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Speaker 1: Welcome to the Bloomberg Surveillance Podcast. I'm Tom Keane jay Ley.

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Speaker 1: We bring you insight from the best in economics, finance, investment,

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Speaker 1: and international relations. Find Bloomberg Surveillance on Apple Podcasts, SoundCloud,

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Speaker 1: Bloomberg dot Com, and of course on the Bloomberg. We

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Speaker 1: begin our study with Jeffrey Rosenberg of Black Rock. Jeff,

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Speaker 1: you know, well the squishiness of how we measure labor

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Speaker 1: dynamics in America. When you dive into this analysis for

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Speaker 1: your market analysis, what will you search for in this

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Speaker 1: shocking report. Well, it is a surprise, and certainly the

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Speaker 1: market was looking for much weaker numbers. And you know,

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Speaker 1: we're all kind of diving into the details here, but

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Speaker 1: the message just appears to be about the pace of

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Speaker 1: returning workers relative to the pay of additional layoffs. And

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Speaker 1: that's a that's a that's a clear positive trend that

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Speaker 1: the opening up in the economy across the various states

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Speaker 1: had been better than what everyone is expecting to see

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Speaker 1: out of this report. The other thing that you're seeing

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Speaker 1: here is is the big distributional shift that we saw

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Speaker 1: in April, where we saw a kind of a surprising

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Speaker 1: or perverse increase in average hourly earnings. That's because you

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Speaker 1: had the biggest impact from the lower worker wage coming out.

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Speaker 1: You're seeing that reverse, and so that's another testament that

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Speaker 1: people are getting back to work. People are returning to work,

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Speaker 1: and and the people who are harmed the most that

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Speaker 1: really dragged down the overall payroll reports and and and

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Speaker 1: perversely pushed up average hourly earnings. You're seeing that reverse

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Speaker 1: as well. So this is clearly a good sign that

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Speaker 1: the market that kind of been telling you for a

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Speaker 1: while that we're getting back to work. Well, there's no

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Speaker 1: question the markets have been leading to this kind and

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Speaker 1: a shock report. But when you look at the social

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Speaker 1: unrest in America, Jeff Rosenberg, is this a surprise in

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Speaker 1: a sense of labor stability for a small part of

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Speaker 1: America that's employable versus everybody else? You mentioned distributions. What's

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Speaker 1: the barbell of our labor economy right now? What we've

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Speaker 1: seen that for a while in terms of up until

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Speaker 1: the coronavirus crisis, this had been a labor market that

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Speaker 1: was benefiting the distributional aspects, meaning it was a very

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Speaker 1: tight labor market. And so you saw the biggest gains

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Speaker 1: coming from the lower end of the labor pool. All

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Speaker 1: of that was turned upside down by the coronavirus crisis

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Speaker 1: in that it had a disproportional impact where you can't

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Speaker 1: simply move the job online, when you can't move to

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Speaker 1: remote working. That's in many areas of service employment, retail employment,

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Speaker 1: things that you had to shut down for coronavirus had

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Speaker 1: the biggest impact, and that that turned on its head

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Speaker 1: what had been a very strong labor market. I think

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Speaker 1: what we see coming out of this report is really,

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Speaker 1: you know, some signs that as we return, we might

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Speaker 1: we're certainly going to ease the pressure relative to the

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Speaker 1: extreme of no work at all. The question will remain

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Speaker 1: what comes back, How far back do we get and

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Speaker 1: how long has it taken. Despite the optimism of the

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Speaker 1: markets and today, there's still the case here that we

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Speaker 1: have had some permanent losses of employers, some permanent losses

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Speaker 1: that are not going to come back, and that will

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Speaker 1: that will be a little bit of a darker cloud today.

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Speaker 1: It'll probably be more of the optimism, but we've got

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Speaker 1: to keep in mind here that that we did see

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Speaker 1: some real scarring. Jeff, I just want to sit on

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Speaker 1: this moment for a minute. This is absolutely stunning for

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Speaker 1: all the right reasons, A huge upside surprise that nobody

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Speaker 1: was anywhere near estimating in our survey. What's the risk

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Speaker 1: care now for you, Jeff, then that we extrapolate this out,

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Speaker 1: this improvement, this rate have changed too far, far too much. Well,

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Speaker 1: you know, you know the risk is in my markets,

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Speaker 1: in in the bond markets here were really at a

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Speaker 1: loss for how to think about what what are the

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Speaker 1: levels of yields? And and and the issue here on

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Speaker 1: the levels of yields is that it's really about the

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Speaker 1: FED saying supporting market functioning in space of an epic

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Speaker 1: increase in issuance. And so the supplied demand uncertainty, well,

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Speaker 1: we have more certainty on the amount of supply that's coming,

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Speaker 1: we have less certainty about how much the FED is

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Speaker 1: going to be willing to support that. And so when

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Speaker 1: you add to this kind of this momentum around growth

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Speaker 1: and increasing yields, it creates a huge uncertainty around the

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Speaker 1: shape of the curve, the pay yield curve steepening, What

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Speaker 1: exactly is the yield curve shape that the FED is

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Speaker 1: gonna want to see. We're gonna pivot that into that

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Speaker 1: next week when we talk about the f m C meeting,

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Speaker 1: but the markets are really uh challenged to figure out

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Speaker 1: what is the right level of yields because it's not

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Speaker 1: driven solely by these economic fundamentals like the payroll report

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Speaker 1: would have done in years gone on past, because we

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Speaker 1: have this historic well it's really analogous to World War

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Speaker 1: two history, but but relative to our modern experiences to

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Speaker 1: historic change in the Fed's operation with regards to well,

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Speaker 1: we're talking about which is yield curve controls and what

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Speaker 1: are the levels around that? Jeff, we're speaking with Jeff

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Speaker 1: Rozenberg of Black Rock, and I should just note right

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Speaker 1: now we are seeing futures rising substantially a new leg

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Speaker 1: higher with the SMPT future is now up one point

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Speaker 1: six percent. Longer dated yields also ripping higher off this report,

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Speaker 1: currently ten year yields at zero point nine three two percent.

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Speaker 1: So people are can question the accuracy of these numbers,

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Speaker 1: but what you're getting in markets is at first to

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Speaker 1: hesitance and then a pile on Jeff, as people expect

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Speaker 1: this to portend good news. Can we just really go

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Speaker 1: over the fact that this does not cohere with the

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Speaker 1: continuing claims and the jobless reports that we're getting out

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Speaker 1: of state and federal agencies over the past few weeks.

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Speaker 1: There is a skepticism that one has to have when

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Speaker 1: looking at these numbers. Can you explain that to us, Jeff,

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Speaker 1: how much confidence can we have that this accurately portrays

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Speaker 1: the picture on the ground. Well, first, one way to

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Speaker 1: sort of ascertain the uncertainty is just look at the

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Speaker 1: spread of market expectations going going into the to the report, right,

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Speaker 1: So nobody had it on the positive side, but even

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Speaker 1: within the negative side, you had you know, minus two

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Speaker 1: and a half millions of minus eight and a half million.

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Speaker 1: I mean, we are in uncharted territory. So when you

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Speaker 1: look at kind of the traditional tools that we use

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Speaker 1: to predict you know, the plus or minus two hundred

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Speaker 1: thousand in the payroll report, you know you can you

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Speaker 1: can map those pretty clearly to the high frequency data

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Speaker 1: that you get ahead of the paywall report. In this case,

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Speaker 1: we're really out of a toolkit to to to know

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Speaker 1: how to do that forecast at all. And so it

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Speaker 1: is possible that the positive number here is reflecting some

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Speaker 1: things in terms of the data, the data calculation. I

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Speaker 1: don't have the response rate at least the April response

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Speaker 1: rate had been in um in the historical averages, so

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Speaker 1: that probably isn't isn't complained here, but it's we're we're

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Speaker 1: really just an uncharted territory for knowing how to address

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Speaker 1: these issues. And so what we're really capturing is the

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Speaker 1: inability to forecast what returning to work and what the

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Speaker 1: pace of returning to work is, and the distributional aspects

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Speaker 1: that we're talking about trying to aggregate up at one

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Speaker 1: number across states level impacts that are highly disparate, very

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Speaker 1: hard to to model that into forecast that. I think

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Speaker 1: that that's what we're seeing here in terms of the

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Speaker 1: surprise relative to the economic forecast. This this is why

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Speaker 1: we love to have Jeffrey Rosenberg on, folks. I can't

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Speaker 1: convey to you the importance of the three sentences Jeffrey

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Speaker 1: Rosenberg just stated there. If you're joining us, uh, just

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Speaker 1: now are simulcast worldwide. I'm Bloomberg Television, I'm Bloomberg Radio.

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Speaker 1: We welcome all of you. I'm not going to give

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Speaker 1: you all of statistics because, as Jeff says, I don't

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Speaker 1: think the statistics here matter. All you need to know is,

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Speaker 1: once again, the markets out front away from the gloom

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Speaker 1: and these are shocking and difficult statistics, but nowhere near

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Speaker 1: the gloom that was expected before this report. Jeffrey, I

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Speaker 1: want to go into your Carnegie Melon mathematics. There, you've

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Speaker 1: used twice the word distributional. I totally agree with you

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Speaker 1: that good people, and I mean this folks, good people

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Speaker 1: are trying to take blended, broad below statistics and aggregate

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Speaker 1: them up to a single reportable unemployment rate. Jeff even

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Speaker 1: in good conditions, that's impossible. How impossible is that to

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Speaker 1: do right now? Well, it's particularly challenged right now because

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Speaker 1: you have the um a shock that is outside of

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Speaker 1: anything we've seen. So remember how these forecast models are

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Speaker 1: built there, they're built off of historical information. They're they're

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Speaker 1: built off of models that have some kind of intuition

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Speaker 1: around how normal economic behaviors respond and and so you're

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Speaker 1: really cast a drift to try to try to do

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Speaker 1: that forecast. You're trying to pull in high frequency data.

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Speaker 1: But again, because of the very specific, differentiated nature of

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Speaker 1: how we're reopening the economy, think about what this labor

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Speaker 1: ward represents, right It's it's total non form payroll across

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Speaker 1: the entirety of the US yet look at the differences

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Speaker 1: and how states have reacted. So to really kind of

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Speaker 1: build that model correctly, you'd have to have state level

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Speaker 1: models filled with state level data, and and that just

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Speaker 1: doesn't exist yet. And that's part of the challenge and

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Speaker 1: part of the explanation I think, for for why you

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Speaker 1: had first such a wide band and how this numbers

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Speaker 1: come outside even that very wide band. To the positive side,

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Speaker 1: I'll tell you, folks, this is what Bloomberg Surveillance is

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Speaker 1: all about. To go from Randall Krassner to David blanche Flower,

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Speaker 1: Tom Porcelia thought was brilliant, and then just to get

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Speaker 1: lucky and have Jeff Rosenberg with us at this time

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Speaker 1: to really explain the noise around this voluminous wall of statistics.

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Speaker 1: It's going to be fascinating to see how this is

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Speaker 1: vamped by the pro economists and by the political voices

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Speaker 1: in the coming hours. Most radian Tom Kanan what we're

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Speaker 1: gonna do here, because this is totally unfair to Tiffty Wilding,

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Speaker 1: She and every other frontline economists are going to go, folks,

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Speaker 1: and you know, the media and I'm as guilty of

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Speaker 1: this is anyone we signed like three statistics and if

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Speaker 1: we're sophisticated, Paul well side a fourth, they're looking at

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Speaker 1: like statistics trying to get a matrix of feeling for

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Speaker 1: the American labor economy activity. And I really want to

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Speaker 1: shout out to zero heads. You's got a great summary

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Speaker 1: going on right now with the revisions job losses of

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Speaker 1: average six point five million per month over the past

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Speaker 1: three months. I mean, is this a point where you

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Speaker 1: take this monthly study and you smooth it out to

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Speaker 1: a moving average. Do you take it as a one

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Speaker 1: off the discard or do you take it and climb

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Speaker 1: on board optimism? Well, um, I mean I think you

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Speaker 1: have to look at this report as tell I knew

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Speaker 1: that we could be you know, the probability that work.

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Speaker 1: You know, we're recovering faster than maybe many people expected.

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Speaker 1: Um is maybe a higher probability than we saw before.

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Speaker 1: You know. So I think that there's always been a

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Speaker 1: lot of caution around the things which people can you know,

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Speaker 1: the labor market can kind of efficiently reallocate once people

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Speaker 1: are laid off, Will those people become long term unemployed?

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Speaker 1: Will they be able to get hired back? I think

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Speaker 1: what this report tells us is that maybe more of

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Speaker 1: those people than we thought, and more quickly than we thought,

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Speaker 1: will actually be hired back. You know, I think that.

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Speaker 1: I think one thing that you know, that that does

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Speaker 1: some out of this is that some of the things

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Speaker 1: that the programs that the government has implemented, like the

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Speaker 1: payroll protection programs, you know, maybe those things are working

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Speaker 1: a lot better than we thought. So that program actually

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Speaker 1: incentifies this business is to rehire back people, um, you know,

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Speaker 1: so they can get loans from the government that ultimately

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Speaker 1: will become grant And so I think this report suggested

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Speaker 1: those kinds of policies and maybe are are much more

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Speaker 1: impact fold and that maybe many people thought. So. Tiffany,

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Speaker 1: how do you kind of square? I guess what I'm

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Speaker 1: trying to do is kind of square the job's claims

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Speaker 1: numbers that we've seen over the last you know, four

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Speaker 1: or five, six weeks with this number we saw today,

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Speaker 1: is it the moderation of jobs claims? Does that explain

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Speaker 1: the fact that we added two and a half million

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Speaker 1: jobs versus the consensus of losing seven and a half million. Yeah,

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Speaker 1: I mean, I think that's a really good question. I mean,

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Speaker 1: so I think you have to be a little bit

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Speaker 1: careful about the initial claims numbers because many people will

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Speaker 1: they'll apply, they might apply more than once, we might

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Speaker 1: get rejected. But those numbers can actually be um inflated.

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Speaker 1: But if it wasn't continuing claim still we had a

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Speaker 1: pretty big increase in continuing claims um you know, certainly

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Speaker 1: not as much as at last month. But that actually

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Speaker 1: underscores how how good this report was because the fact

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Speaker 1: that are you know, around three million people if you

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Speaker 1: just look at the state continuing claims numbers, actually lost

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Speaker 1: their jobs. The fact that this number, this number is

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Speaker 1: a net were it suggested all three million people had

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Speaker 1: to gain employment, um, you know, to get to get that,

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Speaker 1: you know, the total change and then to be positive

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Speaker 1: is furlough in your textbooks? I mean, or what we're

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Speaker 1: arguing about here is people that were laid off. Their

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Speaker 1: statistic they they created claims whatever. But business really meant

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Speaker 1: it when they said this new word furlough. A lot

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Speaker 1: of people were laid off or fired who weren't laid

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Speaker 1: off or fired. They were furloughed. I can't spell it,

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Speaker 1: but there it is furloughed. Tiffany, what's furlough mean to PIMCO? Yeah,

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Speaker 1: I think that's true and you. And I think one

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Speaker 1: thing is that the labor the Labor Department reports and

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Speaker 1: the questions that are asked don't really properly capture furloughs. Um.

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Speaker 1: So the kind of rules around the surveys are if

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Speaker 1: you if you weren't paid, whether you were on furlough

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Speaker 1: or not, but you weren't paid, you're going to be

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Speaker 1: counted as unemployed wid. So many of those unemployed workers,

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Speaker 1: you know, could have been on furlough, they could have

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Speaker 1: still been receiving benefits, and they could have still been

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Speaker 1: very connected to their employers, um you know, but they

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Speaker 1: would think, I think we're seeing we're seeing some of

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Speaker 1: that in this report as well, Paul. I can't tell

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Speaker 1: you how unusual this is if you're just joining as folks,

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Speaker 1: Paul Sweeney and Tom Keene trying to piece together the

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Speaker 1: oddest labor report in my umpteen years of doing this,

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Speaker 1: and folks, there's been some you know, real emotion over

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Speaker 1: the years around certain events like September eleventh of two

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Speaker 1: thousand one, and you know, other moments like the financial crisis, Paul,

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Speaker 1: But this is I just really want to underscore to people.

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Speaker 1: I don't want to hear a lot of certitude right now, Paul,

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Speaker 1: I just want to see adults like Tiffany wilding to

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Speaker 1: the romantic analysis that nobody sees on television. Exactly right. So, Tiffany,

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Speaker 1: what do you think this report will mean to the

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Speaker 1: Federal Reserve? Well, I mean, obviously, no one wants to

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Speaker 1: overreact to anyone report, um, and you have it has

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Speaker 1: to be taken into broader context. I mean, the unemployment rate,

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Speaker 1: you know, obviously surprised consensus, and I think the Bloomberg

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Speaker 1: consisers went around you clearly a big surprise to that.

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Speaker 1: But it's still a thirteenth percent, which is very high historically. Um.

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Speaker 1: So there there's still a lot of question around the recovery. UM.

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Speaker 1: I just think that, you know, the good news is

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Speaker 1: is that maybe we're you know, the at least the

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Speaker 1: labor market isn't sinking into the depths and week that

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Speaker 1: we previously expected. So I think, you know the including

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Speaker 1: you know what. I don't mean to cut off, Tiffanty,

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Speaker 1: but I'm all riled up here after the week we

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Speaker 1: had in New York. I mean, I know you're out

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Speaker 1: of Newport Beach over a peanut Colada enjoyed the view Tiffany.

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Speaker 1: You know, come on, it has been a week of

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Speaker 1: historic unrest. There's a twelve foot high fence that Attorney

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Speaker 1: bar has around, you know, the broader White House. Margaret

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Speaker 1: Brennan and viewing Attorney bar on Face the Nation. You'll

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Speaker 1: hear that on Bloomberg Radio Sunday afternoon. Tiffany, come on,

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Speaker 1: we're not measuring America. I think we're measuring like jobs

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Speaker 1: at Boeing, jobs at Bloomberg, jobs at Pimple. Okay, great.

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Speaker 1: Are we capturing the unemployed the sort of employee I

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Speaker 1: hate this phrase, Paul three to one, the gig economy.

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Speaker 1: Are we measuring at Tiffany? Yeah, I mean I think

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Speaker 1: there's some question around the instance which you do in

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Speaker 1: these surveys. That's absolutely true, and I know that on

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Speaker 1: on claim that's just seen the pandemic from Graham claims,

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Speaker 1: which were a part of the Cares Act, which we're

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Speaker 1: basically geared towards the gig economy because many of them

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Speaker 1: don't um they can't get the usual unemploment claims from

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Speaker 1: states they're not eligible. Those kinds of claims actually shot

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Speaker 1: up quite a bit during the month. You know, I

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Speaker 1: think the household survey. Actually maybe these a little bit

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Speaker 1: better of a job. Although you know it's difficult to

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Speaker 1: know how much they captured. The gave economy to house

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Speaker 1: as the capture that and the household survey was also

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Speaker 1: pretty good. I mean, there's nothing really that you can

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Speaker 1: um you know, you can stay in terms of the

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Speaker 1: surprise at least that it was bad about this report

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Speaker 1: to participation ratey on the household survey and and Harold

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Speaker 1: employment also increased quite a bit terrific Worctivity World, thank

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Speaker 1: you so much, greatly appreciated with pimco. Uh this morning.

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Speaker 1: You know, the extraordinary number we saw today out of

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Speaker 1: the jobs. You know we're talking about consensus a twenty

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Speaker 1: unemployment rate. The actual number came in about thirteen point

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Speaker 1: so much much better than expected. Prom Us, Well, it's

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Speaker 1: a massive victory. Left, I mean, rick Oddonna, they killed it.

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Speaker 1: Now did they get it to the single point to

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Speaker 1: decimal point? We expect that from rick O Donna, but

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Speaker 1: maybe not. They nailed the directional call, Paul, they nailed it. Unfortunately,

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Speaker 1: have Yelena Shiltieva joining us from Bloomber Economics. Delena, thanks

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Speaker 1: so much for joining us. You know, just a shocking

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Speaker 1: number this morning. What's your key takeaway here? I'm worried

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Speaker 1: that this you know, victory lap and better than you

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Speaker 1: know expected statistics on payrolls will make authorities think that

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Speaker 1: we the crisis is over and it's not. Look at

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Speaker 1: the wage income growth and the gap between the trends,

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Speaker 1: the pre crisis trend and income growth after the crisis hit.

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Speaker 1: It's still a significantly wide gap, which need to be

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Speaker 1: supported by additional income coming from a jobless benefit. I

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Speaker 1: think that this these numbers will just simply say, Okay,

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Speaker 1: everything is fine, everything is fixed, whereas the additional systeal

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Speaker 1: measures may be needed in the second half of the

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Speaker 1: year to support that awning gap between wage growth and

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Speaker 1: the pre crisis trend. Elena, how do you how do

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Speaker 1: you square some of these childish claims numbers that we've

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Speaker 1: seen over the last five six weeks, which had just

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Speaker 1: been staggeringly bad on the downside, with kind of the

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Speaker 1: number we saw today, which two and a half million

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Speaker 1: jobs added. I think a lot of it has to

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Speaker 1: do with this UH, you know, augmented UH claims program

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Speaker 1: that that doesn't cover payrol employees, so a lot of

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Speaker 1: people are still unemployed and uh, you know, yes, we

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Speaker 1: did see an improvement in the unemployment rate still double digit,

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Speaker 1: you know, thirteen point three percent is way above the level. Okay,

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Speaker 1: so during the great procession as you do, you nail it. Okay,

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Speaker 1: you go right to the mystery here, which is a

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Speaker 1: gig workers is self employed? That big plugged hole that

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Speaker 1: we have to fill in here from the data release today,

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Speaker 1: Can our listeners easily figure out that whole that gap

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Speaker 1: of people not counted, the unaccountable within this report. I

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Speaker 1: think what we need to look at really going forward

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Speaker 1: to kind of gauge the strength of the recovery is

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Speaker 1: growth in incomes, growth in aggregate hours work. Because if

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Speaker 1: you look at the two months combined, we now have

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Speaker 1: two months of data for the second quarter, the aggregate

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Speaker 1: hours are still declining at annualized, which means the GDP

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Speaker 1: report will still be absolutely uh terrible, you know, but

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Speaker 1: we will need time. We will need months and months

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Speaker 1: to get back to the level where we were. Elena,

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Speaker 1: I know, you know you were late coming to us

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Speaker 1: because you're on the phone with the President of the

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Speaker 1: United States. He's going to get out there, Elena, He's

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Speaker 1: gonna be all mental about what a great report that says,

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Speaker 1: as would any other president, I'm not picking on President Trump.

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Speaker 1: What would you say to the president, what would be

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Speaker 1: your advice on getting to the report after this one?

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Speaker 1: I would point out exactly to what I was saying before,

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Speaker 1: that the wage statistics are still looking very terrible. Yes,

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Speaker 1: you know, we did see a rebound and it was

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Speaker 1: natural given that you know, uh, businesses are going back

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Speaker 1: and we're listening the lockdown measures. But the gap between

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Speaker 1: where we were and the wealth of people before the

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Speaker 1: crisis and right now these are two different things. So

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Speaker 1: just real quickly, Elena, just thinking about this. What's you

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Speaker 1: when you plug this kind of jobs number into your model?

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Speaker 1: What's your GDP out look for the remainder of the year.

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Speaker 1: How do you think this is gonna play? So I

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Speaker 1: think this actually, this report is still consistent with our

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Speaker 1: projections for thirty seven percent decline in GDT in the

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Speaker 1: second quarter, but perhaps maybe a slightly better rebound in

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Speaker 1: the third and the fourth quarter of the year. It

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Speaker 1: was it will still be a tremendous decline in GDP

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Speaker 1: for the years a whole. Elena, congratulations to you at

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Speaker 1: our Bloomberg Economics team. You absolutely nailed the directional life

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Speaker 1: isn't so gloomy gloomy call over the last week or so,

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Speaker 1: you guys just nailed it that it would be better

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Speaker 1: than a good report. Every day we focus on COVID nineteen.

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Speaker 1: We try and track it with One of the experts

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Speaker 1: in this joining us now is Andrew Pekosh, virologist and

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Speaker 1: Johns Hopkins University, Bloomberg School of Public Health professor, Professor Podkash.

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Speaker 1: Thanks so much for joining us. When you look at

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Speaker 1: some of the vaccine trials that we've seen, how are

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Speaker 1: the antibodies and the antibody testing actually developing. Can they

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Speaker 1: be used to make sure that we have a safer vaccine? Yeah?

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Speaker 1: Thanks for progressing UM at a very nice pace When

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Speaker 1: it comes to some of the vaccine trials, studies are

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Speaker 1: moving forward into their second phase, which is oftentimes the

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Speaker 1: phase that we really start to get signs of whether

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Speaker 1: vaccines have the potential to be efficacious or work well

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Speaker 1: in the population. I think in addition to that, there

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Speaker 1: have been a few studies that have started using just

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Speaker 1: antibodies as a therapy, so vaccines induced antibodies UM. Some

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Speaker 1: companies have actually jumped to the fact of giving people

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Speaker 1: those antibodies directly in terms of treatment, and those studies

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Speaker 1: are also moving forward, UM, and have shown some good

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Speaker 1: promise initially. So UM, from the side of your immune response, UM,

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Speaker 1: there's been there's been continued good progress towards towards seeing

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Speaker 1: whether or not we have some good future treatments for this.

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Speaker 1: Are we losening public health restrictions in a good way?

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Speaker 1: Are we able to contact trace of people that may

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Speaker 1: fall ill again to make sure that it's contained. Yeah,

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Speaker 1: that's a great question. UM. You know there are mixed results, UM,

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Speaker 1: I would say, And you look across particularly the United States.

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Speaker 1: There are some states that are doing a good job

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Speaker 1: in terms of keeping cases level. UM, while they're loosening restrictions,

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Speaker 1: there are a few states where you're starting to see

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Speaker 1: little upticks in terms of the numbers of cases that

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Speaker 1: are there. UM. Everything comes down to being um good

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Speaker 1: about identifying cases and then being able to track those

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Speaker 1: cases so that the people who are coming in contact

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Speaker 1: with those individuals can be identified and isolated. That's the

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Speaker 1: phase that we're moving into now as we try to

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Speaker 1: expand our economy. UM. Loosen public health restrictions can still

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Speaker 1: keep the virus down so states are gonna have to

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Speaker 1: be very very UM proactive, monitor how they're doing in

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Speaker 1: that and really work towards um optimizing those contact identification

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Speaker 1: testing and contact tracing strategies for these rollouts to be

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Speaker 1: uh able to be sustained. Well, contact tracing really work.

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Speaker 1: Is there a better way that than just, you know,

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Speaker 1: simply reopening the economy and actually seeing the number of

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Speaker 1: deaths to try and track where it is. Yeah, you know,

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Speaker 1: we're going to have to really change the way that

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00:26:08,880 --> 00:26:12,919
Speaker 1: we're approaching our our day and day out life, the

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00:26:13,080 --> 00:26:17,240
Speaker 1: social distancing mask, wearing various other things in terms of

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00:26:17,600 --> 00:26:20,399
Speaker 1: living crowds and places. These are going to be the

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00:26:20,440 --> 00:26:22,000
Speaker 1: things that we're going to have to deal with for

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00:26:22,040 --> 00:26:25,879
Speaker 1: the next you know, at least six months to if

435
00:26:25,960 --> 00:26:28,800
Speaker 1: not a year or more UM to make sure that

436
00:26:28,840 --> 00:26:32,399
Speaker 1: we're keeping this virus down and not seeing these surge

437
00:26:32,400 --> 00:26:34,280
Speaker 1: of cases that so many parts of the country saw.

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00:26:34,760 --> 00:26:38,080
Speaker 1: Um you know this spring when the virus first made

439
00:26:38,119 --> 00:26:41,639
Speaker 1: its way across the United States, Um, Andrew, what do

440
00:26:41,680 --> 00:26:44,680
Speaker 1: we know about antibodies? So are antibodies you know, something

441
00:26:44,720 --> 00:26:48,399
Speaker 1: that actually protects you against being reinfected or for the

442
00:26:48,480 --> 00:26:51,520
Speaker 1: moment to do these tests only truth that you've had

443
00:26:51,560 --> 00:26:57,760
Speaker 1: COVID nineteen. Yeah, So, um, another great question. There's two

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00:26:57,800 --> 00:27:00,399
Speaker 1: parts to this. We're learning more and more about the

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00:27:00,440 --> 00:27:04,160
Speaker 1: antibody responses that are being induced by infection, and there's

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00:27:04,160 --> 00:27:06,760
Speaker 1: some good results coming from that showing that people are

447
00:27:06,880 --> 00:27:10,960
Speaker 1: generating what we believe our protective responses after infection. Now,

448
00:27:11,000 --> 00:27:13,120
Speaker 1: the important thing to note though, is that the tests

449
00:27:13,640 --> 00:27:15,520
Speaker 1: are a bit more limited in what they tell you.

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00:27:16,080 --> 00:27:19,199
Speaker 1: The tests can tell you if you've been infected, but

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00:27:19,320 --> 00:27:23,359
Speaker 1: they can't tell you if they have these protective antibody levels,

452
00:27:23,359 --> 00:27:25,320
Speaker 1: at least it's not the tests that are around right now.

453
00:27:25,840 --> 00:27:28,960
Speaker 1: So what has to be very careful about the antibody testing,

454
00:27:29,400 --> 00:27:31,720
Speaker 1: which of course is increasing across the country as it

455
00:27:31,760 --> 00:27:34,800
Speaker 1: becomes more available. It tells you if you've been exposed,

456
00:27:35,080 --> 00:27:37,480
Speaker 1: but it doesn't necessarily tell you at this point in

457
00:27:37,520 --> 00:27:42,080
Speaker 1: time whether you're protected from reinfection. So does it make

458
00:27:42,080 --> 00:27:44,960
Speaker 1: sense to to, you know, get tested one time, but

459
00:27:45,000 --> 00:27:49,160
Speaker 1: then also wait for more sophisticated antibody testing. Will will

460
00:27:49,200 --> 00:27:51,399
Speaker 1: these come out to actually be able to tell you

461
00:27:51,440 --> 00:27:55,400
Speaker 1: if you're protected from the virus and for how long? Yeah?

462
00:27:55,520 --> 00:27:57,359
Speaker 1: I think there will be There is a there is

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00:27:57,400 --> 00:28:01,199
Speaker 1: a hope that once we really identify what arm of

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00:28:01,320 --> 00:28:05,200
Speaker 1: the antibody responses are are providing the protection of people,

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00:28:05,440 --> 00:28:08,159
Speaker 1: that the test can then be fine tuned so that

466
00:28:08,200 --> 00:28:10,920
Speaker 1: we're asked answering both of those questions, have you been

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00:28:10,920 --> 00:28:14,280
Speaker 1: exposed and are you protected? At the same time, it's

468
00:28:14,320 --> 00:28:15,719
Speaker 1: going to take a little bit of time for us

469
00:28:15,760 --> 00:28:19,240
Speaker 1: to to come up with those tests simply because we

470
00:28:19,320 --> 00:28:21,640
Speaker 1: have to wait and make sure that the people who

471
00:28:21,640 --> 00:28:25,040
Speaker 1: have been infected have these immune responses for longer periods

472
00:28:25,040 --> 00:28:28,840
Speaker 1: of time. Some things can be moved forward quickly. Um,

473
00:28:28,880 --> 00:28:32,520
Speaker 1: we have two other things, such as understanding if you're

474
00:28:32,520 --> 00:28:37,080
Speaker 1: still protected from infections six months after you've been infected. Um,

475
00:28:37,200 --> 00:28:40,120
Speaker 1: just take time, which is kind of the obvious thing there,

476
00:28:40,400 --> 00:28:42,240
Speaker 1: but it really does take some time for us to

477
00:28:42,320 --> 00:28:45,920
Speaker 1: really understand the full length of protection that's induced by infection.

478
00:28:47,400 --> 00:28:50,200
Speaker 1: And how much do we understand about certain communities, you know,

479
00:28:50,280 --> 00:28:54,200
Speaker 1: and also certain countries have been affected more than others.

480
00:28:54,520 --> 00:28:56,360
Speaker 1: Is it just the spreading of the virus and some

481
00:28:56,520 --> 00:28:59,080
Speaker 1: of the social distancing put in place, or is there

482
00:28:59,320 --> 00:29:02,760
Speaker 1: a much deep are in concerning response that could be

483
00:29:02,760 --> 00:29:06,920
Speaker 1: linked to genetics. So um, those are the studies that

484
00:29:06,960 --> 00:29:10,040
Speaker 1: are still in place right now. We've got population studies

485
00:29:10,080 --> 00:29:13,960
Speaker 1: going on. We certainly know that there are parts of

486
00:29:13,960 --> 00:29:18,080
Speaker 1: the population that seem to be more susceptible to severe

487
00:29:18,080 --> 00:29:25,080
Speaker 1: disease UM. Separating out socioeconomic factors from genetic factors is

488
00:29:25,120 --> 00:29:28,960
Speaker 1: something that is really a high priority level UM. We've

489
00:29:28,960 --> 00:29:30,680
Speaker 1: had a lot of unrest this week in terms of

490
00:29:30,760 --> 00:29:33,040
Speaker 1: racial tensions, and certainly one of the things that we've

491
00:29:33,080 --> 00:29:37,080
Speaker 1: noticed in the US is that UM minority populations, urban

492
00:29:37,120 --> 00:29:40,640
Speaker 1: populations seem to be UM much more strongly hit by

493
00:29:40,680 --> 00:29:44,200
Speaker 1: the virus than other populations. UM. We also know the elderly,

494
00:29:44,240 --> 00:29:46,680
Speaker 1: and in fact, there's many much data suggesting that men

495
00:29:46,720 --> 00:29:50,240
Speaker 1: are more susceptible to severe disease UM than the rest

496
00:29:50,240 --> 00:29:53,080
Speaker 1: of the population. So there are a lot of factors

497
00:29:53,120 --> 00:29:56,640
Speaker 1: that are coming out from the data these days that

498
00:29:56,720 --> 00:30:00,240
Speaker 1: merit more investigation. The US just today is the looking

499
00:30:00,280 --> 00:30:04,800
Speaker 1: for additional demographic information on COVID nineteen infected patients, so

500
00:30:04,840 --> 00:30:08,080
Speaker 1: that can really sort of target and understand UM the

501
00:30:08,160 --> 00:30:12,960
Speaker 1: subpopulations that are being affected by severe disease. So there's

502
00:30:12,960 --> 00:30:14,680
Speaker 1: still a lot to learn, but the data is coming

503
00:30:14,720 --> 00:30:17,160
Speaker 1: in and we're collecting the data in ways there are

504
00:30:17,160 --> 00:30:19,720
Speaker 1: going to allow us to really identify these high risk

505
00:30:19,760 --> 00:30:22,440
Speaker 1: populations and get at the reasons why they're at risk,

506
00:30:23,400 --> 00:30:26,760
Speaker 1: and it really quickly. Is a second wave likely over

507
00:30:27,480 --> 00:30:31,560
Speaker 1: the winter time? Is it you know, seasonal? Yeah, so

508
00:30:31,960 --> 00:30:34,720
Speaker 1: it's looking more and more like you know, there is

509
00:30:34,800 --> 00:30:38,200
Speaker 1: some seasonality to this virus. UM. Right now, we're going

510
00:30:38,200 --> 00:30:41,320
Speaker 1: to continue to see cases. There's so many people that

511
00:30:41,480 --> 00:30:44,520
Speaker 1: have no immunity to this virus that it's relatively easy

512
00:30:44,560 --> 00:30:48,800
Speaker 1: for the virus to find people who can infect Right now, UM,

513
00:30:48,880 --> 00:30:53,360
Speaker 1: once we moved back inside, once humidity and temperature drops, UM,

514
00:30:53,400 --> 00:30:55,400
Speaker 1: we expect that there are going to be a surge

515
00:30:55,400 --> 00:30:58,280
Speaker 1: of cases very similar to what we saw and perhaps

516
00:30:58,360 --> 00:31:01,280
Speaker 1: in the two thousand and nine each one in one pandemic,

517
00:31:01,600 --> 00:31:04,000
Speaker 1: where the virus sort of stammered over the summer and

518
00:31:04,040 --> 00:31:09,360
Speaker 1: then UMS caused a strong surge of cases in the fall.

519
00:31:10,040 --> 00:31:12,720
Speaker 1: We hope to be much more prepared for that UM

520
00:31:12,720 --> 00:31:16,480
Speaker 1: and be able to deal with that much more better. Andrew,

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00:31:16,520 --> 00:31:18,280
Speaker 1: thank you so much for your time. Andrew Peckosh is

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00:31:18,280 --> 00:31:20,240
Speaker 1: there and be sure to check out v r us

523
00:31:20,440 --> 00:31:23,640
Speaker 1: go on the Bloomberg for the latest information. Thanks for

524
00:31:23,720 --> 00:31:28,080
Speaker 1: listening to the Bloomberg surveillance podcast. Subscribe and listen to

525
00:31:28,240 --> 00:31:34,000
Speaker 1: interviews on Apple Podcasts, SoundCloud, or whichever podcast platform you prefer.

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00:31:34,560 --> 00:31:37,880
Speaker 1: I'm on Twitter at Tom Keane before the podcast. You

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00:31:37,920 --> 00:31:41,320
Speaker 1: can always catch us worldwide. I'm Bloomberg Radio