Speaker 1: Pushkin from Pushkin Industries. This is Deep Background, the show
Speaker 1: where we explore the stories behind the stories in the news.
Speaker 1: I'm Noah Feld. Today we wanted to share with you
Speaker 1: a special episode special because its subject matter just seemed
Speaker 1: so pressing that we didn't think it was appropriate to wait.
Speaker 1: We're going to talk to Mark Lipsch about the coronavirus
Speaker 1: COVID nineteen. Mark isn't just another expert on these topics.
Speaker 1: He's Professor of epidemiology at the Harvard chance School of
Speaker 1: Public Health and director of the Center for Communicable Disease
Speaker 1: Dynamics there. In other words, Mark is someone who has
Speaker 1: spent his entire career trying to understand the behavior of
Speaker 1: viruses and what they do when they spread around the world,
Speaker 1: and he is at the forefront of analyzing what's going
Speaker 1: on with the coronavirus right now in real time. What
Speaker 1: Mark has to say is more than a little bit disturbing. Mark,
Speaker 1: thank you so much for taking time out of your
Speaker 1: busy schedules saving the world to talk to me. I'm
Speaker 1: very very grateful, happy to do it. I want to
Speaker 1: just start by diving into something that you said to
Speaker 1: the Washington Post the other day, and I know because
Speaker 1: I know you, that you said much more than this.
Speaker 1: They quoted you as saying there was a chance, and
Speaker 1: they didn't say how big a chance that forty to
Speaker 1: seventy percent of the world's population could end up infected
Speaker 1: with the coronavirus. And now knowing you, you would never
Speaker 1: have said that unless you assign some probability to that judgment.
Speaker 1: What did you actually say and what would you say
Speaker 1: about that subject? That is a roughly correct quote I
Speaker 1: should have said of the adult population, and I'm trying
Speaker 1: to make amends for that, and I'll explain that in
Speaker 1: a little bit. But I think that there's a very
Speaker 1: good chance that we are in the beginning of a
Speaker 1: pandemic of this novel coronavirus right now, and that pandemic
Speaker 1: spread of that virus, given what we currently understand about
Speaker 1: its epidemiology, is likely to infect something on that order
Speaker 1: of the adult population. The reason for the adult qualification
Speaker 1: is that we don't really understand what's going on with children.
Speaker 1: They are very few known cases in children, and that
Speaker 1: either means that they're not getting infected, or perhaps means
Speaker 1: that they are getting infected, maybe transmitting, but just sufficiently
Speaker 1: mild cases that we aren't detecting them with current surveillance.
Speaker 1: So forty percent infected doesn't mean forty percent get very
Speaker 1: very ill or that forty percent die, but it does
Speaker 1: mean that some proportion of them get symptomatic. And of those,
Speaker 1: the current estimates are that around one or two percent
Speaker 1: may die, with a very much larger risk of dying
Speaker 1: if you're over sixty five. So I really did say
Speaker 1: essentially what they quoted me as saying, and with the
Speaker 1: caveat about the adults, I still mean it. Mark what's
Speaker 1: your confidence level in that forty to seventy percent estimate.
Speaker 1: It's hard to put a number exactly on that sort
Speaker 1: of hyper prior, but I think the justification for it
Speaker 1: is that transmission of novel viruses with contagiousness level, which
Speaker 1: we quantify as a reproductive number similar to this, is
Speaker 1: something we've observed before, and that's influenza during pandemics, and
Speaker 1: in the two largest pandemics of the twentieth century, around
Speaker 1: thirty to almost forty percent of people became symptomatically infected.
Speaker 1: And that's lower than the proportion that got infected because
Speaker 1: some people don't get very sick even with flu. So
Speaker 1: we have an analog which is one reason for that number,
Speaker 1: And we also have mathematical models, which are simplifications of reality,
Speaker 1: and we know that and we don't believe their outputs literally,
Speaker 1: but mathematical models would say that it could be that
Speaker 1: range or higher depending on how much mixing there is
Speaker 1: in the population. So that's the basis for it. The
Speaker 1: ways it could be wrong are if transmission outside of
Speaker 1: China is fundamentally different in some way from transmission in Wohan,
Speaker 1: if there's unexpectedly much benefit from warm or weather, we
Speaker 1: expect that the seasonal changes will modestly reduce transmission, but
Speaker 1: not very much and certainly not enough to stop it.
Speaker 1: Or if interventions are sufficiently intensive as to stop the
Speaker 1: spread or very much slow the spread. That I think
Speaker 1: is a possible scenario, at least in certain play. It's
Speaker 1: challenging because those interventions would have to be in place
Speaker 1: for a very long time and will be very unpleasant
Speaker 1: for many people, are worse than unpleasant. But I think
Speaker 1: probably another qualification that should have gone into that forty
Speaker 1: to seventy percent is in the absence of strong countermeasures.
Speaker 1: Mark when you say interventions, you're picturing quarantines, shelter in place,
Speaker 1: something stronger in terms of lockdown, what's the kind of
Speaker 1: intervention that you're describing. They are kind of two categories.
Speaker 1: The one that I think increasingly we think won't work
Speaker 1: is isolation of cases and quarantine of their contacts and
Speaker 1: then making them isolate. I mean, I think that will
Speaker 1: be a part of it, and obviously people will self
Speaker 1: isolate to protect their family and to protect their contacts
Speaker 1: if they're sick, and because they're sick, they'll isolate to
Speaker 1: some extent. But it looks like those kinds of interventions
Speaker 1: by themselves probably aren't good enough for a virus like this,
Speaker 1: where there seems to be evidence of at least some
Speaker 1: transmission from people who are mildly ill or not yet ill.
Speaker 1: So I think the complement to that and the sort
Speaker 1: of more large scale interventions will be population level interventions
Speaker 1: where they don't depend on knowing who's sick and who's transmitting,
Speaker 1: and those are things like canceling public gatherings, potentially closing schools,
Speaker 1: although the relevance of that is unclear until we understand
Speaker 1: better how children are involved in transmission, and other kinds
Speaker 1: of working from home and other kinds of ways of
Speaker 1: reducing contact between people regardless of whether they're currently showing
Speaker 1: any symptoms. And that's what the Chinese have successfully done,
Speaker 1: among other things, is very much clamped down on letting
Speaker 1: people out on the streets. As I understand it, there's
Speaker 1: a sort of you can go out a certain frequency
Speaker 1: per week, you have to have a pass, you get
Speaker 1: your food and other supplies delivered with electronic payment. Not
Speaker 1: every place can do that, but some places can do
Speaker 1: that to some extent. I think China may be one
Speaker 1: of the most able to do that of anywhere in
Speaker 1: a world. I want to talk about consequences now for
Speaker 1: a moment. In a world where let's say in the
Speaker 1: United States, between forty and seventy percent of the adult
Speaker 1: population is infected on a daily basis, when it reaches
Speaker 1: that depth of penetration, I assume you can't diagnose that
Speaker 1: forty to seventy percent of the people. You can only
Speaker 1: diagnose the symptomatic ones because there's just too many people.
Speaker 1: And so do people more or less go about their
Speaker 1: daily lives, or everyone is expected to more or less
Speaker 1: shelter in place, except for the people who are necessary
Speaker 1: to preserve basic services. We're not going to identify every case,
Speaker 1: as you say, for the reasons you say, it won't
Speaker 1: be forty to seventy percent at the same time. Obviously
Speaker 1: it will take it will go over some period of time.
Speaker 1: And the problem is, even if we could diagnose every
Speaker 1: one of those people, there seems to be some transmission
Speaker 1: at least and maybe a fair amount of transmission from
Speaker 1: people who are not yet diagnosed able. So that's why
Speaker 1: I say there are these two categories of interventions, those
Speaker 1: that depend on cases and those that depend on general
Speaker 1: social distancing and for disinfection. It's looking for now, unless
Speaker 1: our picture of it changes like the second category is
Speaker 1: going to be more important. And unfortunately, as we'll see,
Speaker 1: if China goes back to work as they're talking about doing,
Speaker 1: those interventions only work while they're in place as long
Speaker 1: as there's some virus still circulating. When you let up
Speaker 1: on the interventions to keep people at home, transmission begins again.
Speaker 1: So it's a real challenge. And the ways to get
Speaker 1: to the end of it are either that enough will
Speaker 1: get infected so that transmission can't take off again, and
Speaker 1: given our current estimates, forty or fifty percent would be
Speaker 1: enough to do that. The seventy percent scenario would be
Speaker 1: the sort of tail end of that after the virus
Speaker 1: is starting to go away. But if fifty percent, and
Speaker 1: this is very rough because the numbers are changing about
Speaker 1: how contagious this is every time I have a phone call,
Speaker 1: but if say forty or fifty percent got infected, then
Speaker 1: that would be enough to prevent new outbreaks in that
Speaker 1: population until immunity declines, which it may do. So that's
Speaker 1: one way, and all these control measures will make it
Speaker 1: more likely that that happens, that the minimum number needed
Speaker 1: rather than more than that minimum. And the other way
Speaker 1: is that we delay it long enough to have a vaccine,
Speaker 1: but I think the timescale for that is not realistic
Speaker 1: to do that for most of the world, in most
Speaker 1: of the unless we get extremely lucky with the quality
Speaker 1: and producibility of the vaccines. I think I'm experiencing some
Speaker 1: cognitive denial in listening to you, as I indeed I
Speaker 1: was when I read your initial forty to seventy percent
Speaker 1: estimate it. So I guess I'm wondering if you can
Speaker 1: help me and help listeners in sort of taking on
Speaker 1: board whether we just I'm just being irrational, you know,
Speaker 1: is my denial irrational when I'm saying to myself, I
Speaker 1: just can't picture a world where something like half of
Speaker 1: the US population adult population has coronavirus, where we're sort
Speaker 1: of looking at each other, where you know every other
Speaker 1: person is infected at one point or another, where you know,
Speaker 1: potentially in the United States, that would mean well over
Speaker 1: a million people could die, and globally obviously much much, much,
Speaker 1: much more. I mean, is my reaction just standard denialism,
Speaker 1: or is there some underlying sense where maybe my skepticism
Speaker 1: is warranted in light of the imperfection of statistical projections.
Speaker 1: I think it depends on how on whether you can
Speaker 1: find an reason to think that the pieces of the
Speaker 1: projections are wrong. I mean, all projections could be wrong
Speaker 1: for reasons that we can't imagine. So I'll give you
Speaker 1: ten percent or even twenty percent chance that there's just
Speaker 1: something nobody's thought of that is going to completely invalidate this.
Speaker 1: Beyond that, you know, I've been thinking as hard as
Speaker 1: I can because I don't like the conclusion either. I
Speaker 1: think the data on which we're basing this are not
Speaker 1: very good, So the bounds around what we're trying to
Speaker 1: estimate are very large. But I mean, I think one
Speaker 1: way to think of it is this is not an
Speaker 1: existential threat as the people who think about those things
Speaker 1: define it. This is a really potentially very bad thing
Speaker 1: that could happen, that we'll touch many lives in a
Speaker 1: bad way. But I think that's sort of how I'm
Speaker 1: thinking about it, and it's not fun to think about.
Speaker 1: You wrote a terrific piece in the Scientific American trying
Speaker 1: to explain to the rest of us how we should
Speaker 1: think about what we read any here. I think it
Speaker 1: would be great for listeners if you would share your
Speaker 1: three categories in your analysis, because I found it really helpful. Sure,
Speaker 1: we wrote this because when I've been talking to journalists,
Speaker 1: I find that I'm constantly trying to hedge and say
Speaker 1: this is a fact, this is something I believe but
Speaker 1: isn't yet resolved as true or false. And so the
Speaker 1: three categories that we laid out with Bill Hannage, my colleague,
Speaker 1: were that when scientists talk about this pandemic, they can
Speaker 1: describe facts, they can describe informed inference, where facts are
Speaker 1: at the base of it, but some extrapolation is being
Speaker 1: made based on analogies to other viruses or information. We
Speaker 1: know from different sources that we're putting together in a
Speaker 1: particular way. And then the third category is opinion and
Speaker 1: speculation on topics like other people's motives, which are sort
Speaker 1: of irresolvable as matters of fact. But the first two
Speaker 1: categories I think are interesting in that they're very fluid.
Speaker 1: So matters like whether there was undetected transmission in many
Speaker 1: countries that were matters of speculation informed by some calculations
Speaker 1: and facts are now becoming matters of fact, meaning we
Speaker 1: now know there were a lot of undetected cases in Italy,
Speaker 1: there were a lot of undetected cases in Iran. A
Speaker 1: week ago, there were no detected cases in either place,
Speaker 1: and I wouldn't have picked those as the countries I
Speaker 1: would expect first to pop up. I would have picked
Speaker 1: perhaps Indonesia or some other countries, and I got a
Speaker 1: lot of flak for that. But the fact is now
Speaker 1: we know there are cases in a bunch of countries
Speaker 1: where we were just speculating that because of the low
Speaker 1: amount of testing we couldn't know, and there probably was some.
Speaker 1: So I think in this epidemic, hopefully most topics are
Speaker 1: moving up the list from speculation informed by facts to
Speaker 1: real facts. But the public health surveillance system is not
Speaker 1: set up, unfortunately, to provide factual answers to all these
Speaker 1: questions at once, because we just don't have ways of
Speaker 1: detecting things we've never seen before until we invent them,
Speaker 1: and then those have to be rolled out. And so,
Speaker 1: for example, diagnostic testing is still a very very limiting
Speaker 1: factor in many countries, including the US. So we don't
Speaker 1: know how many cases there are in this country because
Speaker 1: we're testing only people who have contacts with China or
Speaker 1: with known cases. And so if somebody was missed in
Speaker 1: this country coming through the border, then anyone they infected,
Speaker 1: and anyone those people infected and so on is untestable
Speaker 1: under current protocols. That will change eventually, but at the moment,
Speaker 1: we can't see them because we're not looking at them.
Speaker 1: And I take it that in your view, it's overwhelmingly
Speaker 1: probable that there are such people. I mean, you say
Speaker 1: you got flack for expecting more cases in Indonesia, but
Speaker 1: really you should get some credit for saying that there
Speaker 1: were going to be undetected cases, you know, somewhere, and
Speaker 1: sure enough there were, So in that sense, there was
Speaker 1: a validation of your position. Thank you. I agree. I
Speaker 1: think that it's very very likely that there are cases,
Speaker 1: many cases in this country that have not been detected.
Speaker 1: And reading between the lines of statements from CDC officials,
Speaker 1: including the director last week or two weeks ago, it
Speaker 1: seems as if they think that's probably true also in
Speaker 1: that they're saying they expect ongoing transmission. Robert Redfield, the director,
Speaker 1: said that he expected that this would be to come
Speaker 1: the next seasonal virus and which would be just with
Speaker 1: us for the long term. You're forty to seventy percent
Speaker 1: in the adult population, as is that in category two
Speaker 1: for you? That's you know, an inference based on based
Speaker 1: on facts, yes, facts, models and analogies. And do you
Speaker 1: want to just say a word for listeners who might
Speaker 1: be interested about how you build those models. I'm fascinated
Speaker 1: by this. How do you go about doing this the
Speaker 1: rest of the time when you're not you know, all
Speaker 1: the preparation as it were, for when a potential pandemic
Speaker 1: actually does it and suddenly you're, you know, on the
Speaker 1: front page of the newspapers making your predictions. But the
Speaker 1: rest of the time you're building the models and gathering
Speaker 1: the data. How do you go about that at the
Speaker 1: at the broadest level of generality. Yeah, So I think
Speaker 1: the models that we work with are that they're base
Speaker 1: very simple. They they are models in which, in a
Speaker 1: computer or on a mathematical formula, you take one infectious
Speaker 1: individual and one susceptible individual. They bump into each other
Speaker 1: figuratively and create another infectious individual out of the susceptible.
Speaker 1: So it's like a chemical reaction A S plus I
Speaker 1: too I susceptible plus infected equals to infected, and then
Speaker 1: the other chemical reaction is infected yields recovered over some timescale.
Speaker 1: That's at the heart of all of these models. So
Speaker 1: in the very simplest version of this, where we're all
Speaker 1: like little atoms bouncing around in an ideal gas, if
Speaker 1: you build that model and put the parameters in that
Speaker 1: have been measured and estimated for this virus, you run
Speaker 1: it to its end and you end up with about
Speaker 1: eighty to ninety percent of the population infected, depending on
Speaker 1: which numbers you put in. But we know that people
Speaker 1: are not ideal gases, and there's structure in the population,
Speaker 1: and there's seasonal variability, and there's different levels of susceptibility
Speaker 1: and different age groups and things, and so when you're
Speaker 1: building a model for an infection that you know something
Speaker 1: about and where there's already data, you can upbrate all
Speaker 1: of those types of heterogeneity based on measurements from the data,
Speaker 1: sometimes with more certainty and sometimes with less, and then
Speaker 1: you can run the model and see what happens. And
Speaker 1: usually the almost always the ideal gas kind of approximation
Speaker 1: is the worst case, and all the structure tends to
Speaker 1: reduce infection levels, which is one reason I wouldn't have
Speaker 1: put eighty percent out there. So that's kind of the approach,
Speaker 1: and then there are many kind of curly cues you
Speaker 1: can build on top of it that are ways of
Speaker 1: accommodating the particularities of certain infections. Like some infections there's
Speaker 1: a carrier state where you're not sick, but you are transmitting.
Speaker 1: Some infections you become immune and you stay immune. Others
Speaker 1: you become a little more immune, and then a little
Speaker 1: more immune after multiple exposures. So you can elaborate that
Speaker 1: basic structure, but the fundamental idea is that you're catalyzing
Speaker 1: new infections with old infections, and then people are recovering
Speaker 1: and everything else is a variation on that mark, What
Speaker 1: are you doing in your own real life to prepare
Speaker 1: for this? I mean from what you're doing for your
Speaker 1: family to did you cash out your your retirement accounts
Speaker 1: out of the market and go into cash. I mean,
Speaker 1: I just really curious, in practical, real world terms, what
Speaker 1: are you doing? Yeah, I mean, I think at the moment,
Speaker 1: we're all we're all a little in denial because this
Speaker 1: doesn't seem it really doesn't feel real because nothing's happening
Speaker 1: visibly in our country. So it's a process and I
Speaker 1: think we're all kind of coming to the idea that
Speaker 1: if this scenario plays out as it feels like it might,
Speaker 1: then we might be doing a lot of work from home,
Speaker 1: we might have our schools canceled for goodness knows how
Speaker 1: many months, and in the short term, you know, the
Speaker 1: recommendations from the government about getting supplies of essential goods,
Speaker 1: including medications, seem like a good idea to me. Not
Speaker 1: a year's supply, but a ninety days supply is what
Speaker 1: they often recommend, and so we are trying to do that. Obviously,
Speaker 1: the ability to do that is a luxury that not
Speaker 1: everybody has, and that's a problem. I mean, what are
Speaker 1: people anticipated to do about food if they're sheltering in
Speaker 1: place in that way. Well, I think that's a really
Speaker 1: good question. I'm kind of astonished how well it seems
Speaker 1: to have worked from what I hear in China and
Speaker 1: my friends attribute that to the completely electronic economy. That
Speaker 1: everybody uses phones to pay for things with one of
Speaker 1: the two systems that they have there, and so that
Speaker 1: eases things. So everything is basically being delivered. In other words,
Speaker 1: it's all being paid for electronically and delivered. Everything's being delivered,
Speaker 1: but of course there have to be delivery people, and
Speaker 1: those people have to have some contact with one another
Speaker 1: and with the warehouse, etc. And this has been thought
Speaker 1: through bit in this country for just this reason, when
Speaker 1: people were more concerned about bird flu pandemics a decade ago.
Speaker 1: So I think we will have to see, and it's
Speaker 1: not my expertise, but I'm concerned about that as well.
Speaker 1: What am I not asking you that you think is
Speaker 1: important for people to hear in terms of preparing. I
Speaker 1: think the things that I described and just sort of
Speaker 1: mentally getting in the into the idea that there is
Speaker 1: a possibility, a significant possibility of a really disrupted time
Speaker 1: is kind of my major recommendation and making some material
Speaker 1: plans for that, in so far as people can Where
Speaker 1: should people look up guidance on this? If they're not
Speaker 1: sure what are the essential medicines? I'm not sure? I
Speaker 1: know what are the essential medicines I ought to have
Speaker 1: for ninety days? Right? Well, what you have prescribed to you.
Speaker 1: There's not very many essential non prescription medicines, but right
Speaker 1: maybe some painkiller or anti fever medications. And there's a
Speaker 1: good website called ready dot gov slash pandemic which has
Speaker 1: got some of that information on it. You know. Politically,
Speaker 1: I think it would be good if people recognize that
Speaker 1: the administration has just asked for a tiny appropriation of
Speaker 1: two and a half billion dollars to deal with this,
Speaker 1: half of which is supposed to come from Ebola funding
Speaker 1: that was not yet spent. That's not a serious response,
Speaker 1: and I hope that people will recognize that and press
Speaker 1: for more. I hope they will demand better leadership than
Speaker 1: having the President say repeatedly again recently, that this is
Speaker 1: all going to go away the way SARS did. SARS
Speaker 1: didn't go away. SARS was beaten back with very intensive measures,
Speaker 1: and this is much bigger than SARS, so I think
Speaker 1: there's the political dimension to it. I'm concerned also about
Speaker 1: the impact on our elections. If the virus really does
Speaker 1: start to be detected in more places in the United States,
Speaker 1: I think it could lead to people not wanting to
Speaker 1: go vote in the primaries. I think that could be
Speaker 1: used by various sides to try to make mischief over
Speaker 1: the results. You know, I think there are lots of
Speaker 1: downstream consequences that will understand better when we see them,
Speaker 1: But those are some of my concerns. Mark. I'm really
Speaker 1: grateful to you for speaking so clearly and so calmly
Speaker 1: about something that, as you say, doesn't really feel real
Speaker 1: yet and therefore is really susceptible to our powerful instinct
Speaker 1: to deny. But you know, as we all know, denying
Speaker 1: something doesn't mean it's not going to happen. So I
Speaker 1: think we all do need to change our mindset and
Speaker 1: start being a little more realistic about what the probabilities
Speaker 1: are as as you've laid them out. So I just
Speaker 1: want to really thank you for the time to clarify
Speaker 1: this and first saying a little bit about how you
Speaker 1: reach these conclusions. Getting behind the story can also help us.
Speaker 1: I think get to terms with the reality. So really,
Speaker 1: thank you very very much for what you're doing. Thank
Speaker 1: you for having me. Having spoken to Mark, I still
Speaker 1: am having trouble with my sense of unreality. I just
Speaker 1: don't want to believe what he's telling us about the
Speaker 1: forty to seventy percent number of adults around the world
Speaker 1: who can end up infected with the coronavirus. And yet
Speaker 1: at the same time, I know I'm talking to Mark Lipsitch.
Speaker 1: I'm talking to a calm, reasonable, extremely brilliant epidemiologist who's
Speaker 1: made a career of studying the questions that he's working on. Now,
Speaker 1: if he's not the expert to be believed, there is
Speaker 1: no expert to be believed. So I think what I'm
Speaker 1: going to be trying to do going forward, and maybe
Speaker 1: you'll choose to do the same, is to try to
Speaker 1: update my understanding of where we are and where we're
Speaker 1: going on the basis of his data. When I do that,
Speaker 1: I admit it makes me afraid. But Mark also said
Speaker 1: very calmly that this is not an existential threat, or
Speaker 1: at least it's not an existential threat to society as
Speaker 1: a whole. So perhaps we can take a deep breath
Speaker 1: and perhaps take some small solace in that, even as
Speaker 1: we prepare ourselves for what could be a long and
Speaker 1: very difficult ride. Deep Background is brought to you by
Speaker 1: Pushkin Industries. Our producer is Lydia Genecott, with studio recording
Speaker 1: by Joseph Friedman and mastering by Jason Gambrell and Jason Roskowski.
Speaker 1: Our showrunner is Sophie mckibbn. Our theme music is composed
Speaker 1: by Luis Garat special thanks to the Pushkin Brass, Malcolm Gladwell,
Speaker 1: Jacob Weisberg, and Mia Lobel. I'm Noah Feldman. You can
Speaker 1: follow me on Twitter at Noah R. Feldman. This is
Speaker 1: Deep Background