1 00:00:04,920 --> 00:00:10,319 Speaker 1: Welcome to Prognosis. I'm Laura Carlson. It's day seventeen since 2 00:00:10,320 --> 00:00:14,520 Speaker 1: coronavirus was declared a global pandemic. Cases in the United 3 00:00:14,560 --> 00:00:18,000 Speaker 1: States soared to more than eighty five thousand, making the 4 00:00:18,120 --> 00:00:23,680 Speaker 1: US the world leader in COVID nineteen cases on today's episode, 5 00:00:24,239 --> 00:00:29,200 Speaker 1: The Coronavirus Detectives. But first, here are the top stories 6 00:00:29,400 --> 00:00:36,680 Speaker 1: from today. The US Senate passed a historic two trillion 7 00:00:36,720 --> 00:00:40,040 Speaker 1: dollar relief package late on Friday morning, which promises to 8 00:00:40,080 --> 00:00:45,160 Speaker 1: deliver payments and benefits to individuals, businesses, and states affected 9 00:00:45,159 --> 00:00:48,680 Speaker 1: by the pandemic. Italy had its deadliest twenty four hours, 10 00:00:49,040 --> 00:00:52,520 Speaker 1: recording almost one thousand fatalities from the virus in one day. 11 00:00:53,360 --> 00:00:56,800 Speaker 1: Spain's death rate also soared. Cases are jumping in the 12 00:00:56,880 --> 00:01:00,480 Speaker 1: United Kingdom and the US raised past China to become 13 00:01:00,480 --> 00:01:04,360 Speaker 1: the country with the most cases in the world. Meanwhile, 14 00:01:04,520 --> 00:01:08,240 Speaker 1: in China, where the outbreak began, virtually all the latest 15 00:01:08,240 --> 00:01:12,040 Speaker 1: cases came from people arriving from overseas, prompting the government 16 00:01:12,080 --> 00:01:15,679 Speaker 1: to temporarily suspend the entry of foreigners with valid visas 17 00:01:15,680 --> 00:01:20,120 Speaker 1: and residence permits in the US. A deal to produce 18 00:01:20,280 --> 00:01:24,240 Speaker 1: life saving ventilators that a massive scale faltered as President 19 00:01:24,319 --> 00:01:29,479 Speaker 1: Donald Trump attacked manufacturers. Carmakers General Motors, and Ford, as 20 00:01:29,520 --> 00:01:33,240 Speaker 1: well as medical device manufacturer Ventech Life Systems, were set 21 00:01:33,280 --> 00:01:36,039 Speaker 1: to ramp up production of the breathing machines, waiting on 22 00:01:36,040 --> 00:01:40,679 Speaker 1: the Trump administration to place orders and cut checks, but 23 00:01:40,760 --> 00:01:44,760 Speaker 1: then the President published a series of angry tweets accusing 24 00:01:44,760 --> 00:01:49,200 Speaker 1: GM of moving too slowly and charging too much, calling 25 00:01:49,200 --> 00:01:52,120 Speaker 1: on the company to produce the machines in an Ohio 26 00:01:52,280 --> 00:01:56,680 Speaker 1: plant it no longer owns. Later, GM said it would 27 00:01:56,680 --> 00:01:59,400 Speaker 1: stop waiting on a federal contract and produce the machines 28 00:01:59,440 --> 00:02:02,880 Speaker 1: at an Indie, Yana plant. GM says it can eventually 29 00:02:02,960 --> 00:02:05,280 Speaker 1: ramp up to making as many as one hundred thousand 30 00:02:05,400 --> 00:02:09,880 Speaker 1: ventilators per day. Other carmakers are contributing to the effort 31 00:02:09,880 --> 00:02:13,799 Speaker 1: to mass produce badly needed supplies. Toyota is planning to 32 00:02:13,880 --> 00:02:16,120 Speaker 1: use its shut down car plants in the US to 33 00:02:16,200 --> 00:02:19,919 Speaker 1: make masks and face shields. The company said it would 34 00:02:19,960 --> 00:02:23,359 Speaker 1: start producing these items early next week to supply hospitals 35 00:02:23,400 --> 00:02:28,079 Speaker 1: near its plants in Indiana, Kentucky, Michigan, and Texas. It's 36 00:02:28,120 --> 00:02:31,760 Speaker 1: also finalizing deals with medical supply companies to make ventilators 37 00:02:31,800 --> 00:02:36,560 Speaker 1: and respirator hoods. UK Prime Minister Boris Johnson reported on 38 00:02:36,600 --> 00:02:40,400 Speaker 1: Friday that he had tested positive for COVID nineteen and 39 00:02:40,400 --> 00:02:44,120 Speaker 1: with suffering symptoms including a fever and persistent cough. The 40 00:02:44,240 --> 00:02:47,399 Speaker 1: United Kingdom is seeing an overall surge in cases with 41 00:02:47,560 --> 00:02:57,840 Speaker 1: deaths from the virus jumping. And now for our main story, 42 00:02:58,560 --> 00:03:04,720 Speaker 1: the coronavirus detectives. If you wanted to learn everything you 43 00:03:04,760 --> 00:03:07,880 Speaker 1: could about an organism, a good place to start would 44 00:03:07,919 --> 00:03:11,160 Speaker 1: be its genome. The genome is the complete set of 45 00:03:11,200 --> 00:03:14,760 Speaker 1: genetic information found in the d n A of any 46 00:03:14,800 --> 00:03:18,600 Speaker 1: and every organism, whether that's a human being, a plant, 47 00:03:19,200 --> 00:03:23,560 Speaker 1: or even a virus. And inside hundreds of viral genomes 48 00:03:23,600 --> 00:03:26,600 Speaker 1: from patients around the globe there may be clues to 49 00:03:26,720 --> 00:03:31,079 Speaker 1: where the infection came from and, most importantly, where it's 50 00:03:31,120 --> 00:03:34,960 Speaker 1: going next. A little known geneticist in Seattle has become 51 00:03:35,000 --> 00:03:38,680 Speaker 1: something of a c s I detective unraveling the origins 52 00:03:38,720 --> 00:03:42,680 Speaker 1: of COVID nineteen in the US. Could his research hold 53 00:03:42,720 --> 00:03:46,640 Speaker 1: secrets to a better understanding of the disease. Some policy 54 00:03:46,680 --> 00:03:49,920 Speaker 1: makers seem to think so. I talked to Bloomberg's Bob 55 00:03:50,000 --> 00:03:56,920 Speaker 1: lang Grath for more scientists around the world have been 56 00:03:56,960 --> 00:03:59,640 Speaker 1: trying to analyze COVID nineteen figure out exactly where this 57 00:03:59,800 --> 00:04:02,200 Speaker 1: vibe came from. And you've been talking to one of 58 00:04:02,240 --> 00:04:05,040 Speaker 1: these scientists, Trevor Bedford, and I was just hoping you 59 00:04:05,040 --> 00:04:07,160 Speaker 1: could tell us a little bit about his research just 60 00:04:07,240 --> 00:04:09,720 Speaker 1: to start off. So he is one of a he's 61 00:04:09,760 --> 00:04:12,120 Speaker 1: at the Fred Hutchinson Cancer Research gener and he's one 62 00:04:12,160 --> 00:04:15,320 Speaker 1: of a new breed of epidemiologists that doesn't do kind 63 00:04:15,320 --> 00:04:17,960 Speaker 1: of the shoe leather work this traditional epidemiologists, which is 64 00:04:18,200 --> 00:04:21,440 Speaker 1: tracking all the cases and finding other contacts. Instead, you know, 65 00:04:21,480 --> 00:04:23,360 Speaker 1: he kind of sits by the laptop with a handful 66 00:04:23,400 --> 00:04:26,560 Speaker 1: of collaborators around the world and waits for new UH 67 00:04:26,839 --> 00:04:30,320 Speaker 1: genome data genome sequencing data from patients that have had 68 00:04:30,320 --> 00:04:32,760 Speaker 1: the virus for it to come in. So he analyzes 69 00:04:32,760 --> 00:04:35,480 Speaker 1: the genome data over time to see, you know, how 70 00:04:35,520 --> 00:04:37,960 Speaker 1: the virus is muted, and that gives some clues as 71 00:04:38,000 --> 00:04:40,039 Speaker 1: to you know, how it is spreading, where is it 72 00:04:40,080 --> 00:04:43,680 Speaker 1: coming from, and what places are starting to have new 73 00:04:43,680 --> 00:04:47,880 Speaker 1: clusters next, So how can scientists predict where these clusters 74 00:04:47,920 --> 00:04:51,040 Speaker 1: go and how they travel. You can think of its 75 00:04:51,080 --> 00:04:53,640 Speaker 1: sort of like a genetic family trees of the virus. 76 00:04:53,680 --> 00:04:56,000 Speaker 1: As the virus spreads from one person to the next 77 00:04:56,040 --> 00:04:59,040 Speaker 1: to the next, there are occasional small mutations in the 78 00:04:59,120 --> 00:05:02,080 Speaker 1: virus and uh, you know, most of these mutations, they 79 00:05:02,080 --> 00:05:04,760 Speaker 1: don't change how your sycomics people, or don't change anything 80 00:05:04,760 --> 00:05:08,320 Speaker 1: functional about the virus, but they do allow the genetic detectives, 81 00:05:08,360 --> 00:05:10,800 Speaker 1: the kind of genome genetic detectives as I like to 82 00:05:10,839 --> 00:05:13,440 Speaker 1: call them, uh, to kind of track the virus in 83 00:05:13,480 --> 00:05:16,560 Speaker 1: near real time. And his one of his big breakthroughs 84 00:05:17,200 --> 00:05:20,400 Speaker 1: came three or four weeks ago at a time when 85 00:05:20,640 --> 00:05:22,800 Speaker 1: a lot of people in public health authorities in the 86 00:05:22,880 --> 00:05:25,120 Speaker 1: US thought we had a kind of mostly under control. 87 00:05:25,560 --> 00:05:29,480 Speaker 1: And as he was analyzing genomes in the virus case 88 00:05:29,560 --> 00:05:33,200 Speaker 1: it was detected in a teenager in Washington, one of 89 00:05:33,200 --> 00:05:35,760 Speaker 1: the first community cases. He found that it was almost 90 00:05:35,920 --> 00:05:39,600 Speaker 1: identical to the virus in the very first Washington case 91 00:05:39,680 --> 00:05:43,039 Speaker 1: in January, and that indicated to him very strongly, to 92 00:05:43,120 --> 00:05:46,720 Speaker 1: Dr Bedford, that the community spread in Washington was closely 93 00:05:46,800 --> 00:05:50,400 Speaker 1: linked to that first Washington case in January, and in fact, 94 00:05:51,040 --> 00:05:54,120 Speaker 1: it had been spreading undetected in Washington for quite a 95 00:05:54,120 --> 00:05:56,679 Speaker 1: few weeks. So basically it was a kind of early 96 00:05:56,720 --> 00:06:01,720 Speaker 1: alarm signal you can get by tracking these viral genomes. Yeah, 97 00:06:01,839 --> 00:06:05,160 Speaker 1: and I'm hoping perhaps that this might actually help us 98 00:06:05,160 --> 00:06:07,960 Speaker 1: predict where the virus is going next and perhaps how 99 00:06:08,000 --> 00:06:10,600 Speaker 1: long it will last. Are there elements to his research 100 00:06:10,720 --> 00:06:13,600 Speaker 1: that give us some indication to that extent as well. 101 00:06:14,080 --> 00:06:15,920 Speaker 1: You know, you can't predict whether someone is going to 102 00:06:15,960 --> 00:06:18,480 Speaker 1: get on a plane uh and flat to some new 103 00:06:18,520 --> 00:06:21,240 Speaker 1: destination or sneak into some new destination and then that 104 00:06:21,279 --> 00:06:23,599 Speaker 1: patient is sick and spreads the virus. It can't really 105 00:06:23,600 --> 00:06:26,039 Speaker 1: predict that. But what what it's very good at doing 106 00:06:26,680 --> 00:06:31,000 Speaker 1: this genetic work is kind of piecing together seemingly outbreaks 107 00:06:31,000 --> 00:06:33,440 Speaker 1: and case clusters that may appear on the surface to 108 00:06:33,440 --> 00:06:36,800 Speaker 1: be unrelated because these patients that got sick around the 109 00:06:36,839 --> 00:06:40,280 Speaker 1: same time in one geographic area like in Washington, you 110 00:06:40,320 --> 00:06:42,760 Speaker 1: didn't have any obvious contacts or points of contact. But 111 00:06:42,760 --> 00:06:44,840 Speaker 1: when you look at the genetics and looks very similar, 112 00:06:44,880 --> 00:06:47,400 Speaker 1: that gives you an indication the fact might be related 113 00:06:47,839 --> 00:06:50,600 Speaker 1: and and might have derived from some of the same 114 00:06:50,640 --> 00:06:53,080 Speaker 1: original index cases and that so it's kind of an 115 00:06:53,080 --> 00:06:57,440 Speaker 1: early warning signal that helps supplement traditional epidemiology method that 116 00:06:57,520 --> 00:07:00,680 Speaker 1: seems to be a really important element of the fact 117 00:07:00,680 --> 00:07:03,720 Speaker 1: that Dr Bedford isn't working alone but with this global team, 118 00:07:03,960 --> 00:07:06,240 Speaker 1: and that the global team really allows him to get 119 00:07:06,240 --> 00:07:10,800 Speaker 1: this almost real time data about where the virus came from, 120 00:07:10,840 --> 00:07:13,640 Speaker 1: the details of how it's spreading. How is he sinking 121 00:07:13,680 --> 00:07:16,520 Speaker 1: up with this team that scattered essentially across the globe. 122 00:07:17,600 --> 00:07:20,480 Speaker 1: Trevor Bedford is at in Seattle at the fred Hut 123 00:07:20,960 --> 00:07:24,600 Speaker 1: Cancer Center and one of his main collaborators is at 124 00:07:24,600 --> 00:07:28,040 Speaker 1: the Universe of Bosel, Switzerland, So and that's a nine 125 00:07:28,040 --> 00:07:31,000 Speaker 1: hour time difference. And then as it happens, he also 126 00:07:31,080 --> 00:07:35,080 Speaker 1: has another researcher in his lab who's from New Zealand 127 00:07:35,440 --> 00:07:38,520 Speaker 1: and apparently got I wanted to go back to New Zealand, 128 00:07:38,560 --> 00:07:41,360 Speaker 1: so that researcher, as luck would have it, is working 129 00:07:41,360 --> 00:07:43,280 Speaker 1: you know, from home in New Zealand. So they have 130 00:07:43,480 --> 00:07:45,200 Speaker 1: as it turns out, even though it's a small group 131 00:07:45,240 --> 00:07:48,080 Speaker 1: of people, they have people in three very disparate time zones. 132 00:07:48,120 --> 00:07:52,240 Speaker 1: That kind of allows them to you know, get an 133 00:07:52,240 --> 00:07:55,280 Speaker 1: analyzed data and almost real time as it comes in. 134 00:07:55,840 --> 00:07:58,280 Speaker 1: And how exactly are they sharing the data? Is this 135 00:07:58,440 --> 00:08:01,280 Speaker 1: publicly available? Can any would go and see the real 136 00:08:01,320 --> 00:08:05,520 Speaker 1: time research. Dr Bedford and his collaborators they have software 137 00:08:05,560 --> 00:08:07,520 Speaker 1: there was basically ready to go already. They've been working 138 00:08:07,520 --> 00:08:10,520 Speaker 1: on this for a while, they and so they've for influenza, 139 00:08:10,640 --> 00:08:12,600 Speaker 1: for a Bowl and for other outbreaks. So they basically 140 00:08:12,600 --> 00:08:15,160 Speaker 1: it's software ready to go that can analyze and compare 141 00:08:15,200 --> 00:08:18,160 Speaker 1: the different viruses and how they've mutated, you know, very 142 00:08:18,240 --> 00:08:20,679 Speaker 1: quickly that can you give us some results in twenty 143 00:08:20,680 --> 00:08:24,360 Speaker 1: to thirty minutes. So they have a website, a interactive 144 00:08:24,400 --> 00:08:26,920 Speaker 1: website called next train dot org and that is updated, 145 00:08:27,000 --> 00:08:29,360 Speaker 1: you know, frequently with new viral data as it comes in. 146 00:08:29,440 --> 00:08:31,560 Speaker 1: And they literally put up like these little I call 147 00:08:31,880 --> 00:08:34,920 Speaker 1: virus family trees that the technical name is bhilogenies. But 148 00:08:34,960 --> 00:08:37,760 Speaker 1: they show like a different you know, clusters of virus 149 00:08:37,800 --> 00:08:40,240 Speaker 1: and you know, how they've mutated over time, and like 150 00:08:40,320 --> 00:08:42,400 Speaker 1: what parts of the world they've come from. And so 151 00:08:42,440 --> 00:08:46,160 Speaker 1: it's a powerful technique. It provides you know, circumstantial not 152 00:08:46,360 --> 00:08:50,480 Speaker 1: definitive evidence, but circumstantial, very strong, circumstantial evidence for how 153 00:08:50,520 --> 00:08:53,360 Speaker 1: the virus spread and which towns it has come from 154 00:08:53,360 --> 00:08:56,840 Speaker 1: and gone to. But it's not just this website. Dr 155 00:08:56,880 --> 00:08:59,240 Speaker 1: Bedford seems to have become a bit of a social 156 00:08:59,240 --> 00:09:03,200 Speaker 1: media filipper by sharing his work online. Dr Bedford is 157 00:09:03,240 --> 00:09:06,560 Speaker 1: a very good at using Twitter u and these Twitter threads. 158 00:09:06,600 --> 00:09:08,160 Speaker 1: You know, he now has more than a hundred seventy 159 00:09:08,200 --> 00:09:11,240 Speaker 1: thousand Twitter followers. It was pretty amazing from someone who know, 160 00:09:11,320 --> 00:09:15,520 Speaker 1: previously was an obscure computational biologist, you know, known to 161 00:09:15,559 --> 00:09:17,800 Speaker 1: people in his field, but you know, not outside his 162 00:09:17,880 --> 00:09:20,720 Speaker 1: very technical field. And now he's being followed by public 163 00:09:20,720 --> 00:09:24,240 Speaker 1: health experts around the world, including former FDA commissioners, as 164 00:09:24,280 --> 00:09:28,080 Speaker 1: being kind of one of the most prescient commentators, you know, 165 00:09:28,160 --> 00:09:31,240 Speaker 1: And what's happening with this virus, how do you see 166 00:09:31,240 --> 00:09:36,120 Speaker 1: maybe policymakers starting to use and apply Bedford's work more 167 00:09:36,160 --> 00:09:39,000 Speaker 1: broadly again to hopefully stop the spread of the virus 168 00:09:39,080 --> 00:09:43,160 Speaker 1: more generally. Yes, he is, uh, he told me he 169 00:09:43,240 --> 00:09:45,520 Speaker 1: was in regular contact with people are both at the 170 00:09:45,559 --> 00:09:48,600 Speaker 1: CDC and the public health authorities in his state, Washington, 171 00:09:49,360 --> 00:09:52,160 Speaker 1: because obviously Washington was the first state to be heavily 172 00:09:52,480 --> 00:09:54,720 Speaker 1: hit by the coronavirus in the in the US obviously 173 00:09:54,720 --> 00:09:57,080 Speaker 1: it's been now surpassed by New York. So he's in 174 00:09:57,120 --> 00:10:00,520 Speaker 1: regular contact with you know, public health authorities and basically 175 00:10:00,640 --> 00:10:02,680 Speaker 1: although they're putting all the data out there. I mean, 176 00:10:02,720 --> 00:10:04,839 Speaker 1: it is the genome data is it's kind of like 177 00:10:04,880 --> 00:10:08,640 Speaker 1: a new thing for epidemiologists that traditionally trained epidemiologists, you know, 178 00:10:08,720 --> 00:10:11,679 Speaker 1: might not have as much expertise and interpreting it. And 179 00:10:12,840 --> 00:10:15,280 Speaker 1: so he's in regular contact with the health authorities, you know, 180 00:10:15,520 --> 00:10:17,560 Speaker 1: to tell him, hey, here's what I think this means 181 00:10:17,880 --> 00:10:19,840 Speaker 1: this new set of data. You know, here's what you 182 00:10:20,000 --> 00:10:21,320 Speaker 1: make of it, Here's what you think of it. So 183 00:10:21,360 --> 00:10:25,240 Speaker 1: he's regularly talking to to the various health authorities. Bob Blankra, 184 00:10:25,360 --> 00:10:32,880 Speaker 1: thank you very much, great, thank you, And that's it 185 00:10:32,960 --> 00:10:36,360 Speaker 1: for the Prognosis Daily Edition. For more on the coronavirus 186 00:10:36,400 --> 00:10:39,120 Speaker 1: crisis from a hundred and twenty bureaus around the world, 187 00:10:39,640 --> 00:10:45,240 Speaker 1: visit Bloomberg dot com slash coronavirus and if you appreciate 188 00:10:45,240 --> 00:10:47,760 Speaker 1: the podcast, please take a moment to rate us and 189 00:10:47,840 --> 00:10:50,520 Speaker 1: leave us a review on Apple Podcasts to help more 190 00:10:50,559 --> 00:10:55,280 Speaker 1: listeners find our global reporting. The Prognosis Daily Edition is 191 00:10:55,320 --> 00:10:58,400 Speaker 1: hosted by me Laura Carlson. The show is produced by 192 00:10:58,400 --> 00:11:03,480 Speaker 1: me To for Foreheads, Orton Gospore, and Magnus Henriksen. Reporting 193 00:11:03,480 --> 00:11:08,200 Speaker 1: by Jason Gale. Original music by Leo sidron Our editors 194 00:11:08,200 --> 00:11:12,679 Speaker 1: are Francesca Levi and Rick Shine. Francesca Levie is Bloomberg's 195 00:11:12,800 --> 00:11:13,800 Speaker 1: head of podcasts,