last week?

Jay Bhattacharya: About two weeks ago I think.

Peter Robinson: Two weeks ago, all right.

Jay Bhattacharya: And so yeah, they took the finger prick, and then we just looked to see if the finger prick test showed evidence of antibodies to COVID-19. Now, 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. Now 2.8, actually the numbers run, but depending on your assumptions that we've made, somewhere between 2.8 and 4%, which doesn't sound like a lot. That means somewhere between 96 and 98% of people haven't got--

Peter Robinson: Sorry, I have to ask laymen questions here. That sample of 3,000 and some odd people, you designed the Facebook ads to make that sample as random or as representative of the entire county as possible? Is that, you're trying to get a result that implies something for the larger population, but it's the larger population of what? Of the county?

Jay Bhattacharya: County, right. Although that strategy worked very well to some extent, we also had to make some adjustments because a lot of the richer parts of the county, they respond to the ads in higher rate than the poor parts of the county, but we know how many people live in each of these rich and poor parts of town. So, anyone who showed up from a poor part of the county, we counted essentially more than people who showed up from poorer county so that when we did the final estimate it represents the entire county, rich and poor alike at the right proportions as opposed to over counting the rich. So then basically with that number, with that procedure, we figured out about somewhere between 2.8 and 4% of Santa Clara County has had evidence of COVID infection. So what does that mean? First thing, right around the time when we were doing this study, there had been about 1,000 cases of COVID found of COVID infection. Active SARS-CoV-2 infection found within the county.

Peter Robinson: Right.

Jay Bhattacharya: So there's about two million people in the county. If 4% have it, evidence of infection, that means that there's about 85 times more people who've had it per person that actually identified having it.

Peter Robinson: That's the critical finding.

Jay Bhattacharya: Yeah and if it's on the low end it'd be 2.8 it'd 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 that never showed up with a test of positive test. The COVID infection is substantially more common in the population than we realized prior to the study.

Peter Robinson: And can I just, I want to make sure I frame the test correctly. It's my understanding and I take my instruction in these matters from you.

Jay Bhattacharya: Don't you have a pre-med son, Peter?

Peter Robinson: He's very much pre-med. So, Jay, you were using what is so far in our experience with COVID, a new kind of test. Is that correct? You explained this the last time we spoke a month ago that the test that was being used to determine whether people had COVID at that moment was one kind of test. Another kind of test was being developed, technically as I recall more difficult to develop, and that test would test not only for whether you have the COVID infection now, but whether you have had it in the past and cleared it from your system and that is what is now available and your test, as far as I'm aware, represents the first rigorous, serious, important results from this new kind of test, correct?

Jay Bhattacharya: You characterized the test right. So the test that people have been using sort of early on in this epidemic is a test that checks to see if the virus is present. It's called a PCR test, RTPCR test for those who follow that kind of thing. We're looking for antibodies. So it's an antibody test and of course, you produce antibodies a few days after you get infected. Those antibodies last. Actually there's an active debate over to the extent to which those antibodies actually protect you from a second COVID infection or how long, but any case, the fact that those antibodies are present are evidence that you were infected, even if the virus is gone and that's what we're looking for.

Peter Robinson: All right so now again more on the results. There's good news, bad news here to the layman's mind. The bad news is, wait a minute. Even by your high estimate, only 4% of my fellow citizens of Santa Clara County have been infected. We still have 96% of us to go. That sounds as though we're still at the very beginning. I've had it. I'm sick of the shutdown and your result suggest that in fact, the trouble is just beginning. The good news is, the good news is, so tell me the good news, bad news. Is the bad news not as bad as I think? As it feels to me?

Jay Bhattacharya: I'll start with the good news. So the good news is it's not as deadly as we might have thought. So if 50%, 50 people had it for every person that we've identified having it, well previously we were counting the number of deaths, we'd divide by the number of people we think have it and we say okay gosh, 1% 2%, somewhere between 1 and 2% of people who have it die, but if there's 50 people who have had it and cleared it

Peter Robinson: And that your test now picks up.

Jay Bhattacharya: Yeah, then you can get a much lower death rate. Now there's some more nuance to that calculation that'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 per case.

Peter Robinson: Okay.

Jay Bhattacharya: Instead of having a death rate like the World Health Organization said, three and 100. So you get COVID and three out of 100 people die. Instead, our estimates suggest about somewhere between one and two in 1,000 die from getting infected with COVID. So that's the good news.

Peter Robinson: And what about the bad news. It fills me with a certain trepidation because it suggests were just getting started. If you had said 30% had been infected, I'd have felt more relieved, but am I understanding that in the wrong way?

Jay Bhattacharya: No I mean it is in some sense it's bad news. So the traditional strategy to control new epidemics involves a strategy called contact tracing right? So they find that Peter has the virus. Sorry, Peter.

Peter Robinson: I'll volunteer.

Jay Bhattacharya: They quarantine you. Then they interrogate you and ask who else have you been in touch with? Oh, you've been in touch with Jay. Well, that means Jay now needs to get tested and possibly quarantined also or isolated and then you just keep tracking outside until you've characterized the full set of people that Peter has been in touch with and the set of people that his friends have been in touch with and so on and so forth until you've sort of collared everybody that potentially could have it and tested them and move outward and every time you find a new case you do the same thing over and over again until you've identified everyone who has it. So if this many people have it that we've found, it is almost impossible to do that kind of contact tracing.

Peter Robinson: Because you end up with the whole population.

Jay Bhattacharya: Yeah I mean that's hopefully not the whole population, but it's certainly a very, very difficult challenge even in the best of cases, but if you have tens of thousands or hundreds of thousands of people that need to get contact traced, then there's no way to do it. It's just not a feasible strategy.

Peter Robinson: So back if I may to what the new information contained in your results about the Coronavirus, this new Coronavirus. Again, I'm summarizing things as best this little layman's mind can grasp them, but basically we've been told that there are three ways in which the Coronavirus differs from the ordinary flu and the first is the long latency period. You can go a long time without becoming aware that you have it and the second which is related to the first is the high infection rate. What was it, I saw figures that each person who came down with COVID-19 infected three others. Well, of course, that's exponential. You'll end up in real trouble very quickly. Do your results affect either of those two?

Jay Bhattacharya: Also that second number you said, the three people infected, has to do with the policies that you follow. If you have a shelter in place order, each person won't be infecting three. That's part of the reasoning behind the shelter in place orders.

Peter Robinson: The latency period, what you've discovered is that the latency period is often infinite. Lots of people get this thing and don't even know they've had it.

Jay Bhattacharya: That latency period has to do with the biological property of the thing, but it certainly hasn't reached what we've found. A lot of the people that have it probably never knew that they had it, have had it and cleared it and they could have infected other people very easily. It is very infectious as you say.

Peter Robinson: Even when you're totally asymptomatic.

Jay Bhattacharya: Yeah I think it's less likely that you'll spread it if you're asymptomatic, but it's possible. It just makes it very difficult. One more corollary to that piece of bad news that I gave you earlier. Economists like to deliver bad news right? The disease eradication is probably not possible. Making COVID go away altogether from the face of the Earth is probably not possible.

Peter Robinson: All right. I can live with that because I think I've already adjusted my thinking to that. Here's the third way. The third fundamental way that COVID differs from the flu and I'm just gonna quote Dr. Fauci, Dr. Anthony Fauci, and he's speaking early last month, early in March. Quote, I'm quoting him. "The flu has a mortality rate of 0.1%." One-tenth of one percent. "This", meaning COVID-19. "This has a mortality rate of 10 times that." close quote and your results say?

Jay Bhattacharya: It's about the same as the flu on that order. It could be a little less, could be a little more. The estimates again there's some nuance to them and that latency period actually matters. I mean we're presenting a conservative estimate that I think roughly it's on line with the flu. Like I said, it could be a little bit more. One in a thousand, two in a thousand, not one in a 100, two in a 100.

Peter Robinson: All right you're conducting another couple of tests. Very briefly, you've got one that's underway or soon to be started in L.A. county and another with MLB, the Major League Baseball Organization? Just briefly tell us about that.

Jay Bhattacharya: Sure yeah. So actually the L.A. county study is in some way similar to the Santa Clara study, but it's in a bigger metropolitan area represents about 10 million people. That data collection happened last week.

Peter Robinson: Oh so okay.

Jay Bhattacharya: That's all done. We're gonna release the paper. We sent it to journal and hopefully, I can start talking about it very, very soon, and then the Major League Baseball study is with 27 different teams all around the country. Now it's the employees of Major League Baseball, not the athletes. In fact mostly employees. So concessionaires, front office, and back-office staff.

Peter Robinson: Will that get you closer to a sample that's closer to the way ordinary right?

Jay Bhattacharya: Presentative. They're unique individuals working for a unique organization, but they are a picture into 27 different places, very, very quickly with an organization that has fantastic medical personnel and fantastic people to help organize the study that normally to get into all those places all at once would have taken years. Because it's Major League Baseball, now we're gonna have a picture in those communities within a few weeks we organized the study.

Peter Robinson: Spectacular. And when will those results begin to become available?

Jay Bhattacharya: Well we just finished the data collection.

Peter Robinson: Oh you've done that as well?

Jay Bhattacharya: Yeah so

Peter Robinson: You've been busier than I thought, Jay.

Jay Bhattacharya: I've been very busy this past month and some. I'm gonna try to write that paper this weekend. We'll see how far I get.

Peter Robinson: All right, we'll speed up this conversation so you can get back to work. So Jay, the implications of your study. I'll come in a moment to the implications for what we ought to do now, but of course, the first question is what does this study say about what we have done? If I understand you correctly and I hope I've misunderstood, but what's in my mind right now is that we have just shut down the economy and thrown over the last month, it's in the Wall Street Journal this morning, 22 million people in the last month have filed for unemployment insurance and those numbers are bigger than the nation has ever seen before. You're telling me that we shut down the American economy for the flu?

Jay Bhattacharya: No because the flu doesn't, we don't have a vaccine for this thing. Many more people will die total for it. So it's not for nothing. I mean we also didn't know the number like we talked about last time and so people were reacting to a scenario where it just looked catastrophic. I mean two million American's dead? That would have been catastrophic. I don't really want to spend time too much on recriminations Peter because--

Peter Robinson: No recriminations because people were behaving reason, what has been done was within the realm of reasonable action.

Jay Bhattacharya: Yeah I think so. I mean we could go back and second guess, but it's not productive. I think what is productive is to think about what the right thing to do is now that we're starting to have a better picture of how extensive the epidemic actually is and where we actually are. I think one of the major themes has been and completely reasonably, when is this virus most deadly? Well, it's most deadly when you are living in a place with an overwhelmed hospital system or healthcare system or nursing home or whatever it is. You've got a huge amount of viral load floating around, the people taking care of the patients themselves are getting sick. You know Milan, Wuhan, New York, we've seen dramatic evidence that this virus can become incredibly deadly when it overwhelms a healthcare system. So that's the basis for the famous "let's flatten the curve" kind of idea. I think that's completely reasonable. We basically should work very hard to avoid that because that's when the virus kills people at high rates. So how can we do that now? Well now that we have an idea of how extensive the virus is and how deadly it is, let's rework those models with this improved understanding and then see where in the country, if we lift up the cap, would we get near the capacity of the healthcare systems if you will and in places that it seems safe, then let's start to lift the caps. In places where it's not safe, again with the new data we're gonna be able to start to be able to make those much more accurate projections about what would happen if we lift the cap. In places where it's not safe, well then we keep the cap on. Another thing is gonna be very, very important is to protect the vulnerable right? If you're older or you have some other health conditions, I mean we know this virus is deadly for you. So working very hard to isolate those folks. You've seen it in the three-phase plan

Peter Robinson: That the President announced yesterday, right. Have you had a chance to look at that?

Jay Bhattacharya: Briefly. I haven't had a ton of time to look at that I will confess.

Peter Robinson: But it looks reasonable?

Jay Bhattacharya: I mean it needs to get flushed out. I mean I think the main thing I would add to it is let's do these kinds of studies that I'm doing. Let's do them everywhere. If we actually get a picture in every community in the country and actually frankly the world of where we are in the epidemics and then we can start to make reason decisions about where it's safe to lift the cap.

Peter Robinson: And, Jay, are you, so you've already conducted three tests. The results of one of those tests are up today. The results of the other two will be up in another, well if I can let you get off the computer and get back to work, you'll get that done sometime next week. Perhaps 10 days you'll have two more results. What other kinds of tests are taking place in the country? Is the country engaged in the kind of testing regime that you believe ought to be taking place right now?

Jay Bhattacharya: Yeah I mean I think people all over the country, actually all over the world, are starting these kinds of studies and I saw someone in Germany, there was one in Denmark, there's one in Finland. They're coming out all around the world because this is exactly the kind of information you need to make the right kind of decisions and in the U.S, I know there are groups all over the country that are gearing up to do these studies everywhere. I'm gonna try my hardest to start to help to make those, but it won't be me, it'll be lots and lots of people doing these kinds of studies.

Peter Robinson: Okay so if I may ask, roughly does study like your Santa Clara County study cost?

Jay Bhattacharya: Well we did that kind of on a shoestring. If we had a professional organization do it, maybe $200,000.

Peter Robinson: $200,000. So the Federal Reserve has made it 2.3 trillion in emergency lending available. Congress and the President, Congress passed and the President signed a $2.2 trillion stimulus package. We've shut the economy down for six weeks. That's got to be at least two or three trillion off the GDP. Should it be little groups running around, scraping to get their hands on 200 grand at a time, be doing this testing? Six weeks ago why didn't Fauci say Mr. President, I need $100 million to the CDC to start serological testing the moment it becomes available, across the country. It just feels to me as though the Feds, you're serious. The Feds are serious about the wrong things.

Jay Bhattacharya: Well I mean I think part of the issue is, as we talked about last month, the test themselves are relatively new and I've learned a lot about the characteristics of these tests and errors they have in the course of doing this research. Stuff that people didn't know. I mean I didn't know a month ago. I think 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, but maybe I'm foolish to have gone ahead, but I think it was the right thing to do. I believe this is critically important information, even though I know the tests are gonna get better as better research gets done on them I mean all over the country. In a sense, I'm 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 than a late version of this number with better test is incredibly important is my view and I think 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.

Peter Robinson: So you're rolling out more tests. That's weeks. More tests are springing up here, there and everywhere. That's weeks and months. A year from now, two years from now, as we adjust to some sort of regime of permanent vigilance. You and I discussed this last time and you said between SARS, there's just been too many outbreaks over the last decade or decade and a half, this is a new normal. You recall saying that. So is it too much to ask to imagine at least that CDC or somebody becomes charged with a regime of regular testing? What's in my mind, correct the image I have in my mind, is something like poling during a political campaign where you understand the statistical analysis involved, you can use small samples to represent big samples, you know how people are infected in a certain city, you have a snapshot of the entire nation, you know state by state, county by county, we get very good at the testing. Is that what we need?

Jay Bhattacharya: Yeah I think so. I mean I think that would be a really, really big step forward so that when something like this happens again and it will, we can much more rapidly deploy this kind of testing apparatus across the country. I mean poling is a good example, a good analogy I mean. Another one is unemployment numbers. The unemployment numbers are based on a study of 100,000 people conducted every month from the Bureau of Labor Statistics. Every month they just go and ask 100,000 people did you work this month or something close to that and that's where the unemployment numbers come from. We could have a panel like that for this kind of disease tracking and then as soon as a new disease comes up and tests start to get developed for checking it, we just go to that same panel

Peter Robinson: And we'd have the equipment already in place. What about the civil rights aspects of this? We're in a phase right now where you and I are seated where we're seated because the Governor of California told us to go home and stay there. You're in the office because you're an essential worker.

Jay Bhattacharya: Got a letter, should I show you my letter?

Peter Robinson: I haven't seen that letter, but if you're not essential, nobody is, but can you get a rigorous enough permanent regime of testing based on volunteers only?

Jay Bhattacharya: Yeah, I think so.

Peter Robinson: Oh, you can?

Jay Bhattacharya: The BLS, Bureau of Labor Statistics, they're not forcing people to answer their questions about are you working. They're just people who they reach out to them and say would you be willing to answer these questions. They say yeah and then they ask them. People will say no. That's fine. You can make statistical adjustments for that non response or refusal. That's fine, but I think there's no reason, that's not a reason to not set up that kind of infrastructure.

Peter Robinson: Got it. All right, a few last questions, Jay. India. We mentioned last week you still have cousins back in India.

Jay Bhattacharya: Uncles, aunts.

Peter Robinson: I'm sorry?

Jay Bhattacharya: Uncles, aunts, you know you name it.

Peter Robinson: All kinds of family. On April 14th, that's just a few days ago, Prime Minister Modi announced a strict three week national lockdown and here we are only a couple of days later and he's already made clear that he intends to extend that three week lockdown. As I understand it he hasn't yet said how long the final date will be, but it's going to last longer than three weeks. What implications does your study have for India?

Jay Bhattacharya: It seems very likely that this disease is more widespread than people believe everywhere. I think India is gonna start to do these sorts of serologic studies as well and I think they'll find the same thing as well. Obviously this is a guess because I don't know their numbers from there, but that's what's happened in Europe, that's what's happened here. I think it's likely to be true in India as well and the number, because there are many, many, very densely packed cities in India, the spread of the disease, is probably faster there than here. Now, of course, there's a lot we don't know about the virus. That guess could be wrong, but again it's just like we talked about last month. We won't know until we run the numbers there.

Peter Robinson: So, Jay, question that slipped my mind until this moment, what implications do your numbers have for a concept that I barely understand, which is the development of herd immunity? Might we be farther along in developing a kind of broad-based immunity than we supposed earlier or no?

Jay Bhattacharya: We have to be careful about that. So this study and the antibody test provide evidence that you had the infection. There's an active debate among immunologists and scientists about the extent to which these antibodies confer immunity. So we don't know how long they confer immunity. If they do it may be partial immunity. It might protect you partly from the infection, but not completely. So it's a little hard to say anything about herd immunity until that debate starts to get settled.

Peter Robinson: That's not just a conceptual debate. Presumably there's going to be testing on that question as well is that so?

Jay Bhattacharya: Absolutely. In fact, the vaccine development that's there, I'm sure you've heard about, have to do with that debate. What antibodies actually confer immunity the best and how do you induce those antibodies to form without actually infecting somebody? That's what the vaccine is about. That's not anywhere near resolved yet. I mean people are still studying that particular question. It's a new virus. We've only known about it basically, in the U.S, for three months. I'm not comfortable talking about herd immunity just because we don't know the extent to which

Peter Robinson: Nobody knows what immunity looks like.

Jay Bhattacharya: But I think if we were to talk about herd immunity, three-four percent of the population is nowhere near that.

Peter Robinson: Okay, all right. 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 would you have to say to the Governor of California? You've just 2 million people in Santa Clara County. You've just discovered what you've discovered, what would you say to the Governor? What does that imply?

Jay Bhattacharya: One, run these studies everywhere and keep running them until the epidemics done. That seems obvious to me, but I'm sure

Peter Robinson: And these studies only cost $200,000 a pop?

Jay Bhattacharya: I mean maybe I don't know how to do budgets, but I think so

Peter Robinson: But the point is, if we're talking about Governor Newsom, we're talking about a man who has an annual, oversees an annual budget of well over $100 billion a year. There's room in there somewhere to run these studies.

Jay Bhattacharya: I think these are really high-value studies because like I said, they're gonna provide information about policy in a way that we desperately need 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 resources of the hospital resources available and ask if I lift the caps will I be over, will I really stress the hospitals or not? We can follow the same structure of the policy we've been following, but now with real numbers it could be that many, many places around the country, including California, it's safe to lift the caps. So the next step to 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.

Peter Robinson: Jay, you talk, everybody's talking now about modeling and modeling means doing the most rigorous analyst you can and making the most reasonable assumptions, most realistic assumptions you can and projecting into the future and there's a lot of modeling being done about the Coronavirus. I don't see anybody modeling the health effects of what we're doing to the economy. You can not engage in cost-benefit analyst unless you know the costs. So we know as a general matter that unemployment is associated with depression, alcohol and drug abuse, domestic violence, suicide, we know for fact certain that all kinds of hospitals are under intense pressure now. I just read a statistic the other day, the Mayo Clinic of all places is expecting a $900 million shortfall. Why? Because the procedures that they ordinarily perform have been stopped in favor because nobody will show up. We also know for certain that some number of colon cancers are going to go undetected because people are not getting their colonoscopies, that the pap smears are not taking place, that the PSA tests are not taking place, who's doing the rigorous weighing of the costs of what we're doing? It just seems to me that we have this very vivid image of to the nearest dozen how many deaths might occur under this scenario or that scenario or the other scenario. We just shut down the economy and thrown 22 million people out of work, nobody really seems to be modeling the health effects of that. Am I wrong about that?

Jay Bhattacharya: Not wrong, Peter, and it's even broader than you say because it's not just the United States. Nearly every country on Earth has implemented these kinds of economic caps. The macroeconomic numbers are incredibly, the global ones, incredibly scary. It looks like a Great Depression. It looks like on that order and the health effects of that and the United States are gonna be bad as you described correctly, but in poor countries and in poor people in poor countries, it's gonna be absolutely catastrophic. Those lives count for something and they should count for something I think in the calculus and they're not counted right now in the calculus. We're just counting COVID deaths.

Peter Robinson: What would you say to Dr. Fauci? What would your one or two lines of advice be to him?

Jay Bhattacharya: Do serologic studies everywhere. They're not that expensive. 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 start to do the right thing. It's not that I should be careful. I don't mean to say they're doing the wrong thing. I mean to say we'll now start to have enough information so that we can figure out what the right thing is.

Peter Robinson: And for the President? For Donald Trump? If you could give him a sentence or two of advice?

Jay Bhattacharya: These studies are really, really critical. I'd give him the same advice I'd give Dr. Fauci. I think weighing the effects of the shutdown policy on other, on non-COVID deaths should also weigh on President Trump's mind as well and I think that he must be thinking along those lines if the three-phase plan I saw is any indication. Those lives matter too. The health and well being of those people matter as well, that are thrown out of work, that die of depression or all of those really bad things. Those lives count and so separate from Dr. Fauci, if it's President Trump, I say count both those. Both the lives of the people that die dying from COVID and the people that are going to be dying from the depression that's coming.

Peter Robinson: I saw a headline this morning. Last question, Jay. You've gotta get back to work. You really do have to get back to work. The nation awaits the results of your next test. I saw a headline this morning on the President's three-part plan. I can't quote it exactly, but it was something like Trump puts burden of decision on Governors and so the question is, is this a moment, we live under a constitution that's 240 years old and that gives us a Federalist system where the president has some powers, but so does Gavin Newsom and so does Andrew Cuomo, so do the Governors. Is this a moment when we should be rejoicing at the structure the founders gave us or thinking, really we could use more federal control right now?

Jay Bhattacharya: I think every community is going to be different. This is why I'm interested in the baseball numbers.

Peter Robinson: That's a fantastic thing. 27 different cities. That's spectacular.

Jay Bhattacharya: Well teams, but yeah I think the key thing there is that the communities are going to be in different places on the epidemic. Different responses will be required, different things will be optimal in Cincinnati than they will be in Los Angeles. So I think starting to understand what the right thing to do locally is going to be very, very important. It's not enough just to have a single national number. This is a disease that you can see already. It hit New York much harder than it hit the rest of the country. New York is further. I mean 15% of pregnant women giving birth in New York were virus positive. Forget about antibody positive.

Peter Robinson: Wow, wow.

Jay Bhattacharya: So clearly it's very, very much more common there. So the right response in New York is gonna be different than the right response in Dallas. We need this kind of intelligence everywhere.

Peter Robinson: Distributed intelligence. In other words federalism.

Jay Bhattacharya: I mean you could call it that. Sure. I don't know much about political science, Peter.

Peter Robinson: Jay, help me to understand yet another aspect of this. We've been told that we need to flatten the curve. Now I get the impression that when some officials talk about fattening the curve, all they're saying is that we need to slow the advance of the virus and that's all that we can do. Just retard the advance, but others seem to be suggesting we need to flatten the curve so that we slow the advance of the virus until we get a vaccine or until warmer weather comes along, until we eradicate this thing. Can we eradicate the Coronavirus?

Jay Bhattacharya: I don't see how that's possible at this point based on the findings we have. It's too widespread and it's not just, we picked Santa Clara, we picked L.A because they were places that we have connections and we could actually get the study up and running quickly. Those places are not unique in this sense. There's gonna be lots of places around the country with these kinds of numbers, probably more. New york certainly more. I don't see how you make disease eradication as a goal. That means that we have to think very carefully about how to manage the disease and the epidemic. It's not going to go away and suppressing the economy forever so that it goes away seems that it's too costly. I mean it is too costly in lives. So the question is what do we do about it? I mean it's like we're gonna have to learn to live with it in some sense. If it's a one in a 1,000 risk of dying from getting it, we can learn to live with it right? If it's three in a 100 maybe not, but its more like one in a 1,000.

Peter Robinson: So is the ultimate aim and now I'm talking about farther out in time, the first thing that we begin to reopen as you've said and testing needs to take place, but the ultimate aim is to reduce this, to contain it the way we have contained what we think of as the ordinary flu. That is to say we develop vaccines which are at least largely effective. People understand what to do if they get the symptom. You go to bed, you force fluids, but to put it into the category of things that become routine, is that the correct hope?

Jay Bhattacharya: Yeah, that's my hope and also that better treatments could become available. All of that is fine, it's just the question is do we shut everything down until that happens? The only reason you would do that, if it was a three and 100 death rate, you might do that. If it's 1 in 1,000 and you know there's deaths on the other side of the policy, then you wouldn't do that.

Peter Robinson: So you can escape a swarm of hornets by putting your head under water, but if the hornets are still there when you take your head out, you have not solved the problem and the hornets will still be there. Correct?

Jay Bhattacharya: Yeah I mean you lift the cap up. The models say the thing comes back.

Peter Robinson: Could you end on a cheerier note, please?

Jay Bhattacharya: I'm an economist. We're the dismal science for a reason, Peter.

Peter Robinson: Jay, you have work to do but thank you and before we say goodbye, look right at the little green light on the camera and say hello to your mom.

Jay Bhattacharya: Hi, mom.

Peter Robinson: Dr. Jay Bhattacharya, big shot, Professor of Medicine here at Stanford, but still his mother's son. Thanks, Jay. Back to work now. For Uncommon Knowledge, The Hoover Institution, and Fox Nation, I'm Peter Robinson.

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