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Adam Kucharski on what should (and shouldn't) worry us about the coronavirus

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    Hello, I'm Chris Anderson.
    Welcome to The TED Interview.
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    We're gearing up for season four
    with some extraordinary guests,
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    but I don't want to wait for that
    for today's episode,
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    because we're in the middle of a pandemic,
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    and there's a guest
    I really wanted to talk to now.
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    He is Adam Kucharski,
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    an infectious diseases scientist
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    who focuses on the mathematical
    modeling of pandemics.
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    He's an associate professor
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    at the London School of Hygiene
    and Tropical Medicine
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    and a TED Fellow.
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    (Music)
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    (TED Talk) Adam Kucharski:
    So what kind of behavior
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    is actually important for epidemics?
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    Conversations, close physical contacts?
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    What sort of data should we be collecting
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    before an outbreak
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    if we want to predict
    how infection might spread?
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    To find out, our team
    built a mathematical model ...
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    Chris Anderson: When it comes
    to figuring out what to make of
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    this pandemic,
    known technically as COVID-19,
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    and informally as just the coronavirus,
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    I find his thinking unbelievably helpful.
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    And I'm excited to dive into it with you.
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    A special callout to my friends on Twitter
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    who offered up many
    suggestions for questions.
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    I know this topic
    is on everyone's mind right now.
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    And what I hope this episode does
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    is give us all a more nuanced way
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    of thinking about how this pandemic
    has unfolded so far,
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    what might be to come
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    and what we all collectively
    can do about it.
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    Let's dive in.
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    (Music)
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    Adam, welcome to the TED Interview.
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    Adam Kucharski: Thank you.
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    CA: So let's just start
    with a couple of basics.
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    A lot of skeptical people's response --
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    certainly over the last few weeks,
    maybe less so now --
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    has been, "Oh, come on,
    this isn't such a big deal,
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    there's a relatively tiny number of cases.
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    Compare it to the flu,
    compare it to anything else.
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    There are much bigger
    problems in the world.
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    Why are we making such a fuss about this?"
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    And I guess the answer to that fuss
    is that it comes down to the mathematics.
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    We're talking about the mathematics
    of exponential growth,
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    fundamentally, right?
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    AK: Exactly.
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    And there's a number that we use
    to get a sense of how easy things spread
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    and the level of transmission
    we're dealing with.
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    We call that the reproduction number,
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    and conceptually, it's just,
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    for each case you have, on average,
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    how many others are they infecting?
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    And that gives you a sense
    of how much is this scaling,
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    how much this growth
    is going to look like.
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    For coronavirus, we're now seeing,
    across multiple countries,
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    we're seeing each person on average
    giving it to two or three more.
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    CA: So that reproduction number,
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    the first thing to understand
    is that any number above one
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    means that this thing is going to grow.
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    Any number below one
    means it's going to diminish.
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    AK: Exactly -- if you have it above one,
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    then each group of people infected
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    are going to be generating more infection
    than there was before.
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    And you will see the exponential effect,
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    so if it's two, you will be doubling
    every round of infection,
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    and if it's below one,
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    you're going to get something
    that's going to decline, on average.
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    CA: So that number two or higher,
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    I think everyone here is maybe familiar
    with the famous story
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    of the chessboard and the grains of rice,
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    and if you double the number of grains
    for every square of the chessboard,
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    for the first 10 or 15 squares
    nothing much happens,
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    but by the time you've got
    to the 64th square,
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    you suddenly have tons of rice
    for every individual on the planet.
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    (Laughs)
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    Exponential growth is an incredible thing.
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    And the small numbers now
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    are really not what you
    should be paying attention to --
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    you should be paying attention
    to the models of what could be to come.
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    AK: Exactly.
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    Obviously, if you continue
    the exponential growth,
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    you do sometimes get
    these incredibly large,
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    maybe implausibly large numbers.
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    But even looking at a timescale
    of say, a month,
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    if the reproduction number is three,
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    each person is infecting three on average.
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    The gap between these rounds
    of infection is about five days.
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    So if you imagine
    that you've got one case now,
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    that's, kind of, six of these
    five-day steps in a month.
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    So by the end of that month,
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    that one person could have generated,
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    I think it works out at about 729 cases.
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    So even in a month,
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    just the scale of this thing
    can really shoot up
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    if it's not controlled.
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    CA: And so certainly,
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    that seems to be happening
    on most numbers that you look at now,
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    certainly where the virus
    is in the early stages
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    of entering a country.
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    You've given a model
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    whereby we can much more clearly
    understand this reproduction number,
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    because it seems to me this is almost
    like the core to how we think of the virus
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    and how we respond to it
    and how much we should fear it, almost.
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    And in your thinking,
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    you sort of break it down
    into four components,
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    which you call DOTS:
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    Duration, Opportunities,
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    Transmission probability
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    and Susceptibility.
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    And I think it would be
    really helpful, Adam,
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    for you to just explain each of these,
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    because it's quite a simple equation
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    that links those four things
    to the actual reproduction number.
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    So talk about them in turn.
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    Duration, what does that mean?
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    AK: Duration measures
    how long someone is infectious for.
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    If, for example,
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    intuitively, if someone is infectious
    for a longer period of time,
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    say, twice as long as someone else,
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    then that's twice the length
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    that they've got
    to be spreading infection.
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    CA: And what is the duration
    number for this virus,
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    compared with, say, flu
    or with other pathogens?
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    AK: It depends a little bit
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    on what happens
    when people are infectious,
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    if they're being isolated very quickly,
    that shortens that period of time,
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    but potentially, we're looking
    at around a week
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    people are effectively infectious
    before they might be isolated in hospital.
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    CA: And during that week,
    they may not even be showing symptoms
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    for that full week either, right?
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    So someone gets infected,
    there's an incubation period.
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    There's a period some way
    into that incubation period
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    where they start being infectious,
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    and there may be a period after that,
    where they start to show symptoms,
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    and it's not clear, quite,
    how those dates align.
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    Is that right?
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    AK: No, we're getting more information.
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    One of the signals we see in data
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    that suggest that you may have
    that early transmission going on
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    is when you have this delay
    from one infection to the next.
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    So that seems to be around five days.
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    That incubation period,
    the time for symptoms to appear,
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    is also about five days.
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    So if you imagine that most people
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    are only infecting others
    when they're symptomatic,
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    you'd have that incubation period
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    and then you'd have some more time
    when they're infecting others.
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    So the fact that those values
    seem to be similar,
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    suggesting that some people
    are transmitting
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    either very early on or potentially
    before they're showing clear symptoms.
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    CA: So almost implies that on average,
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    people are infecting others
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    as much before
    they show symptoms as after.
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    AK: Potentially.
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    Obviously these are early data sets,
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    but I think there's good evidence
    that a fair number of people,
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    either before they're
    showing clear symptoms
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    or maybe they're not showing the kind of
    very distinctive fever and cough
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    but they're feeling unwell
    and they're shedding virus
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    during that period.
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    CA: And does that make it
    quite different from the flu, for example?
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    AK: It makes it actually
    similar to flu in that regard.
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    One of the reasons pandemic flu
    is so hard to control
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    and so feared as a threat
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    is because so much transmission happens
    before people are severely ill.
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    And that means that by the time
    you identify these cases,
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    they've probably actually spread it
    to a number of other people.
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    CA: Yeah, so this is
    the trickery of the thing,
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    and why it's so hard
    to do anything about it.
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    It is ahead of us all the time,
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    and you can't just pay attention
    to how someone feels
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    or what they're doing.
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    I mean, how does that happen, by the way?
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    How does someone infect someone else
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    before they're even showing
    symptoms themselves,
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    because classically, we think of,
    you know, the person sneezing
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    and droplets go through the air
    and someone else breathes them in
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    and that's how infection happens.
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    What is actually going on
    for infection pre-symptoms?
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    AK: So the level of transmission
    we see with this virus
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    isn't what we see,
    for example, with measles,
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    where someone sneezes
    and a lot of virus gets out
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    and potentially lots of susceptible
    people can get exposed.
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    So potentially, it could be quite early on
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    that even if someone
    has quite mild symptoms,
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    maybe a bit of a cough,
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    that's enough for some virus
    to be getting out
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    and particularly,
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    some of the work that we've done
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    trying to look at sort of
    close gatherings,
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    so very tight-knit meals,
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    there was an example in a ski chalet --
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    and even in those situations,
    you might have someone mildly ill,
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    but enough virus is getting out
    and somehow exposing others,
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    we're still trying
    to work out exactly how,
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    but there's enough there
    to cause some infection.
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    CA: But if someone's mildly ill,
    don't they still have symptoms?
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    Isn't there evidence that even before
    they know that they're ill,
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    something is going on?
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    There was a German paper
    published this week
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    that seemed to suggest
    that even really early on,
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    you take a swab from the back
    of someone's throat
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    and they have hundreds
    of thousands of these viruses
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    already reproducing there.
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    Like, can someone just
    literally just be breathing normally
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    and there is some transmission
    of virus out into the air
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    that they don't even know about
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    and is either infecting people directly
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    or settling on surfaces, is that possible?
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    AK: I think that's what
    we're trying to pin down,
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    how much that [unclear].
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    As you said,
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    there's evidence that you can have
    people without symptoms
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    and you can get virus out their throats.
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    And so certainly there's potential
    that it can be breathed out,
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    but is that a fairly rare event
    for that actual transmission to happen,
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    or are we potentially seeing more
    infections occur through that route?
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    So it's really early data,
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    and I think it's a piece of the puzzle,
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    but we're trying to work out
    where that fits in
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    with what we know about the kind of
    other transmission events we've seen.
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    CA: Alright, so, duration is the duration
    of the period of infectiousness.
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    We think is five to six days,
    is that what I heard you say?
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    AK: Potentially around a week,
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    depending on exactly what happens
    to people when they're infectious.
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    CA: And there are cases
    of people testing positive
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    way, way later,
    after they've got infected.
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    That may be true, but they are probably
    not as infectious then.
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    Is that basically right,
    that's the way to think of this?
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    AK: I think that's our working theory,
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    that a lot of that infection
    is happening early on.
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    And we see that for a number
    of respiratory infections,
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    that when people obviously
    become severely ill,
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    their behavior is very different
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    to when they may be walking around
    and going about their normal day.
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    CA: And so again, comparing
    that D number to other cases,
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    like the flu,
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    is flu similar?
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    What's the D number for flu?
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    AK: So for flu,
    it's probably slightly shorter
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    in terms of the period
    that people are actively infectious.
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    I mean, for flu,
    it's a very quick turnover
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    from one case to the next, actually.
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    Even a matter of
    about three days, potentially,
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    from one infection
    to the person that they infect.
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    And then at the other end of the scale,
    you get things like STDs,
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    where that duration could be
    several months, potentially.
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    CA: Right.
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    OK, really nothing that unusual so far,
    in terms of this particular virus.
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    Let's look at the O, opportunity.
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    What is that?
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    AK: So opportunity is a measure
    of how many chances
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    the virus has to spread
    through interactions
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    while someone is infectious.
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    So typically, it's a measure
    of social behavior.
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    On average, how many
    social contacts do people make
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    that create opportunities for transmission
    while they're infectious.
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    CA: So it's how many people
    have you got close enough to
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    during a day, during a given day,
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    to have a chance of infecting them.
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    And that number could be,
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    if people aren't taking precautions
    in a normal, sort of, urban setting,
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    I mean, that could run
    into the hundreds, presumably?
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    AK: Potentially, for some people.
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    We've done a number of studies
    looking at that in recent years,
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    and the average,
    in terms of physical contacts,
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    is about five people per day.
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    Most people will have
    conversation or contacts
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    generally with about 10, 15,
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    but obviously,
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    between cultures,
    we see quite a lot of variation
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    in the level of physical greetings
    that might happen.
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    CA: And presumably, that number
    again is no different for this virus
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    than for any other.
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    I mean, that's just a feature
    of the lives that we live.
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    AK: I think for this one,
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    if it's driven through
    these kind of interactions,
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    and we've seen for flu,
    for other respiratory infections,
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    those kinds of fairly close contacts
    and everyday physical interactions
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    seem to be the ones
    that are important for transmission.
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    CA: Perhaps there is one difference.
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    The fact that if you're
    infectious pre-symptoms,
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    perhaps that means that actually,
    there are more opportunities here.
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    This is part of the virus's
    genius, as it were,
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    that by not letting on
    that it's inside someone,
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    people continue to interact and go to work
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    and take the subway and so forth,
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    not even knowing that they're sick.
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    AK: Exactly.
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    And for something like flu,
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    you see when people get ill, clearly,
    their social contacts drop off.
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    So to have a virus that can be infectious
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    while people are going around
    their everyday lives,
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    really gives it an advantage
    in terms of transmission.
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    CA: In your modeling,
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    do you actually have this
    opportunities number higher than for flu?
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    AK: So for the moment,
    we're kind of using similar values,
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    so we're trying to look at, for example,
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    physical contacts
    within different populations.
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    But what we are doing is scaling the risk.
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    So that's coming on to the T term.
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    So that between each contact,
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    what's the risk that a transmission
    event will occur.
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    CA: Alright, so let's go on
    to this next number,
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    the T, transmission probability.
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    How do you define that?
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    AK: So this measures the chance
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    that, essentially,
    the virus will get across
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    during a particular opportunity
    or a particular interaction.
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    So you may well have
    a conversation with somebody,
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    but actually, you don't cough
    or you don't sneeze
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    or for some reason,
    the virus doesn't actually get across
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    and expose the other person.
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    And so, for this virus, as I mentioned,
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    say people are having
    10 conversations a day,
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    but we're not seeing infected people
    infect 10 others a day.
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    So it suggests that not all
    of those opportunities
  • 14:31 - 14:34
    are actually resulting
    in the virus getting across.
  • 14:35 - 14:39
    CA: But people say
    that this is an infectious virus.
  • 14:39 - 14:42
    Like, what is that transmission
    probability number,
  • 14:42 - 14:44
    again, compared with, say, the flu?
  • 14:45 - 14:49
    AK: So, we did some analysis
    looking at these very close gatherings.
  • 14:49 - 14:51
    We looked at about 10
    different case studies,
  • 14:51 - 14:55
    and we found that about a third
    of the contacts in those settings
  • 14:55 - 14:57
    subsequently got infected
  • 14:57 - 14:59
    in these early stages,
    when people weren't aware.
  • 14:59 - 15:01
    So if you had these, kind of,
    big group meals,
  • 15:01 - 15:05
    potentially, each contact
    had about, a kind of, one in three chance
  • 15:05 - 15:07
    of getting exposed.
  • 15:07 - 15:10
    For seasonal flu,
    that tends to be slightly lower,
  • 15:10 - 15:12
    even within households
    and close settings,
  • 15:12 - 15:14
    you don't necessarily
    get values that high.
  • 15:15 - 15:18
    And even for something like SARS,
    those values have, kind of --
  • 15:18 - 15:22
    the risk per interaction you had
  • 15:22 - 15:24
    was lower than what we seem
    to be getting for coronavirus.
  • 15:24 - 15:26
    Which intuitively makes sense,
  • 15:26 - 15:28
    there must be a higher risk
    per interaction
  • 15:28 - 15:30
    if this thing is spreading so easily.
  • 15:30 - 15:31
    CA: Hm.
  • 15:32 - 15:36
    OK, and then the fourth letter of DOTS
  • 15:36 - 15:38
    is S for susceptibility.
  • 15:40 - 15:41
    What's that?
  • 15:41 - 15:46
    AK: So that is a measure of the proportion
    of the population who are susceptible.
  • 15:46 - 15:48
    If you imagine you have
    this interaction with someone,
  • 15:48 - 15:50
    the virus gets across, it exposes them,
  • 15:50 - 15:52
    but some people may have been vaccinated
  • 15:52 - 15:54
    or otherwise have some immunity
  • 15:54 - 15:56
    and not develop infection themselves
  • 15:56 - 15:58
    and not be infectious to others.
  • 15:58 - 16:01
    So we've got to account for this
    potential proportion of people
  • 16:01 - 16:04
    who are not actually
    going to turn into cases themselves.
  • 16:06 - 16:12
    CA: And obviously, there's no vaccine yet
    for this coronavirus,
  • 16:12 - 16:16
    nor is anyone, at least initially,
    immune, as far as we know.
  • 16:16 - 16:20
    So are you modeling that
    susceptibility number pretty high,
  • 16:20 - 16:22
    is that part of the issue here?
  • 16:22 - 16:24
    AK: Yeah, I think the evidence
  • 16:24 - 16:27
    is that this is going to fully
    susceptible populations,
  • 16:27 - 16:29
    and even in areas,
    for example, like China,
  • 16:29 - 16:31
    where there's been a lot of transmission
  • 16:31 - 16:33
    but there's been very strong
    control measures,
  • 16:33 - 16:35
    we estimated that up to
    the end of January,
  • 16:35 - 16:38
    probably about 95 percent of Wuhan
    are still susceptible.
  • 16:38 - 16:40
    So there's been a lot of infection,
  • 16:40 - 16:43
    but it hasn't really taken much
    of that component,
  • 16:43 - 16:46
    of the DOTS, of those four things
    that drive transmission.
  • 16:47 - 16:49
    CA: And so the way the mathematics works,
  • 16:49 - 16:55
    I have to confess, amidst the stress
    of this whole situation,
  • 16:55 - 16:58
    the nerd in me kind of loves
    the elegance of the mathematics here,
  • 16:58 - 17:01
    because I'd never really
    thought about it this way,
  • 17:01 - 17:04
    but you basically just multiply
    those numbers together
  • 17:04 - 17:06
    to get the reproduction number.
  • 17:06 - 17:07
    Is that right?
  • 17:07 - 17:08
    AK: Exactly, yeah,
  • 17:08 - 17:11
    you almost take the path
    of the infection during transmission
  • 17:11 - 17:12
    as you multiply those together,
  • 17:12 - 17:15
    and that gives you
    the number for that virus.
  • 17:15 - 17:18
    CA: And so there's just
    a total logic to that.
  • 17:18 - 17:21
    It's the number of days,
    duration that you're infectious,
  • 17:21 - 17:23
    it's the number of people
    you're seeing on average
  • 17:23 - 17:26
    during those days
    that you have a chance to infect.
  • 17:27 - 17:32
    Then you multiply that
    by the transmission probability,
  • 17:32 - 17:35
    is virus getting into them, essentially,
  • 17:35 - 17:37
    that's what you mean by crossing over.
  • 17:37 - 17:39
    And then by the susceptibility number.
  • 17:39 - 17:42
    By the way, what do you think
    the susceptibility probability is
  • 17:42 - 17:43
    for this case?
  • 17:44 - 17:47
    AK: I think we have to assume
    that it's near 100 percent
  • 17:47 - 17:49
    in terms of spread, yeah.
  • 17:50 - 17:52
    CA: Alright, you multiply
    those numbers together,
  • 17:52 - 17:57
    and right now, it looks like,
    for this coronavirus,
  • 17:57 - 18:02
    that you say two to three
    is the most plausible current number,
  • 18:03 - 18:05
    which implies very rapid growth.
  • 18:05 - 18:06
    AK: Exactly.
  • 18:06 - 18:08
    In these uncontrolled outbreaks,
  • 18:08 - 18:10
    we're seeing now a number
    of countries in this stage --
  • 18:11 - 18:14
    you are going to get
    this really rapid growth occurring.
  • 18:14 - 18:19
    CA: And so how does
    that two to three compare with flu?
  • 18:19 - 18:22
    And I guess, there's seasonal flu,
  • 18:22 - 18:24
    in the winter, when it's spreading,
  • 18:24 - 18:28
    and at other times during the year
    drops well below one
  • 18:28 - 18:30
    as a reproduction number, right?
  • 18:30 - 18:33
    But what is it during seasonal flu time?
  • 18:34 - 18:36
    AK: During the early stage
    when it's taking off
  • 18:36 - 18:38
    at the start of the flu season,
  • 18:38 - 18:42
    it's probably, we reckon,
    somewhere between about maybe 1.2, 1.4.
  • 18:42 - 18:44
    So it's not incredibly transmissible,
  • 18:44 - 18:48
    if you imagine you do have some immunity
    in your population from vaccination
  • 18:48 - 18:49
    and from other things.
  • 18:49 - 18:51
    So it can spread, it's above one,
  • 18:51 - 18:54
    but it's not taking off, necessarily,
    as quickly as the coronavirus is.
  • 18:55 - 18:58
    CA: So I want to come back
    to two of those elements,
  • 18:58 - 19:01
    specifically opportunity
    and transmission probability,
  • 19:01 - 19:06
    because those seem to have the most
    chance to actually do something
  • 19:06 - 19:07
    about this infection rate.
  • 19:07 - 19:09
    Before we go there,
  • 19:09 - 19:11
    let's talk about another
    key number on this,
  • 19:11 - 19:14
    which is the fatality rate.
  • 19:14 - 19:16
    First of all, could you define --
  • 19:16 - 19:18
    I think there's two different versions
    of the fatality rate
  • 19:18 - 19:20
    that maybe confuse people.
  • 19:20 - 19:22
    Could you define them?
  • 19:22 - 19:27
    AK: So the one that we often talk about
    is what's known as the case fatality rate,
  • 19:27 - 19:30
    and that's of the proportion who show up
    with symptoms as cases,
  • 19:31 - 19:34
    what proportion of those
    will subsequently be fatal.
  • 19:34 - 19:36
    And we also sometimes talk
    about what's known
  • 19:36 - 19:38
    as the infection fatality rate,
  • 19:38 - 19:40
    which is, of everyone who gets infected,
  • 19:40 - 19:41
    regardless of symptoms,
  • 19:41 - 19:44
    how many of those infections
    will subsequently be fatal.
  • 19:44 - 19:46
    But most of the values
    we see kicking around
  • 19:46 - 19:49
    are the case fatality rate, or the CFR,
    as it's sometimes known.
  • 19:50 - 19:54
    CA: And so what is
    that fatality rate for this virus,
  • 19:54 - 19:57
    and again, how does that compare
    with other pathogens?
  • 19:58 - 20:00
    AK: So there's a few numbers
    that have been bouncing around.
  • 20:01 - 20:04
    One of the challenges in real time
    is you often don't see all of your cases,
  • 20:04 - 20:07
    you have people symptomatic
    not being reported.
  • 20:07 - 20:08
    You also have a delay.
  • 20:08 - 20:09
    If you imagine, for example,
  • 20:09 - 20:12
    100 people turn up to a hospital
    with coronavirus
  • 20:12 - 20:13
    and none have died yet,
  • 20:13 - 20:16
    that doesn't imply
    that the fatality rate is zero,
  • 20:16 - 20:19
    because you've got to wait to see
    what might happen to them.
  • 20:19 - 20:21
    So when you adjust for that
    underreporting and delays,
  • 20:22 - 20:25
    best estimate for the case fatality
    is about one percent.
  • 20:25 - 20:27
    So about one percent
    of people with symptoms,
  • 20:27 - 20:28
    on average,
  • 20:28 - 20:29
    those outcomes are fatal.
  • 20:29 - 20:32
    And that's probably about 10 times
    worse than seasonal flu.
  • 20:34 - 20:37
    CA: Yeah, so that's a scary
    comparison right there,
  • 20:37 - 20:40
    given how many people die of flu.
  • 20:40 - 20:45
    So when the World Health Organization
    mentioned a higher number,
  • 20:45 - 20:48
    a little while back, of 3.4 percent,
  • 20:48 - 20:50
    they were criticized a bit for that.
  • 20:50 - 20:54
    Explain why that
    might have been misleading
  • 20:54 - 20:57
    and how to think about it
    and adjust for that.
  • 20:57 - 21:00
    AK: It's incredibly common that people
    look at these raw numbers,
  • 21:00 - 21:03
    they say, "How many deaths
    are there so far, how many cases,"
  • 21:03 - 21:05
    and they look at that ratio,
  • 21:05 - 21:08
    and even a couple of weeks ago,
    that number produced a two percent value.
  • 21:08 - 21:11
    But if you imagine
    you have this delay effect,
  • 21:11 - 21:13
    then even if you stop all your cases,
  • 21:13 - 21:16
    you will still have these kind of
    fatal outcomes over time,
  • 21:16 - 21:18
    so that number will creep up.
  • 21:18 - 21:22
    This has occurred in every epidemic
    from pandemic flu to Ebola,
  • 21:22 - 21:23
    we see this again and again.
  • 21:23 - 21:27
    And I made the point to a number of people
    that this number is going to go up,
  • 21:27 - 21:29
    because as China's cases slow,
  • 21:29 - 21:30
    it will look like it's increasing,
  • 21:30 - 21:33
    and that's just kind of
    a statistical quirk.
  • 21:33 - 21:35
    There's nothing really
    kind of, behind a change,
  • 21:35 - 21:38
    there's no mutations or anything going on.
  • 21:39 - 21:42
    CA: If I have this right,
    there are two effects going on.
  • 21:42 - 21:45
    One is that the number of fatalities
  • 21:45 - 21:48
    from the existing caseload will rise,
  • 21:48 - 21:52
    which actually would boost
    that 3.4 even higher.
  • 21:52 - 21:55
    But then you have to offset that
    against the fact that, apparently,
  • 21:56 - 21:58
    huge numbers of cases
    have just gone undetected
  • 21:58 - 22:00
    and that we haven't,
  • 22:00 - 22:02
    due to bad testing,
  • 22:02 - 22:05
    that the number of fatalities don't --
  • 22:05 - 22:08
    they probably reflect
    a much larger number of early cases.
  • 22:08 - 22:09
    Is that it?
  • 22:09 - 22:10
    AK: Exactly.
  • 22:10 - 22:12
    So you have one thing
    pulling the number up
  • 22:12 - 22:14
    and one thing pulling it down.
  • 22:14 - 22:16
    And it means that on these
    kind of early values,
  • 22:16 - 22:18
    if you actually just adjust for the delay
  • 22:18 - 22:20
    and don't think
    about these unreported cases,
  • 22:20 - 22:23
    you start getting really
    very scary numbers indeed.
  • 22:23 - 22:25
    You get up to 20, 30 percent potentially,
  • 22:25 - 22:26
    which really doesn't align
  • 22:26 - 22:29
    with what we know
    about this virus in general.
  • 22:30 - 22:31
    CA: Alright.
  • 22:32 - 22:33
    There's a lot more data in now.
  • 22:33 - 22:37
    From your point of view,
    you think the likely fatality rate,
  • 22:37 - 22:42
    at least in the earlier stage
    of an infection,
  • 22:42 - 22:44
    is about two percent?
  • 22:44 - 22:45
    AK: I think overall,
  • 22:46 - 22:49
    I think we can put something probably
    in the 0.5 to two percent range,
  • 22:49 - 22:52
    and that's on a number
    of different data sets.
  • 22:52 - 22:54
    And that's for people who are symptomatic.
  • 22:54 - 22:57
    I think on average, one percent
    is a good number to work with.
  • 22:57 - 22:58
    CA: OK, one percent,
  • 22:58 - 23:01
    So flu is often quoted
    as a tenth of a percent,
  • 23:01 - 23:07
    so it's five to 10 times or more
    more dangerous than flu.
  • 23:07 - 23:10
    And that danger is not symmetric
    across age groups,
  • 23:10 - 23:11
    as is well known.
  • 23:11 - 23:14
    It primarily affects the elderly.
  • 23:14 - 23:17
    AK: Yeah, we've seen
    that one percent on average,
  • 23:17 - 23:19
    but once you start getting
    into the over 60s, over 70s,
  • 23:19 - 23:21
    that number really starts to shoot up.
  • 23:21 - 23:24
    I mean, we're estimating
    potentially in these older groups,
  • 23:24 - 23:29
    you're looking at maybe five,
    10 percent fatality.
  • 23:29 - 23:31
    And then of course, on top of that,
  • 23:31 - 23:34
    you've got to add what
    are going to be the severe cases
  • 23:34 - 23:36
    and people are going to require
    hospitalization.
  • 23:36 - 23:39
    And those risks get very large
    in the older groups indeed.
  • 23:41 - 23:43
    CA: Adam, put these numbers
    together for us.
  • 23:43 - 23:44
    In your models,
  • 23:44 - 23:49
    if you put together
    a reproduction rate of two to three
  • 23:49 - 23:54
    and a fatality rate
    of 0.5 percent to one percent
  • 23:54 - 23:56
    and you run the simulation,
  • 23:56 - 23:58
    what does it look like?
  • 23:59 - 24:02
    AK: So if you have
    this uncontrolled transmission,
  • 24:02 - 24:04
    and you have this reproduction
    number of two or three
  • 24:04 - 24:06
    and you don't do anything about it,
  • 24:06 - 24:07
    the only way the outbreak ends
  • 24:08 - 24:11
    is enough people get it
    and immunity builds up
  • 24:11 - 24:15
    and the outbreak kind of ends on its own.
  • 24:15 - 24:17
    And in that case,
  • 24:17 - 24:20
    you would expect very large numbers
    of the population to be infected.
  • 24:20 - 24:21
    It's what we see, for example,
  • 24:21 - 24:24
    with many other uncontained outbreaks,
  • 24:24 - 24:26
    that it essentially burns
    through the population,
  • 24:26 - 24:28
    you get large numbers infected
  • 24:28 - 24:31
    and with this kind of fatality rate
    and hospitalization rate,
  • 24:31 - 24:35
    that would really be hugely damaging
    if that were to occur.
  • 24:35 - 24:37
    Certainly at the country level,
    we're seeing --
  • 24:37 - 24:39
    Italy is a good example at the moment,
  • 24:39 - 24:42
    that if you have that early
    transmission that's undetected,
  • 24:42 - 24:43
    that rapid growth,
  • 24:43 - 24:47
    you very quickly get to a situation
    where your health systems are overwhelmed.
  • 24:47 - 24:51
    I think one of the nastiest
    aspects of this virus
  • 24:51 - 24:55
    is that because you have the delay
    between infection and symptoms
  • 24:55 - 24:57
    and people showing up in health care,
  • 24:57 - 24:59
    if your health system is overwhelmed,
  • 24:59 - 25:00
    even on that day,
  • 25:00 - 25:02
    if you completely stop transmission,
  • 25:02 - 25:05
    you've got all of these people
    who have already been exposed,
  • 25:05 - 25:08
    so you're still going to have cases
    and severe cases appearing
  • 25:08 - 25:10
    for maybe another couple of weeks.
  • 25:10 - 25:13
    So it's really this huge
    accumulation of infection and burden
  • 25:13 - 25:16
    that's coming through the system
    on your population.
  • 25:17 - 25:20
    CA: So there's another
    key number, actually,
  • 25:20 - 25:24
    is how does the total case number
  • 25:24 - 25:28
    compare to the capacity
    of a country's health system
  • 25:28 - 25:30
    to process that number of cases.
  • 25:31 - 25:33
    Presumably that issue
    makes a huge difference
  • 25:33 - 25:34
    to the fatality rate,
  • 25:34 - 25:37
    the difference between people
    coming in with severe illness
  • 25:37 - 25:40
    and a health system that's able
    to respond and one that's overwhelmed.
  • 25:40 - 25:43
    The fatality rate is going to be
    very different at that point.
  • 25:43 - 25:45
    AK: If someone requires an ICU bed,
  • 25:45 - 25:48
    that's a couple of weeks
    they're going to require it for
  • 25:48 - 25:50
    and you've got more cases
    coming through the system,
  • 25:50 - 25:52
    so it very quickly gets very tough.
  • 25:52 - 25:55
    CA: So talk about the difference
    between containment
  • 25:55 - 25:57
    and mitigation.
  • 25:57 - 26:00
    These are different terms
    that we're hearing a lot about.
  • 26:00 - 26:06
    In the early stages of the virus,
    governments are focused on containment.
  • 26:06 - 26:08
    What does that mean?
  • 26:08 - 26:11
    AK: Containment is this idea
    that you can focus your effort on control
  • 26:11 - 26:14
    very much on the cases and their contacts.
  • 26:14 - 26:16
    So you're not causing disruption
    to the wider population,
  • 26:16 - 26:19
    you have a case that comes in,
    you isolate them,
  • 26:19 - 26:21
    you work out who they've come
    into contact with,
  • 26:21 - 26:25
    who's potentially these
    opportunities for exposure
  • 26:25 - 26:27
    and then you can follow up those people,
  • 26:27 - 26:30
    maybe quarantine them to make sure
    that no further transmission happens.
  • 26:30 - 26:33
    So it's a very focused, targeted method,
  • 26:33 - 26:35
    and for SARS, it worked remarkably well.
  • 26:36 - 26:38
    But I think for this infection,
  • 26:38 - 26:41
    because some cases are going to be missed
    or undetected,
  • 26:41 - 26:45
    you've really got to be capturing
    a large chunk of people at risk.
  • 26:45 - 26:46
    If a few slip through the net,
  • 26:46 - 26:48
    potentially, you're going
    to get an outbreak.
  • 26:48 - 26:50
    CA: Are there any countries
  • 26:50 - 26:52
    that have been able
    to employ this strategy
  • 26:52 - 26:55
    and effectively contain the virus?
  • 26:55 - 26:59
    AK: So Singapore have been doing
    a really remarkable job of this
  • 26:59 - 27:00
    for the last six weeks or so.
  • 27:01 - 27:03
    So as well as some wider measures,
  • 27:03 - 27:05
    they've been working incredibly hard
  • 27:05 - 27:07
    to trace people
    who have come into contact.
  • 27:08 - 27:10
    Looking at CCTV,
  • 27:10 - 27:13
    going through to find out
    which taxi someone might have gotten,
  • 27:13 - 27:14
    who might be at risk --
  • 27:14 - 27:15
    really, really thorough follow-up.
  • 27:15 - 27:19
    And for about six weeks,
    that has kept a lid on transmission.
  • 27:19 - 27:21
    CA: So that's amazing.
  • 27:21 - 27:23
    So someone comes into the country,
  • 27:23 - 27:25
    they test positive --
  • 27:25 - 27:27
    they go to work, and with a massive team,
  • 27:27 - 27:29
    and trace everything
  • 27:29 - 27:31
    to the level of actually saying,
  • 27:31 - 27:33
    "Oh, you don't know what taxi you went in?
  • 27:33 - 27:35
    Let us find that out for you."
  • 27:35 - 27:37
    And presumably,
    when they find the taxi driver,
  • 27:37 - 27:40
    they then have to try and figure out
    everyone else who was in that taxi?
  • 27:40 - 27:43
    AK: So they will focus on
    close contacts of people most at risk,
  • 27:43 - 27:47
    but they're really minimizing the chance
    that anyone slips through the net.
  • 27:48 - 27:52
    CA: But even in Singapore,
    if I'm not mistaken,
  • 27:52 - 27:54
    numbers started to trend
    back down to zero,
  • 27:54 - 27:57
    but I think recently,
    they've picked up again a bit.
  • 27:57 - 27:58
    It's still unclear
  • 27:58 - 28:02
    whether they will actually
    be able to sustain containment.
  • 28:02 - 28:03
    AK: Exactly.
  • 28:03 - 28:05
    If we talk in terms
    of the reproduction number,
  • 28:05 - 28:07
    we saw it dipped to maybe 0.8, 0.9,
  • 28:07 - 28:09
    so under that crucial value of one.
  • 28:10 - 28:12
    But in the last week or two,
  • 28:12 - 28:15
    it does seem to be ticking up
    and they're getting more cases appearing.
  • 28:15 - 28:16
    I think a lot of it is,
  • 28:16 - 28:18
    even if they are containing it,
  • 28:18 - 28:20
    the world is experiencing outbreaks
  • 28:20 - 28:22
    and just keeps throwing
    sparks of infection,
  • 28:22 - 28:24
    and it becomes harder and harder
  • 28:24 - 28:27
    with that level of intensive effort
    to stamp them all out.
  • 28:27 - 28:32
    (Music)
  • 28:48 - 28:50
    CA: In the case of this virus,
  • 28:50 - 28:53
    you know, there was warning
    to most countries in the world
  • 28:53 - 28:54
    that this thing was happening.
  • 28:54 - 28:58
    The news out of China
    very quickly became very bleak,
  • 28:58 - 29:01
    and people had time to prepare.
  • 29:01 - 29:06
    I mean, what would ideal
    preparation look like
  • 29:06 - 29:08
    if you know that something
    like this is coming
  • 29:08 - 29:10
    and you know that there's
    a lot on the line
  • 29:10 - 29:13
    if you can successfully contain it
    before it really escapes?
  • 29:13 - 29:16
    AK: I think two things
    would make a really big difference.
  • 29:16 - 29:21
    One is having as thorough a follow-up
    and detection as possible.
  • 29:21 - 29:22
    We've done some modeling analyses,
  • 29:22 - 29:26
    looking at how effective
    that kind of early containment is.
  • 29:26 - 29:30
    And it can be, if you're identifying
    maybe 70 or 80 percent
  • 29:30 - 29:33
    of the people who might have
    come into contact.
  • 29:33 - 29:36
    But if you're not detecting
    those cases coming in,
  • 29:36 - 29:38
    if you're not detecting their contacts --
  • 29:38 - 29:42
    and a lot of the early focus, for example,
    was on travel history to China,
  • 29:42 - 29:45
    and then it became clear
    that the situation was changing,
  • 29:45 - 29:48
    but because you were relying on that
    as your definition of a case,
  • 29:48 - 29:51
    it meant a lot of maybe other cases
    that matched the definition
  • 29:51 - 29:52
    weren't being tested
  • 29:52 - 29:55
    because they didn't seem
    to be potentially at risk.
  • 29:55 - 29:59
    CA: So I mean, if you know
    that early detection is key to this,
  • 29:59 - 30:01
    an essential early measure, I guess,
  • 30:01 - 30:06
    would be to rapidly ensure
    that you had enough tests available
  • 30:06 - 30:08
    and where needed,
  • 30:08 - 30:10
    so that you could respond,
  • 30:10 - 30:14
    be ready to swing into action
    as soon as someone was detected,
  • 30:14 - 30:19
    you then have to very quickly,
    I guess, test their contacts and so forth,
  • 30:19 - 30:22
    to have a chance
    of keeping this under control.
  • 30:22 - 30:23
    AK: Exactly.
  • 30:23 - 30:26
    In my line of work, we say
    there's value in a negative test,
  • 30:26 - 30:30
    because it shows that you're looking
    for something and it's not there.
  • 30:30 - 30:33
    And so I think having
    small numbers of people tested
  • 30:33 - 30:36
    doesn't give you confidence
    that you're not missing infections,
  • 30:36 - 30:39
    whereas if you are doing
    really thorough follow-up on contacts,
  • 30:39 - 30:41
    we've seen countries even like Korea now,
  • 30:41 - 30:43
    huge numbers of people tested.
  • 30:43 - 30:45
    So although there are still
    cases appearing,
  • 30:45 - 30:46
    it gives them more confidence
  • 30:46 - 30:49
    that they have some sense
    of where those infections are.
  • 30:49 - 30:52
    CA: I mean, you're in the UK right now,
  • 30:52 - 30:55
    I'm in the US.
  • 30:55 - 30:58
    How likely is it that the UK
    is going to be able to contain,
  • 30:58 - 31:02
    how likely is it that the US
    is going to be able to contain this?
  • 31:03 - 31:07
    AK: I think it's pretty
    unlikely in both cases.
  • 31:07 - 31:10
    I think the UK is going to have
    to introduce some additional measures.
  • 31:10 - 31:12
    I think when that happens
    obviously depends a bit
  • 31:12 - 31:14
    on the current situation,
  • 31:14 - 31:16
    but we've tested almost 30,000 people now.
  • 31:17 - 31:22
    Frankly, I think the US
    may well be moving beyond that point,
  • 31:22 - 31:25
    given how much evidence
    of extensive transmission that has,
  • 31:25 - 31:28
    and I think without clear ideas
    of how much infection there is
  • 31:28 - 31:30
    and that level of testing,
  • 31:30 - 31:34
    it's quite hard to actually see
    what the picture currently is in the US.
  • 31:35 - 31:39
    CA: I mean, I definitely don't want to get
    too political about this,
  • 31:39 - 31:41
    but I mean, does this strike you as --
  • 31:41 - 31:43
    you just said that the UK
    has tested 30,000 people --
  • 31:43 - 31:46
    the US is five or six times bigger
  • 31:46 - 31:49
    and I think the total number
    of tests here is five or six thousand,
  • 31:49 - 31:50
    or it was a few days ago.
  • 31:50 - 31:53
    Does that strike you as bizarre?
  • 31:53 - 31:57
    I don't understand, honestly,
    how that happened in an educated country
  • 31:57 - 31:59
    that has so much knowledge
    about infectious diseases.
  • 32:00 - 32:01
    AK: It does,
  • 32:01 - 32:05
    and I think there's obviously
    a number of factors playing in there,
  • 32:05 - 32:06
    logistics and so on,
  • 32:06 - 32:08
    but there has been that period of warning
  • 32:08 - 32:10
    that this is a threat
    and this is coming in.
  • 32:10 - 32:14
    And I think countries need to make sure
    that they've got the capacity
  • 32:14 - 32:17
    to really do as much detection as they can
    in those early stages,
  • 32:17 - 32:19
    because that's where
    you're going to catch it
  • 32:19 - 32:22
    and that's where you're going to have
    a better chance of containing it.
  • 32:22 - 32:25
    CA: OK, so if you fail to contain,
  • 32:25 - 32:28
    then you have to move
    to some kind of mitigation strategy.
  • 32:28 - 32:31
    So what comes into play there?
  • 32:32 - 32:35
    And I think I almost want
    to bring that back
  • 32:35 - 32:38
    to two of your DOTS factors,
  • 32:38 - 32:41
    opportunity and transmission probability,
  • 32:41 - 32:44
    because it seems like
    the virus is what it is,
  • 32:44 - 32:47
    the actual duration when someone
    is potentially infectious,
  • 32:47 - 32:48
    we can't do much about.
  • 32:48 - 32:50
    The susceptibility side,
  • 32:50 - 32:53
    we can't do much about
    until there's a vaccine.
  • 32:53 - 32:55
    We could maybe talk about that in a bit.
  • 32:55 - 32:58
    But the middle two of opportunity
    and transmission probability,
  • 32:58 - 33:00
    we can do something about.
  • 33:00 - 33:03
    Do you want to maybe talk
    about those in turn,
  • 33:03 - 33:05
    of what that looks like,
  • 33:05 - 33:09
    how would you build a mitigation strategy?
  • 33:09 - 33:12
    I mean, first of all,
    thinking about opportunity,
  • 33:12 - 33:14
    how do you reduce
    the number of opportunities
  • 33:14 - 33:15
    to pass on the bug?
  • 33:16 - 33:18
    AK: And so I think in that respect,
  • 33:18 - 33:21
    it would be about massive changes
    in our social interactions.
  • 33:21 - 33:24
    And if you think in terms
    of the reproduction number
  • 33:24 - 33:26
    of being about two or three,
  • 33:26 - 33:27
    to get that number below one,
  • 33:27 - 33:31
    you've really got to cut
    some aspect of that transmission
  • 33:31 - 33:32
    in half or in two-thirds
  • 33:32 - 33:34
    to get that below one.
  • 33:34 - 33:36
    And so that would require,
  • 33:36 - 33:38
    of the opportunities
    that could spread the virus,
  • 33:38 - 33:40
    so these kind of close contacts,
  • 33:40 - 33:42
    everybody in the population, on average,
  • 33:42 - 33:45
    will be needing to reduce
    those interactions
  • 33:45 - 33:48
    potentially by two-thirds
    to bring it under control.
  • 33:48 - 33:51
    That might be through working from home,
  • 33:51 - 33:53
    from changing lifestyle
  • 33:53 - 33:56
    and kind of where you go
    in crowded places and dinners.
  • 33:57 - 33:59
    And of course, these measures,
    things like school closures,
  • 34:00 - 34:02
    and other things
    that just attempt to reduce
  • 34:02 - 34:03
    the social mixing of a population.
  • 34:04 - 34:07
    CA: Well, actually, talk to me more
    about school closures,
  • 34:07 - 34:09
    because that, if I remember,
  • 34:09 - 34:15
    often in past pandemics has been cited
    as an absolutely key measure,
  • 34:15 - 34:19
    that schools represent this sort of
    coming together of people,
  • 34:19 - 34:21
    children are often --
  • 34:21 - 34:24
    certainly when
    it comes to flu and colds --
  • 34:24 - 34:25
    they're carriers.
  • 34:26 - 34:27
    But on this case,
  • 34:27 - 34:31
    children don't seem to be getting sick
    from this particular virus,
  • 34:31 - 34:34
    or at least very few of them are.
  • 34:34 - 34:39
    Do we know whether they
    can still be infectious?
  • 34:39 - 34:42
    They can be the unintended carriers of it.
  • 34:42 - 34:45
    Or actually, is there evidence
    that school closures
  • 34:45 - 34:49
    may not be as important
    in this instance as it is in others?
  • 34:49 - 34:51
    AK: So that point
    on what role children play
  • 34:51 - 34:52
    is a crucial one,
  • 34:52 - 34:55
    and there's still not
    a good evidence base there.
  • 34:55 - 34:57
    From following up of contacts of cases,
  • 34:57 - 35:00
    there's now evidence
    that children are getting infected,
  • 35:00 - 35:02
    so when you're testing,
    they are getting exposed,
  • 35:02 - 35:06
    it's not that somehow they're just
    not getting the infection at all,
  • 35:06 - 35:09
    but as you said, they're not showing
    symptoms in the same way.
  • 35:09 - 35:11
    And particularly for flu,
  • 35:11 - 35:14
    when we see the implications
    of school closures,
  • 35:14 - 35:17
    even in the UK in 2009 during swine flu,
  • 35:17 - 35:20
    there was a dip in the outbreak
    during the school holidays,
  • 35:20 - 35:22
    you could see it on the epidemic curve,
  • 35:22 - 35:25
    it kind of comes back down in the summer
    and goes back up in the autumn.
  • 35:25 - 35:28
    But of course, in 2009,
    there was some immunity in older groups.
  • 35:28 - 35:32
    That kind of shifted more the transmission
    into the younger ones.
  • 35:32 - 35:35
    So I think it's really something
    we're trying to work to understand.
  • 35:35 - 35:38
    Obviously, it will reduce interactions,
    with school closures,
  • 35:38 - 35:40
    but then there's knock-on social effects,
  • 35:40 - 35:42
    there's potential
    knock-on changes in mixing,
  • 35:42 - 35:46
    maybe grandparents and their role,
    in terms of alternative carers
  • 35:46 - 35:47
    if parents have to work.
  • 35:47 - 35:51
    So I think there's a lot of pieces
    that need to be considered.
  • 35:52 - 35:57
    CA: I mean, based on all of the different
    pieces of evidence you've seen,
  • 35:57 - 35:58
    if it were down to you,
  • 35:58 - 36:02
    would you be recommending
    that most countries at this point
  • 36:02 - 36:06
    do look hard at extensive school closures
    as a precautionary measure,
  • 36:06 - 36:09
    that it's just worth it to do that
  • 36:09 - 36:15
    as a sort of painful two,
    three, four, five-month strategy?
  • 36:15 - 36:16
    What would you recommend?
  • 36:16 - 36:18
    AK: I think the key thing,
  • 36:18 - 36:21
    given the age distribution of risk
    and the severity in older groups
  • 36:21 - 36:25
    is reduce interactions that bring
    the infection into those groups.
  • 36:26 - 36:29
    And then amongst everyone else,
    reduce interactions as much as possible.
  • 36:29 - 36:31
    I think the key thing is
  • 36:31 - 36:34
    we've got so much of the disease burden
    in the kind of 60-plus group
  • 36:34 - 36:38
    that it's not just about
    everyone trying to avoid
  • 36:38 - 36:39
    everyone's interactions,
  • 36:39 - 36:41
    but it's the kind of behaviors
  • 36:41 - 36:43
    that would drive infections
    into those groups.
  • 36:44 - 36:47
    CA: Does that mean
    that people should think twice
  • 36:47 - 36:50
    before, I don't know, visiting a loved one
  • 36:50 - 36:55
    in an old people's home
    or in a residential facility?
  • 36:55 - 36:59
    Like that, we should just pay
    super special attention to that,
  • 36:59 - 37:02
    should all these facilities
    be taking great care
  • 37:02 - 37:04
    about who they admit,
  • 37:04 - 37:07
    taking temperature and checking
    for symptoms or something like that?
  • 37:07 - 37:10
    AK: I think those measures
    definitely need to be considered.
  • 37:10 - 37:11
    In the UK, we're getting plans
  • 37:11 - 37:14
    for potentially what's known
    as a cocooning strategy
  • 37:14 - 37:15
    for these older groups
  • 37:15 - 37:18
    that we can really try
    and seal off interactions
  • 37:18 - 37:19
    as much as possible
  • 37:19 - 37:22
    from people who might
    be bringing infection in.
  • 37:22 - 37:25
    And ultimately, because as you said,
  • 37:25 - 37:27
    we can't target these other
    aspects of transmission,
  • 37:27 - 37:30
    it is just reducing the risk
    of exposure in these groups,
  • 37:30 - 37:34
    and so I think anything
    at the individual level you can do
  • 37:34 - 37:36
    to get people reducing their risk,
  • 37:36 - 37:39
    if either they're elderly
    or in other risk groups,
  • 37:39 - 37:41
    I think is crucial.
  • 37:41 - 37:43
    And I think more at the general level
  • 37:43 - 37:47
    those kind of more large-scale measures
    can help reduce interactions overall,
  • 37:47 - 37:50
    but I think if those
    reductions are happening
  • 37:50 - 37:51
    and not reducing the risk
  • 37:51 - 37:53
    for people who are going
    to get severe disease,
  • 37:53 - 37:57
    then you're still going to get
    this really remarkably severe burden.
  • 37:58 - 38:03
    CA: I mean, do people have to almost
    apply this double lens
  • 38:03 - 38:05
    as they think about this stuff?
  • 38:05 - 38:07
    There's the risk to you
    as you go about your life,
  • 38:07 - 38:09
    of you catching this bug.
  • 38:10 - 38:13
    But there's also the risk
    of you being, unintentionally, a carrier
  • 38:13 - 38:16
    to someone who would suffer
    much more than you might.
  • 38:16 - 38:20
    And that both those things
    have to be top of mind right now.
  • 38:20 - 38:22
    AK: Yeah, and it's not just
    whose hand you shake,
  • 38:22 - 38:24
    it's whose hand that person
    goes on to shake.
  • 38:24 - 38:27
    And I think we need to think
    about these second-degree steps,
  • 38:27 - 38:30
    that you might think you have low risk
  • 38:30 - 38:31
    and you're in a younger group,
  • 38:31 - 38:34
    but you're often going to be
    a very short step away
  • 38:34 - 38:37
    from someone who is going to get hit
    very hard by this.
  • 38:37 - 38:40
    And I think we really need
    to be socially minded
  • 38:40 - 38:43
    and think this could be quite dramatic
    in terms of change of behavior,
  • 38:43 - 38:45
    but it needs to be
  • 38:45 - 38:47
    to reduce the impact
    that we're potentially facing.
  • 38:49 - 38:51
    CA: So the opportunity
    number, we bring down
  • 38:51 - 38:54
    by just reducing the number
    of physical contacts we have
  • 38:54 - 38:56
    with other people.
  • 38:56 - 38:59
    And I guess the transmission
    probability number,
  • 38:59 - 39:01
    how do we bring that down?
  • 39:01 - 39:03
    That impacts how we interact.
  • 39:03 - 39:05
    You mentioned hand-shaking,
  • 39:05 - 39:07
    I'm guessing you're going to say
    no handshaking.
  • 39:07 - 39:09
    AK: Yeah, so changes like that.
  • 39:09 - 39:10
    I mean, another one, I think,
  • 39:10 - 39:12
    handwashing operates in a way
  • 39:12 - 39:16
    that we might be still be doing
    activities that we've previously done,
  • 39:16 - 39:21
    but handwashing reduces the chance
    that from one interaction to another,
  • 39:21 - 39:22
    we might be spreading infection,
  • 39:22 - 39:24
    so it's all of these measures
  • 39:24 - 39:26
    that mean that even
    if we're having these exposures,
  • 39:26 - 39:30
    we're taking additional steps
    to avoid any transmission happening.
  • 39:30 - 39:33
    CA: I still think most people
    don't fully understand
  • 39:33 - 39:35
    or don't have a clear model of the pathway
  • 39:35 - 39:39
    by which this thing spreads.
  • 39:39 - 39:41
    So you think definitely people understand
  • 39:41 - 39:43
    that you don't breathe in
  • 39:43 - 39:47
    the water droplets of someone
    who has just coughed or sneezed.
  • 39:47 - 39:49
    So how does it spread?
  • 39:49 - 39:51
    It gets onto surfaces. How?
  • 39:51 - 39:55
    Do people just breathe out
    and it goes on from people who are sick,
  • 39:55 - 39:57
    they touch their mouth
    or something like that,
  • 39:57 - 39:59
    and then touch a surface
    and it gets on that way?
  • 39:59 - 40:01
    How does it actually get onto surfaces?
  • 40:01 - 40:04
    AK: I think a lot of it would be
    that you cough in your hand
  • 40:04 - 40:05
    and it ends up on a surface.
  • 40:06 - 40:10
    But I think the challenge, obviously,
    is untangling these questions
  • 40:10 - 40:11
    of how transmission happens.
  • 40:11 - 40:13
    You have transmission in a household,
  • 40:13 - 40:15
    and is it that someone coughed
    and it got on a surface,
  • 40:15 - 40:17
    is it direct contact, is it a handshake,
  • 40:17 - 40:19
    and even for things like flu,
  • 40:19 - 40:22
    that's something that we work
    quite hard to try and unpick,
  • 40:22 - 40:25
    how does social behavior correspond
    to infection risk.
  • 40:25 - 40:29
    Because it's clearly important,
    but pinning it down is really tough.
  • 40:29 - 40:32
    CA: It's almost like embracing the fact
  • 40:32 - 40:35
    that for a lot of these things,
    we actually don't know
  • 40:35 - 40:39
    and that we're all
    in this game of probabilities.
  • 40:39 - 40:42
    Which, in a way, is why I think
    the math is so important here.
  • 40:42 - 40:48
    That you have to think of this
    as these multiple numbers
  • 40:48 - 40:50
    working together on each other,
  • 40:50 - 40:52
    they all have their part to play.
  • 40:52 - 40:57
    And any of them that you can
    take down by a percentage
  • 40:57 - 40:58
    is likely contributing,
  • 40:58 - 41:01
    not just to you but to everyone.
  • 41:01 - 41:05
    And people don't actually know
    in detail how the numbers go together,
  • 41:05 - 41:07
    but they know that they
    probably all matter.
  • 41:07 - 41:12
    We almost need people to, somehow,
    you know, embrace that uncertainty
  • 41:12 - 41:17
    and then try to get some satisfaction
    by acting on every single part of it.
  • 41:17 - 41:18
    AK: I think it's this idea
  • 41:18 - 41:22
    that if on average,
    you're infecting, say, three people,
  • 41:22 - 41:25
    what's driving that and how can you
    chip away at that value?
  • 41:25 - 41:26
    If you're washing your hands,
  • 41:26 - 41:29
    how much might that chip away
    in terms of the handshakes,
  • 41:29 - 41:32
    you know, you may have had virus
    and you no longer do,
  • 41:32 - 41:36
    or if you are changing
    your social behavior in a certain way,
  • 41:36 - 41:38
    is that taking away
    a couple of interactions,
  • 41:38 - 41:39
    is that taking away half?
  • 41:39 - 41:43
    How can you really chip into that number
    as much as you possibly can?
  • 41:44 - 41:47
    CA: Is there anything else to say
    about how we could reduce
  • 41:47 - 41:52
    that transmission probability
    in our interactions?
  • 41:52 - 41:55
    Like, what is the physical distance
  • 41:55 - 42:00
    that it's wise to stay away
    from other people if we can?
  • 42:01 - 42:03
    AK: I think it's hard to pin down exactly,
  • 42:03 - 42:06
    but I think one thing to bear in mind
    is that there's not so much evidence
  • 42:06 - 42:09
    that this is a kind of aerosol
    and it goes really far --
  • 42:09 - 42:10
    it's reasonably short distances.
  • 42:10 - 42:12
    I don't think it's the case
  • 42:12 - 42:15
    that you're sitting a few meters
    away from someone
  • 42:15 - 42:17
    and the virus is somehow
    going to get across.
  • 42:18 - 42:19
    It's in closer interactions,
  • 42:19 - 42:22
    and it's why we're seeing
    so many transmission events
  • 42:22 - 42:26
    occur in things like meals
    and really tight-knit groups.
  • 42:26 - 42:27
    Because if you imagine
  • 42:27 - 42:30
    that's where you can get
    a virus out and onto surfaces
  • 42:30 - 42:32
    and onto hands and onto faces,
  • 42:32 - 42:36
    and it's really situations like that
    we've got to think more about.
  • 42:38 - 42:39
    CA: So in a way,
  • 42:39 - 42:42
    some of the fears that people have
    may actually be overstated,
  • 42:42 - 42:45
    like, if you're in the middle
    of an airplane
  • 42:45 - 42:47
    and someone at the front sneezes,
  • 42:47 - 42:49
    I mean, that's annoying,
  • 42:49 - 42:53
    but it's actually not the thing
    you should be most freaked out about.
  • 42:53 - 42:57
    There are much smarter ways
    to pay attention to your well-being.
  • 42:57 - 43:01
    AK: Yeah, if it was measles
    and the plane was susceptible people,
  • 43:01 - 43:03
    you would see a lot
    of infections after that.
  • 43:03 - 43:05
    I think it is, bear in mind,
    that this is, on average,
  • 43:05 - 43:07
    people infecting two or three others,
  • 43:07 - 43:11
    so it's not the case of your
    maybe 50 interactions over a week,
  • 43:11 - 43:13
    all of those people are at risk.
  • 43:13 - 43:15
    But it's going to be some of them,
  • 43:15 - 43:17
    particularly those close contacts,
  • 43:17 - 43:19
    that are going to be
    where transmission's occurring.
  • 43:19 - 43:21
    CA: So talk about,
  • 43:22 - 43:26
    from a sort of national
    strategy point of view.
  • 43:26 - 43:30
    There's a lot of talk about the need
    to "flatten the curve."
  • 43:30 - 43:31
    What does that mean?
  • 43:31 - 43:36
    AK: I think it refers to this idea
    that for your health systems,
  • 43:36 - 43:39
    you don't want all your cases
    to appear at the same time.
  • 43:39 - 43:41
    So if we sat back and did nothing
  • 43:41 - 43:42
    and just let the epidemic grow,
  • 43:42 - 43:45
    and you had this growth rate
    that, at the moment,
  • 43:45 - 43:46
    in some places is looking like maybe
  • 43:46 - 43:49
    three to four days,
    you're getting doubling.
  • 43:49 - 43:51
    So every three or four days,
    the epidemic is doubling.
  • 43:51 - 43:53
    It will skyrocket and you'll end up
  • 43:53 - 43:56
    with a whole bunch
    of really severely ill people
  • 43:56 - 43:58
    needing hospital care
    all at the same time,
  • 43:58 - 44:00
    and you just won't have capacity for it.
  • 44:00 - 44:03
    So the idea of flattening the curve
    is if we can slow transmission,
  • 44:03 - 44:05
    if we can get that
    reproduction number down,
  • 44:05 - 44:07
    then there may still be an outbreak,
  • 44:07 - 44:09
    but it will be much flatter,
  • 44:09 - 44:10
    it will be longer
  • 44:10 - 44:12
    and there will be fewer
    severe cases showing up,
  • 44:12 - 44:15
    which means that they can get
    the health care they need.
  • 44:16 - 44:23
    CA: Does it imply that there will be
    fewer cases overall, or --
  • 44:24 - 44:27
    When you look at the actual images
    of people showing
  • 44:27 - 44:29
    what flattening the curve looks like,
  • 44:29 - 44:32
    it almost looks as if you've got
    the same area still under the graph,
  • 44:32 - 44:35
    i.e. that the same number of people,
    ultimately, are infected
  • 44:35 - 44:38
    but over a longer period.
  • 44:38 - 44:40
    Is that typically what happens,
  • 44:40 - 44:45
    and even if you adopt
    all these strategies of social distancing
  • 44:45 - 44:49
    and washing hands and etc.
  • 44:49 - 44:52
    that the best you can hope for
    is that you slow the thing down,
  • 44:52 - 44:55
    you actually will get
    as many people infected in the end?
  • 44:55 - 44:58
    AK: Not necessarily --
    it depends on the measures that go in.
  • 44:58 - 45:00
    There are some measures like,
    shutting down travel,
  • 45:00 - 45:03
    which typically delay the spread
    rather than reduce it.
  • 45:03 - 45:06
    So you're still going to get
    the same outbreaks,
  • 45:06 - 45:08
    but you're stretching out the outbreaks.
  • 45:08 - 45:10
    But there are other measures.
  • 45:10 - 45:12
    If we talk about reducing interactions,
  • 45:12 - 45:14
    if your reproduction number's lower,
  • 45:14 - 45:16
    you would expect fewer cases overall.
  • 45:16 - 45:18
    And eventually, in your population,
  • 45:18 - 45:20
    you will get some buildup of immunity,
  • 45:20 - 45:23
    which would help you out
    if you think about the components,
  • 45:23 - 45:24
    reducing susceptibility,
  • 45:24 - 45:27
    alongside what's going on elsewhere.
  • 45:27 - 45:29
    So the hope is that the two things
    will work together.
  • 45:30 - 45:34
    CA: So help me understand
    what the endgame is here.
  • 45:35 - 45:37
    So, take China, for example.
  • 45:39 - 45:43
    Whatever you make
    of the early suppression of data
  • 45:43 - 45:44
    and so forth
  • 45:44 - 45:48
    that seems pretty troubling there.
  • 45:48 - 45:53
    The intensity of the response
    come January time or whatever,
  • 45:53 - 45:57
    with the shutdown
    of this huge area of the country,
  • 45:57 - 45:59
    seems to have actually been effective.
  • 45:59 - 46:05
    The number of cases there are falling
    at a shockingly high rate in some ways.
  • 46:05 - 46:07
    Falling to almost nothing.
  • 46:07 - 46:10
    And I can't understand that.
  • 46:10 - 46:14
    You are talking about a country
    of, whatever, 1.4 billion people.
  • 46:14 - 46:16
    There have been a huge
    number of cases there,
  • 46:16 - 46:20
    but it was a tiny fraction
    of the population have actually got sick.
  • 46:20 - 46:25
    And yet, they've got the number way down.
  • 46:25 - 46:29
    It's not like every other person in China
    has somehow developed immunity.
  • 46:29 - 46:33
    Is it that they have been
    absolutely disciplined
  • 46:33 - 46:38
    about shutting down travel
    from the infected regions
  • 46:38 - 46:43
    and somehow really dialed up,
    massively dialed up
  • 46:43 - 46:46
    testing at any sign of any problem,
  • 46:46 - 46:50
    so that literally, they are back
    in containment mode
  • 46:50 - 46:52
    in most parts of China?
  • 46:52 - 46:56
    I can't get my head around it,
    help me understand it.
  • 46:56 - 46:58
    AK: So we estimated,
    in the last two weeks of January,
  • 46:58 - 47:00
    when these measures went in,
  • 47:00 - 47:02
    the reproduction number
    went from about 2.4 to 1.1.
  • 47:02 - 47:04
    So about 60 percent decline
    in transmission
  • 47:04 - 47:06
    in the space of a week or two.
  • 47:06 - 47:09
    Which is remarkable and really,
  • 47:09 - 47:13
    a lot of it is likely to be driven
    by just fundamental change
  • 47:14 - 47:15
    in social behavior,
  • 47:15 - 47:16
    huge social distancing,
  • 47:16 - 47:19
    really intensive follow-up,
    intensive testing.
  • 47:20 - 47:22
    And it got to the point
  • 47:22 - 47:24
    where it took enough
    off the reproduction number
  • 47:24 - 47:25
    to cause the decline,
  • 47:25 - 47:28
    and now, of course,
    we're seeing, in many areas,
  • 47:28 - 47:31
    a transition back to more
    of this kind of containment,
  • 47:31 - 47:33
    because there's few cases,
    it's more manageable.
  • 47:34 - 47:36
    But we're also seeing them
    face a challenge,
  • 47:37 - 47:40
    because a lot of these cities
    have basically been locked down
  • 47:40 - 47:42
    for six weeks
  • 47:42 - 47:44
    and there's a limit to how long
    you can do that for.
  • 47:44 - 47:47
    And so some of these measures
    are gradually starting to be lifted,
  • 47:47 - 47:49
    which of course creates the risk
  • 47:49 - 47:52
    that cases that are appearing
    from other countries
  • 47:52 - 47:55
    may subsequently go in
    and reintroduce transmission.
  • 47:58 - 48:01
    CA: But given how infectious the bug is,
  • 48:01 - 48:05
    and how many theoretical pathways
    and connection points there are
  • 48:05 - 48:09
    between people in Wuhan, even in shutdown,
  • 48:09 - 48:10
    or relatively shut down,
  • 48:10 - 48:13
    or the other places
    where there's been some infection
  • 48:13 - 48:15
    and the rest of the country,
  • 48:15 - 48:21
    does it surprise you how quickly
    that curve has gone down to nearly zero?
  • 48:22 - 48:23
    AK: Yes.
  • 48:23 - 48:27
    Early on when we saw
    that flattening off in cases
  • 48:27 - 48:29
    in those first few days,
  • 48:29 - 48:32
    we did wonder whether it was just
    they hit a limit in testing capacity
  • 48:32 - 48:34
    and they were reporting 1,000 a day,
  • 48:34 - 48:36
    because that's all the kits they had.
  • 48:36 - 48:38
    But it continued, thankfully,
  • 48:38 - 48:41
    and it shows that it is possible
    to turn this over
  • 48:42 - 48:43
    with that level of intervention.
  • 48:43 - 48:47
    I think the key thing now
    is seeing how it works in other settings.
  • 48:47 - 48:51
    Italy now are putting in
    really dramatic interventions.
  • 48:51 - 48:53
    But of course,
    because of this delay effect,
  • 48:53 - 48:55
    if you put them in today,
  • 48:55 - 48:57
    you won't necessarily see
    the effects on cases
  • 48:57 - 48:58
    for another week or two.
  • 48:58 - 49:00
    So I think working out
    what impact that's had
  • 49:00 - 49:03
    is going to be key for helping
    other countries
  • 49:03 - 49:04
    work on how to contain this.
  • 49:05 - 49:06
    CA: To have a picture, Adam,
  • 49:06 - 49:10
    of how this is likely to play out
    over the next month or two,
  • 49:10 - 49:14
    give us a couple of scenarios
    that are in your head.
  • 49:15 - 49:17
    AK: I think the optimistic scenario
  • 49:17 - 49:20
    is that we're going to learn a lot
    from places like Italy
  • 49:20 - 49:23
    that have unfortunately
    been hit very hard.
  • 49:23 - 49:25
    And that countries are going to take
    this very seriously
  • 49:25 - 49:28
    and that we're not going to get
    this continued growth
  • 49:28 - 49:29
    that's going to overwhelm totally,
  • 49:29 - 49:33
    that we're going to be able
    to sufficiently slow it down,
  • 49:33 - 49:35
    that we are going to get
    large numbers of cases,
  • 49:35 - 49:38
    we're probably going to get
    a lot of severe cases,
  • 49:38 - 49:40
    but that will be more manageable,
  • 49:40 - 49:42
    that's the kind of optimistic scenario.
  • 49:42 - 49:43
    I think if we have a point
  • 49:43 - 49:46
    where countries either
    don't take this seriously
  • 49:46 - 49:50
    or populations don't respond well
    to control measures
  • 49:50 - 49:51
    or it's not detected,
  • 49:51 - 49:53
    we could get situations --
  • 49:53 - 49:55
    I think Iran is probably
    the closest one at the moment --
  • 49:55 - 49:58
    where there's been extensive
    widespread transmission,
  • 49:59 - 50:01
    and by the time it's being responded to,
  • 50:02 - 50:04
    those infections are already in the system
  • 50:04 - 50:06
    and they are going to turn up
    as cases and severe illness.
  • 50:06 - 50:08
    So I'm hoping we're not at that point,
  • 50:08 - 50:10
    but we've certainly got, at the moment,
  • 50:10 - 50:14
    potentially about 10 countries
    on that trajectory
  • 50:14 - 50:16
    to have the same outlook as Italy.
  • 50:16 - 50:19
    So it's really crucial what happens
    in the next couple of weeks.
  • 50:20 - 50:22
    CA: Is there a real chance
    that quite a few countries
  • 50:22 - 50:25
    end up having, this year,
  • 50:25 - 50:31
    substantially more deaths from this virus
    than from seasonal flu?
  • 50:32 - 50:35
    AK: I think for some countries
    that is likely, yeah.
  • 50:35 - 50:37
    I think if control is not possible,
  • 50:37 - 50:39
    and we've seen it happen in China,
  • 50:39 - 50:43
    but that was really just an unprecedented
    level of intervention.
  • 50:43 - 50:46
    It was really just changing
    the social fabric.
  • 50:46 - 50:52
    I think people, many of us,
    don't really appreciate, at a glance,
  • 50:52 - 50:53
    just what that means,
  • 50:53 - 50:56
    to reduce your interactions
    to that extent.
  • 50:56 - 50:59
    I think many countries just simply
    won't be able to manage that.
  • 51:01 - 51:03
    CA: It's almost a challenge
    to democracies, isn't it --
  • 51:03 - 51:08
    "OK, show us what you can do
    without that kind of draconian control.
  • 51:08 - 51:10
    If you don't like the thought of that,
  • 51:10 - 51:13
    come on, citizens, step up,
    show us what you're capable of,
  • 51:13 - 51:15
    show that you can be wise about this
  • 51:15 - 51:17
    and smart and self-disciplined,
  • 51:17 - 51:20
    and get ahead of the damn bug."
  • 51:20 - 51:21
    AK: Yeah.
  • 51:21 - 51:25
    CA: I mean, I'm not personally
    superoptimistic about that,
  • 51:25 - 51:30
    because there's such conflicting messaging
    coming out in so many different places,
  • 51:30 - 51:36
    and people don't like
    to short-term sacrifice.
  • 51:36 - 51:38
    I mean, is there almost a case that --
  • 51:39 - 51:41
    I mean, what's your view
  • 51:41 - 51:44
    on whether the media has played
    a helpful role here
  • 51:44 - 51:45
    or an unhelpful role?
  • 51:45 - 51:47
    Is it actually, in some ways, helpful
  • 51:47 - 51:51
    to, if anything, overstate
    the concern, the fear,
  • 51:51 - 51:53
    and actually make people
    panic a little bit?
  • 51:53 - 51:56
    AK: I think it's a really
    tough balance to strike,
  • 51:56 - 51:58
    because of course, early on,
    if you don't have cases,
  • 51:58 - 52:01
    if you don't have any evidence
    of potential pressure,
  • 52:01 - 52:05
    it's very hard to get that message
    and convince people to take it seriously
  • 52:05 - 52:06
    if you're overhyping it.
  • 52:06 - 52:09
    But equally, if you're waiting too long,
  • 52:09 - 52:12
    and saying it's not a concern yet,
    we're OK for the moment,
  • 52:12 - 52:14
    a lot of people think it's just flu.
  • 52:15 - 52:18
    By the time it hits hard, as I've said,
  • 52:18 - 52:21
    you're going to have weeks
    of an overburdened health system,
  • 52:21 - 52:24
    because even if you take interventions,
  • 52:24 - 52:27
    it's too late to control the infections
    that have happened.
  • 52:27 - 52:28
    So I think it's a fine line,
  • 52:28 - 52:31
    and my hope is there is
    this ramp-up in messaging,
  • 52:31 - 52:33
    now people have these
    tangible examples like Italy,
  • 52:33 - 52:37
    where they can see what's going to happen
    if they don't take it seriously.
  • 52:37 - 52:40
    But certainly, of all
    the diseases I've seen,
  • 52:40 - 52:42
    I think many of my colleagues
    who are much older than me
  • 52:42 - 52:44
    and have memories of other outbreaks,
  • 52:44 - 52:48
    it's the scariest thing we've seen
    in terms of the impact it could have,
  • 52:48 - 52:50
    and I think we need to respond to that.
  • 52:50 - 52:52
    CA: It's the scariest disease you've seen.
  • 52:53 - 52:54
    Wow.
  • 52:54 - 52:59
    I've got some questions for you
    from my friends on Twitter.
  • 52:59 - 53:05
    Everyone is obviously
    super exercised about this topic.
  • 53:05 - 53:07
    Hypothetically,
  • 53:07 - 53:09
    if everyone stayed home for three weeks,
  • 53:09 - 53:12
    would that effectively wipe this out?
  • 53:12 - 53:15
    Is there a way to socially
    distance ourselves out of this?
  • 53:15 - 53:20
    AK: Yeah, I think in certain countries
    with reasonably small household sizes,
  • 53:20 - 53:23
    I think average in the UK, US
    is about two and a half,
  • 53:23 - 53:26
    so even if you had a round of infection
    within the household,
  • 53:26 - 53:28
    that would probably stamp it out.
  • 53:28 - 53:29
    As a secondary benefit,
  • 53:29 - 53:32
    you may well stamp out
    a few other infections, too.
  • 53:32 - 53:33
    Measles only circulates in humans,
  • 53:33 - 53:35
    so you may have some knock-on effect,
  • 53:35 - 53:38
    if, of course, that were
    ever to be possible.
  • 53:38 - 53:42
    CA: I mean, obviously that would be
    a huge dent to the economy,
  • 53:42 - 53:46
    and this is in a way almost, like,
    one of the underlying challenges here
  • 53:46 - 53:50
    is that you can't optimize public policy
  • 53:50 - 53:55
    for both economic health
    and fighting a virus.
  • 53:55 - 53:58
    Like, those two things are,
    to some extent, in conflict,
  • 53:58 - 54:02
    or at least, short-term
    economic health and fighting a virus.
  • 54:02 - 54:04
    Those two things are in conflict, right?
  • 54:04 - 54:07
    And societies need to pick one.
  • 54:07 - 54:11
    AK: It is tough to convince
    people of that balance,
  • 54:11 - 54:13
    the thing we always say
    of pandemic planning
  • 54:13 - 54:15
    is it's cheap to put
    this stuff in place now --
  • 54:15 - 54:17
    otherwise, you've got to pay for it later.
  • 54:18 - 54:20
    But unfortunately,
    as we've seen with this,
  • 54:20 - 54:23
    that a lot of early money
    for response wasn't there.
  • 54:23 - 54:27
    And it's only when it has an impact
    and when it's going to get expensive
  • 54:27 - 54:31
    that people are happy to take
    that cost on board, it seems.
  • 54:32 - 54:34
    CA: OK, some more Twitter questions.
  • 54:34 - 54:36
    Will the rising temperature
    in coming weeks and months
  • 54:36 - 54:39
    slow down the COVID-19 spread?
  • 54:40 - 54:42
    AK: I haven't seen any convincing evidence
  • 54:42 - 54:44
    that there's that strong pattern
    with temperature,
  • 54:45 - 54:49
    and we've seen it for other infections
    that there is this seasonal pattern,
  • 54:49 - 54:51
    but I think the fact
    we're getting widespread outbreaks
  • 54:51 - 54:54
    makes it hard to identify, and of course,
  • 54:54 - 54:55
    there's other things going on.
  • 54:55 - 54:59
    So even if one country doesn't have
    as big an outbreak as another,
  • 54:59 - 55:01
    that's going to be influenced
    by control measures,
  • 55:01 - 55:04
    by social behavior, by opportunities
    and these things as well.
  • 55:04 - 55:07
    So it would be really reassuring
    if this was the case,
  • 55:07 - 55:09
    but I don't think
    we can say that just yet.
  • 55:10 - 55:12
    CA: Continuing from Twitter,
  • 55:12 - 55:15
    I mean, is there a standardized
    global recommendation
  • 55:15 - 55:17
    for all countries
  • 55:17 - 55:18
    on how to do this?
  • 55:18 - 55:20
    And if not, why not?
  • 55:21 - 55:23
    AK: I think that's what people
    are trying to piece together,
  • 55:23 - 55:25
    first in terms of what works.
  • 55:25 - 55:28
    It's only really in the last
    sort of few weeks
  • 55:29 - 55:31
    we've got a sense that this thing
    can be controllable
  • 55:31 - 55:33
    with this extent of interventions,
  • 55:33 - 55:36
    but of course, not all countries
    can do what China have done,
  • 55:36 - 55:37
    some of these measures
  • 55:37 - 55:41
    incur a huge social, economic,
    psychological burden
  • 55:41 - 55:43
    on populations.
  • 55:43 - 55:45
    And of course, there's the time limit.
  • 55:45 - 55:47
    In China they've had
    them in for six weeks
  • 55:47 - 55:48
    it's tough to maintain that,
  • 55:48 - 55:50
    so we need to think of these tradeoffs
  • 55:50 - 55:53
    of all the things we can ask people to do,
  • 55:53 - 55:57
    what's going to have the most impact
    on actually reducing the burden.
  • 55:58 - 55:59
    CA: Another question:
  • 55:59 - 56:02
    How did this happen
    and is it likely to happen again?
  • 56:03 - 56:08
    AK: So it's likely that this originated
    with the virus that was circling in bats
  • 56:08 - 56:11
    and then probably made its way
    through another species
  • 56:11 - 56:12
    into humans somehow,
  • 56:12 - 56:15
    there's a lot of bits
    of evidence around this,
  • 56:15 - 56:17
    there's not kind of single, clear story,
  • 56:17 - 56:19
    but even for SARS, it took several years
  • 56:19 - 56:22
    for genomics to piece together
    the exact route that it happened.
  • 56:22 - 56:25
    But certainly, I think it's plausible
    that it could happen again.
  • 56:25 - 56:28
    Nature is throwing out
    these viruses constantly.
  • 56:28 - 56:31
    Many of them aren't
    well-adapted to humans,
  • 56:31 - 56:32
    don't pick up,
  • 56:32 - 56:35
    you know, there may well have been
    a virus like this a few years ago
  • 56:35 - 56:37
    that just happened to infect someone
  • 56:37 - 56:40
    who just didn't have any contacts
    and didn't go any further.
  • 56:40 - 56:42
    I think we are going to face these things
  • 56:42 - 56:44
    and we need to think
    about how can we get in early
  • 56:44 - 56:47
    at the stage where we're talking
    small numbers of cases,
  • 56:47 - 56:49
    and even something like this
    is containable,
  • 56:49 - 56:51
    rather than the situation we've got now.
  • 56:51 - 56:53
    CA: It seems like
    this isn't the first time
  • 56:53 - 56:57
    that a virus seems to have emerged
    from, like, a wild meat market.
  • 56:58 - 57:00
    That's certainly how
    it happens in the movies. (Laughs)
  • 57:00 - 57:03
    And I think China has already taken
    some steps this time
  • 57:03 - 57:06
    to try to crack down on that.
  • 57:06 - 57:09
    I guess that's potentially
    quite a big deal for the future
  • 57:09 - 57:12
    if that can be properly maintained.
  • 57:12 - 57:14
    AK: It is, and we saw, for example,
  • 57:14 - 57:16
    the H7N9 avian flu,
  • 57:16 - 57:20
    over the last few years, in 2013,
    it was a big emerging concern,
  • 57:20 - 57:22
    and China made a very extensive response
  • 57:22 - 57:24
    in terms of changing
    how they operate their markets
  • 57:25 - 57:26
    and vaccination of birds
  • 57:26 - 57:30
    and that seems
    to have removed that threat.
  • 57:30 - 57:34
    So I think these measures can be effective
    if they're identified early on.
  • 57:34 - 57:36
    CA: So talk about vaccinations.
  • 57:36 - 57:38
    That's the key measure, I guess,
  • 57:38 - 57:41
    to change that susceptibility
    factor in your equation.
  • 57:45 - 57:49
    There's obviously a race on
    to get these vaccinations out there,
  • 57:49 - 57:52
    there are some candidate
    vaccinations there.
  • 57:52 - 57:54
    How do you see that playing out?
  • 57:55 - 57:59
    AK: I think there's certainly some
    promising development happening,
  • 57:59 - 58:01
    but I think the timescales of these things
  • 58:01 - 58:04
    are really on the order
    of maybe a year, 18 months
  • 58:04 - 58:06
    before these things be widely available.
  • 58:06 - 58:09
    Obviously, a vaccine has to go
    through these stages of trials,
  • 58:09 - 58:11
    that takes time,
    so even if by the end of the year,
  • 58:11 - 58:13
    we have something
    which is viable and works,
  • 58:14 - 58:17
    we're still going to see a delay
    before everyone can get ahold of it.
  • 58:17 - 58:19
    CA: So this really puzzles me, actually,
  • 58:19 - 58:22
    and I'd love to ask you
    as a mathematician about this as well.
  • 58:22 - 58:24
    There are already several companies
  • 58:24 - 58:28
    believing that they have
    plausible candidate vaccines.
  • 58:28 - 58:32
    As you say, the process
    of testing takes forever.
  • 58:33 - 58:38
    Is there a case that we're not
    thinking about this right
  • 58:38 - 58:43
    when we're looking at the way
    that testing is done
  • 58:43 - 58:45
    and that the safety calculations are made?
  • 58:45 - 58:48
    Because it's one thing
    if you're going to introduce
  • 58:48 - 58:49
    a brand new drug or something --
  • 58:49 - 58:54
    yes, you want to test to make sure
    that there are no side effects,
  • 58:54 - 58:55
    and that can take a long time
  • 58:55 - 58:58
    by the time you've done all
    the control trials and all the rest of it.
  • 58:59 - 59:00
    If there's a global emergency,
  • 59:01 - 59:03
    isn't there a case,
  • 59:03 - 59:05
    both mathematically and ethically,
  • 59:05 - 59:07
    that there should just be
    a different calculation,
  • 59:07 - 59:09
    the question shouldn't be
  • 59:09 - 59:14
    "Is there any possible case
    where this vaccine can do harm,"
  • 59:14 - 59:16
    the question surely should be,
  • 59:16 - 59:18
    "On the net probabilities,
  • 59:18 - 59:22
    isn't there a case
    to roll this out at scale,
  • 59:22 - 59:27
    to have a shot at nipping
    this thing in the bud?"
  • 59:27 - 59:30
    I mean, what am I missing
    in thinking that way?
  • 59:31 - 59:33
    AK: I mean, we do see that
    in other situations,
  • 59:33 - 59:37
    for example, the Ebola vaccine in 2015
  • 59:37 - 59:40
    showed, within a few months,
    very promising evidence
  • 59:40 - 59:45
    and interim results of the trial in humans
  • 59:45 - 59:47
    showed what seemed very high efficacy.
  • 59:47 - 59:50
    And even though
    it hadn't been licensed fully,
  • 59:50 - 59:53
    it was employed for what is known
    as compassionate use
  • 59:53 - 59:55
    in subsequent other outbreaks.
  • 59:55 - 59:57
    So there are these mechanisms
  • 59:57 - 59:59
    where vaccines can be
    fast-tracked in this way.
  • 60:00 - 60:03
    But of course, we're currently
    in a situation where we have no idea
  • 60:03 - 60:05
    if these things will do anything at all.
  • 60:05 - 60:08
    So I think we need
    to accrue enough evidence
  • 60:08 - 60:10
    that it could have an impact,
  • 60:10 - 60:13
    but obviously, fast-track that
    as much as possible.
  • 60:14 - 60:17
    CA: But the skeptic in me
    still doesn't fully get this.
  • 60:17 - 60:19
    I don't understand
  • 60:19 - 60:25
    why there isn't more energy
    behind bolder thinking on this.
  • 60:25 - 60:28
    Everyone seems, despite the overall risk,
  • 60:28 - 60:31
    incredibly risk-averse
    about how to build the response to it.
  • 60:32 - 60:33
    AK: So with the caveat that,
  • 60:33 - 60:35
    yeah, there's a lot
    of good questions on this,
  • 60:35 - 60:38
    and some of them are slightly
    outside my wheelhouse,
  • 60:38 - 60:41
    but I agree that we need to do more
    to get timescales out.
  • 60:41 - 60:42
    The example I always quote
  • 60:42 - 60:45
    is it takes us six months
    to choose a seasonal flu strain
  • 60:45 - 60:47
    and get the vaccines out there to people.
  • 60:47 - 60:51
    We always have to try and predict ahead
    which strains are going to be circulating.
  • 60:51 - 60:53
    And that's for something
    we know how to make
  • 60:53 - 60:55
    and has been manufactured for a long time.
  • 60:56 - 60:59
    So there is definitely more
    that needs to be done
  • 60:59 - 61:01
    to get these timescales shorter.
  • 61:01 - 61:03
    But I think we do have to balance that,
  • 61:03 - 61:06
    especially if we're exposing
    large numbers of people to something
  • 61:06 - 61:08
    to make sure that we're confident
    it's safe
  • 61:08 - 61:11
    and that it's going to have
    some benefit, potentially.
  • 61:13 - 61:15
    CA: And so, finally,
  • 61:15 - 61:18
    Adam, I guess going into this --
  • 61:19 - 61:23
    There's another set of infectious things
    happening around the world
  • 61:23 - 61:24
    at the same time,
  • 61:24 - 61:28
    which is ideas and the communication
    around this thing.
  • 61:28 - 61:34
    They really are two very dynamic,
    interactive systems of infectiousness --
  • 61:34 - 61:37
    there's some very damaging
    information out there.
  • 61:37 - 61:42
    Is it fair to think of this as battle
    of credible knowledge and measures
  • 61:42 - 61:44
    against the bug,
  • 61:44 - 61:48
    and just bad information --
  • 61:48 - 61:50
    You know, part of what
    we have to think about here
  • 61:50 - 61:55
    is how to suppress one set of things
    and boost the other, actually,
  • 61:55 - 61:57
    turbocharge the other.
  • 61:57 - 61:58
    How should we think of this?
  • 61:58 - 62:02
    AK: I think we can definitely think of it
    almost as competition for our attention,
  • 62:02 - 62:04
    and we see similarly, with diseases,
  • 62:04 - 62:06
    you have viruses competing
    to infect susceptible hosts.
  • 62:06 - 62:08
    And I think we're now seeing,
  • 62:08 - 62:11
    I guess over the last few years
    with fake news and misinformation
  • 62:11 - 62:13
    and the emergence of awareness,
  • 62:13 - 62:14
    more of a transition
  • 62:14 - 62:17
    to thinking about how do we
    reduce that susceptibility
  • 62:17 - 62:19
    if we have people that can be
    in these different states,
  • 62:19 - 62:22
    how can we try and preempt
    better with information.
  • 62:22 - 62:24
    I think the challenge
    for an outbreak is obviously,
  • 62:24 - 62:27
    early on, we have
    very little good information,
  • 62:27 - 62:31
    and it's very easy for certainty
    and confidence to fill that vacuum.
  • 62:31 - 62:33
    And so I think that is something --
  • 62:33 - 62:37
    I know platforms are working
    on how can we get people exposed
  • 62:37 - 62:38
    to good information earlier,
  • 62:38 - 62:41
    so hopefully protect them
    against other stuff.
  • 62:42 - 62:44
    CA: One of the big unknowns to me
    in the year ahead --
  • 62:44 - 62:48
    let's say that the year ahead includes
    many, many more weeks,
  • 62:48 - 62:49
    for many people,
  • 62:49 - 62:53
    of actually self-isolating.
  • 62:53 - 62:58
    Those of us who are lucky enough
    to have jobs where you can do that.
  • 62:58 - 62:59
    You know, staying home.
  • 62:59 - 63:02
    By the way, the whole injustice
    of this situation,
  • 63:02 - 63:06
    where so many people can't do that
    and continue to make a living,
  • 63:06 - 63:10
    is, I'm sure, going to be
    a huge deal in the year ahead
  • 63:10 - 63:16
    and if it turns out that death rates
    are much higher in the latter group
  • 63:16 - 63:18
    than in the former group,
  • 63:18 - 63:19
    and especially in a country like the US,
  • 63:20 - 63:23
    where the latter group
    doesn't even have proper health insurance
  • 63:23 - 63:24
    and so forth.
  • 63:25 - 63:31
    That feels like right there,
    that could just become a huge debate,
  • 63:31 - 63:34
    hopefully a huge source
    of change at some level.
  • 63:34 - 63:36
    AK: I think that's an incredibly
    important point,
  • 63:36 - 63:38
    because it's very easy --
  • 63:38 - 63:41
    I similarly have a job
    where remote working is fairly easy,
  • 63:41 - 63:45
    and it's very easy to say
    we should just stop social interactions,
  • 63:45 - 63:48
    but of course, that could have
    an enormous impact on people
  • 63:48 - 63:51
    and the choices and the routine
    that they can have.
  • 63:51 - 63:53
    And I think those do need
    to be accounted for,
  • 63:53 - 63:56
    both now and what the effect
    is going to look like
  • 63:56 - 63:57
    a few months down the line.
  • 63:57 - 63:59
    CA: When all's said and done,
  • 63:59 - 64:04
    is it fair to say that the world has
    faced, actually, much graver problems
  • 64:04 - 64:05
    in the past,
  • 64:05 - 64:08
    that on any scenario,
  • 64:08 - 64:12
    it's highly likely that at some point
    in the next 18 months, let's say,
  • 64:12 - 64:16
    a vaccine is there and starts
    to get wide distribution,
  • 64:16 - 64:22
    that we will have learned
    lots of other ways to manage this problem?
  • 64:22 - 64:24
    But at some point, next year probably,
  • 64:24 - 64:30
    the world will feel like
    it's got on top of this
  • 64:30 - 64:32
    and can move on.
  • 64:32 - 64:34
    Is that likely to be it,
  • 64:34 - 64:37
    or is this more likely to be,
    you know, it escapes,
  • 64:37 - 64:42
    it's now an endemic nightmare
    that every year picks off far more people
  • 64:42 - 64:45
    than are picked off by the flu currently.
  • 64:45 - 64:47
    What are the likely ways forward,
  • 64:48 - 64:50
    just taking a slightly longer-term view?
  • 64:50 - 64:53
    AK: I think there's plausible ways
    you could see all of those
  • 64:53 - 64:54
    potentially playing out.
  • 64:54 - 64:59
    I think the most plausible is probably
    that we'll see very rapid growth this year
  • 64:59 - 65:03
    and lots of large outbreaks
    that don't recur, necessarily.
  • 65:03 - 65:06
    But there is a potential
    sequence of events
  • 65:06 - 65:10
    that could end up with these kind of
    multiyear outbreaks in different places
  • 65:10 - 65:11
    and reemerging.
  • 65:11 - 65:13
    But I think it's likely we'll see
  • 65:13 - 65:16
    most transmission concentrated
    in the next year or so.
  • 65:16 - 65:19
    And then, obviously,
    if there's a vaccine available,
  • 65:19 - 65:21
    we can move past this,
    and hopefully learn from this.
  • 65:21 - 65:25
    I think a lot of the countries
    that responded very strongly to this
  • 65:25 - 65:26
    were hit very hard by SARS.
  • 65:26 - 65:29
    Singapore, Hong Kong,
    that really did leave an impact,
  • 65:29 - 65:32
    and I think that's something
    they've drawn on very heavily
  • 65:32 - 65:33
    in their response to this.
  • 65:33 - 65:34
    CA: Alright.
  • 65:34 - 65:37
    So let's wrap up maybe by just
    encouraging people
  • 65:37 - 65:39
    to channel their inner mathematician
  • 65:39 - 65:44
    and especially think
    about the opportunities
  • 65:44 - 65:48
    and the transmission probabilities
    that they can help shift.
  • 65:48 - 65:53
    Just remind us of the top
    three or four or five or six things
  • 65:53 - 65:55
    that you would love to see people doing.
  • 65:55 - 65:58
    AK: I think at the individual level,
    just thinking a lot more
  • 65:58 - 66:00
    about your interactions
    and your risk of infection
  • 66:00 - 66:02
    and obviously, what gets onto your hands
  • 66:02 - 66:04
    and once that gets onto your face,
  • 66:04 - 66:06
    and how do you potentially
    create that risk for others.
  • 66:06 - 66:09
    I think also, in terms of interactions,
  • 66:09 - 66:14
    with things like handshakes
    and maybe contacts you don't need to have.
  • 66:14 - 66:17
    You know, how can we get those down
    as much as possible.
  • 66:17 - 66:19
    If each person's giving it
    to two or three others,
  • 66:19 - 66:22
    how do we get that number
    down to one, through our behavior.
  • 66:22 - 66:26
    And then it's likely that we'll need
    some larger-scale interventions
  • 66:26 - 66:29
    in terms of gatherings, conferences,
  • 66:29 - 66:32
    other things where
    there's a lot of opportunities
  • 66:32 - 66:33
    for transmission.
  • 66:33 - 66:36
    And really, I think that combination
    of that individual level,
  • 66:36 - 66:39
    you know, if you're ill
    or potentially you're going to get ill,
  • 66:39 - 66:41
    reducing that risk,
  • 66:41 - 66:42
    but then also us working together
  • 66:42 - 66:44
    to prevent it getting
    into those groups who,
  • 66:44 - 66:46
    if it continues to be uncontrolled,
  • 66:46 - 66:48
    could really hit some people
    very, very hard.
  • 66:49 - 66:51
    CA: Yeah, there's a lot of things
  • 66:51 - 66:54
    that we may need
    to gently let go of for a bit.
  • 66:54 - 66:59
    And maybe try to reinvent
    the best aspects of them.
  • 66:59 - 67:00
    Thank you so much.
  • 67:00 - 67:03
    If people want to keep up with you,
  • 67:03 - 67:06
    first of all, they can follow you
    on Twitter, for example.
  • 67:06 - 67:07
    What's your Twitter handle?
  • 67:07 - 67:10
    AK: So @AdamJKucharski, all one word.
  • 67:10 - 67:13
    CA: Adam, thank you so much
    for your time, stay well.
  • 67:13 - 67:14
    AK: Thank you.
  • 67:14 - 67:21
    (Music)
  • 67:29 - 67:33
    CA: Associate professor
    and TED Fellow Adam Kucharski.
  • 67:33 - 67:36
    We'd love to hear what you think
    of this bonus episode.
  • 67:36 - 67:39
    Please tell us by rating
    and reviewing us in Apple Podcasts
  • 67:39 - 67:41
    or your favorite podcast app.
  • 67:41 - 67:43
    Those reviews are influential, actually.
  • 67:43 - 67:45
    We certainly read every one,
  • 67:45 - 67:47
    and truly appreciate your feedback.
  • 67:47 - 67:49
    (Music)
  • 67:49 - 67:53
    This week's show was produced
    by Dan O'Donnell at Transmitter Media.
  • 67:53 - 67:55
    Our production manager
    is Roxanne Hai Lash,
  • 67:55 - 67:57
    our fact-checker Nicole Bode.
  • 67:57 - 67:59
    This episode was mixed by Sam Bair.
  • 67:59 - 68:01
    Our theme music
    is by Allison Layton-Brown.
  • 68:01 - 68:04
    Special thanks to my colleague
    Michelle Quint.
  • 68:04 - 68:06
    Thanks for listening to the TED Interview.
  • 68:06 - 68:08
    We'll be back later this spring
  • 68:08 - 68:11
    with a whole new season's worth
    of deep dives with great minds.
  • 68:12 - 68:15
    I hope you'll enjoy them
    whether or not life is back to normal.
  • 68:16 - 68:17
    I'm Chris Anderson,
  • 68:17 - 68:19
    thanks for listening and stay well.
Títol:
Adam Kucharski on what should (and shouldn't) worry us about the coronavirus
Speaker:
The TED Interview
Descripció:

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Video Language:
English
Team:
TED
Projecte:
TEDTalks
Duration:
01:08:24

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