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

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Showing Revision 16 created 03/19/2020 by Krystian Aparta.

  1. Hello, I'm Chris Anderson.
    Welcome to The TED Interview.
  2. We're gearing up for season four
    with some extraordinary guests,
  3. but I don't want to wait for that
    for today's episode,
  4. because we're in the middle of a pandemic,
  5. and there's a guest
    I really wanted to talk to now.
  6. He is Adam Kucharski,

  7. an infectious diseases scientist
  8. who focuses on the mathematical
    modeling of pandemics.
  9. He's an associate professor
  10. at the London School of Hygiene
    and Tropical Medicine
  11. and a TED Fellow.
  12. (Music)

  13. (TED Talk) Adam Kucharski:
    So what kind of behavior

  14. is actually important for epidemics?
  15. Conversations, close physical contacts?
  16. What sort of data should we be collecting
  17. before an outbreak
  18. if we want to predict
    how infection might spread?
  19. To find out, our team
    built a mathematical model ...
  20. Chris Anderson: When it comes
    to figuring out what to make of

  21. this pandemic,
    known technically as COVID-19,
  22. and informally as just the coronavirus,
  23. I find his thinking unbelievably helpful.
  24. And I'm excited to dive into it with you.
  25. A special callout to my friends on Twitter
  26. who offered up many
    suggestions for questions.
  27. I know this topic
    is on everyone's mind right now.
  28. And what I hope this episode does
  29. is give us all a more nuanced way
  30. of thinking about how this pandemic
    has unfolded so far,
  31. what might be to come
  32. and what we all collectively
    can do about it.
  33. Let's dive in.
  34. (Music)

  35. Adam, welcome to the TED Interview.

  36. Adam Kucharski: Thank you.

  37. CA: So let's just start
    with a couple of basics.

  38. A lot of skeptical people's response --
  39. certainly over the last few weeks,
    maybe less so now --
  40. has been, "Oh, come on,
    this isn't such a big deal,
  41. there's a relatively tiny number of cases.
  42. Compare it to the flu,
    compare it to anything else.
  43. There are much bigger
    problems in the world.
  44. Why are we making such a fuss about this?"
  45. And I guess the answer to that fuss
    is that it comes down to the mathematics.
  46. We're talking about the mathematics
    of exponential growth,
  47. fundamentally, right?
  48. AK: Exactly.

  49. And there's a number that we use
    to get a sense of how easy things spread
  50. and the level of transmission
    we're dealing with.
  51. We call that the reproduction number,
  52. and conceptually, it's just,
  53. for each case you have, on average,
  54. how many others are they infecting?
  55. And that gives you a sense
    of how much is this scaling,
  56. how much this growth
    is going to look like.
  57. For coronavirus, we're now seeing,
    across multiple countries,
  58. we're seeing each person on average
    giving it to two or three more.
  59. CA: So that reproduction number,

  60. the first thing to understand
    is that any number above one
  61. means that this thing is going to grow.
  62. Any number below one
    means it's going to diminish.
  63. AK: Exactly -- if you have it above one,

  64. then each group of people infected
  65. are going to be generating more infection
    than there was before.
  66. And you will see the exponential effect,
  67. so if it's two, you will be doubling
    every round of infection,
  68. and if it's below one,
  69. you're going to get something
    that's going to decline, on average.
  70. CA: So that number two or higher,

  71. I think everyone here is maybe familiar
    with the famous story
  72. of the chessboard and the grains of rice,
  73. and if you double the number of grains
    for every square of the chessboard,
  74. for the first 10 or 15 squares
    nothing much happens,
  75. but by the time you've got
    to the 64th square,
  76. you suddenly have tons of rice
    for every individual on the planet.
  77. (Laughs)

  78. Exponential growth is an incredible thing.

  79. And the small numbers now
  80. are really not what you
    should be paying attention to --
  81. you should be paying attention
    to the models of what could be to come.
  82. AK: Exactly.

  83. Obviously, if you continue
    the exponential growth,
  84. you do sometimes get
    these incredibly large,
  85. maybe implausibly large numbers.
  86. But even looking at a timescale
    of say, a month,
  87. if the reproduction number is three,
  88. each person is infecting three on average.
  89. The gap between these rounds
    of infection is about five days.
  90. So if you imagine
    that you've got one case now,
  91. that's, kind of, six of these
    five-day steps in a month.
  92. So by the end of that month,
  93. that one person could have generated,
  94. I think it works out at about 729 cases.
  95. So even in a month,
  96. just the scale of this thing
    can really shoot up
  97. if it's not controlled.
  98. CA: And so certainly,

  99. that seems to be happening
    on most numbers that you look at now,
  100. certainly where the virus
    is in the early stages
  101. of entering a country.
  102. You've given a model
  103. whereby we can much more clearly
    understand this reproduction number,
  104. because it seems to me this is almost
    like the core to how we think of the virus
  105. and how we respond to it
    and how much we should fear it, almost.
  106. And in your thinking,
  107. you sort of break it down
    into four components,
  108. which you call DOTS:
  109. Duration, Opportunities,
  110. Transmission probability
  111. and Susceptibility.
  112. And I think it would be
    really helpful, Adam,
  113. for you to just explain each of these,
  114. because it's quite a simple equation
  115. that links those four things
    to the actual reproduction number.
  116. So talk about them in turn.
  117. Duration, what does that mean?
  118. AK: Duration measures
    how long someone is infectious for.

  119. If, for example,
  120. intuitively, if someone is infectious
    for a longer period of time,
  121. say, twice as long as someone else,
  122. then that's twice the length
  123. that they've got
    to be spreading infection.
  124. CA: And what is the duration
    number for this virus,

  125. compared with, say, flu
    or with other pathogens?
  126. AK: It depends a little bit

  127. on what happens
    when people are infectious,
  128. if they're being isolated very quickly,
    that shortens that period of time,
  129. but potentially, we're looking
    at around a week
  130. people are effectively infectious
    before they might be isolated in hospital.
  131. CA: And during that week,
    they may not even be showing symptoms

  132. for that full week either, right?
  133. So someone gets infected,
    there's an incubation period.
  134. There's a period some way
    into that incubation period
  135. where they start being infectious,
  136. and there may be a period after that,
    where they start to show symptoms,
  137. and it's not clear, quite,
    how those dates align.
  138. Is that right?
  139. AK: No, we're getting more information.

  140. One of the signals we see in data
  141. that suggest that you may have
    that early transmission going on
  142. is when you have this delay
    from one infection to the next.
  143. So that seems to be around five days.
  144. That incubation period,
    the time for symptoms to appear,
  145. is also about five days.
  146. So if you imagine that most people
  147. are only infecting others
    when they're symptomatic,
  148. you'd have that incubation period
  149. and then you'd have some more time
    when they're infecting others.
  150. So the fact that those values
    seem to be similar,
  151. suggesting that some people
    are transmitting
  152. either very early on or potentially
    before they're showing clear symptoms.
  153. CA: So almost implies that on average,

  154. people are infecting others
  155. as much before
    they show symptoms as after.
  156. AK: Potentially.

  157. Obviously these are early data sets,
  158. but I think there's good evidence
    that a fair number of people,
  159. either before they're
    showing clear symptoms
  160. or maybe they're not showing the kind of
    very distinctive fever and cough
  161. but they're feeling unwell
    and they're shedding virus
  162. during that period.
  163. CA: And does that make it
    quite different from the flu, for example?

  164. AK: It makes it actually
    similar to flu in that regard.

  165. One of the reasons pandemic flu
    is so hard to control
  166. and so feared as a threat
  167. is because so much transmission happens
    before people are severely ill.
  168. And that means that by the time
    you identify these cases,
  169. they've probably actually spread it
    to a number of other people.
  170. CA: Yeah, so this is
    the trickery of the thing,

  171. and why it's so hard
    to do anything about it.
  172. It is ahead of us all the time,
  173. and you can't just pay attention
    to how someone feels
  174. or what they're doing.
  175. I mean, how does that happen, by the way?
  176. How does someone infect someone else
  177. before they're even showing
    symptoms themselves,
  178. because classically, we think of,
    you know, the person sneezing
  179. and droplets go through the air
    and someone else breathes them in
  180. and that's how infection happens.
  181. What is actually going on
    for infection pre-symptoms?
  182. AK: So the level of transmission
    we see with this virus

  183. isn't what we see,
    for example, with measles,
  184. where someone sneezes
    and a lot of virus gets out
  185. and potentially lots of susceptible
    people can get exposed.
  186. So potentially, it could be quite early on
  187. that even if someone
    has quite mild symptoms,
  188. maybe a bit of a cough,
  189. that's enough for some virus
    to be getting out
  190. and particularly,
  191. some of the work that we've done
  192. trying to look at sort of
    close gatherings,
  193. so very tight-knit meals,
  194. there was an example in a ski chalet --
  195. and even in those situations,
    you might have someone mildly ill,
  196. but enough virus is getting out
    and somehow exposing others,
  197. we're still trying
    to work out exactly how,
  198. but there's enough there
    to cause some infection.
  199. CA: But if someone's mildly ill,
    don't they still have symptoms?

  200. Isn't there evidence that even before
    they know that they're ill,
  201. something is going on?
  202. There was a German paper
    published this week
  203. that seemed to suggest
    that even really early on,
  204. you take a swab from the back
    of someone's throat
  205. and they have hundreds
    of thousands of these viruses
  206. already reproducing there.
  207. Like, can someone just
    literally just be breathing normally
  208. and there is some transmission
    of virus out into the air
  209. that they don't even know about
  210. and is either infecting people directly
  211. or settling on surfaces, is that possible?
  212. AK: I think that's what
    we're trying to pin down,

  213. how much that [unclear].
  214. As you said,
  215. there's evidence that you can have
    people without symptoms
  216. and you can get virus out their throats.
  217. And so certainly there's potential
    that it can be breathed out,
  218. but is that a fairly rare event
    for that actual transmission to happen,
  219. or are we potentially seeing more
    infections occur through that route?
  220. So it's really early data,
  221. and I think it's a piece of the puzzle,
  222. but we're trying to work out
    where that fits in
  223. with what we know about the kind of
    other transmission events we've seen.
  224. CA: Alright, so, duration is the duration
    of the period of infectiousness.

  225. We think is five to six days,
    is that what I heard you say?
  226. AK: Potentially around a week,

  227. depending on exactly what happens
    to people when they're infectious.
  228. CA: And there are cases
    of people testing positive

  229. way, way later,
    after they've got infected.
  230. That may be true, but they are probably
    not as infectious then.
  231. Is that basically right,
    that's the way to think of this?
  232. AK: I think that's our working theory,

  233. that a lot of that infection
    is happening early on.
  234. And we see that for a number
    of respiratory infections,
  235. that when people obviously
    become severely ill,
  236. their behavior is very different
  237. to when they may be walking around
    and going about their normal day.
  238. CA: And so again, comparing
    that D number to other cases,

  239. like the flu,
  240. is flu similar?
  241. What's the D number for flu?
  242. AK: So for flu,
    it's probably slightly shorter

  243. in terms of the period
    that people are actively infectious.
  244. I mean, for flu,
    it's a very quick turnover
  245. from one case to the next, actually.
  246. Even a matter of
    about three days, potentially,
  247. from one infection
    to the person that they infect.
  248. And then at the other end of the scale,
    you get things like STDs,
  249. where that duration could be
    several months, potentially.
  250. CA: Right.

  251. OK, really nothing that unusual so far,
    in terms of this particular virus.
  252. Let's look at the O, opportunity.
  253. What is that?
  254. AK: So opportunity is a measure
    of how many chances

  255. the virus has to spread
    through interactions
  256. while someone is infectious.
  257. So typically, it's a measure
    of social behavior.
  258. On average, how many
    social contacts do people make
  259. that create opportunities for transmission
    while they're infectious.
  260. CA: So it's how many people
    have you got close enough to

  261. during a day, during a given day,
  262. to have a chance of infecting them.
  263. And that number could be,
  264. if people aren't taking precautions
    in a normal, sort of, urban setting,
  265. I mean, that could run
    into the hundreds, presumably?
  266. AK: Potentially, for some people.

  267. We've done a number of studies
    looking at that in recent years,
  268. and the average,
    in terms of physical contacts,
  269. is about five people per day.
  270. Most people will have
    conversation or contacts
  271. generally with about 10, 15,
  272. but obviously,
  273. between cultures,
    we see quite a lot of variation
  274. in the level of physical greetings
    that might happen.
  275. CA: And presumably, that number
    again is no different for this virus

  276. than for any other.
  277. I mean, that's just a feature
    of the lives that we live.
  278. AK: I think for this one,

  279. if it's driven through
    these kind of interactions,
  280. and we've seen for flu,
    for other respiratory infections,
  281. those kinds of fairly close contacts
    and everyday physical interactions
  282. seem to be the ones
    that are important for transmission.
  283. CA: Perhaps there is one difference.

  284. The fact that if you're
    infectious pre-symptoms,
  285. perhaps that means that actually,
    there are more opportunities here.
  286. This is part of the virus's
    genius, as it were,
  287. that by not letting on
    that it's inside someone,
  288. people continue to interact and go to work
  289. and take the subway and so forth,
  290. not even knowing that they're sick.
  291. AK: Exactly.

  292. And for something like flu,
  293. you see when people get ill, clearly,
    their social contacts drop off.
  294. So to have a virus that can be infectious
  295. while people are going around
    their everyday lives,
  296. really gives it an advantage
    in terms of transmission.
  297. CA: In your modeling,

  298. do you actually have this
    opportunities number higher than for flu?
  299. AK: So for the moment,
    we're kind of using similar values,

  300. so we're trying to look at, for example,
  301. physical contacts
    within different populations.
  302. But what we are doing is scaling the risk.
  303. So that's coming on to the T term.
  304. So that between each contact,
  305. what's the risk that a transmission
    event will occur.
  306. CA: Alright, so let's go on
    to this next number,

  307. the T, transmission probability.
  308. How do you define that?
  309. AK: So this measures the chance

  310. that, essentially,
    the virus will get across
  311. during a particular opportunity
    or a particular interaction.
  312. So you may well have
    a conversation with somebody,
  313. but actually, you don't cough
    or you don't sneeze
  314. or for some reason,
    the virus doesn't actually get across
  315. and expose the other person.
  316. And so, for this virus, as I mentioned,
  317. say people are having
    10 conversations a day,
  318. but we're not seeing infected people
    infect 10 others a day.
  319. So it suggests that not all
    of those opportunities
  320. are actually resulting
    in the virus getting across.
  321. CA: But people say
    that this is an infectious virus.

  322. Like, what is that transmission
    probability number,
  323. again, compared with, say, the flu?
  324. AK: So, we did some analysis
    looking at these very close gatherings.

  325. We looked at about 10
    different case studies,
  326. and we found that about a third
    of the contacts in those settings
  327. subsequently got infected
  328. in these early stages,
    when people weren't aware.
  329. So if you had these, kind of,
    big group meals,
  330. potentially, each contact
    had about, a kind of, one in three chance
  331. of getting exposed.
  332. For seasonal flu,
    that tends to be slightly lower,
  333. even within households
    and close settings,
  334. you don't necessarily
    get values that high.
  335. And even for something like SARS,
    those values have, kind of --
  336. the risk per interaction you had
  337. was lower than what we seem
    to be getting for coronavirus.
  338. Which intuitively makes sense,
  339. there must be a higher risk
    per interaction
  340. if this thing is spreading so easily.
  341. CA: Hm.

  342. OK, and then the fourth letter of DOTS
  343. is S for susceptibility.
  344. What's that?
  345. AK: So that is a measure of the proportion
    of the population who are susceptible.

  346. If you imagine you have
    this interaction with someone,
  347. the virus gets across, it exposes them,
  348. but some people may have been vaccinated
  349. or otherwise have some immunity
  350. and not develop infection themselves
  351. and not be infectious to others.
  352. So we've got to account for this
    potential proportion of people
  353. who are not actually
    going to turn into cases themselves.
  354. CA: And obviously, there's no vaccine yet
    for this coronavirus,

  355. nor is anyone, at least initially,
    immune, as far as we know.
  356. So are you modeling that
    susceptibility number pretty high,
  357. is that part of the issue here?
  358. AK: Yeah, I think the evidence

  359. is that this is going to fully
    susceptible populations,
  360. and even in areas,
    for example, like China,
  361. where there's been a lot of transmission
  362. but there's been very strong
    control measures,
  363. we estimated that up to
    the end of January,
  364. probably about 95 percent of Wuhan
    are still susceptible.
  365. So there's been a lot of infection,
  366. but it hasn't really taken much
    of that component,
  367. of the DOTS, of those four things
    that drive transmission.
  368. CA: And so the way the mathematics works,

  369. I have to confess, amidst the stress
    of this whole situation,
  370. the nerd in me kind of loves
    the elegance of the mathematics here,
  371. because I'd never really
    thought about it this way,
  372. but you basically just multiply
    those numbers together
  373. to get the reproduction number.
  374. Is that right?
  375. AK: Exactly, yeah,

  376. you almost take the path
    of the infection during transmission
  377. as you multiply those together,
  378. and that gives you
    the number for that virus.
  379. CA: And so there's just
    a total logic to that.

  380. It's the number of days,
    duration that you're infectious,
  381. it's the number of people
    you're seeing on average
  382. during those days
    that you have a chance to infect.
  383. Then you multiply that
    by the transmission probability,
  384. is virus getting into them, essentially,
  385. that's what you mean by crossing over.
  386. And then by the susceptibility number.
  387. By the way, what do you think
    the susceptibility probability is
  388. for this case?
  389. AK: I think we have to assume
    that it's near 100 percent

  390. in terms of spread, yeah.
  391. CA: Alright, you multiply
    those numbers together,

  392. and right now, it looks like,
    for this coronavirus,
  393. that you say two to three
    is the most plausible current number,
  394. which implies very rapid growth.
  395. AK: Exactly.

  396. In these uncontrolled outbreaks,
  397. we're seeing now a number
    of countries in this stage --
  398. you are going to get
    this really rapid growth occurring.
  399. CA: And so how does
    that two to three compare with flu?

  400. And I guess, there's seasonal flu,
  401. in the winter, when it's spreading,
  402. and at other times during the year
    drops well below one
  403. as a reproduction number, right?
  404. But what is it during seasonal flu time?
  405. AK: During the early stage
    when it's taking off

  406. at the start of the flu season,
  407. it's probably, we reckon,
    somewhere between about maybe 1.2, 1.4.
  408. So it's not incredibly transmissible,
  409. if you imagine you do have some immunity
    in your population from vaccination
  410. and from other things.
  411. So it can spread, it's above one,
  412. but it's not taking off, necessarily,
    as quickly as the coronavirus is.
  413. CA: So I want to come back
    to two of those elements,

  414. specifically opportunity
    and transmission probability,
  415. because those seem to have the most
    chance to actually do something
  416. about this infection rate.
  417. Before we go there,
  418. let's talk about another
    key number on this,
  419. which is the fatality rate.
  420. First of all, could you define --
  421. I think there's two different versions
    of the fatality rate
  422. that maybe confuse people.
  423. Could you define them?
  424. AK: So the one that we often talk about
    is what's known as the case fatality rate,

  425. and that's of the proportion who show up
    with symptoms as cases,
  426. what proportion of those
    will subsequently be fatal.
  427. And we also sometimes talk
    about what's known
  428. as the infection fatality rate,
  429. which is, of everyone who gets infected,
  430. regardless of symptoms,
  431. how many of those infections
    will subsequently be fatal.
  432. But most of the values
    we see kicking around
  433. are the case fatality rate, or the CFR,
    as it's sometimes known.
  434. CA: And so what is
    that fatality rate for this virus,

  435. and again, how does that compare
    with other pathogens?
  436. AK: So there's a few numbers
    that have been bouncing around.

  437. One of the challenges in real time
    is you often don't see all of your cases,
  438. you have people symptomatic
    not being reported.
  439. You also have a delay.
  440. If you imagine, for example,
  441. 100 people turn up to a hospital
    with coronavirus
  442. and none have died yet,
  443. that doesn't imply
    that the fatality rate is zero,
  444. because you've got to wait to see
    what might happen to them.
  445. So when you adjust for that
    underreporting and delays,
  446. best estimate for the case fatality
    is about one percent.
  447. So about one percent
    of people with symptoms,
  448. on average,
  449. those outcomes are fatal.
  450. And that's probably about 10 times
    worse than seasonal flu.
  451. CA: Yeah, so that's a scary
    comparison right there,

  452. given how many people die of flu.
  453. So when the World Health Organization
    mentioned a higher number,
  454. a little while back, of 3.4 percent,
  455. they were criticized a bit for that.
  456. Explain why that
    might have been misleading
  457. and how to think about it
    and adjust for that.
  458. AK: It's incredibly common that people
    look at these raw numbers,

  459. they say, "How many deaths
    are there so far, how many cases,"
  460. and they look at that ratio,
  461. and even a couple of weeks ago,
    that number produced a two percent value.
  462. But if you imagine
    you have this delay effect,
  463. then even if you stop all your cases,
  464. you will still have these kind of
    fatal outcomes over time,
  465. so that number will creep up.
  466. This has occurred in every epidemic
    from pandemic flu to Ebola,
  467. we see this again and again.
  468. And I made the point to a number of people
    that this number is going to go up,
  469. because as China's cases slow,
  470. it will look like it's increasing,
  471. and that's just kind of
    a statistical quirk.
  472. There's nothing really
    kind of, behind a change,
  473. there's no mutations or anything going on.
  474. CA: If I have this right,
    there are two effects going on.

  475. One is that the number of fatalities
  476. from the existing caseload will rise,
  477. which actually would boost
    that 3.4 even higher.
  478. But then you have to offset that
    against the fact that, apparently,
  479. huge numbers of cases
    have just gone undetected
  480. and that we haven't,
  481. due to bad testing,
  482. that the number of fatalities don't --
  483. they probably reflect
    a much larger number of early cases.
  484. Is that it?
  485. AK: Exactly.

  486. So you have one thing
    pulling the number up
  487. and one thing pulling it down.
  488. And it means that on these
    kind of early values,
  489. if you actually just adjust for the delay
  490. and don't think
    about these unreported cases,
  491. you start getting really
    very scary numbers indeed.
  492. You get up to 20, 30 percent potentially,
  493. which really doesn't align
  494. with what we know
    about this virus in general.
  495. CA: Alright.

  496. There's a lot more data in now.
  497. From your point of view,
    you think the likely fatality rate,
  498. at least in the earlier stage
    of an infection,
  499. is about two percent?
  500. AK: I think overall,

  501. I think we can put something probably
    in the 0.5 to two percent range,
  502. and that's on a number
    of different data sets.
  503. And that's for people who are symptomatic.
  504. I think on average, one percent
    is a good number to work with.
  505. CA: OK, one percent,

  506. So flu is often quoted
    as a tenth of a percent,
  507. so it's five to 10 times or more
    more dangerous than flu.
  508. And that danger is not symmetric
    across age groups,
  509. as is well known.
  510. It primarily affects the elderly.
  511. AK: Yeah, we've seen
    that one percent on average,

  512. but once you start getting
    into the over 60s, over 70s,
  513. that number really starts to shoot up.
  514. I mean, we're estimating
    potentially in these older groups,
  515. you're looking at maybe five,
    10 percent fatality.
  516. And then of course, on top of that,
  517. you've got to add what
    are going to be the severe cases
  518. and people are going to require
    hospitalization.
  519. And those risks get very large
    in the older groups indeed.
  520. CA: Adam, put these numbers
    together for us.

  521. In your models,
  522. if you put together
    a reproduction rate of two to three
  523. and a fatality rate
    of 0.5 percent to one percent
  524. and you run the simulation,
  525. what does it look like?
  526. AK: So if you have
    this uncontrolled transmission,

  527. and you have this reproduction
    number of two or three
  528. and you don't do anything about it,
  529. the only way the outbreak ends
  530. is enough people get it
    and immunity builds up
  531. and the outbreak kind of ends on its own.
  532. And in that case,
  533. you would expect very large numbers
    of the population to be infected.
  534. It's what we see, for example,
  535. with many other uncontained outbreaks,
  536. that it essentially burns
    through the population,
  537. you get large numbers infected
  538. and with this kind of fatality rate
    and hospitalization rate,
  539. that would really be hugely damaging
    if that were to occur.
  540. Certainly at the country level,
    we're seeing --
  541. Italy is a good example at the moment,
  542. that if you have that early
    transmission that's undetected,
  543. that rapid growth,
  544. you very quickly get to a situation
    where your health systems are overwhelmed.
  545. I think one of the nastiest
    aspects of this virus
  546. is that because you have the delay
    between infection and symptoms
  547. and people showing up in health care,
  548. if your health system is overwhelmed,
  549. even on that day,
  550. if you completely stop transmission,
  551. you've got all of these people
    who have already been exposed,
  552. so you're still going to have cases
    and severe cases appearing
  553. for maybe another couple of weeks.
  554. So it's really this huge
    accumulation of infection and burden
  555. that's coming through the system
    on your population.
  556. CA: So there's another
    key number, actually,

  557. is how does the total case number
  558. compare to the capacity
    of a country's health system
  559. to process that number of cases.
  560. Presumably that issue
    makes a huge difference
  561. to the fatality rate,
  562. the difference between people
    coming in with severe illness
  563. and a health system that's able
    to respond and one that's overwhelmed.
  564. The fatality rate is going to be
    very different at that point.
  565. AK: If someone requires an ICU bed,

  566. that's a couple of weeks
    they're going to require it for
  567. and you've got more cases
    coming through the system,
  568. so it very quickly gets very tough.
  569. CA: So talk about the difference
    between containment

  570. and mitigation.
  571. These are different terms
    that we're hearing a lot about.
  572. In the early stages of the virus,
    governments are focused on containment.
  573. What does that mean?
  574. AK: Containment is this idea
    that you can focus your effort on control

  575. very much on the cases and their contacts.
  576. So you're not causing disruption
    to the wider population,
  577. you have a case that comes in,
    you isolate them,
  578. you work out who they've come
    into contact with,
  579. who's potentially these
    opportunities for exposure
  580. and then you can follow up those people,
  581. maybe quarantine them to make sure
    that no further transmission happens.
  582. So it's a very focused, targeted method,
  583. and for SARS, it worked remarkably well.
  584. But I think for this infection,
  585. because some cases are going to be missed
    or undetected,
  586. you've really got to be capturing
    a large chunk of people at risk.
  587. If a few slip through the net,
  588. potentially, you're going
    to get an outbreak.
  589. CA: Are there any countries

  590. that have been able
    to employ this strategy
  591. and effectively contain the virus?
  592. AK: So Singapore have been doing
    a really remarkable job of this

  593. for the last six weeks or so.
  594. So as well as some wider measures,
  595. they've been working incredibly hard
  596. to trace people
    who have come into contact.
  597. Looking at CCTV,
  598. going through to find out
    which taxi someone might have gotten,
  599. who might be at risk --
  600. really, really thorough follow-up.
  601. And for about six weeks,
    that has kept a lid on transmission.
  602. CA: So that's amazing.

  603. So someone comes into the country,
  604. they test positive --
  605. they go to work, and with a massive team,
  606. and trace everything
  607. to the level of actually saying,
  608. "Oh, you don't know what taxi you went in?
  609. Let us find that out for you."
  610. And presumably,
    when they find the taxi driver,
  611. they then have to try and figure out
    everyone else who was in that taxi?
  612. AK: So they will focus on
    close contacts of people most at risk,

  613. but they're really minimizing the chance
    that anyone slips through the net.
  614. CA: But even in Singapore,
    if I'm not mistaken,

  615. numbers started to trend
    back down to zero,
  616. but I think recently,
    they've picked up again a bit.
  617. It's still unclear
  618. whether they will actually
    be able to sustain containment.
  619. AK: Exactly.

  620. If we talk in terms
    of the reproduction number,
  621. we saw it dipped to maybe 0.8, 0.9,
  622. so under that crucial value of one.
  623. But in the last week or two,
  624. it does seem to be ticking up
    and they're getting more cases appearing.
  625. I think a lot of it is,
  626. even if they are containing it,
  627. the world is experiencing outbreaks
  628. and just keeps throwing
    sparks of infection,
  629. and it becomes harder and harder
  630. with that level of intensive effort
    to stamp them all out.
  631. (Music)

  632. CA: In the case of this virus,

  633. you know, there was warning
    to most countries in the world
  634. that this thing was happening.
  635. The news out of China
    very quickly became very bleak,
  636. and people had time to prepare.
  637. I mean, what would ideal
    preparation look like
  638. if you know that something
    like this is coming
  639. and you know that there's
    a lot on the line
  640. if you can successfully contain it
    before it really escapes?
  641. AK: I think two things
    would make a really big difference.

  642. One is having as thorough a follow-up
    and detection as possible.
  643. We've done some modeling analyses,
  644. looking at how effective
    that kind of early containment is.
  645. And it can be, if you're identifying
    maybe 70 or 80 percent
  646. of the people who might have
    come into contact.
  647. But if you're not detecting
    those cases coming in,
  648. if you're not detecting their contacts --
  649. and a lot of the early focus, for example,
    was on travel history to China,
  650. and then it became clear
    that the situation was changing,
  651. but because you were relying on that
    as your definition of a case,
  652. it meant a lot of maybe other cases
    that matched the definition
  653. weren't being tested
  654. because they didn't seem
    to be potentially at risk.
  655. CA: So I mean, if you know
    that early detection is key to this,

  656. an essential early measure, I guess,
  657. would be to rapidly ensure
    that you had enough tests available
  658. and where needed,
  659. so that you could respond,
  660. be ready to swing into action
    as soon as someone was detected,
  661. you then have to very quickly,
    I guess, test their contacts and so forth,
  662. to have a chance
    of keeping this under control.
  663. AK: Exactly.

  664. In my line of work, we say
    there's value in a negative test,
  665. because it shows that you're looking
    for something and it's not there.
  666. And so I think having
    small numbers of people tested
  667. doesn't give you confidence
    that you're not missing infections,
  668. whereas if you are doing
    really thorough follow-up on contacts,
  669. we've seen countries even like Korea now,
  670. huge numbers of people tested.
  671. So although there are still
    cases appearing,
  672. it gives them more confidence
  673. that they have some sense
    of where those infections are.
  674. CA: I mean, you're in the UK right now,

  675. I'm in the US.
  676. How likely is it that the UK
    is going to be able to contain,
  677. how likely is it that the US
    is going to be able to contain this?
  678. AK: I think it's pretty
    unlikely in both cases.

  679. I think the UK is going to have
    to introduce some additional measures.
  680. I think when that happens
    obviously depends a bit
  681. on the current situation,
  682. but we've tested almost 30,000 people now.
  683. Frankly, I think the US
    may well be moving beyond that point,
  684. given how much evidence
    of extensive transmission that has,
  685. and I think without clear ideas
    of how much infection there is
  686. and that level of testing,
  687. it's quite hard to actually see
    what the picture currently is in the US.
  688. CA: I mean, I definitely don't want to get
    too political about this,

  689. but I mean, does this strike you as --
  690. you just said that the UK
    has tested 30,000 people --
  691. the US is five or six times bigger
  692. and I think the total number
    of tests here is five or six thousand,
  693. or it was a few days ago.
  694. Does that strike you as bizarre?
  695. I don't understand, honestly,
    how that happened in an educated country
  696. that has so much knowledge
    about infectious diseases.
  697. AK: It does,

  698. and I think there's obviously
    a number of factors playing in there,
  699. logistics and so on,
  700. but there has been that period of warning
  701. that this is a threat
    and this is coming in.
  702. And I think countries need to make sure
    that they've got the capacity
  703. to really do as much detection as they can
    in those early stages,
  704. because that's where
    you're going to catch it
  705. and that's where you're going to have
    a better chance of containing it.
  706. CA: OK, so if you fail to contain,

  707. then you have to move
    to some kind of mitigation strategy.
  708. So what comes into play there?
  709. And I think I almost want
    to bring that back
  710. to two of your DOTS factors,
  711. opportunity and transmission probability,
  712. because it seems like
    the virus is what it is,
  713. the actual duration when someone
    is potentially infectious,
  714. we can't do much about.
  715. The susceptibility side,
  716. we can't do much about
    until there's a vaccine.
  717. We could maybe talk about that in a bit.
  718. But the middle two of opportunity
    and transmission probability,
  719. we can do something about.
  720. Do you want to maybe talk
    about those in turn,
  721. of what that looks like,
  722. how would you build a mitigation strategy?
  723. I mean, first of all,
    thinking about opportunity,
  724. how do you reduce
    the number of opportunities
  725. to pass on the bug?
  726. AK: And so I think in that respect,

  727. it would be about massive changes
    in our social interactions.
  728. And if you think in terms
    of the reproduction number
  729. of being about two or three,
  730. to get that number below one,
  731. you've really got to cut
    some aspect of that transmission
  732. in half or in two-thirds
  733. to get that below one.
  734. And so that would require,
  735. of the opportunities
    that could spread the virus,
  736. so these kind of close contacts,
  737. everybody in the population, on average,
  738. will be needing to reduce
    those interactions
  739. potentially by two-thirds
    to bring it under control.
  740. That might be through working from home,
  741. from changing lifestyle
  742. and kind of where you go
    in crowded places and dinners.
  743. And of course, these measures,
    things like school closures,
  744. and other things
    that just attempt to reduce
  745. the social mixing of a population.
  746. CA: Well, actually, talk to me more
    about school closures,

  747. because that, if I remember,
  748. often in past pandemics has been cited
    as an absolutely key measure,
  749. that schools represent this sort of
    coming together of people,
  750. children are often --
  751. certainly when
    it comes to flu and colds --
  752. they're carriers.
  753. But on this case,
  754. children don't seem to be getting sick
    from this particular virus,
  755. or at least very few of them are.
  756. Do we know whether they
    can still be infectious?
  757. They can be the unintended carriers of it.
  758. Or actually, is there evidence
    that school closures
  759. may not be as important
    in this instance as it is in others?
  760. AK: So that point
    on what role children play

  761. is a crucial one,
  762. and there's still not
    a good evidence base there.
  763. From following up of contacts of cases,
  764. there's now evidence
    that children are getting infected,
  765. so when you're testing,
    they are getting exposed,
  766. it's not that somehow they're just
    not getting the infection at all,
  767. but as you said, they're not showing
    symptoms in the same way.
  768. And particularly for flu,
  769. when we see the implications
    of school closures,
  770. even in the UK in 2009 during swine flu,
  771. there was a dip in the outbreak
    during the school holidays,
  772. you could see it on the epidemic curve,
  773. it kind of comes back down in the summer
    and goes back up in the autumn.
  774. But of course, in 2009,
    there was some immunity in older groups.
  775. That kind of shifted more the transmission
    into the younger ones.
  776. So I think it's really something
    we're trying to work to understand.
  777. Obviously, it will reduce interactions,
    with school closures,
  778. but then there's knock-on social effects,
  779. there's potential
    knock-on changes in mixing,
  780. maybe grandparents and their role,
    in terms of alternative carers
  781. if parents have to work.
  782. So I think there's a lot of pieces
    that need to be considered.
  783. CA: I mean, based on all of the different
    pieces of evidence you've seen,

  784. if it were down to you,
  785. would you be recommending
    that most countries at this point
  786. do look hard at extensive school closures
    as a precautionary measure,
  787. that it's just worth it to do that
  788. as a sort of painful two,
    three, four, five-month strategy?
  789. What would you recommend?
  790. AK: I think the key thing,

  791. given the age distribution of risk
    and the severity in older groups
  792. is reduce interactions that bring
    the infection into those groups.
  793. And then amongst everyone else,
    reduce interactions as much as possible.
  794. I think the key thing is
  795. we've got so much of the disease burden
    in the kind of 60-plus group
  796. that it's not just about
    everyone trying to avoid
  797. everyone's interactions,
  798. but it's the kind of behaviors
  799. that would drive infections
    into those groups.
  800. CA: Does that mean
    that people should think twice

  801. before, I don't know, visiting a loved one
  802. in an old people's home
    or in a residential facility?
  803. Like that, we should just pay
    super special attention to that,
  804. should all these facilities
    be taking great care
  805. about who they admit,
  806. taking temperature and checking
    for symptoms or something like that?
  807. AK: I think those measures
    definitely need to be considered.

  808. In the UK, we're getting plans
  809. for potentially what's known
    as a cocooning strategy
  810. for these older groups
  811. that we can really try
    and seal off interactions
  812. as much as possible
  813. from people who might
    be bringing infection in.
  814. And ultimately, because as you said,
  815. we can't target these other
    aspects of transmission,
  816. it is just reducing the risk
    of exposure in these groups,
  817. and so I think anything
    at the individual level you can do
  818. to get people reducing their risk,
  819. if either they're elderly
    or in other risk groups,
  820. I think is crucial.
  821. And I think more at the general level
  822. those kind of more large-scale measures
    can help reduce interactions overall,
  823. but I think if those
    reductions are happening
  824. and not reducing the risk
  825. for people who are going
    to get severe disease,
  826. then you're still going to get
    this really remarkably severe burden.
  827. CA: I mean, do people have to almost
    apply this double lens

  828. as they think about this stuff?
  829. There's the risk to you
    as you go about your life,
  830. of you catching this bug.
  831. But there's also the risk
    of you being, unintentionally, a carrier
  832. to someone who would suffer
    much more than you might.
  833. And that both those things
    have to be top of mind right now.
  834. AK: Yeah, and it's not just
    whose hand you shake,

  835. it's whose hand that person
    goes on to shake.
  836. And I think we need to think
    about these second-degree steps,
  837. that you might think you have low risk
  838. and you're in a younger group,
  839. but you're often going to be
    a very short step away
  840. from someone who is going to get hit
    very hard by this.
  841. And I think we really need
    to be socially minded
  842. and think this could be quite dramatic
    in terms of change of behavior,
  843. but it needs to be
  844. to reduce the impact
    that we're potentially facing.
  845. CA: So the opportunity
    number, we bring down

  846. by just reducing the number
    of physical contacts we have
  847. with other people.
  848. And I guess the transmission
    probability number,
  849. how do we bring that down?
  850. That impacts how we interact.
  851. You mentioned hand-shaking,
  852. I'm guessing you're going to say
    no handshaking.
  853. AK: Yeah, so changes like that.

  854. I mean, another one, I think,
  855. handwashing operates in a way
  856. that we might be still be doing
    activities that we've previously done,
  857. but handwashing reduces the chance
    that from one interaction to another,
  858. we might be spreading infection,
  859. so it's all of these measures
  860. that mean that even
    if we're having these exposures,
  861. we're taking additional steps
    to avoid any transmission happening.
  862. CA: I still think most people
    don't fully understand

  863. or don't have a clear model of the pathway
  864. by which this thing spreads.
  865. So you think definitely people understand
  866. that you don't breathe in
  867. the water droplets of someone
    who has just coughed or sneezed.
  868. So how does it spread?
  869. It gets onto surfaces. How?
  870. Do people just breathe out
    and it goes on from people who are sick,
  871. they touch their mouth
    or something like that,
  872. and then touch a surface
    and it gets on that way?
  873. How does it actually get onto surfaces?
  874. AK: I think a lot of it would be
    that you cough in your hand

  875. and it ends up on a surface.
  876. But I think the challenge, obviously,
    is untangling these questions
  877. of how transmission happens.
  878. You have transmission in a household,
  879. and is it that someone coughed
    and it got on a surface,
  880. is it direct contact, is it a handshake,
  881. and even for things like flu,
  882. that's something that we work
    quite hard to try and unpick,
  883. how does social behavior correspond
    to infection risk.
  884. Because it's clearly important,
    but pinning it down is really tough.
  885. CA: It's almost like embracing the fact

  886. that for a lot of these things,
    we actually don't know
  887. and that we're all
    in this game of probabilities.
  888. Which, in a way, is why I think
    the math is so important here.
  889. That you have to think of this
    as these multiple numbers
  890. working together on each other,
  891. they all have their part to play.
  892. And any of them that you can
    take down by a percentage
  893. is likely contributing,
  894. not just to you but to everyone.
  895. And people don't actually know
    in detail how the numbers go together,
  896. but they know that they
    probably all matter.
  897. We almost need people to, somehow,
    you know, embrace that uncertainty
  898. and then try to get some satisfaction
    by acting on every single part of it.
  899. AK: I think it's this idea

  900. that if on average,
    you're infecting, say, three people,
  901. what's driving that and how can you
    chip away at that value?
  902. If you're washing your hands,
  903. how much might that chip away
    in terms of the handshakes,
  904. you know, you may have had virus
    and you no longer do,
  905. or if you are changing
    your social behavior in a certain way,
  906. is that taking away
    a couple of interactions,
  907. is that taking away half?
  908. How can you really chip into that number
    as much as you possibly can?
  909. CA: Is there anything else to say
    about how we could reduce

  910. that transmission probability
    in our interactions?
  911. Like, what is the physical distance
  912. that it's wise to stay away
    from other people if we can?
  913. AK: I think it's hard to pin down exactly,

  914. but I think one thing to bear in mind
    is that there's not so much evidence
  915. that this is a kind of aerosol
    and it goes really far --
  916. it's reasonably short distances.
  917. I don't think it's the case
  918. that you're sitting a few meters
    away from someone
  919. and the virus is somehow
    going to get across.
  920. It's in closer interactions,
  921. and it's why we're seeing
    so many transmission events
  922. occur in things like meals
    and really tight-knit groups.
  923. Because if you imagine
  924. that's where you can get
    a virus out and onto surfaces
  925. and onto hands and onto faces,
  926. and it's really situations like that
    we've got to think more about.
  927. CA: So in a way,

  928. some of the fears that people have
    may actually be overstated,
  929. like, if you're in the middle
    of an airplane
  930. and someone at the front sneezes,
  931. I mean, that's annoying,
  932. but it's actually not the thing
    you should be most freaked out about.
  933. There are much smarter ways
    to pay attention to your well-being.
  934. AK: Yeah, if it was measles
    and the plane was susceptible people,

  935. you would see a lot
    of infections after that.
  936. I think it is, bear in mind,
    that this is, on average,
  937. people infecting two or three others,
  938. so it's not the case of your
    maybe 50 interactions over a week,
  939. all of those people are at risk.
  940. But it's going to be some of them,
  941. particularly those close contacts,
  942. that are going to be
    where transmission's occurring.
  943. CA: So talk about,

  944. from a sort of national
    strategy point of view.
  945. There's a lot of talk about the need
    to "flatten the curve."
  946. What does that mean?
  947. AK: I think it refers to this idea
    that for your health systems,

  948. you don't want all your cases
    to appear at the same time.
  949. So if we sat back and did nothing
  950. and just let the epidemic grow,
  951. and you had this growth rate
    that, at the moment,
  952. in some places is looking like maybe
  953. three to four days,
    you're getting doubling.
  954. So every three or four days,
    the epidemic is doubling.
  955. It will skyrocket and you'll end up
  956. with a whole bunch
    of really severely ill people
  957. needing hospital care
    all at the same time,
  958. and you just won't have capacity for it.
  959. So the idea of flattening the curve
    is if we can slow transmission,
  960. if we can get that
    reproduction number down,
  961. then there may still be an outbreak,
  962. but it will be much flatter,
  963. it will be longer
  964. and there will be fewer
    severe cases showing up,
  965. which means that they can get
    the health care they need.
  966. CA: Does it imply that there will be
    fewer cases overall, or --

  967. When you look at the actual images
    of people showing
  968. what flattening the curve looks like,
  969. it almost looks as if you've got
    the same area still under the graph,
  970. i.e. that the same number of people,
    ultimately, are infected
  971. but over a longer period.
  972. Is that typically what happens,
  973. and even if you adopt
    all these strategies of social distancing
  974. and washing hands and etc.
  975. that the best you can hope for
    is that you slow the thing down,
  976. you actually will get
    as many people infected in the end?
  977. AK: Not necessarily --
    it depends on the measures that go in.

  978. There are some measures like,
    shutting down travel,
  979. which typically delay the spread
    rather than reduce it.
  980. So you're still going to get
    the same outbreaks,
  981. but you're stretching out the outbreaks.
  982. But there are other measures.
  983. If we talk about reducing interactions,
  984. if your reproduction number's lower,
  985. you would expect fewer cases overall.
  986. And eventually, in your population,
  987. you will get some buildup of immunity,
  988. which would help you out
    if you think about the components,
  989. reducing susceptibility,
  990. alongside what's going on elsewhere.
  991. So the hope is that the two things
    will work together.
  992. CA: So help me understand
    what the endgame is here.

  993. So, take China, for example.
  994. Whatever you make
    of the early suppression of data
  995. and so forth
  996. that seems pretty troubling there.
  997. The intensity of the response
    come January time or whatever,
  998. with the shutdown
    of this huge area of the country,
  999. seems to have actually been effective.
  1000. The number of cases there are falling
    at a shockingly high rate in some ways.
  1001. Falling to almost nothing.
  1002. And I can't understand that.
  1003. You are talking about a country
    of, whatever, 1.4 billion people.
  1004. There have been a huge
    number of cases there,
  1005. but it was a tiny fraction
    of the population have actually got sick.
  1006. And yet, they've got the number way down.
  1007. It's not like every other person in China
    has somehow developed immunity.
  1008. Is it that they have been
    absolutely disciplined
  1009. about shutting down travel
    from the infected regions
  1010. and somehow really dialed up,
    massively dialed up
  1011. testing at any sign of any problem,
  1012. so that literally, they are back
    in containment mode
  1013. in most parts of China?
  1014. I can't get my head around it,
    help me understand it.
  1015. AK: So we estimated,
    in the last two weeks of January,

  1016. when these measures went in,
  1017. the reproduction number
    went from about 2.4 to 1.1.
  1018. So about 60 percent decline
    in transmission
  1019. in the space of a week or two.
  1020. Which is remarkable and really,
  1021. a lot of it is likely to be driven
    by just fundamental change
  1022. in social behavior,
  1023. huge social distancing,
  1024. really intensive follow-up,
    intensive testing.
  1025. And it got to the point
  1026. where it took enough
    off the reproduction number
  1027. to cause the decline,
  1028. and now, of course,
    we're seeing, in many areas,
  1029. a transition back to more
    of this kind of containment,
  1030. because there's few cases,
    it's more manageable.
  1031. But we're also seeing them
    face a challenge,
  1032. because a lot of these cities
    have basically been locked down
  1033. for six weeks
  1034. and there's a limit to how long
    you can do that for.
  1035. And so some of these measures
    are gradually starting to be lifted,
  1036. which of course creates the risk
  1037. that cases that are appearing
    from other countries
  1038. may subsequently go in
    and reintroduce transmission.
  1039. CA: But given how infectious the bug is,

  1040. and how many theoretical pathways
    and connection points there are
  1041. between people in Wuhan, even in shutdown,
  1042. or relatively shut down,
  1043. or the other places
    where there's been some infection
  1044. and the rest of the country,
  1045. does it surprise you how quickly
    that curve has gone down to nearly zero?
  1046. AK: Yes.

  1047. Early on when we saw
    that flattening off in cases
  1048. in those first few days,
  1049. we did wonder whether it was just
    they hit a limit in testing capacity
  1050. and they were reporting 1,000 a day,
  1051. because that's all the kits they had.
  1052. But it continued, thankfully,
  1053. and it shows that it is possible
    to turn this over
  1054. with that level of intervention.
  1055. I think the key thing now
    is seeing how it works in other settings.
  1056. Italy now are putting in
    really dramatic interventions.
  1057. But of course,
    because of this delay effect,
  1058. if you put them in today,
  1059. you won't necessarily see
    the effects on cases
  1060. for another week or two.
  1061. So I think working out
    what impact that's had
  1062. is going to be key for helping
    other countries
  1063. work on how to contain this.
  1064. CA: To have a picture, Adam,

  1065. of how this is likely to play out
    over the next month or two,
  1066. give us a couple of scenarios
    that are in your head.
  1067. AK: I think the optimistic scenario

  1068. is that we're going to learn a lot
    from places like Italy
  1069. that have unfortunately
    been hit very hard.
  1070. And that countries are going to take
    this very seriously
  1071. and that we're not going to get
    this continued growth
  1072. that's going to overwhelm totally,
  1073. that we're going to be able
    to sufficiently slow it down,
  1074. that we are going to get
    large numbers of cases,
  1075. we're probably going to get
    a lot of severe cases,
  1076. but that will be more manageable,
  1077. that's the kind of optimistic scenario.
  1078. I think if we have a point
  1079. where countries either
    don't take this seriously
  1080. or populations don't respond well
    to control measures
  1081. or it's not detected,
  1082. we could get situations --
  1083. I think Iran is probably
    the closest one at the moment --
  1084. where there's been extensive
    widespread transmission,
  1085. and by the time it's being responded to,
  1086. those infections are already in the system
  1087. and they are going to turn up
    as cases and severe illness.
  1088. So I'm hoping we're not at that point,
  1089. but we've certainly got, at the moment,
  1090. potentially about 10 countries
    on that trajectory
  1091. to have the same outlook as Italy.
  1092. So it's really crucial what happens
    in the next couple of weeks.
  1093. CA: Is there a real chance
    that quite a few countries

  1094. end up having, this year,
  1095. substantially more deaths from this virus
    than from seasonal flu?
  1096. AK: I think for some countries
    that is likely, yeah.

  1097. I think if control is not possible,
  1098. and we've seen it happen in China,
  1099. but that was really just an unprecedented
    level of intervention.
  1100. It was really just changing
    the social fabric.
  1101. I think people, many of us,
    don't really appreciate, at a glance,
  1102. just what that means,
  1103. to reduce your interactions
    to that extent.
  1104. I think many countries just simply
    won't be able to manage that.
  1105. CA: It's almost a challenge
    to democracies, isn't it --

  1106. "OK, show us what you can do
    without that kind of draconian control.
  1107. If you don't like the thought of that,
  1108. come on, citizens, step up,
    show us what you're capable of,
  1109. show that you can be wise about this
  1110. and smart and self-disciplined,
  1111. and get ahead of the damn bug."
  1112. AK: Yeah.

  1113. CA: I mean, I'm not personally
    superoptimistic about that,

  1114. because there's such conflicting messaging
    coming out in so many different places,
  1115. and people don't like
    to short-term sacrifice.
  1116. I mean, is there almost a case that --
  1117. I mean, what's your view
  1118. on whether the media has played
    a helpful role here
  1119. or an unhelpful role?
  1120. Is it actually, in some ways, helpful
  1121. to, if anything, overstate
    the concern, the fear,
  1122. and actually make people
    panic a little bit?
  1123. AK: I think it's a really
    tough balance to strike,

  1124. because of course, early on,
    if you don't have cases,
  1125. if you don't have any evidence
    of potential pressure,
  1126. it's very hard to get that message
    and convince people to take it seriously
  1127. if you're overhyping it.
  1128. But equally, if you're waiting too long,
  1129. and saying it's not a concern yet,
    we're OK for the moment,
  1130. a lot of people think it's just flu.
  1131. By the time it hits hard, as I've said,
  1132. you're going to have weeks
    of an overburdened health system,
  1133. because even if you take interventions,
  1134. it's too late to control the infections
    that have happened.
  1135. So I think it's a fine line,
  1136. and my hope is there is
    this ramp-up in messaging,
  1137. now people have these
    tangible examples like Italy,
  1138. where they can see what's going to happen
    if they don't take it seriously.
  1139. But certainly, of all
    the diseases I've seen,
  1140. I think many of my colleagues
    who are much older than me
  1141. and have memories of other outbreaks,
  1142. it's the scariest thing we've seen
    in terms of the impact it could have,
  1143. and I think we need to respond to that.
  1144. CA: It's the scariest disease you've seen.

  1145. Wow.
  1146. I've got some questions for you
    from my friends on Twitter.
  1147. Everyone is obviously
    super exercised about this topic.
  1148. Hypothetically,
  1149. if everyone stayed home for three weeks,
  1150. would that effectively wipe this out?
  1151. Is there a way to socially
    distance ourselves out of this?
  1152. AK: Yeah, I think in certain countries
    with reasonably small household sizes,

  1153. I think average in the UK, US
    is about two and a half,
  1154. so even if you had a round of infection
    within the household,
  1155. that would probably stamp it out.
  1156. As a secondary benefit,
  1157. you may well stamp out
    a few other infections, too.
  1158. Measles only circulates in humans,
  1159. so you may have some knock-on effect,
  1160. if, of course, that were
    ever to be possible.
  1161. CA: I mean, obviously that would be
    a huge dent to the economy,

  1162. and this is in a way almost, like,
    one of the underlying challenges here
  1163. is that you can't optimize public policy
  1164. for both economic health
    and fighting a virus.
  1165. Like, those two things are,
    to some extent, in conflict,
  1166. or at least, short-term
    economic health and fighting a virus.
  1167. Those two things are in conflict, right?
  1168. And societies need to pick one.
  1169. AK: It is tough to convince
    people of that balance,

  1170. the thing we always say
    of pandemic planning
  1171. is it's cheap to put
    this stuff in place now --
  1172. otherwise, you've got to pay for it later.
  1173. But unfortunately,
    as we've seen with this,
  1174. that a lot of early money
    for response wasn't there.
  1175. And it's only when it has an impact
    and when it's going to get expensive
  1176. that people are happy to take
    that cost on board, it seems.
  1177. CA: OK, some more Twitter questions.

  1178. Will the rising temperature
    in coming weeks and months
  1179. slow down the COVID-19 spread?
  1180. AK: I haven't seen any convincing evidence

  1181. that there's that strong pattern
    with temperature,
  1182. and we've seen it for other infections
    that there is this seasonal pattern,
  1183. but I think the fact
    we're getting widespread outbreaks
  1184. makes it hard to identify, and of course,
  1185. there's other things going on.
  1186. So even if one country doesn't have
    as big an outbreak as another,
  1187. that's going to be influenced
    by control measures,
  1188. by social behavior, by opportunities
    and these things as well.
  1189. So it would be really reassuring
    if this was the case,
  1190. but I don't think
    we can say that just yet.
  1191. CA: Continuing from Twitter,

  1192. I mean, is there a standardized
    global recommendation
  1193. for all countries
  1194. on how to do this?
  1195. And if not, why not?
  1196. AK: I think that's what people
    are trying to piece together,

  1197. first in terms of what works.
  1198. It's only really in the last
    sort of few weeks
  1199. we've got a sense that this thing
    can be controllable
  1200. with this extent of interventions,
  1201. but of course, not all countries
    can do what China have done,
  1202. some of these measures
  1203. incur a huge social, economic,
    psychological burden
  1204. on populations.
  1205. And of course, there's the time limit.
  1206. In China they've had
    them in for six weeks
  1207. it's tough to maintain that,
  1208. so we need to think of these tradeoffs
  1209. of all the things we can ask people to do,
  1210. what's going to have the most impact
    on actually reducing the burden.
  1211. CA: Another question:

  1212. How did this happen
    and is it likely to happen again?
  1213. AK: So it's likely that this originated
    with the virus that was circling in bats

  1214. and then probably made its way
    through another species
  1215. into humans somehow,
  1216. there's a lot of bits
    of evidence around this,
  1217. there's not kind of single, clear story,
  1218. but even for SARS, it took several years
  1219. for genomics to piece together
    the exact route that it happened.
  1220. But certainly, I think it's plausible
    that it could happen again.
  1221. Nature is throwing out
    these viruses constantly.
  1222. Many of them aren't
    well-adapted to humans,
  1223. don't pick up,
  1224. you know, there may well have been
    a virus like this a few years ago
  1225. that just happened to infect someone
  1226. who just didn't have any contacts
    and didn't go any further.
  1227. I think we are going to face these things
  1228. and we need to think
    about how can we get in early
  1229. at the stage where we're talking
    small numbers of cases,
  1230. and even something like this
    is containable,
  1231. rather than the situation we've got now.
  1232. CA: It seems like
    this isn't the first time

  1233. that a virus seems to have emerged
    from, like, a wild meat market.
  1234. That's certainly how
    it happens in the movies. (Laughs)
  1235. And I think China has already taken
    some steps this time
  1236. to try to crack down on that.
  1237. I guess that's potentially
    quite a big deal for the future
  1238. if that can be properly maintained.
  1239. AK: It is, and we saw, for example,

  1240. the H7N9 avian flu,
  1241. over the last few years, in 2013,
    it was a big emerging concern,
  1242. and China made a very extensive response
  1243. in terms of changing
    how they operate their markets
  1244. and vaccination of birds
  1245. and that seems
    to have removed that threat.
  1246. So I think these measures can be effective
    if they're identified early on.
  1247. CA: So talk about vaccinations.

  1248. That's the key measure, I guess,
  1249. to change that susceptibility
    factor in your equation.
  1250. There's obviously a race on
    to get these vaccinations out there,
  1251. there are some candidate
    vaccinations there.
  1252. How do you see that playing out?
  1253. AK: I think there's certainly some
    promising development happening,

  1254. but I think the timescales of these things
  1255. are really on the order
    of maybe a year, 18 months
  1256. before these things be widely available.
  1257. Obviously, a vaccine has to go
    through these stages of trials,
  1258. that takes time,
    so even if by the end of the year,
  1259. we have something
    which is viable and works,
  1260. we're still going to see a delay
    before everyone can get ahold of it.
  1261. CA: So this really puzzles me, actually,

  1262. and I'd love to ask you
    as a mathematician about this as well.
  1263. There are already several companies
  1264. believing that they have
    plausible candidate vaccines.
  1265. As you say, the process
    of testing takes forever.
  1266. Is there a case that we're not
    thinking about this right
  1267. when we're looking at the way
    that testing is done
  1268. and that the safety calculations are made?
  1269. Because it's one thing
    if you're going to introduce
  1270. a brand new drug or something --
  1271. yes, you want to test to make sure
    that there are no side effects,
  1272. and that can take a long time
  1273. by the time you've done all
    the control trials and all the rest of it.
  1274. If there's a global emergency,
  1275. isn't there a case,
  1276. both mathematically and ethically,
  1277. that there should just be
    a different calculation,
  1278. the question shouldn't be
  1279. "Is there any possible case
    where this vaccine can do harm,"
  1280. the question surely should be,
  1281. "On the net probabilities,
  1282. isn't there a case
    to roll this out at scale,
  1283. to have a shot at nipping
    this thing in the bud?"
  1284. I mean, what am I missing
    in thinking that way?
  1285. AK: I mean, we do see that
    in other situations,

  1286. for example, the Ebola vaccine in 2015
  1287. showed, within a few months,
    very promising evidence
  1288. and interim results of the trial in humans
  1289. showed what seemed very high efficacy.
  1290. And even though
    it hadn't been licensed fully,
  1291. it was employed for what is known
    as compassionate use
  1292. in subsequent other outbreaks.
  1293. So there are these mechanisms
  1294. where vaccines can be
    fast-tracked in this way.
  1295. But of course, we're currently
    in a situation where we have no idea
  1296. if these things will do anything at all.
  1297. So I think we need
    to accrue enough evidence
  1298. that it could have an impact,
  1299. but obviously, fast-track that
    as much as possible.
  1300. CA: But the skeptic in me
    still doesn't fully get this.

  1301. I don't understand
  1302. why there isn't more energy
    behind bolder thinking on this.
  1303. Everyone seems, despite the overall risk,
  1304. incredibly risk-averse
    about how to build the response to it.
  1305. AK: So with the caveat that,

  1306. yeah, there's a lot
    of good questions on this,
  1307. and some of them are slightly
    outside my wheelhouse,
  1308. but I agree that we need to do more
    to get timescales out.
  1309. The example I always quote
  1310. is it takes us six months
    to choose a seasonal flu strain
  1311. and get the vaccines out there to people.
  1312. We always have to try and predict ahead
    which strains are going to be circulating.
  1313. And that's for something
    we know how to make
  1314. and has been manufactured for a long time.
  1315. So there is definitely more
    that needs to be done
  1316. to get these timescales shorter.
  1317. But I think we do have to balance that,
  1318. especially if we're exposing
    large numbers of people to something
  1319. to make sure that we're confident
    it's safe
  1320. and that it's going to have
    some benefit, potentially.
  1321. CA: And so, finally,

  1322. Adam, I guess going into this --
  1323. There's another set of infectious things
    happening around the world
  1324. at the same time,
  1325. which is ideas and the communication
    around this thing.
  1326. They really are two very dynamic,
    interactive systems of infectiousness --
  1327. there's some very damaging
    information out there.
  1328. Is it fair to think of this as battle
    of credible knowledge and measures
  1329. against the bug,
  1330. and just bad information --
  1331. You know, part of what
    we have to think about here
  1332. is how to suppress one set of things
    and boost the other, actually,
  1333. turbocharge the other.
  1334. How should we think of this?
  1335. AK: I think we can definitely think of it
    almost as competition for our attention,

  1336. and we see similarly, with diseases,
  1337. you have viruses competing
    to infect susceptible hosts.
  1338. And I think we're now seeing,
  1339. I guess over the last few years
    with fake news and misinformation
  1340. and the emergence of awareness,
  1341. more of a transition
  1342. to thinking about how do we
    reduce that susceptibility
  1343. if we have people that can be
    in these different states,
  1344. how can we try and preempt
    better with information.
  1345. I think the challenge
    for an outbreak is obviously,
  1346. early on, we have
    very little good information,
  1347. and it's very easy for certainty
    and confidence to fill that vacuum.
  1348. And so I think that is something --
  1349. I know platforms are working
    on how can we get people exposed
  1350. to good information earlier,
  1351. so hopefully protect them
    against other stuff.
  1352. CA: One of the big unknowns to me
    in the year ahead --

  1353. let's say that the year ahead includes
    many, many more weeks,
  1354. for many people,
  1355. of actually self-isolating.
  1356. Those of us who are lucky enough
    to have jobs where you can do that.
  1357. You know, staying home.
  1358. By the way, the whole injustice
    of this situation,
  1359. where so many people can't do that
    and continue to make a living,
  1360. is, I'm sure, going to be
    a huge deal in the year ahead
  1361. and if it turns out that death rates
    are much higher in the latter group
  1362. than in the former group,
  1363. and especially in a country like the US,
  1364. where the latter group
    doesn't even have proper health insurance
  1365. and so forth.
  1366. That feels like right there,
    that could just become a huge debate,
  1367. hopefully a huge source
    of change at some level.
  1368. AK: I think that's an incredibly
    important point,

  1369. because it's very easy --
  1370. I similarly have a job
    where remote working is fairly easy,
  1371. and it's very easy to say
    we should just stop social interactions,
  1372. but of course, that could have
    an enormous impact on people
  1373. and the choices and the routine
    that they can have.
  1374. And I think those do need
    to be accounted for,
  1375. both now and what the effect
    is going to look like
  1376. a few months down the line.
  1377. CA: When all's said and done,

  1378. is it fair to say that the world has
    faced, actually, much graver problems
  1379. in the past,
  1380. that on any scenario,
  1381. it's highly likely that at some point
    in the next 18 months, let's say,
  1382. a vaccine is there and starts
    to get wide distribution,
  1383. that we will have learned
    lots of other ways to manage this problem?
  1384. But at some point, next year probably,
  1385. the world will feel like
    it's got on top of this
  1386. and can move on.
  1387. Is that likely to be it,
  1388. or is this more likely to be,
    you know, it escapes,
  1389. it's now an endemic nightmare
    that every year picks off far more people
  1390. than are picked off by the flu currently.
  1391. What are the likely ways forward,
  1392. just taking a slightly longer-term view?
  1393. AK: I think there's plausible ways
    you could see all of those

  1394. potentially playing out.
  1395. I think the most plausible is probably
    that we'll see very rapid growth this year
  1396. and lots of large outbreaks
    that don't recur, necessarily.
  1397. But there is a potential
    sequence of events
  1398. that could end up with these kind of
    multiyear outbreaks in different places
  1399. and reemerging.
  1400. But I think it's likely we'll see
  1401. most transmission concentrated
    in the next year or so.
  1402. And then, obviously,
    if there's a vaccine available,
  1403. we can move past this,
    and hopefully learn from this.
  1404. I think a lot of the countries
    that responded very strongly to this
  1405. were hit very hard by SARS.
  1406. Singapore, Hong Kong,
    that really did leave an impact,
  1407. and I think that's something
    they've drawn on very heavily
  1408. in their response to this.
  1409. CA: Alright.

  1410. So let's wrap up maybe by just
    encouraging people
  1411. to channel their inner mathematician
  1412. and especially think
    about the opportunities
  1413. and the transmission probabilities
    that they can help shift.
  1414. Just remind us of the top
    three or four or five or six things
  1415. that you would love to see people doing.
  1416. AK: I think at the individual level,
    just thinking a lot more

  1417. about your interactions
    and your risk of infection
  1418. and obviously, what gets onto your hands
  1419. and once that gets onto your face,
  1420. and how do you potentially
    create that risk for others.
  1421. I think also, in terms of interactions,
  1422. with things like handshakes
    and maybe contacts you don't need to have.
  1423. You know, how can we get those down
    as much as possible.
  1424. If each person's giving it
    to two or three others,
  1425. how do we get that number
    down to one, through our behavior.
  1426. And then it's likely that we'll need
    some larger-scale interventions
  1427. in terms of gatherings, conferences,
  1428. other things where
    there's a lot of opportunities
  1429. for transmission.
  1430. And really, I think that combination
    of that individual level,
  1431. you know, if you're ill
    or potentially you're going to get ill,
  1432. reducing that risk,
  1433. but then also us working together
  1434. to prevent it getting
    into those groups who,
  1435. if it continues to be uncontrolled,
  1436. could really hit some people
    very, very hard.
  1437. CA: Yeah, there's a lot of things

  1438. that we may need
    to gently let go of for a bit.
  1439. And maybe try to reinvent
    the best aspects of them.
  1440. Thank you so much.

  1441. If people want to keep up with you,
  1442. first of all, they can follow you
    on Twitter, for example.
  1443. What's your Twitter handle?
  1444. AK: So @AdamJKucharski, all one word.

  1445. CA: Adam, thank you so much
    for your time, stay well.

  1446. AK: Thank you.

  1447. (Music)

  1448. CA: Associate professor
    and TED Fellow Adam Kucharski.

  1449. We'd love to hear what you think
    of this bonus episode.
  1450. Please tell us by rating
    and reviewing us in Apple Podcasts
  1451. or your favorite podcast app.
  1452. Those reviews are influential, actually.
  1453. We certainly read every one,
  1454. and truly appreciate your feedback.
  1455. (Music)

  1456. This week's show was produced
    by Dan O'Donnell at Transmitter Media.

  1457. Our production manager
    is Roxanne Hai Lash,
  1458. our fact-checker Nicole Bode.
  1459. This episode was mixed by Sam Bair.
  1460. Our theme music
    is by Allison Layton-Brown.
  1461. Special thanks to my colleague
    Michelle Quint.
  1462. Thanks for listening to the TED Interview.

  1463. We'll be back later this spring
  1464. with a whole new season's worth
    of deep dives with great minds.
  1465. I hope you'll enjoy them
    whether or not life is back to normal.
  1466. I'm Chris Anderson,

  1467. thanks for listening and stay well.