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Speaker 1: Pushkin from Pushkin Industries. This is Deep Background, the show

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Speaker 1: where we explore the stories behind the stories in the news.

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Speaker 1: I'm Noah Feld. Today we wanted to share with you

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Speaker 1: a special episode special because its subject matter just seemed

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Speaker 1: so pressing that we didn't think it was appropriate to wait.

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Speaker 1: We're going to talk to Mark Lipsch about the coronavirus

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Speaker 1: COVID nineteen. Mark isn't just another expert on these topics.

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Speaker 1: He's Professor of epidemiology at the Harvard chance School of

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Speaker 1: Public Health and director of the Center for Communicable Disease

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Speaker 1: Dynamics there. In other words, Mark is someone who has

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Speaker 1: spent his entire career trying to understand the behavior of

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Speaker 1: viruses and what they do when they spread around the world,

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Speaker 1: and he is at the forefront of analyzing what's going

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Speaker 1: on with the coronavirus right now in real time. What

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Speaker 1: Mark has to say is more than a little bit disturbing. Mark,

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Speaker 1: thank you so much for taking time out of your

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Speaker 1: busy schedules saving the world to talk to me. I'm

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Speaker 1: very very grateful, happy to do it. I want to

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Speaker 1: just start by diving into something that you said to

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Speaker 1: the Washington Post the other day, and I know because

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Speaker 1: I know you, that you said much more than this.

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Speaker 1: They quoted you as saying there was a chance, and

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Speaker 1: they didn't say how big a chance that forty to

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Speaker 1: seventy percent of the world's population could end up infected

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Speaker 1: with the coronavirus. And now knowing you, you would never

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Speaker 1: have said that unless you assign some probability to that judgment.

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Speaker 1: What did you actually say and what would you say

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Speaker 1: about that subject? That is a roughly correct quote I

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Speaker 1: should have said of the adult population, and I'm trying

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Speaker 1: to make amends for that, and I'll explain that in

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Speaker 1: a little bit. But I think that there's a very

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Speaker 1: good chance that we are in the beginning of a

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Speaker 1: pandemic of this novel coronavirus right now, and that pandemic

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Speaker 1: spread of that virus, given what we currently understand about

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Speaker 1: its epidemiology, is likely to infect something on that order

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Speaker 1: of the adult population. The reason for the adult qualification

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Speaker 1: is that we don't really understand what's going on with children.

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Speaker 1: They are very few known cases in children, and that

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Speaker 1: either means that they're not getting infected, or perhaps means

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Speaker 1: that they are getting infected, maybe transmitting, but just sufficiently

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Speaker 1: mild cases that we aren't detecting them with current surveillance.

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Speaker 1: So forty percent infected doesn't mean forty percent get very

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Speaker 1: very ill or that forty percent die, but it does

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Speaker 1: mean that some proportion of them get symptomatic. And of those,

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Speaker 1: the current estimates are that around one or two percent

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Speaker 1: may die, with a very much larger risk of dying

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Speaker 1: if you're over sixty five. So I really did say

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Speaker 1: essentially what they quoted me as saying, and with the

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Speaker 1: caveat about the adults, I still mean it. Mark what's

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Speaker 1: your confidence level in that forty to seventy percent estimate.

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Speaker 1: It's hard to put a number exactly on that sort

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Speaker 1: of hyper prior, but I think the justification for it

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Speaker 1: is that transmission of novel viruses with contagiousness level, which

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Speaker 1: we quantify as a reproductive number similar to this, is

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Speaker 1: something we've observed before, and that's influenza during pandemics, and

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Speaker 1: in the two largest pandemics of the twentieth century, around

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Speaker 1: thirty to almost forty percent of people became symptomatically infected.

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Speaker 1: And that's lower than the proportion that got infected because

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Speaker 1: some people don't get very sick even with flu. So

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Speaker 1: we have an analog which is one reason for that number,

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Speaker 1: And we also have mathematical models, which are simplifications of reality,

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Speaker 1: and we know that and we don't believe their outputs literally,

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Speaker 1: but mathematical models would say that it could be that

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Speaker 1: range or higher depending on how much mixing there is

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Speaker 1: in the population. So that's the basis for it. The

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Speaker 1: ways it could be wrong are if transmission outside of

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Speaker 1: China is fundamentally different in some way from transmission in Wohan,

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Speaker 1: if there's unexpectedly much benefit from warm or weather, we

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Speaker 1: expect that the seasonal changes will modestly reduce transmission, but

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Speaker 1: not very much and certainly not enough to stop it.

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Speaker 1: Or if interventions are sufficiently intensive as to stop the

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Speaker 1: spread or very much slow the spread. That I think

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Speaker 1: is a possible scenario, at least in certain play. It's

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Speaker 1: challenging because those interventions would have to be in place

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Speaker 1: for a very long time and will be very unpleasant

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Speaker 1: for many people, are worse than unpleasant. But I think

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Speaker 1: probably another qualification that should have gone into that forty

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Speaker 1: to seventy percent is in the absence of strong countermeasures.

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Speaker 1: Mark when you say interventions, you're picturing quarantines, shelter in place,

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Speaker 1: something stronger in terms of lockdown, what's the kind of

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Speaker 1: intervention that you're describing. They are kind of two categories.

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Speaker 1: The one that I think increasingly we think won't work

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Speaker 1: is isolation of cases and quarantine of their contacts and

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Speaker 1: then making them isolate. I mean, I think that will

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Speaker 1: be a part of it, and obviously people will self

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Speaker 1: isolate to protect their family and to protect their contacts

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Speaker 1: if they're sick, and because they're sick, they'll isolate to

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Speaker 1: some extent. But it looks like those kinds of interventions

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Speaker 1: by themselves probably aren't good enough for a virus like this,

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Speaker 1: where there seems to be evidence of at least some

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Speaker 1: transmission from people who are mildly ill or not yet ill.

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Speaker 1: So I think the complement to that and the sort

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Speaker 1: of more large scale interventions will be population level interventions

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Speaker 1: where they don't depend on knowing who's sick and who's transmitting,

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Speaker 1: and those are things like canceling public gatherings, potentially closing schools,

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Speaker 1: although the relevance of that is unclear until we understand

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Speaker 1: better how children are involved in transmission, and other kinds

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Speaker 1: of working from home and other kinds of ways of

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Speaker 1: reducing contact between people regardless of whether they're currently showing

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Speaker 1: any symptoms. And that's what the Chinese have successfully done,

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Speaker 1: among other things, is very much clamped down on letting

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Speaker 1: people out on the streets. As I understand it, there's

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Speaker 1: a sort of you can go out a certain frequency

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Speaker 1: per week, you have to have a pass, you get

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Speaker 1: your food and other supplies delivered with electronic payment. Not

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Speaker 1: every place can do that, but some places can do

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Speaker 1: that to some extent. I think China may be one

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Speaker 1: of the most able to do that of anywhere in

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Speaker 1: a world. I want to talk about consequences now for

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Speaker 1: a moment. In a world where let's say in the

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Speaker 1: United States, between forty and seventy percent of the adult

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Speaker 1: population is infected on a daily basis, when it reaches

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Speaker 1: that depth of penetration, I assume you can't diagnose that

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Speaker 1: forty to seventy percent of the people. You can only

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Speaker 1: diagnose the symptomatic ones because there's just too many people.

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Speaker 1: And so do people more or less go about their

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Speaker 1: daily lives, or everyone is expected to more or less

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Speaker 1: shelter in place, except for the people who are necessary

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Speaker 1: to preserve basic services. We're not going to identify every case,

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Speaker 1: as you say, for the reasons you say, it won't

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Speaker 1: be forty to seventy percent at the same time. Obviously

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Speaker 1: it will take it will go over some period of time.

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Speaker 1: And the problem is, even if we could diagnose every

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Speaker 1: one of those people, there seems to be some transmission

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Speaker 1: at least and maybe a fair amount of transmission from

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Speaker 1: people who are not yet diagnosed able. So that's why

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Speaker 1: I say there are these two categories of interventions, those

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Speaker 1: that depend on cases and those that depend on general

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Speaker 1: social distancing and for disinfection. It's looking for now, unless

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Speaker 1: our picture of it changes like the second category is

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Speaker 1: going to be more important. And unfortunately, as we'll see,

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Speaker 1: if China goes back to work as they're talking about doing,

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Speaker 1: those interventions only work while they're in place as long

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Speaker 1: as there's some virus still circulating. When you let up

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Speaker 1: on the interventions to keep people at home, transmission begins again.

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Speaker 1: So it's a real challenge. And the ways to get

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Speaker 1: to the end of it are either that enough will

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Speaker 1: get infected so that transmission can't take off again, and

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Speaker 1: given our current estimates, forty or fifty percent would be

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Speaker 1: enough to do that. The seventy percent scenario would be

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Speaker 1: the sort of tail end of that after the virus

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Speaker 1: is starting to go away. But if fifty percent, and

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Speaker 1: this is very rough because the numbers are changing about

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Speaker 1: how contagious this is every time I have a phone call,

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Speaker 1: but if say forty or fifty percent got infected, then

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Speaker 1: that would be enough to prevent new outbreaks in that

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Speaker 1: population until immunity declines, which it may do. So that's

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Speaker 1: one way, and all these control measures will make it

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Speaker 1: more likely that that happens, that the minimum number needed

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Speaker 1: rather than more than that minimum. And the other way

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Speaker 1: is that we delay it long enough to have a vaccine,

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Speaker 1: but I think the timescale for that is not realistic

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Speaker 1: to do that for most of the world, in most

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Speaker 1: of the unless we get extremely lucky with the quality

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Speaker 1: and producibility of the vaccines. I think I'm experiencing some

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Speaker 1: cognitive denial in listening to you, as I indeed I

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Speaker 1: was when I read your initial forty to seventy percent

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Speaker 1: estimate it. So I guess I'm wondering if you can

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Speaker 1: help me and help listeners in sort of taking on

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Speaker 1: board whether we just I'm just being irrational, you know,

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Speaker 1: is my denial irrational when I'm saying to myself, I

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Speaker 1: just can't picture a world where something like half of

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Speaker 1: the US population adult population has coronavirus, where we're sort

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Speaker 1: of looking at each other, where you know every other

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Speaker 1: person is infected at one point or another, where you know,

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Speaker 1: potentially in the United States, that would mean well over

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Speaker 1: a million people could die, and globally obviously much much, much,

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Speaker 1: much more. I mean, is my reaction just standard denialism,

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Speaker 1: or is there some underlying sense where maybe my skepticism

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Speaker 1: is warranted in light of the imperfection of statistical projections.

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Speaker 1: I think it depends on how on whether you can

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Speaker 1: find an reason to think that the pieces of the

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Speaker 1: projections are wrong. I mean, all projections could be wrong

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Speaker 1: for reasons that we can't imagine. So I'll give you

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Speaker 1: ten percent or even twenty percent chance that there's just

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Speaker 1: something nobody's thought of that is going to completely invalidate this.

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Speaker 1: Beyond that, you know, I've been thinking as hard as

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Speaker 1: I can because I don't like the conclusion either. I

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Speaker 1: think the data on which we're basing this are not

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Speaker 1: very good, So the bounds around what we're trying to

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Speaker 1: estimate are very large. But I mean, I think one

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Speaker 1: way to think of it is this is not an

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Speaker 1: existential threat as the people who think about those things

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Speaker 1: define it. This is a really potentially very bad thing

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Speaker 1: that could happen, that we'll touch many lives in a

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Speaker 1: bad way. But I think that's sort of how I'm

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Speaker 1: thinking about it, and it's not fun to think about.

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Speaker 1: You wrote a terrific piece in the Scientific American trying

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Speaker 1: to explain to the rest of us how we should

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Speaker 1: think about what we read any here. I think it

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Speaker 1: would be great for listeners if you would share your

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Speaker 1: three categories in your analysis, because I found it really helpful. Sure,

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Speaker 1: we wrote this because when I've been talking to journalists,

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Speaker 1: I find that I'm constantly trying to hedge and say

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Speaker 1: this is a fact, this is something I believe but

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Speaker 1: isn't yet resolved as true or false. And so the

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Speaker 1: three categories that we laid out with Bill Hannage, my colleague,

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Speaker 1: were that when scientists talk about this pandemic, they can

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Speaker 1: describe facts, they can describe informed inference, where facts are

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Speaker 1: at the base of it, but some extrapolation is being

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Speaker 1: made based on analogies to other viruses or information. We

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Speaker 1: know from different sources that we're putting together in a

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Speaker 1: particular way. And then the third category is opinion and

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Speaker 1: speculation on topics like other people's motives, which are sort

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Speaker 1: of irresolvable as matters of fact. But the first two

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Speaker 1: categories I think are interesting in that they're very fluid.

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Speaker 1: So matters like whether there was undetected transmission in many

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Speaker 1: countries that were matters of speculation informed by some calculations

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Speaker 1: and facts are now becoming matters of fact, meaning we

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Speaker 1: now know there were a lot of undetected cases in Italy,

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Speaker 1: there were a lot of undetected cases in Iran. A

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Speaker 1: week ago, there were no detected cases in either place,

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Speaker 1: and I wouldn't have picked those as the countries I

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Speaker 1: would expect first to pop up. I would have picked

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Speaker 1: perhaps Indonesia or some other countries, and I got a

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Speaker 1: lot of flak for that. But the fact is now

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Speaker 1: we know there are cases in a bunch of countries

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Speaker 1: where we were just speculating that because of the low

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Speaker 1: amount of testing we couldn't know, and there probably was some.

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Speaker 1: So I think in this epidemic, hopefully most topics are

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Speaker 1: moving up the list from speculation informed by facts to

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Speaker 1: real facts. But the public health surveillance system is not

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Speaker 1: set up, unfortunately, to provide factual answers to all these

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Speaker 1: questions at once, because we just don't have ways of

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Speaker 1: detecting things we've never seen before until we invent them,

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Speaker 1: and then those have to be rolled out. And so,

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Speaker 1: for example, diagnostic testing is still a very very limiting

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Speaker 1: factor in many countries, including the US. So we don't

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Speaker 1: know how many cases there are in this country because

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Speaker 1: we're testing only people who have contacts with China or

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Speaker 1: with known cases. And so if somebody was missed in

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Speaker 1: this country coming through the border, then anyone they infected,

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Speaker 1: and anyone those people infected and so on is untestable

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Speaker 1: under current protocols. That will change eventually, but at the moment,

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Speaker 1: we can't see them because we're not looking at them.

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Speaker 1: And I take it that in your view, it's overwhelmingly

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Speaker 1: probable that there are such people. I mean, you say

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Speaker 1: you got flack for expecting more cases in Indonesia, but

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Speaker 1: really you should get some credit for saying that there

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Speaker 1: were going to be undetected cases, you know, somewhere, and

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Speaker 1: sure enough there were, So in that sense, there was

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Speaker 1: a validation of your position. Thank you. I agree. I

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Speaker 1: think that it's very very likely that there are cases,

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Speaker 1: many cases in this country that have not been detected.

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Speaker 1: And reading between the lines of statements from CDC officials,

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Speaker 1: including the director last week or two weeks ago, it

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Speaker 1: seems as if they think that's probably true also in

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Speaker 1: that they're saying they expect ongoing transmission. Robert Redfield, the director,

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Speaker 1: said that he expected that this would be to come

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Speaker 1: the next seasonal virus and which would be just with

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Speaker 1: us for the long term. You're forty to seventy percent

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Speaker 1: in the adult population, as is that in category two

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Speaker 1: for you? That's you know, an inference based on based

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Speaker 1: on facts, yes, facts, models and analogies. And do you

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Speaker 1: want to just say a word for listeners who might

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Speaker 1: be interested about how you build those models. I'm fascinated

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Speaker 1: by this. How do you go about doing this the

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Speaker 1: rest of the time when you're not you know, all

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Speaker 1: the preparation as it were, for when a potential pandemic

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Speaker 1: actually does it and suddenly you're, you know, on the

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Speaker 1: front page of the newspapers making your predictions. But the

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Speaker 1: rest of the time you're building the models and gathering

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Speaker 1: the data. How do you go about that at the

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Speaker 1: at the broadest level of generality. Yeah, So I think

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Speaker 1: the models that we work with are that they're base

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Speaker 1: very simple. They they are models in which, in a

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Speaker 1: computer or on a mathematical formula, you take one infectious

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Speaker 1: individual and one susceptible individual. They bump into each other

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Speaker 1: figuratively and create another infectious individual out of the susceptible.

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Speaker 1: So it's like a chemical reaction A S plus I

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Speaker 1: too I susceptible plus infected equals to infected, and then

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Speaker 1: the other chemical reaction is infected yields recovered over some timescale.

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Speaker 1: That's at the heart of all of these models. So

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Speaker 1: in the very simplest version of this, where we're all

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Speaker 1: like little atoms bouncing around in an ideal gas, if

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Speaker 1: you build that model and put the parameters in that

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Speaker 1: have been measured and estimated for this virus, you run

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Speaker 1: it to its end and you end up with about

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Speaker 1: eighty to ninety percent of the population infected, depending on

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Speaker 1: which numbers you put in. But we know that people

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Speaker 1: are not ideal gases, and there's structure in the population,

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Speaker 1: and there's seasonal variability, and there's different levels of susceptibility

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Speaker 1: and different age groups and things, and so when you're

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Speaker 1: building a model for an infection that you know something

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Speaker 1: about and where there's already data, you can upbrate all

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Speaker 1: of those types of heterogeneity based on measurements from the data,

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Speaker 1: sometimes with more certainty and sometimes with less, and then

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Speaker 1: you can run the model and see what happens. And

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Speaker 1: usually the almost always the ideal gas kind of approximation

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Speaker 1: is the worst case, and all the structure tends to

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Speaker 1: reduce infection levels, which is one reason I wouldn't have

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Speaker 1: put eighty percent out there. So that's kind of the approach,

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Speaker 1: and then there are many kind of curly cues you

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Speaker 1: can build on top of it that are ways of

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Speaker 1: accommodating the particularities of certain infections. Like some infections there's

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Speaker 1: a carrier state where you're not sick, but you are transmitting.

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Speaker 1: Some infections you become immune and you stay immune. Others

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Speaker 1: you become a little more immune, and then a little

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Speaker 1: more immune after multiple exposures. So you can elaborate that

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Speaker 1: basic structure, but the fundamental idea is that you're catalyzing

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Speaker 1: new infections with old infections, and then people are recovering

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Speaker 1: and everything else is a variation on that mark, What

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Speaker 1: are you doing in your own real life to prepare

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Speaker 1: for this? I mean from what you're doing for your

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Speaker 1: family to did you cash out your your retirement accounts

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Speaker 1: out of the market and go into cash. I mean,

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Speaker 1: I just really curious, in practical, real world terms, what

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Speaker 1: are you doing? Yeah, I mean, I think at the moment,

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Speaker 1: we're all we're all a little in denial because this

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Speaker 1: doesn't seem it really doesn't feel real because nothing's happening

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Speaker 1: visibly in our country. So it's a process and I

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Speaker 1: think we're all kind of coming to the idea that

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Speaker 1: if this scenario plays out as it feels like it might,

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Speaker 1: then we might be doing a lot of work from home,

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Speaker 1: we might have our schools canceled for goodness knows how

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Speaker 1: many months, and in the short term, you know, the

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Speaker 1: recommendations from the government about getting supplies of essential goods,

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Speaker 1: including medications, seem like a good idea to me. Not

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Speaker 1: a year's supply, but a ninety days supply is what

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Speaker 1: they often recommend, and so we are trying to do that. Obviously,

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Speaker 1: the ability to do that is a luxury that not

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Speaker 1: everybody has, and that's a problem. I mean, what are

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Speaker 1: people anticipated to do about food if they're sheltering in

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Speaker 1: place in that way. Well, I think that's a really

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Speaker 1: good question. I'm kind of astonished how well it seems

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Speaker 1: to have worked from what I hear in China and

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Speaker 1: my friends attribute that to the completely electronic economy. That

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Speaker 1: everybody uses phones to pay for things with one of

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Speaker 1: the two systems that they have there, and so that

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Speaker 1: eases things. So everything is basically being delivered. In other words,

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Speaker 1: it's all being paid for electronically and delivered. Everything's being delivered,

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Speaker 1: but of course there have to be delivery people, and

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Speaker 1: those people have to have some contact with one another

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Speaker 1: and with the warehouse, etc. And this has been thought

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Speaker 1: through bit in this country for just this reason, when

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Speaker 1: people were more concerned about bird flu pandemics a decade ago.

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Speaker 1: So I think we will have to see, and it's

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Speaker 1: not my expertise, but I'm concerned about that as well.

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Speaker 1: What am I not asking you that you think is

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Speaker 1: important for people to hear in terms of preparing. I

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Speaker 1: think the things that I described and just sort of

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Speaker 1: mentally getting in the into the idea that there is

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Speaker 1: a possibility, a significant possibility of a really disrupted time

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Speaker 1: is kind of my major recommendation and making some material

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Speaker 1: plans for that, in so far as people can Where

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Speaker 1: should people look up guidance on this? If they're not

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Speaker 1: sure what are the essential medicines? I'm not sure? I

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Speaker 1: know what are the essential medicines I ought to have

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Speaker 1: for ninety days? Right? Well, what you have prescribed to you.

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Speaker 1: There's not very many essential non prescription medicines, but right

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Speaker 1: maybe some painkiller or anti fever medications. And there's a

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Speaker 1: good website called ready dot gov slash pandemic which has

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Speaker 1: got some of that information on it. You know. Politically,

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Speaker 1: I think it would be good if people recognize that

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Speaker 1: the administration has just asked for a tiny appropriation of

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Speaker 1: two and a half billion dollars to deal with this,

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Speaker 1: half of which is supposed to come from Ebola funding

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Speaker 1: that was not yet spent. That's not a serious response,

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Speaker 1: and I hope that people will recognize that and press

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Speaker 1: for more. I hope they will demand better leadership than

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Speaker 1: having the President say repeatedly again recently, that this is

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Speaker 1: all going to go away the way SARS did. SARS

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Speaker 1: didn't go away. SARS was beaten back with very intensive measures,

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Speaker 1: and this is much bigger than SARS, so I think

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Speaker 1: there's the political dimension to it. I'm concerned also about

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Speaker 1: the impact on our elections. If the virus really does

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Speaker 1: start to be detected in more places in the United States,

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Speaker 1: I think it could lead to people not wanting to

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Speaker 1: go vote in the primaries. I think that could be

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Speaker 1: used by various sides to try to make mischief over

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Speaker 1: the results. You know, I think there are lots of

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Speaker 1: downstream consequences that will understand better when we see them,

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Speaker 1: But those are some of my concerns. Mark. I'm really

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Speaker 1: grateful to you for speaking so clearly and so calmly

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Speaker 1: about something that, as you say, doesn't really feel real

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Speaker 1: yet and therefore is really susceptible to our powerful instinct

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Speaker 1: to deny. But you know, as we all know, denying

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Speaker 1: something doesn't mean it's not going to happen. So I

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Speaker 1: think we all do need to change our mindset and

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Speaker 1: start being a little more realistic about what the probabilities

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Speaker 1: are as as you've laid them out. So I just

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Speaker 1: want to really thank you for the time to clarify

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Speaker 1: this and first saying a little bit about how you

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Speaker 1: reach these conclusions. Getting behind the story can also help us.

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Speaker 1: I think get to terms with the reality. So really,

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Speaker 1: thank you very very much for what you're doing. Thank

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Speaker 1: you for having me. Having spoken to Mark, I still

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Speaker 1: am having trouble with my sense of unreality. I just

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Speaker 1: don't want to believe what he's telling us about the

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Speaker 1: forty to seventy percent number of adults around the world

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Speaker 1: who can end up infected with the coronavirus. And yet

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Speaker 1: at the same time, I know I'm talking to Mark Lipsitch.

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Speaker 1: I'm talking to a calm, reasonable, extremely brilliant epidemiologist who's

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Speaker 1: made a career of studying the questions that he's working on. Now,

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Speaker 1: if he's not the expert to be believed, there is

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Speaker 1: no expert to be believed. So I think what I'm

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Speaker 1: going to be trying to do going forward, and maybe

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Speaker 1: you'll choose to do the same, is to try to

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Speaker 1: update my understanding of where we are and where we're

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Speaker 1: going on the basis of his data. When I do that,

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Speaker 1: I admit it makes me afraid. But Mark also said

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Speaker 1: very calmly that this is not an existential threat, or

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Speaker 1: at least it's not an existential threat to society as

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Speaker 1: a whole. So perhaps we can take a deep breath

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Speaker 1: and perhaps take some small solace in that, even as

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Speaker 1: we prepare ourselves for what could be a long and

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Speaker 1: very difficult ride. Deep Background is brought to you by

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Speaker 1: Pushkin Industries. Our producer is Lydia Genecott, with studio recording

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Speaker 1: Jacob Weisberg, and Mia Lobel. I'm Noah Feldman. You can

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Speaker 1: follow me on Twitter at Noah R. Feldman. This is

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