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  1. hello and welcome you are watching
  2. lockdown TV this is the pop up news and
  3. discussion program from unheard and
  4. during these strange and concerning
  5. lockdown weeks we have been trying to
  6. bring interesting and brilliant brains
  7. to you from around the world to give
  8. alternative perspectives on what is
  9. actually happening you've spoken to
  10. scientists thinkers political people and
  11. today I am delighted that we have a very
  12. brilliant person called professor
  13. Michael let it by way of introduction
  14. and you are a professor of computer
  15. science and structural biology at the
  16. University of Stanford based at the
  17. Medical School and you were winner of
  18. the 2013 Nobel Prize for chemistry for
  19. work on computer models for complex
  20. chemical interactions and since January
  21. you have been taking a close interest in
  22. the corona virus pandemic so let's just
  23. dive straight in basically so what well
  24. the rest of the world and many of us
  25. hadn't even really noticed the corona
  26. virus issue
  27. you were already following the numbers
  28. very closely coming out of China tell us
  29. about that so basically my wife is
  30. actually a curator of Chinese art and as
  31. a result we spend quite a lot of time in
  32. China we had been there until the end of
  33. November last year and we have lots of
  34. friends there when the outbreak started
  35. today in Wuhan I think I first heard
  36. about a number 20th of January which was
  37. just a form of the Chinese Spring
  38. Festival or new year we were very
  39. concerned for our friends we wrote them
  40. a kind of consoling message in the
  41. Chinese equivalent of Facebook and
  42. what's up called WeChat wishing and
  43. basically they were very excited to get
  44. some news from out of the world so I
  45. then decided that maybe I should use my
  46. skills with numbers to look at the
  47. numbers I first compared when this is
  48. probably 25th or 22
  49. of January by that time we had a but I
  50. would guess a few thousand cases already
  51. maybe a hundred or so deaths and solid
  52. initially just to compare coronavirus to
  53. to the society which were already scared
  54. about and it was clear that coronavirus
  55. was growing much much fossil and the
  56. very first thing I noticed even before I
  57. started looking at the numbers China
  58. actually had two separate outbreaks in
  59. those days we looked at the numbers from
  60. Chinese websites I can't be Chinese but
  61. numbers are fine and what we saw is that
  62. the outbreak in Hebei Province was had a
  63. much much higher death rate and the
  64. outbreak everywhere else in China by
  65. about a factor of 15 so that was scary
  66. most of our friends were not included
  67. they were in Shanghai in Beijing and so
  68. on so the first thing I taught it out is
  69. not the death rate in goodbay was about
  70. four or five percent whereas outside who
  71. Bay was one tenth or even 115 to that
  72. when I started to get more into the
  73. numbers and on the second of February I
  74. had been playing with itself and look at
  75. the numbers and saw that the rate of
  76. increase of digits were slowing down
  77. this is a very easy thing to do if you
  78. take the number of deaths today the
  79. total number let's that's basically a 25
  80. percent increase between 80 and 100 but
  81. if the day afterwards the increase is no
  82. longer 25% let's say 22% in the day are
  83. 18 percent you start to feel knees break
  84. break to run on bogus pre exponential
  85. growth the number should be the same
  86. every day it should be 15 percent 15
  87. percent 15 percent or were an even
  88. number it's growing out so the slowdown
  89. of the ratio of today divided by
  90. yesterday either for cases or for deaths
  91. indicates slowing on the Twitter on the
  92. 2nd of February I noticed that there
  93. were four days in a row when they being
  94. steady linear decrease in the ratio of
  95. yesterday and
  96. basically wrote a reach cap message like
  97. my wife potential the same people our
  98. friends saying that things looked like
  99. it was slowing down we then got on a
  100. plane and flew from Israel to New York
  101. City and when I learned all these emails
  102. from me saying are you the Michael
  103. Edwards who posted this message in
  104. Chinese saying that everything is
  105. slowing down and what had happened is
  106. that somebody I don't even know who had
  107. taken the two pages of PDF that I'd sent
  108. them had it translated into Chinese and
  109. actually posted it on the Facebook
  110. equivalent or the Twitter equivalent in
  111. China in English and there were already
  112. two million views of it see you then so
  113. there's paying attention every single
  114. day thereafter so each day the total
  115. number of cases is always increasing but
  116. the number of new cases increases and
  117. then gets to a maximum
  118. it gets to a maximum and then decreases
  119. at the same rate now the epidemics both
  120. in China in today in China a star Thuban
  121. I didn't I kept on looking at them
  122. separately those behaves very well they
  123. basically got to their maximum started
  124. slowing down so it was relatively easy
  125. maybe by the 15th of February to predict
  126. the total discounts for the epidemic
  127. inside the bay as well as the outside
  128. could be it was about 3,000 inside Hubei
  129. and about 120 outside who Bay so that
  130. was basically how things were but so so
  131. you noticed that the curve was less of a
  132. sort of exponential curve than we might
  133. have feared in those early days some
  134. ways there was never any exponential
  135. growth from the minute I looked at it
  136. there were never any two days that had
  137. exactly the same growth rate and they
  138. were getting slow of course you could
  139. have non exponential growth where every
  140. single day they're getting more than
  141. exponential but the growth was always
  142. subjects financial so that's the first
  143. step
  144. so because we talk of at least over here
  145. we talk endlessly about the our rate the
  146. reproduction rate and apparently that
  147. began very high maybe as high as 3 and
  148. now is down we've got it down below 1 in
  149. the UK intuitively if there's a high
  150. reproduction rate you should see that
  151. exponential curve just going up and up
  152. well no wait okay so let's just see this
  153. the r0 which is very popular is it is a
  154. summers I think a faulted number let me
  155. explain why the rate of growth dust it
  156. depends on R 0 it depends on R 0 and the
  157. time you infectious so if you have twice
  158. as long infectious and half the R 0
  159. you'll get exactly the same growth rate
  160. and this is sort of intuitive but it's
  161. not explained and therefore it seems to
  162. me that I I would say at the present
  163. time in a lot of I think i 0 became so
  164. important a lot of movies it was very
  165. popular talked about R 0
  166. didn't the other just talk about us
  167. but looking at all the mathematics you
  168. have to specify the time infectious at
  169. the same time to have any meaning and
  170. the other problem is is that r0
  171. decreases we don't know why our zero
  172. decreases it could be social distancing
  173. it could be prior to immunity it could
  174. be in cases that you've been observing
  175. the shapes of these curves and how the
  176. our number tends to come down and the
  177. curve tends to flatten in some kind of
  178. natural way regardless of intervention
  179. is that what you observe
  180. we don't know I think the big test is
  181. going to be Sweden Sweden is practicing
  182. a level of social distancing that is
  183. keeping children in schools you can
  184. people at work they are obviously having
  185. more deaths in countries like Israel or
  186. Austria that are practicing very very
  187. strict social distancing but I think it
  188. is not a crazy policy the reason I felt
  189. that social distancing was unimportant
  190. is that I had two examples in China to
  191. start with and then we had the
  192. additional examples the first one was
  193. Korea yeah and it's the beginning of all
  194. the epidemics sure the slowing down and
  195. it was very hard for me to believe there
  196. those three countries could be social
  197. distancing as well as China China was
  198. amazing especially outside who Bay in
  199. that they had no additional outbreaks
  200. people left who they were very carefully
  201. tracked had to wear face masks all the
  202. time
  203. had to take their temperatures all the
  204. time and it was no further outbreaks so
  205. this did not happen in either in South
  206. Korea or in Italy or mini run now two
  207. months later something else suggest that
  208. social distancing might not be important
  209. and that is that the total number of
  210. just we're seeing in New York City in
  211. parts of England in parts of France
  212. in northern Italy all seem to stop at
  213. about the same direction of the
  214. population so are they all practicing
  215. equally good social distancing I don't
  216. think so
  217. the problem I think is outbreaks
  218. occurring in different regions I think
  219. social distance thing that stops people
  220. moving from London to Manchester is
  221. probably a really good idea my feeling
  222. is is that in London
  223. and in New York City all the people who
  224. got infected or got infected before
  225. anybody noticed there's no way that the
  226. infection grew so quickly in New York
  227. City without the infection spreading
  228. very quickly so one of the key things is
  229. to stop people know that they're sick
  230. exactly others and here again
  231. China has three very very important
  232. advantages that are not high-tech that
  233. don't involve security tracking of
  234. telephones what they involve is number
  235. one the tradition in China four years of
  236. wearing a face mask when you're sick as
  237. soon as the coronavirus started
  238. everybody wore a face mask it doesn't
  239. have to be hygienic face mask it just
  240. has to be face covering that stops you
  241. spraying saliva micro droplets of
  242. survivor saliva and somebody who talked
  243. to you the second thing in China is that
  244. because the SARS epidemic they were so
  245. scared of it in most airports stations
  246. will you pay tolls etc there are
  247. thermometers infrared thermometers that
  248. measure your temperature so having your
  249. temperature measured at every single
  250. store entrance either with a handheld
  251. thermometer or with something about her
  252. in the wall is something completely
  253. standard in China and the third thing is
  254. is that almost all payments in China are
  255. made not using a credit card so in some
  256. senses it is very very much easier there
  257. to practice the social distancing of
  258. course in in addition they know where
  259. people are what's your view of the
  260. lockdown policy
  261. European countries and states in America
  262. have introduced I think it is the huge
  263. mistake I think we need smart lockdown
  264. if we were to do this again we would
  265. probably insist on face masks hand
  266. sanitizers and some kind of payment that
  267. did not involve touching right from the
  268. very beginning this would slow down new
  269. outbreaks and I think the for example
  270. they found as I understand that children
  271. even if they're infected never in adults
  272. so why do we not have children at school
  273. why do we not have people working
  274. England is reaching England France Italy
  275. Sweden Belgium Holland are all reaching
  276. levels of saturation to be very very
  277. close to her immunity so basically I
  278. think that's a good thing I think the
  279. policy good immunity is the right policy
  280. I think Britain was on exactly the right
  281. track before they were fed wrong numbers
  282. and they made a huge mistake I see the
  283. standard winners as Germany and Sweden
  284. they didn't practice too much lock down
  285. they got enough people sick to get some
  286. hood immunity the standard losers are
  287. countries like Austria Australia Israel
  288. that actually had very very strict
  289. lockdown but didn't have many cases so
  290. they have damaged their economies caused
  291. massive social damage damage the
  292. educational year of their children but
  293. not obtain any herd immunity I think in
  294. many ways the European countries are
  295. fine they didn't need to have lockdown
  296. but they have will have all reached a
  297. high enough level of infection not to
  298. have to worry about further future
  299. attacks of coronagraph the United States
  300. seems to be heading that way there
  301. certainly that way in New York City but
  302. they still have a long way to go what
  303. you're saying is that you believe
  304. success
  305. as we are currently measuring it which
  306. is as few cases as possible and a
  307. smaller spread of the virus as possible
  308. is actually failure I think if you
  309. really control your your epidemic for
  310. example California it's now had locked
  311. down for six weeks and once another four
  312. weeks they have so far less than a
  313. hundred deaths that means they don't
  314. have more let's say a hundred thousand
  315. in people that is not enough to give
  316. them a significant herd immunity they
  317. didn't need to do all that
  318. lock down the lock down I think is
  319. particularly hurtful in countries that
  320. don't have good social infrastructure
  321. countries like the United States in
  322. Israel many many people have been really
  323. really hurt especially young people you
  324. know I think that everybody panicked
  325. they were fed incorrect numbers by
  326. epidemiologists and you know this I
  327. think led to a situation there's no
  328. doubt in my mind when we come to look
  329. back on this the damage done by knock
  330. down will exceed any saving of glides by
  331. a huge factor one very easy way to see
  332. this is that and again I'm getting it a
  333. sensitive territory here but economists
  334. economists have a very simple way of
  335. looking at death they don't count people
  336. they come to the conclusion that if
  337. you're 20 and you die
  338. that's a greater loss and if you're 85
  339. and you die and it's a hard issue but in
  340. some ways you know are we evaluating the
  341. potential of future like of the 20 year
  342. old are we valuing the loss of senior p
  343. person now by what's called da ly
  344. disability-adjusted life years well
  345. basically if somebody is in the 80s
  346. health has Alzheimer's disease and then
  347. dies from pneumonia perhaps due to
  348. corona that is less of a loss and if a
  349. 15 year old is riding his motorcycle but
  350. his bike and gets run
  351. so this is an important way of looking
  352. at the death it's also you know right
  353. now we know that's the number of excess
  354. deaths in Europe is around a hundred and
  355. thirty thousand up to yesterday this is
  356. for all of Europe this is for a
  357. population of around 330 million people
  358. so in excess of a hundred thousand for
  359. this whole year is actually not that
  360. much in some of the worst flu epidemics
  361. we get so those kinds of numbers
  362. sometimes it's a bit more sometimes a
  363. little bit less now I'm not saying flu
  364. is like coronavirus I'm just simply
  365. saying that the burden of death of flu
  366. is like coronavirus especially when we
  367. correct for the fact that people who die
  368. from coronavirus are older on average
  369. than people who died from flu flu kills
  370. young people it kills two or three times
  371. more people under 65 than does
  372. coronavirus if we put those facts into
  373. the situation we find that the burden of
  374. death from coronavirus and Phillip Shaw
  375. will in Europe where we have good
  376. numbers in less than that of a very very
  377. flu another factor which has not been
  378. considered Oh cancer patients or ants
  379. being treated or all the hot cardiology
  380. patients who aren't being treated
  381. I've got estimates of tens of thousands
  382. of people who are basically going to be
  383. dying because of lack of that treatment
  384. and generally again the age group who
  385. die of cancer are younger than the age
  386. group who died of coronavirus and
  387. there's one very easy way to sort of
  388. summarize coronavirus I put an article
  389. in the medium in response to an article
  390. by the pretty famous British
  391. statistician Sir David Spiegel Hotel
  392. Cambridge and he had said that the
  393. numbers coming from Ferguson suggested
  394. that we had to lose about one year of
  395. people turns out in fact that I
  396. immediately wrote an article in the same
  397. medium and reply to him saying that in
  398. fact the answer was actually one month
  399. not one year so basically my feeling is
  400. it's being supported by the numbers that
  401. the amount of excess death you need to
  402. reach saturation I'm not gonna call it
  403. herd immunity where the virus bites off
  404. stock is on the order of four weeks of
  405. excess to give you some idea in the
  406. European area where there is good
  407. monitoring by a website called Euro mama
  408. run out of Denmark it covers about 300
  409. million people every week in Europe in
  410. that area there's around 50,000 natural
  411. dense so full weeks will be about
  412. 200,000 extra deaths in that year and it
  413. looks like coronavirus in Europe where
  414. it's no doubt that it's the most
  415. severely hit area in all the world we'll
  416. probably reach around 200,000 or 4 weeks
  417. worth so what happens if what you're
  418. saying is there's a sort of them there
  419. seems to be a statistical observation
  420. which is around four weeks of excess
  421. death and then the the pandemic seems to
  422. peter out will begin to flap now what
  423. does that mean policy-wise for these
  424. European countries then so if we could
  425. protect the old people perfectly then
  426. the death rates would be very very low
  427. say for example in Europe I said there
  428. were about a hundred and forty thousand
  429. excess deaths in the last I think nine
  430. weeks the number of those excess deaths
  431. who are younger than 65 is about 10%
  432. so basically 13,000 of 130,000 deaths or
  433. actually under 65 years old so and if we
  434. had simply been able to protect or
  435. elderly people then the death rate would
  436. have been much much less but the key
  437. thing is is to have as much infection
  438. for as little possible death and also do
  439. whatever you can to keep the hospitals
  440. full brada overflowing it's a difficult
  441. calculation it's one which accounts me
  442. like Sweden can do where essentially
  443. there's no political concerns the
  444. trouble is is that in Israel that I know
  445. well in the United States the everything
  446. is political and therefore nobody could
  447. say something like this they would say
  448. ah but you're not valuing depth of the
  449. thing that should have been done is for
  450. the media to stress the people that
  451. everyday somebody dies and these people
  452. are essentially in the same age band
  453. because I'm from Corona have other
  454. comorbidities they have other diseases I
  455. I've become a huge fan of Twitter I'd
  456. never used twitter before and for me
  457. Twitter is the best discussion forum I
  458. had seen since I was a student at the
  459. cameras Laboratory of natural biology
  460. which is that 26 Nobel Prize winning lab
  461. the best lab in the world the Twitter
  462. discussion is phenomenal and I'm getting
  463. documents from Italy showing that many
  464. of the Cova deaths were either dead
  465. before they were tested or else they
  466. ahead up to three other conditions so
  467. this is nothing wrong with this people
  468. die for all sorts of reasons but the
  469. news should be stressing this and maybe
  470. they should be counting it as a point
  471. one code that their countries seem to be
  472. racing have as many koga the distance
  473. they could and this is a huge mistake in
  474. the flu season no one cares about these
  475. people I mean the the total number of
  476. deaths in Europe will be very similar to
  477. a severe flu season and you know this is
  478. serious flu is a serious disease maybe
  479. we should just shut down the economy
  480. during the flu season I mean we need
  481. people people should have been made to
  482. understand it unfortunately I think in
  483. Britain they started out wanting to go
  484. for herd immunity without too much
  485. lockdown there was then a scary paper
  486. which is likely to be retracted which
  487. improves Italy as well where basically
  488. it was claimed they were could we just
  489. spend a moment on that paper professor I
  490. know you you had some specific queries
  491. about Neil Ferguson's paper we had him
  492. repeat on the show last week so what
  493. what did you think he got wrong in those
  494. models and predictions so
  495. so his work was on modeling in the paper
  496. that I saw and of course these are pre
  497. prints initially he found I was
  498. following China very carefully and
  499. around the 10th of February he had his
  500. first paper that I saw and in there he
  501. was getting a case fatality ratio of
  502. around 15% whereas all my observations
  503. were saying that it was around 3 or 4
  504. percent so I was suspicious I looked at
  505. the paper very carefully and in a
  506. footnote to a table it said assuming
  507. exponential growth for six days at
  508. fifteen percent a day now I had looked
  509. at China and never ever seen exponential
  510. growth that wasn't decaying rapidly sara
  511. is suspicious my numbers were about 10
  512. percent less milk 1/10 not it was 10
  513. percent of the numbers that Ferguson had
  514. obtained I basically pointed this out in
  515. reply in the medium which was in out
  516. there it's clear nobody has ever seen it
  517. but it's there and I didn't hide it it
  518. just didn't get any likes and this said
  519. that it was much more like one month
  520. than one year and have an exchange both
  521. in Spiegel hota and Ferguson where I try
  522. to show my case basically saying but all
  523. I was doing was just simple
  524. proportionality using exactly the same
  525. profile of different ages have different
  526. death rates so there's a profile saying
  527. that people over 80 have a certain
  528. fraction of the disk people between 7
  529. 1980 have a different fraction just
  530. using that data from almost anywhere it
  531. could be South Korea
  532. it could be walkin could be anywhere and
  533. just simply saying we want the number of
  534. deaths that occurred on the german
  535. princess to be the same number that we
  536. found which was 708 if you do that and
  537. then you apply that proportionality to
  538. britain in the USA you find that for
  539. britain the half a million drops to
  540. about 50,000 and in the united states
  541. the two million drops to 200,000
  542. essentially a year dropping two a month
  543. and so the the argument that is made
  544. here is that whether you believe
  545. the infection fatality rate is nour
  546. point three percent or whether you
  547. believe it's nor point eight percent
  548. there is still a big chunk of the
  549. population the majority population who
  550. hasn't had been exposed to the disease
  551. or hasn't had it and therefore if we
  552. just let it rip there will be many many
  553. tens possibly even further hundreds
  554. according to Professor Fergusson
  555. of thousands of deaths and that's why
  556. it's politically totally not an option
  557. to be at do anything other than follow
  558. this ultra cautious approach I mean what
  559. do you say what I say is is that the
  560. World Health Organization and I think
  561. demolishes in general can only go wrong
  562. if they get smaller if I said it's going
  563. to be 1 billion deaths from coronavirus
  564. and it's oh sorry you guys have done
  565. what I've said there's only gonna be a
  566. hundred thousands that is considered
  567. good policy be they overestimated bird
  568. flu by a factor of a hundred or ten
  569. thousand paper in The Guardian The
  570. Guardian wrote about this Ebola was
  571. overestimated by a factor of 100 I think
  572. they say their role as scaring the
  573. people into doing something I can
  574. understand that and there's something to
  575. be said for it if you could practice
  576. lock down with zero economic cost and
  577. zero social cost let's do it
  578. but the problem is is that those costs
  579. are huge we're gonna have fatality some
  580. hostel hospitals being closed down we're
  581. going to have additional children in
  582. trauma we're going to have businesses
  583. damaged maybe less so in the UK because
  584. of the compensation policy but certainly
  585. massive economic damage in the USA and
  586. in Israel and in other countries so you
  587. need to balance both of these things and
  588. you know I it seems to me that you know
  589. that's what I don't think is responsible
  590. in my work if I say a number is too
  591. small and I'm wrong or a number is too
  592. big and I'm wrong both of those errors
  593. are the same if I'm 10 percent too high
  594. or 10 percent too low that is okay it
  595. seems
  596. being a factor of a thousand - hi is
  597. perfectly okay in epidemiology but being
  598. effective three too low is too low I'm
  599. trying to think about what this means
  600. for the UK and for these countries that
  601. are trying to work out what to do next
  602. is your view then having looked at the
  603. numbers that if we had not implemented
  604. lockdown
  605. we would have seen a fall off anyway is
  606. that is that a fair summary we could
  607. have had smart lockdown Sweden for
  608. example doesn't allow gatherings of more
  609. than 50 people I think a football game
  610. would be a really bad idea right now
  611. because people shot and therefore spray
  612. saliva on everyone around them and they
  613. could infect a lot of people but you
  614. know Sweden is doing fine and their
  615. deaths again are very localized to
  616. nursing homes like they are in England
  617. it's the same profile I think that you
  618. know again it's Sweden so all the
  619. evidence suggests that so my
  620. contradiction is the following Britain
  621. if they had done nothing would have had
  622. reported deaths not remember there's a
  623. difference of reported death my numbers
  624. are all reported this would have four
  625. weeks of additional reported death when
  626. the numbers actually came in from what
  627. were the real axis death my guess is
  628. they would be less than that so it would
  629. not have been double it wasn't in the
  630. month but maybe one and three quarters
  631. or so on so that is my feeling we're
  632. seeing this in Europe we will know the
  633. answer in three or four weeks time we
  634. will know for all of Europe exactly what
  635. the existed of coronavirus wants right
  636. now it's a hundred and thirty seven
  637. thousand do you find when you've been
  638. making these points on medium or in the
  639. media that you received a lot of
  640. backlash do you think there's a lot of
  641. political pressure and as an academic
  642. you know they're one of your colleagues
  643. in Stanford dr. Yeoh nudie ass has also
  644. put out studies that seem to become
  645. skeptical
  646. and has received a lot of political
  647. blowback I just wonder whether you've
  648. experienced any of that I went on CNN
  649. once when he was CNN Vicky Anderson out
  650. of London I appeared on Fox News a
  651. couple of times basically said this is
  652. all just common sense because I appeared
  653. on Fox News CNN wouldn't have me anymore
  654. so basically I have had very clear of
  655. things I had one article in the National
  656. Los Angeles Times which did great but
  657. since I was not saying things that were
  658. too extreme
  659. none of the East Coast newspapers wanted
  660. me they quoted me but they wouldn't have
  661. me what disconcerting is a few of my
  662. academic colleagues even relatives we're
  663. very upset with me because in my earlier
  664. writings I published a report that was
  665. when was the media report from the 22nd
  666. of March but in a 14 to 13 3rd March I
  667. distributed a 1980 rapport with three
  668. academics got very upset with me I think
  669. they were totally panicked and they felt
  670. that if anyone thought this was truth
  671. they wouldn't lock down as tightly as
  672. they should and in fact friends with all
  673. the people again no hard feelings
  674. let me leave you with one final question
  675. what do you what's your prognosis what
  676. do you think is now gonna happen with
  677. this and you do you think we're now what
  678. happens that there will be a reckoning
  679. maybe countries will start to see that
  680. they need governments that are not pop
  681. not necessarily great in rhetoric but
  682. actually thinking and doing these I
  683. often go back and think about what
  684. Socrates said 2,400 years ago used your
  685. common sense instead of listening to the
  686. rhetoric of leaders and we keep County
  687. very influenced by that I think also
  688. what should happen and this is gonna
  689. sound very strange to you I think this
  690. is another fella on the part of the baby
  691. boomers I am a real baby boomer I was
  692. born in 1947 I'm almost seventy-three
  693. years old but I think we've really
  694. screwed up we cause pollution we allowed
  695. the world's population that we increase
  696. threefold in my
  697. lifetime even more we've caused the
  698. problems of global warming and now even
  699. left your your generation with a real
  700. mess in order to say a really small
  701. number of very old people if I was the
  702. young person now I would say now you
  703. guys are gonna pay for this this is a we
  704. have my family what's up and very early
  705. on I said this is a virus being designed
  706. to get rid of the baby boomers you know
  707. I don't know I think my wife thinks this
  708. is gonna be a sea change and we're gonna
  709. have they're gonna be young people on
  710. the street saying you guys have really
  711. screwed up it's time to go and I always
  712. joke with her and saying well at least
  713. I've made lots of friends among the
  714. young people I'll be okay but quite
  715. frankly you know I've had a great life
  716. and I'd muster all this t-yong faces in
  717. front of me I have a grandson who's 17
  718. I'd much rather have young people then
  719. live for a very long time
  720. they said I do have a mother who's a
  721. hundred and five years old living in
  722. London with my brother she's in lockdown
  723. and I took to her by whatsapp every
  724. single day on FaceTime and she's fine
  725. she still uses her phone and so on so
  726. you know these differences but you guys
  727. should get out there and do something
  728. don't accept this anymore
  729. we screwed up too much professor thank
  730. you so very much for your time Mike no
  731. Jack that was Professor Michael Leavitt
  732. of the University of Stanford and thanks
  733. to him and also his 105 year old mother
  734. we sincerely hope she is doing well here
  735. in London and he is the professor of
  736. structural biology and computer science
  737. at Stanford he was the recipient of the
  738. 2013 Nobel Prize for chemistry and he
  739. was sharing their some of his views
  740. looking at things in it from a bit of a
  741. different angle more of a numbers angle
  742. than a straightforward epidemiology
  743. angle and here on herd we want to get
  744. these different opinions and try and
  745. allow you to make your own mind up so
  746. thank you to him we will be back next
  747. with professor Heinrich streak of the
  748. University of Bonn who has just
  749. completed the first fully Deaton
  750. study of seroprevalence or that is
  751. immunity to the coronavirus and he will
  752. be sharing the findings with us on
  753. Monday so we look forward to that
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