1 00:00:00,120 --> 00:00:02,880 Speaker 1: Hi, this is new I am currently staying at home 2 00:00:03,000 --> 00:00:06,360 Speaker 1: in Rome to bring you this episode this week. I'm 3 00:00:06,400 --> 00:00:09,440 Speaker 1: recording from my home, so you may notice a difference 4 00:00:09,440 --> 00:00:15,800 Speaker 1: in audio quality on this episode of News World. This 5 00:00:15,880 --> 00:00:18,760 Speaker 1: is the second in a series of episodes we're presenting 6 00:00:19,160 --> 00:00:23,599 Speaker 1: about China and COVID nineteen. What role did China play 7 00:00:23,600 --> 00:00:27,360 Speaker 1: in the spread of COVID nineteen globally? What responsibility should 8 00:00:27,360 --> 00:00:31,520 Speaker 1: they bear for the devastation the virus has caused. In 9 00:00:31,560 --> 00:00:34,120 Speaker 1: this episode, I'll look at the role big data and 10 00:00:34,200 --> 00:00:38,960 Speaker 1: artificial intelligence can play and tracking global pandemics or disease 11 00:00:38,960 --> 00:00:42,680 Speaker 1: outbreaks anywhere in the world, and make the case for 12 00:00:42,840 --> 00:00:45,479 Speaker 1: why the United States health authorities should be using this 13 00:00:45,600 --> 00:00:49,919 Speaker 1: advanced technology to create an early warning system to hopefully 14 00:00:50,280 --> 00:00:54,040 Speaker 1: prevent the spread of future pandemics. I'm pleased to welcome 15 00:00:54,080 --> 00:00:58,440 Speaker 1: my guests, doctor Cameron Kahn, founder and CEO of glue 16 00:00:58,480 --> 00:01:12,399 Speaker 1: Dot and infectious disease physician. I'm delighted in the midst 17 00:01:12,440 --> 00:01:14,959 Speaker 1: of everything that's going on to have a chance to 18 00:01:15,360 --> 00:01:19,319 Speaker 1: chat with and to introduce doctor Cameron Cohn. He's the 19 00:01:19,440 --> 00:01:23,560 Speaker 1: founder and chief executive officer of Blue Dot. His background 20 00:01:23,720 --> 00:01:27,120 Speaker 1: is as an infectious disease physician. I think when you 21 00:01:27,160 --> 00:01:29,440 Speaker 1: get done hearing what he has achieved and how he 22 00:01:29,480 --> 00:01:32,560 Speaker 1: has put it together, you're going to be truly amazed 23 00:01:32,959 --> 00:01:35,760 Speaker 1: at some of the opportunities we faced and dramatically better 24 00:01:36,400 --> 00:01:41,920 Speaker 1: anticipation of and management of our response to various pandemics 25 00:01:42,000 --> 00:01:46,160 Speaker 1: or epidemics. Doctor Conn, thank you for joining us, Thank 26 00:01:46,200 --> 00:01:48,880 Speaker 1: you for having me. What led you to become a 27 00:01:48,920 --> 00:01:54,360 Speaker 1: specialist in epidemiology As a physician, I was always fascinated 28 00:01:54,480 --> 00:01:58,480 Speaker 1: with the field of infectious diseases. I think also was 29 00:01:59,120 --> 00:02:04,160 Speaker 1: really interested in understanding not only what's happening with my patients, 30 00:02:04,600 --> 00:02:08,880 Speaker 1: but how their illnesses might spill over into the population 31 00:02:08,919 --> 00:02:12,200 Speaker 1: and affect others. I have a big practice in tuberculosis, 32 00:02:12,200 --> 00:02:16,040 Speaker 1: for example, and so understanding what happens to my patient 33 00:02:16,080 --> 00:02:18,640 Speaker 1: and then when they walk out of the clinic and 34 00:02:18,720 --> 00:02:21,800 Speaker 1: they're out in the community, what's happening there. After I 35 00:02:21,800 --> 00:02:25,120 Speaker 1: finished my medical training at the University of Toronto, came 36 00:02:25,160 --> 00:02:28,040 Speaker 1: down to New York and to Boston and did my 37 00:02:28,120 --> 00:02:30,720 Speaker 1: infectious disease training. I'm trained as a public health and 38 00:02:30,760 --> 00:02:34,520 Speaker 1: permitted medicine position, and then did some of my epidemiologic 39 00:02:34,600 --> 00:02:37,320 Speaker 1: training at Columbia and Harvard, and then came back to 40 00:02:37,320 --> 00:02:40,320 Speaker 1: Toronto in two thousand and three. Really have just always 41 00:02:40,360 --> 00:02:43,800 Speaker 1: been kind of fascinated by infectious diseases and wanted to 42 00:02:43,800 --> 00:02:46,840 Speaker 1: have that understanding that could bridge what's happening with the 43 00:02:46,880 --> 00:02:50,040 Speaker 1: individual and how that goes out into the broader population. 44 00:02:50,800 --> 00:02:53,560 Speaker 1: I was in New York when Wesna virus made the 45 00:02:53,639 --> 00:02:57,200 Speaker 1: leap over into North America nineteen ninety nine, and was 46 00:02:57,240 --> 00:02:59,720 Speaker 1: there as well in two thousand and one and May 47 00:03:00,600 --> 00:03:03,600 Speaker 1: after nine to eleven anthrax was sweaponized and sent through 48 00:03:03,639 --> 00:03:08,040 Speaker 1: the US postal system, so was around these public health emergencies, 49 00:03:08,200 --> 00:03:11,000 Speaker 1: and then came back to Toronto, and then my experience 50 00:03:11,040 --> 00:03:15,600 Speaker 1: in two thousand and three really consolidated my interest in 51 00:03:15,639 --> 00:03:19,760 Speaker 1: emerging diseases and emergency response. When you got back to 52 00:03:19,760 --> 00:03:22,320 Speaker 1: Toronto in two thousand and three, it was right in 53 00:03:22,320 --> 00:03:24,400 Speaker 1: the middle of the Stars a break, wasn't it. It 54 00:03:24,440 --> 00:03:28,720 Speaker 1: was actually just before stars started. Shortly after I got here, 55 00:03:28,840 --> 00:03:31,400 Speaker 1: this virus that nobody had ever heard of our scene 56 00:03:31,440 --> 00:03:34,680 Speaker 1: before showed up in our hospitals, infected Actually one of 57 00:03:34,680 --> 00:03:37,680 Speaker 1: my close colleagues. A number of healthcare workers here in 58 00:03:37,720 --> 00:03:41,120 Speaker 1: the city died in the line of duty. It overwhelmed 59 00:03:41,120 --> 00:03:45,120 Speaker 1: our hospital, our public health infrastructure, and really crippled our 60 00:03:45,160 --> 00:03:48,240 Speaker 1: city for four very very long months. I think in 61 00:03:48,280 --> 00:03:51,480 Speaker 1: many ways what people are feeling around the world today 62 00:03:51,720 --> 00:03:55,120 Speaker 1: with COVID nineteen, we were feeling the same kind of 63 00:03:55,160 --> 00:03:58,960 Speaker 1: thing with just a different coronavirus, the Stars coronavirus back 64 00:03:58,960 --> 00:04:01,560 Speaker 1: in two thousand and three. And for me at the 65 00:04:01,720 --> 00:04:03,760 Speaker 1: end of it, there was a sense that we've never 66 00:04:03,760 --> 00:04:06,560 Speaker 1: seen anything like this before, but this won't be the 67 00:04:06,640 --> 00:04:09,119 Speaker 1: last time, and now's the time to start thinking about 68 00:04:09,160 --> 00:04:12,840 Speaker 1: ways we could perhaps get ourselves better prepared. What was 69 00:04:12,880 --> 00:04:17,280 Speaker 1: the source of the Stars upbreak? It emerged in Guangdong Province, 70 00:04:17,440 --> 00:04:22,040 Speaker 1: probably in late two thousand and two, started to amplify. 71 00:04:22,120 --> 00:04:24,839 Speaker 1: You will see there are parallels here with COVID nineteen 72 00:04:25,440 --> 00:04:29,480 Speaker 1: started to increase in activity in Guangdong Province, then moved 73 00:04:29,520 --> 00:04:33,240 Speaker 1: over into neighboring Hong Kong, and then of course Hong 74 00:04:33,360 --> 00:04:37,520 Speaker 1: Kong being a major global hub for transportation, quickly jumped 75 00:04:37,640 --> 00:04:41,960 Speaker 1: around the globe to about two dozen different countries and cities, 76 00:04:41,960 --> 00:04:44,680 Speaker 1: and then Toronto being one of them, and we just 77 00:04:44,800 --> 00:04:47,640 Speaker 1: were unfortunate and we had an outbreak. There were many 78 00:04:47,680 --> 00:04:50,760 Speaker 1: imported cases around the world, including in the United States, 79 00:04:51,040 --> 00:04:54,320 Speaker 1: but this was one place where the virus caught fire, 80 00:04:54,360 --> 00:04:57,640 Speaker 1: if you will, and started to trigger a few different 81 00:04:57,640 --> 00:05:00,000 Speaker 1: waves of an outbreak that went on for almost four month. 82 00:05:00,800 --> 00:05:03,760 Speaker 1: I happened to be in South Korea at the beginning 83 00:05:04,360 --> 00:05:08,560 Speaker 1: of the coronavirus epidemic. Yeah, it was interesting because the 84 00:05:08,680 --> 00:05:12,919 Speaker 1: Koreans had taken really seriously the experience of SARS, and 85 00:05:13,000 --> 00:05:17,560 Speaker 1: I think both Taiwan and Singapore were much more aggressive 86 00:05:17,640 --> 00:05:19,640 Speaker 1: and sort of had a sense that we've been here before, 87 00:05:20,320 --> 00:05:23,640 Speaker 1: we know how dangerous this is, and they reacted very 88 00:05:23,760 --> 00:05:27,680 Speaker 1: very fast. And it was really somehow SARS had imprinted 89 00:05:28,480 --> 00:05:32,000 Speaker 1: Korea much deeper than it did the United States. Let 90 00:05:32,040 --> 00:05:34,559 Speaker 1: me just first of all say, I think every other 91 00:05:35,480 --> 00:05:38,680 Speaker 1: city or country that has experienced this kind of outbreak, 92 00:05:38,839 --> 00:05:41,359 Speaker 1: Korea had an outbreak of mirrors and soul I believe 93 00:05:41,360 --> 00:05:44,760 Speaker 1: it was back in two and fifteen. Hong Kong, of course, 94 00:05:44,960 --> 00:05:48,520 Speaker 1: was hit very hard during the SARS outbreak. Singapore, Taiwan, 95 00:05:49,240 --> 00:05:52,520 Speaker 1: Toronto also being one of those places where we had 96 00:05:52,520 --> 00:05:55,880 Speaker 1: a significant outbreak. One of my close colleagues got infected 97 00:05:55,920 --> 00:05:59,400 Speaker 1: with SARS. We had other frontline healthcare workers who died, 98 00:06:00,000 --> 00:06:02,560 Speaker 1: and I think what that did is it really sent 99 00:06:02,920 --> 00:06:06,320 Speaker 1: a lot of angst through the healthcare workforce. We're on 100 00:06:06,360 --> 00:06:10,240 Speaker 1: the front lines, and it's hard for us as clinicians 101 00:06:10,360 --> 00:06:13,400 Speaker 1: to be able to have this global panoramic view of 102 00:06:13,440 --> 00:06:16,560 Speaker 1: what's happening around the world. There's an adage in medicine 103 00:06:16,600 --> 00:06:18,440 Speaker 1: I believe was coined by an American physician in the 104 00:06:18,520 --> 00:06:21,520 Speaker 1: nineteen forties that when you hear hoofbeats, think of horses, 105 00:06:21,600 --> 00:06:24,120 Speaker 1: not zebras. And that's kind of what we're used to 106 00:06:24,240 --> 00:06:26,359 Speaker 1: thinking about the things that we see day in and 107 00:06:26,440 --> 00:06:29,640 Speaker 1: day out in our own backyard. But our backyard has 108 00:06:29,680 --> 00:06:32,919 Speaker 1: just gotten a whole lot bigger, and now we have 109 00:06:33,000 --> 00:06:35,839 Speaker 1: to be thinking about events happening all around the world. 110 00:06:36,320 --> 00:06:39,520 Speaker 1: In many ways, that was for me the inspiration to say, 111 00:06:40,200 --> 00:06:43,920 Speaker 1: these diseases spread quickly, but we have the raw ingredients 112 00:06:43,960 --> 00:06:47,159 Speaker 1: to actually spread information and knowledge even faster. We've got 113 00:06:47,360 --> 00:06:50,760 Speaker 1: increasing access to data, we have advanced analytical tools like 114 00:06:50,880 --> 00:06:53,800 Speaker 1: artificial intelligence to make sense of these data, and we 115 00:06:53,920 --> 00:06:57,239 Speaker 1: can spread information through the internet faster than any outbreak 116 00:06:57,279 --> 00:06:59,880 Speaker 1: can spread. So how do we take advantage of these 117 00:07:00,160 --> 00:07:02,440 Speaker 1: types of digital tools and assets that we have and 118 00:07:02,960 --> 00:07:06,800 Speaker 1: use them to be better prepared and to be better coordinated. 119 00:07:07,160 --> 00:07:10,240 Speaker 1: We need to be providing this type of intelligence to 120 00:07:10,280 --> 00:07:13,280 Speaker 1: the frontline healthcare workers because we rely on them to 121 00:07:13,320 --> 00:07:16,280 Speaker 1: protect themselves and to protect the rest of us more 122 00:07:16,320 --> 00:07:19,080 Speaker 1: effective and efficient in the way that we detect these 123 00:07:19,080 --> 00:07:22,840 Speaker 1: threats and respond to them. You really got involved directly 124 00:07:22,880 --> 00:07:26,880 Speaker 1: in thinking about ways to do this that I think 125 00:07:26,960 --> 00:07:30,800 Speaker 1: is pretty intriguing. How you've approached it, What led you 126 00:07:30,840 --> 00:07:34,120 Speaker 1: to go to this kind of quantifiable approach. It did 127 00:07:34,160 --> 00:07:37,520 Speaker 1: strike me that it was remarkably powerful what you've done 128 00:07:38,240 --> 00:07:41,160 Speaker 1: when two thousand and three, I'm a scientist. I'm a 129 00:07:41,200 --> 00:07:43,640 Speaker 1: professor at the University of Toronto. So what I do 130 00:07:43,920 --> 00:07:46,680 Speaker 1: is like many other academics, I write grants and I 131 00:07:46,720 --> 00:07:50,200 Speaker 1: start doing research. I'm interested in understanding if there are 132 00:07:50,200 --> 00:07:52,960 Speaker 1: ways that we can better anticipate how disease is spread. 133 00:07:53,080 --> 00:07:57,880 Speaker 1: I become particularly fascinated with the global airline transportation network 134 00:07:57,920 --> 00:08:02,480 Speaker 1: as a conduit for the global infectious diseases. It's often 135 00:08:02,520 --> 00:08:05,280 Speaker 1: said that we're just an airplane right away. Can we 136 00:08:05,400 --> 00:08:08,800 Speaker 1: understand this global network they're connecting the planet. If we 137 00:08:08,840 --> 00:08:11,560 Speaker 1: can understand those, we may be able to better anticipate 138 00:08:11,640 --> 00:08:14,200 Speaker 1: how diseases will spread. But I think one of the 139 00:08:14,280 --> 00:08:17,920 Speaker 1: key issues really is optimization of time, and this is 140 00:08:17,920 --> 00:08:21,000 Speaker 1: a theme that we're hearing quite a bit discussed around 141 00:08:21,040 --> 00:08:25,880 Speaker 1: COVID nineteen. Time is a non renewable resource. You don't 142 00:08:25,920 --> 00:08:28,720 Speaker 1: get it back, and you've got to be using your 143 00:08:28,760 --> 00:08:33,199 Speaker 1: time in the most effective and efficient way possible. So 144 00:08:33,800 --> 00:08:37,160 Speaker 1: in twenty thirteen, that's when I ended out founding Blue Dot. 145 00:08:37,240 --> 00:08:40,520 Speaker 1: The belief and thesis there was we have the raw 146 00:08:40,640 --> 00:08:43,680 Speaker 1: ingredients to build a digital global early warning system for 147 00:08:43,760 --> 00:08:48,440 Speaker 1: infectious diseases. Let's accelerate that. Let's actually move even faster 148 00:08:48,559 --> 00:08:52,080 Speaker 1: than we can in the academic sector and draw from 149 00:08:52,120 --> 00:08:55,240 Speaker 1: the various talents that are out there in machine learning 150 00:08:55,280 --> 00:08:59,160 Speaker 1: and medicine and epidemiology, and let's bring a diverse group 151 00:08:59,200 --> 00:09:03,280 Speaker 1: together to tackle this problem. Because we know the next 152 00:09:03,320 --> 00:09:05,559 Speaker 1: threat is coming. We can't tell you exactly when it's 153 00:09:05,559 --> 00:09:08,200 Speaker 1: going to appear, but we know that there will be 154 00:09:08,400 --> 00:09:11,400 Speaker 1: threats arising in the not too distant future. I'm happy 155 00:09:11,440 --> 00:09:13,400 Speaker 1: if you'd like to kind of talk you through the 156 00:09:13,400 --> 00:09:15,800 Speaker 1: pillars of this global early warning system and how they're 157 00:09:15,800 --> 00:09:19,079 Speaker 1: all integrated, and how they go from detection to assessment 158 00:09:19,120 --> 00:09:22,640 Speaker 1: of risk, to dissemination of knowledge, to empower or some 159 00:09:22,760 --> 00:09:25,840 Speaker 1: kind of response and timely action. Yeah, I think it'll 160 00:09:25,840 --> 00:09:28,080 Speaker 1: be helpful because it essems to me that you've broken 161 00:09:28,120 --> 00:09:33,440 Speaker 1: the code on thinking about a worldwide management system for 162 00:09:34,240 --> 00:09:40,640 Speaker 1: anticipating and focusing on communicable diseases, not just pandemics viruses. 163 00:09:40,920 --> 00:09:44,720 Speaker 1: So walk us through how this works. The first one 164 00:09:44,840 --> 00:09:47,760 Speaker 1: is detection. We have to be able to detect outbreaks 165 00:09:47,760 --> 00:09:50,760 Speaker 1: and threats early because that's how we give ourselves lead time. 166 00:09:51,679 --> 00:09:53,880 Speaker 1: What we learned during the SARS outbreak was that if 167 00:09:53,920 --> 00:09:58,600 Speaker 1: we wait for official reports from government agencies about outbreaks, 168 00:09:58,720 --> 00:10:01,920 Speaker 1: we may be waiting long than we would like. The 169 00:10:02,040 --> 00:10:06,720 Speaker 1: next is dispersion. We know that humans have become the 170 00:10:06,800 --> 00:10:10,120 Speaker 1: vectors that are carrying many of these diseases around the world. 171 00:10:10,840 --> 00:10:15,000 Speaker 1: Just to put this in perspective, last year in twenty nineteen, 172 00:10:15,120 --> 00:10:19,720 Speaker 1: the world traveled over seven trillion kilometers on commercial flights. 173 00:10:19,720 --> 00:10:22,760 Speaker 1: I mean we actually analyze all these data and to 174 00:10:22,800 --> 00:10:25,720 Speaker 1: put that in perspective, that's over twenty thousand round trips 175 00:10:25,720 --> 00:10:28,280 Speaker 1: to and from the Sun. This is a lot of movement. 176 00:10:28,559 --> 00:10:31,160 Speaker 1: About a trillion of that actually comes just from travel 177 00:10:31,200 --> 00:10:33,360 Speaker 1: to and from the United States, which is the largest 178 00:10:33,360 --> 00:10:37,640 Speaker 1: and most mobile population on Earth, so we recognize that 179 00:10:37,720 --> 00:10:41,480 Speaker 1: these types of diseases can leap across continents. So the 180 00:10:41,520 --> 00:10:45,040 Speaker 1: next pillar, or the third is what we would call disruption, 181 00:10:45,240 --> 00:10:47,000 Speaker 1: and this is a hard one. This is one we're 182 00:10:47,040 --> 00:10:49,880 Speaker 1: continuing to work on now. Disease is spread around the 183 00:10:49,880 --> 00:10:52,520 Speaker 1: world all the time. I see them in the emergency 184 00:10:52,520 --> 00:10:55,760 Speaker 1: department at my hospital, people with malaria and dingy fever 185 00:10:55,840 --> 00:11:00,640 Speaker 1: and other types of infections, but they don't all cause outbreak. Now, 186 00:11:01,280 --> 00:11:04,160 Speaker 1: what is it that actually triggers an outbreak? And this 187 00:11:04,240 --> 00:11:08,040 Speaker 1: is sometimes called the infectious disease triangle, that the impact 188 00:11:08,120 --> 00:11:11,120 Speaker 1: from a microbe really lies at the crossroads of the 189 00:11:11,200 --> 00:11:15,680 Speaker 1: characteristics of the pathogen or the microbe itself, the attributes 190 00:11:15,760 --> 00:11:19,480 Speaker 1: of the population in which it exists, as well as 191 00:11:19,520 --> 00:11:24,520 Speaker 1: the environmental conditions where it exists. So as you can imagine, 192 00:11:24,559 --> 00:11:29,640 Speaker 1: for every single microbe, there's a different footprint. The fourth 193 00:11:29,679 --> 00:11:33,199 Speaker 1: one is dissemination of knowledge. This is a really important 194 00:11:33,200 --> 00:11:37,520 Speaker 1: one for me. And the way that information typically flows 195 00:11:38,080 --> 00:11:41,760 Speaker 1: is it typically goes about an outbreak. If there's news 196 00:11:41,760 --> 00:11:45,040 Speaker 1: of an outbreak, the first audience that typically learns about 197 00:11:45,080 --> 00:11:47,440 Speaker 1: it is the public health sector and that's because their 198 00:11:47,559 --> 00:11:51,199 Speaker 1: job is to understand what's happening in the population. Now, 199 00:11:51,240 --> 00:11:54,280 Speaker 1: certainly we know that not every government, necessarily in public 200 00:11:54,320 --> 00:11:58,760 Speaker 1: health agency, has the appropriate resources, both capital and human resources, 201 00:11:58,760 --> 00:12:00,600 Speaker 1: to be able to conduct this kind of surveillance, but 202 00:12:00,640 --> 00:12:03,920 Speaker 1: nonetheless they typically find out first. Then there's kind of 203 00:12:03,920 --> 00:12:07,640 Speaker 1: a trickle down effect that happens to the healthcare community. 204 00:12:08,040 --> 00:12:11,440 Speaker 1: We need to be empowering governments, public health agencies, and 205 00:12:11,480 --> 00:12:14,760 Speaker 1: other branches of government so that they actually are better 206 00:12:14,840 --> 00:12:34,680 Speaker 1: able to protect their citizens. You sent out an early 207 00:12:34,760 --> 00:12:38,280 Speaker 1: warning on December thirty first, was that a warning of 208 00:12:38,440 --> 00:12:41,800 Speaker 1: something interesting was happening, or warning that there was a 209 00:12:41,800 --> 00:12:45,560 Speaker 1: potential epidemic or what was the intensity of your early warning? Well, 210 00:12:45,600 --> 00:12:49,320 Speaker 1: we were actually very concerned back on December thirty first. 211 00:12:49,360 --> 00:12:51,920 Speaker 1: I do almost remember kind of gasping a little bit 212 00:12:51,960 --> 00:12:55,520 Speaker 1: when we had read this news, and it was largely 213 00:12:55,559 --> 00:12:58,680 Speaker 1: because of the parallels to the stars outbreak. We didn't 214 00:12:58,679 --> 00:13:00,880 Speaker 1: know that it was going to be a pandemic. Normally, 215 00:13:00,920 --> 00:13:03,680 Speaker 1: our information goes out to you based on your location, 216 00:13:03,760 --> 00:13:06,160 Speaker 1: So if I'm in Toronto and there's an outbreak in 217 00:13:06,240 --> 00:13:08,440 Speaker 1: my backyard, I will learn about it. If there's an 218 00:13:08,440 --> 00:13:11,120 Speaker 1: outbreak halfway around the world and there are ten thousand 219 00:13:11,160 --> 00:13:13,680 Speaker 1: people traveling from that location to Toronto. I will learn 220 00:13:13,720 --> 00:13:16,320 Speaker 1: about it. We sent this out to everyone and basically 221 00:13:16,320 --> 00:13:20,319 Speaker 1: what we highlighted was there is an outbreak involving reported 222 00:13:20,400 --> 00:13:24,360 Speaker 1: twenty seven individuals. Seems to be associated with this wet market. 223 00:13:24,520 --> 00:13:27,400 Speaker 1: It doesn't seem to be associated with the usual suspects 224 00:13:27,440 --> 00:13:31,160 Speaker 1: that might cause a pneumonia, and so we will continue 225 00:13:31,160 --> 00:13:33,800 Speaker 1: to monitor this situation and let you know more about 226 00:13:33,840 --> 00:13:36,640 Speaker 1: it as we learn. But this is something that is 227 00:13:36,679 --> 00:13:39,880 Speaker 1: worthy of paying attention to, and so our partners and 228 00:13:39,920 --> 00:13:43,680 Speaker 1: clients are across in twelve countries. We sent that information out. 229 00:13:43,760 --> 00:13:46,800 Speaker 1: It was before ten am on December thirty. First, I 230 00:13:46,840 --> 00:13:49,360 Speaker 1: do want to highlight one other important thing, which is 231 00:13:50,160 --> 00:13:54,840 Speaker 1: we're a very social impact oriented organization. As this happened, 232 00:13:54,880 --> 00:13:58,960 Speaker 1: we immediately wrote this article submitted it to an open access, 233 00:13:59,120 --> 00:14:02,640 Speaker 1: peer reviewed aific journal. We did that on January eighth, 234 00:14:02,640 --> 00:14:05,480 Speaker 1: and we did that because we don't have lines of 235 00:14:05,520 --> 00:14:09,200 Speaker 1: communication with every organization or country, but this would be 236 00:14:09,200 --> 00:14:11,400 Speaker 1: the best way for our work to be peer reviewed 237 00:14:11,720 --> 00:14:15,120 Speaker 1: and then openly accessible to anyone to understand what are 238 00:14:15,120 --> 00:14:17,800 Speaker 1: the places at risk. That's where we had, for example, 239 00:14:17,840 --> 00:14:21,760 Speaker 1: identified that Bangkok, for instance, was right at the top 240 00:14:21,800 --> 00:14:24,040 Speaker 1: of our list of the cities to look for next 241 00:14:24,080 --> 00:14:27,680 Speaker 1: and low and behold, Bangkok was the first city that 242 00:14:27,760 --> 00:14:31,240 Speaker 1: actually had reported cases of COVID nineteen as it spread 243 00:14:31,280 --> 00:14:33,560 Speaker 1: out of mainland China. So that's kind of the warning 244 00:14:33,560 --> 00:14:35,480 Speaker 1: that we had sent to our clients, but then more 245 00:14:35,520 --> 00:14:39,920 Speaker 1: broadly using the scientific literature as a way to open 246 00:14:39,960 --> 00:14:43,320 Speaker 1: this information up to anyone, to make it broadly accessible. 247 00:14:43,880 --> 00:14:48,120 Speaker 1: If the Chinese government had understood how risk of this 248 00:14:48,320 --> 00:14:53,320 Speaker 1: was and had canceled Chinese New Year when they have 249 00:14:53,400 --> 00:14:56,680 Speaker 1: this enormous s flow of people, how big a difference 250 00:14:56,680 --> 00:14:59,640 Speaker 1: do you think that might have made. I've also been 251 00:14:59,680 --> 00:15:03,320 Speaker 1: studied mass gatherings as an academic for years, everything from 252 00:15:03,360 --> 00:15:07,280 Speaker 1: the Olympic Games to the Hodge pilgrimage in Saudi Arabia. 253 00:15:07,640 --> 00:15:12,880 Speaker 1: Mass gatherings can be a massive amplifier and accelerator for outbreaks. 254 00:15:13,200 --> 00:15:15,960 Speaker 1: It's very intuitive. If you bring large numbers of people 255 00:15:16,000 --> 00:15:19,160 Speaker 1: together and it's a disease that spreads from person to person, 256 00:15:20,000 --> 00:15:23,240 Speaker 1: you increase the number of connection points, you increase the 257 00:15:23,280 --> 00:15:26,360 Speaker 1: opportunity for the virus to spread, and then as those 258 00:15:26,400 --> 00:15:30,520 Speaker 1: individuals moved back to their home locations. You now actually 259 00:15:30,560 --> 00:15:34,360 Speaker 1: accelerate that dispersion. So yes, this was happening right around 260 00:15:34,400 --> 00:15:37,400 Speaker 1: the time of the Chinese New Year festivities. And I 261 00:15:37,440 --> 00:15:40,440 Speaker 1: know this is an outstanding question what was known exactly when, 262 00:15:40,960 --> 00:15:45,440 Speaker 1: But I do think had some of those gatherings been avoided, 263 00:15:45,800 --> 00:15:48,720 Speaker 1: the trajectory of this outbreak may have been different. It's 264 00:15:48,760 --> 00:15:52,120 Speaker 1: possible that it would have still unfolded as a pandemic, 265 00:15:52,160 --> 00:15:54,320 Speaker 1: but we may have bought ourselves more time and been 266 00:15:54,360 --> 00:15:58,560 Speaker 1: able to mitigate some of the impacts. President Trump closest 267 00:15:58,680 --> 00:16:02,640 Speaker 1: travel China and the thirty first of January, but we 268 00:16:02,720 --> 00:16:06,880 Speaker 1: don't move to cut to Europe until the eleventh of 269 00:16:06,960 --> 00:16:09,880 Speaker 1: March and written on the fourteenth. And it seemed to 270 00:16:09,920 --> 00:16:13,200 Speaker 1: have been a surprise to people that the disease could 271 00:16:13,240 --> 00:16:16,400 Speaker 1: go both ways, that as you could come from China 272 00:16:16,680 --> 00:16:19,080 Speaker 1: to the US, but you can also come from China 273 00:16:19,320 --> 00:16:22,200 Speaker 1: to Europe to the US. And I think that's part 274 00:16:22,200 --> 00:16:24,560 Speaker 1: of what happened in New York City, is that that 275 00:16:24,760 --> 00:16:26,680 Speaker 1: a false sense of confidence that it was a West 276 00:16:26,720 --> 00:16:29,640 Speaker 1: coast problem, when in fact it was coming right at 277 00:16:29,680 --> 00:16:32,880 Speaker 1: them from Europe. When you were looking at Europe projections, 278 00:16:33,400 --> 00:16:36,200 Speaker 1: did you see this kind of three hundred and sixty 279 00:16:36,200 --> 00:16:41,480 Speaker 1: degree dispersal pattern. Absolutely, I think our analytics identified, just 280 00:16:41,560 --> 00:16:43,840 Speaker 1: if we looked at Wuhan, let alone, some of the 281 00:16:43,880 --> 00:16:47,840 Speaker 1: broader neighboring airports adjacent to Wuhan, because again we didn't 282 00:16:47,880 --> 00:16:50,600 Speaker 1: know the extent of the outbreak. When we did see 283 00:16:50,640 --> 00:16:52,800 Speaker 1: the first case show up in Bangkok, I believe it 284 00:16:52,840 --> 00:16:56,560 Speaker 1: was on January thirteenth, that was an indication to us 285 00:16:57,000 --> 00:16:59,080 Speaker 1: we're not talking about a few dozen cases in a 286 00:16:59,120 --> 00:17:01,720 Speaker 1: city of eleven million. And when we look at the 287 00:17:01,760 --> 00:17:05,040 Speaker 1: flows of travelers, when you see cases showing up in 288 00:17:05,080 --> 00:17:08,439 Speaker 1: another country, the fire, if you will, is bigger than 289 00:17:08,480 --> 00:17:10,680 Speaker 1: a little spark. It's got to be much much larger. 290 00:17:11,040 --> 00:17:14,400 Speaker 1: When we looked at travel we saw in the US 291 00:17:14,760 --> 00:17:18,960 Speaker 1: the places that were at greatest risk from our analysis 292 00:17:19,000 --> 00:17:23,240 Speaker 1: back on December thirty first were San Francisco, Los Angeles, 293 00:17:23,240 --> 00:17:26,640 Speaker 1: and New York City. NonStop flights into San Francisco, NonStop 294 00:17:26,680 --> 00:17:30,879 Speaker 1: flights into New York City Los Angeles had a significant 295 00:17:30,920 --> 00:17:34,879 Speaker 1: number of travelers post connections or weren't NonStop flights. So 296 00:17:34,920 --> 00:17:37,400 Speaker 1: when we do look at San Francisco, and you may 297 00:17:37,440 --> 00:17:40,960 Speaker 1: have read some of the recent reports that the first 298 00:17:41,200 --> 00:17:43,840 Speaker 1: known deaths from COVID nineteen in the US was in 299 00:17:43,880 --> 00:17:48,520 Speaker 1: Santa Clara County, right adjacent to the airport in San Francisco. 300 00:17:48,960 --> 00:17:50,760 Speaker 1: And then of course New York City we can see 301 00:17:50,800 --> 00:17:54,560 Speaker 1: has really suffered an explosive outbreak. There may be multiple reasons, 302 00:17:54,600 --> 00:17:57,240 Speaker 1: but one of them certainly could be that it was 303 00:17:57,280 --> 00:18:00,679 Speaker 1: seeded very early and kind of amplified and grew just 304 00:18:00,720 --> 00:18:03,880 Speaker 1: sort of beneath the surface without recognizing that it was there. 305 00:18:03,920 --> 00:18:06,600 Speaker 1: And these types of things is you know, can grow exponentially. 306 00:18:06,960 --> 00:18:10,159 Speaker 1: If you're caught a little bit late, the growth trajectory 307 00:18:10,200 --> 00:18:13,440 Speaker 1: can be quite rapid. So when we look at the US, 308 00:18:13,480 --> 00:18:16,040 Speaker 1: I think what we have seen with the earliest death 309 00:18:16,119 --> 00:18:18,840 Speaker 1: and the outbreak in New York City is very much 310 00:18:18,880 --> 00:18:21,800 Speaker 1: aligned with the early analytics that we did back at 311 00:18:21,800 --> 00:18:24,639 Speaker 1: the end of December. So we are currently, as I 312 00:18:24,760 --> 00:18:29,520 Speaker 1: understand it, working with both California and the City of Chicago. 313 00:18:30,000 --> 00:18:32,960 Speaker 1: Correct what are the kind of things you provide them, 314 00:18:32,960 --> 00:18:35,800 Speaker 1: whether you help them one Well, what we've been building 315 00:18:35,840 --> 00:18:38,879 Speaker 1: with Blue Dot over the last several years is really 316 00:18:38,920 --> 00:18:43,800 Speaker 1: thinking about solutions across the entire course or life cycle 317 00:18:43,880 --> 00:18:48,320 Speaker 1: of an outbreak. With our work in Chicago, it's about 318 00:18:48,480 --> 00:18:52,320 Speaker 1: not just thinking about COVID nineteen, it's actually about building 319 00:18:52,359 --> 00:18:55,960 Speaker 1: the systems and implementing the systems that are giving that 320 00:18:56,080 --> 00:18:59,359 Speaker 1: three sixty panoramic view of infectious disease threats on a 321 00:18:59,440 --> 00:19:03,000 Speaker 1: day to day basis, and understanding how all those outbreaks 322 00:19:03,000 --> 00:19:05,560 Speaker 1: are connected to Chicago and what kinds of risks there 323 00:19:05,560 --> 00:19:06,960 Speaker 1: may be on a day to day basis, how to 324 00:19:07,000 --> 00:19:11,159 Speaker 1: prioritize and understand all the risks across the globe. In 325 00:19:11,280 --> 00:19:14,800 Speaker 1: working with California, as we start to look at transmission 326 00:19:15,480 --> 00:19:18,879 Speaker 1: within our own communities, not thinking globally now but looking 327 00:19:18,920 --> 00:19:22,840 Speaker 1: literally in our own backyards. Is we've also been working 328 00:19:23,000 --> 00:19:26,080 Speaker 1: with and I want to underscore anonymous data. It's all 329 00:19:26,119 --> 00:19:30,959 Speaker 1: aggregated from the locations of mobile devices in these communities 330 00:19:31,000 --> 00:19:35,199 Speaker 1: to better understand population movements at a local scale, and 331 00:19:35,280 --> 00:19:38,160 Speaker 1: that helps us understand things like stay at home orders 332 00:19:38,200 --> 00:19:41,720 Speaker 1: and our populations adhering to that where might there be 333 00:19:41,760 --> 00:19:45,760 Speaker 1: congregations occurring, so that ultimately your finite health and human 334 00:19:45,840 --> 00:19:49,960 Speaker 1: resources you can be using them in the most effective 335 00:19:50,080 --> 00:19:53,760 Speaker 1: and efficient and coordinated manner possible. And so those are 336 00:19:53,800 --> 00:19:57,200 Speaker 1: a few examples of how we've been able to work 337 00:19:57,240 --> 00:19:59,520 Speaker 1: with the state of California in the city of Chicago 338 00:20:00,040 --> 00:20:03,439 Speaker 1: to generate some really localized insights about the spread of 339 00:20:03,440 --> 00:20:08,040 Speaker 1: COVID nineteen domestically within communities. And as we start to 340 00:20:08,080 --> 00:20:11,760 Speaker 1: think about reopening our economies, how do we look inwards 341 00:20:11,760 --> 00:20:14,040 Speaker 1: and manage the outbreak, but how do we also keep 342 00:20:14,080 --> 00:20:18,200 Speaker 1: one eye looking outwards and thinking about where introductions might 343 00:20:18,240 --> 00:20:39,240 Speaker 1: be coming from next? Are you a little surprised that 344 00:20:39,359 --> 00:20:45,119 Speaker 1: the virus has not been more devastating in the Third World? 345 00:20:45,720 --> 00:20:49,879 Speaker 1: I don't sense the scale of crisis you might have 346 00:20:49,960 --> 00:20:52,199 Speaker 1: expected if you look in literally or a France, or 347 00:20:52,720 --> 00:20:55,960 Speaker 1: Great Britain or the US forever or we seem to 348 00:20:56,000 --> 00:21:00,360 Speaker 1: be in more trouble than places in Africa or parts 349 00:21:00,400 --> 00:21:03,880 Speaker 1: of South Asian. Just because we can't see it right 350 00:21:03,880 --> 00:21:06,679 Speaker 1: now doesn't mean that it's not happening. And it also 351 00:21:06,880 --> 00:21:09,680 Speaker 1: is that many of the developing countries are less connected, 352 00:21:09,720 --> 00:21:12,920 Speaker 1: which means they are probably seeded later, they just maybe 353 00:21:12,960 --> 00:21:16,360 Speaker 1: earlier in the course of the outbreak. I would honestly 354 00:21:16,359 --> 00:21:20,959 Speaker 1: say I would be surprised if somehow developing countries do 355 00:21:21,040 --> 00:21:26,520 Speaker 1: not experience devastating outbreaks. I really worry about those countries 356 00:21:26,560 --> 00:21:31,440 Speaker 1: where there are just enormous disparities in access to housing 357 00:21:31,640 --> 00:21:35,679 Speaker 1: and income, and what the consequences may be. There are 358 00:21:35,760 --> 00:21:39,840 Speaker 1: some questions about what role does the environment play. Most 359 00:21:39,840 --> 00:21:42,280 Speaker 1: of the world has no immunity to this virus, but 360 00:21:42,640 --> 00:21:46,200 Speaker 1: maybe things like temperature and humidity might play a role, 361 00:21:46,359 --> 00:21:49,520 Speaker 1: possibly and maybe introducing a little bit of a headwind. 362 00:21:49,800 --> 00:21:52,440 Speaker 1: I don't think it's going to prevent this from spreading, 363 00:21:52,680 --> 00:21:55,400 Speaker 1: but we are seeing data now from places like Brazil 364 00:21:55,520 --> 00:21:59,480 Speaker 1: and Ecuador and other developing countries where there is a 365 00:21:59,600 --> 00:22:03,119 Speaker 1: swing that is going up. I would be kind of 366 00:22:03,160 --> 00:22:06,639 Speaker 1: presuming that this will get worse and operating under that 367 00:22:06,680 --> 00:22:10,439 Speaker 1: assumption rather than presuming that things will go well. We 368 00:22:10,520 --> 00:22:13,000 Speaker 1: can hope for the best, but I think we want 369 00:22:13,040 --> 00:22:14,840 Speaker 1: to do everything we can to be preparing for the 370 00:22:14,880 --> 00:22:17,879 Speaker 1: worst case scenario. I think it's yet to be seen, 371 00:22:18,040 --> 00:22:20,840 Speaker 1: and we know that if you're not doing widespread testing, 372 00:22:21,440 --> 00:22:24,840 Speaker 1: you just can't actually see what's happening. When you see it, 373 00:22:24,840 --> 00:22:27,240 Speaker 1: it's when there are large numbers of deaths that is 374 00:22:27,240 --> 00:22:30,320 Speaker 1: obviously becomes much more apparent. But right now I think 375 00:22:30,320 --> 00:22:32,879 Speaker 1: we're just seeing the tip of the iceberg. Probably the 376 00:22:32,920 --> 00:22:36,200 Speaker 1: month of May and June will I think reveal whether 377 00:22:36,280 --> 00:22:39,320 Speaker 1: or not, these countries are experiencing larger outbreaks and we 378 00:22:39,359 --> 00:22:41,879 Speaker 1: are aware of it. This time. It seems to be 379 00:22:41,880 --> 00:22:46,000 Speaker 1: a general consensus that there's a real potential danger that 380 00:22:46,119 --> 00:22:48,879 Speaker 1: we will beat this down in the roads of linear 381 00:22:48,960 --> 00:22:52,239 Speaker 1: future in the industrial countries, but that we can get 382 00:22:52,280 --> 00:22:55,399 Speaker 1: a second wave in six or eight or nine months 383 00:22:55,400 --> 00:22:59,560 Speaker 1: from now. Wouldn't it make sense to take your model 384 00:22:59,680 --> 00:23:02,520 Speaker 1: and the things you're trying to do and lay it 385 00:23:02,640 --> 00:23:07,000 Speaker 1: up and actually have our first real intense planning and 386 00:23:07,160 --> 00:23:12,280 Speaker 1: intense implementation in that cycle, because right now people are 387 00:23:12,320 --> 00:23:15,600 Speaker 1: aware of how dangerous this is. We need to find 388 00:23:15,680 --> 00:23:19,000 Speaker 1: a strategy that let's us avoid a second total shutdown 389 00:23:19,040 --> 00:23:21,679 Speaker 1: of the economy, and to do that we need to 390 00:23:21,720 --> 00:23:26,400 Speaker 1: have very early warning and very early management of the process. 391 00:23:27,160 --> 00:23:30,680 Speaker 1: Early on, it's early detection, it's a risk of introduction. 392 00:23:30,800 --> 00:23:34,400 Speaker 1: Now we're dealing with trying to manage the local transmission 393 00:23:34,400 --> 00:23:36,880 Speaker 1: of this. But as we come past the peak, we 394 00:23:36,920 --> 00:23:41,119 Speaker 1: will find ourselves where New Zealand is, or China or 395 00:23:41,160 --> 00:23:45,720 Speaker 1: South Korea is, where there's very little or limited transmission 396 00:23:46,040 --> 00:23:49,200 Speaker 1: locally or domestically within those countries, and so the shift 397 00:23:49,280 --> 00:23:52,760 Speaker 1: is going to then move to as this pandemic rolls 398 00:23:52,800 --> 00:23:55,760 Speaker 1: across different parts of the world. How do we then 399 00:23:56,040 --> 00:23:59,919 Speaker 1: utilize our resources in the most effective way possible, anticipate 400 00:24:00,119 --> 00:24:03,960 Speaker 1: where this may be coming in from, and do that strategically, 401 00:24:03,960 --> 00:24:05,919 Speaker 1: and do it in a way that's really data driven, 402 00:24:06,400 --> 00:24:10,720 Speaker 1: because what will happen is post peak, most of the 403 00:24:10,800 --> 00:24:14,800 Speaker 1: population will still be susceptible to this virus. While it 404 00:24:14,840 --> 00:24:18,600 Speaker 1: has spread significantly, it's not that there's widespread immunity. We 405 00:24:18,640 --> 00:24:22,360 Speaker 1: still have to understand more about what happens after infection 406 00:24:22,359 --> 00:24:25,679 Speaker 1: in our people immune. But the next introduction, the next ember, 407 00:24:25,720 --> 00:24:29,160 Speaker 1: if you will, could trigger another outbreak and lead into 408 00:24:29,200 --> 00:24:30,800 Speaker 1: a second wave. So I think we need to be 409 00:24:31,160 --> 00:24:34,199 Speaker 1: thinking carefully about that. But what I will say is, 410 00:24:34,400 --> 00:24:37,560 Speaker 1: I think it's so important is that we also have 411 00:24:37,640 --> 00:24:41,280 Speaker 1: to be thinking about implementing systems to look past COVID nineteen. 412 00:24:41,359 --> 00:24:45,080 Speaker 1: Everyone knows COVID nineteen is here now, but we don't 413 00:24:45,119 --> 00:24:48,560 Speaker 1: know when the next thing is going to appear, And 414 00:24:48,800 --> 00:24:52,720 Speaker 1: the next outbreak won't necessarily care whether we're experiencing a 415 00:24:53,320 --> 00:24:56,320 Speaker 1: nineteen pandemic or not. It could come whenever, And so 416 00:24:56,359 --> 00:24:59,800 Speaker 1: I think this is ultimately about building systems, building decision 417 00:24:59,840 --> 00:25:04,359 Speaker 1: may processes, empowering the whole of society to mobilize around 418 00:25:04,440 --> 00:25:08,440 Speaker 1: this response for our individual benefit and our collective benefit. 419 00:25:09,320 --> 00:25:11,560 Speaker 1: My thought is that you have an excuse right now, 420 00:25:12,400 --> 00:25:15,840 Speaker 1: take the next six or eight months and develop that 421 00:25:17,040 --> 00:25:21,480 Speaker 1: universal mechanism as a method of responding to the potential 422 00:25:21,520 --> 00:25:24,159 Speaker 1: for a second wave, because right now you're going to 423 00:25:24,240 --> 00:25:27,760 Speaker 1: have the intensity and the awareness of how important it 424 00:25:27,800 --> 00:25:30,439 Speaker 1: is if you wait until after this one fades away. 425 00:25:30,760 --> 00:25:34,800 Speaker 1: As you know, it's radically harder to get resources and 426 00:25:34,880 --> 00:25:41,040 Speaker 1: attention in between crises. That is definitely true. Some refer 427 00:25:41,119 --> 00:25:43,119 Speaker 1: to this as the panic neglect cycle it and you're 428 00:25:43,160 --> 00:25:46,359 Speaker 1: absolutely right. I mean, our brains are hardwired in a 429 00:25:46,359 --> 00:25:49,680 Speaker 1: way where we're reactive. It's hard to get people's attention. 430 00:25:50,080 --> 00:25:52,159 Speaker 1: It's hard to tell someone while their house is on 431 00:25:52,200 --> 00:25:55,359 Speaker 1: fire they should install a smoke detector. But as things 432 00:25:55,400 --> 00:25:57,720 Speaker 1: start to wind down a little bit and we have 433 00:25:57,760 --> 00:26:00,640 Speaker 1: a moment to breathe and reflect, that the right kind 434 00:26:00,640 --> 00:26:04,119 Speaker 1: of moment to be thinking proactively about a resurgence of 435 00:26:04,200 --> 00:26:06,919 Speaker 1: that fire or the next one. Because we have a 436 00:26:06,920 --> 00:26:10,840 Speaker 1: real fear that this thing may come back, we have 437 00:26:10,880 --> 00:26:13,720 Speaker 1: a much higher pressure point to buy the fire detector, 438 00:26:14,359 --> 00:26:17,600 Speaker 1: and we would if we thought this was gone. I 439 00:26:17,640 --> 00:26:20,400 Speaker 1: think it's also the breadth of the impact. In two 440 00:26:20,440 --> 00:26:24,760 Speaker 1: thousand and three, Toronto was a microcosm of what's being 441 00:26:24,800 --> 00:26:27,920 Speaker 1: experienced in the world today, which was we felt that fear, 442 00:26:28,040 --> 00:26:30,600 Speaker 1: we felt that panic. We looked out in the streets 443 00:26:30,600 --> 00:26:33,239 Speaker 1: and they were empty. As a multi billion dollars hit, 444 00:26:33,280 --> 00:26:35,560 Speaker 1: nobody wanted to come to the city of Toronto. So 445 00:26:35,640 --> 00:26:38,879 Speaker 1: that coronavirus crippled cities, this one has crippled the planet. 446 00:26:38,960 --> 00:26:42,840 Speaker 1: So I think there is a broad recognition that these 447 00:26:42,880 --> 00:26:47,960 Speaker 1: types of threats can have devastating health consequences, economic consequences, 448 00:26:48,040 --> 00:26:50,800 Speaker 1: social impacts. And so we're really going to have to 449 00:26:51,400 --> 00:26:54,520 Speaker 1: use this as an opportunity to be thinking ahead and 450 00:26:54,680 --> 00:26:57,960 Speaker 1: building and implementing these systems. We have the capabilities to 451 00:26:58,080 --> 00:27:00,399 Speaker 1: do things in a way that is smarter and faster 452 00:27:00,520 --> 00:27:03,440 Speaker 1: and more timely and better coordinated. I think it's really 453 00:27:03,440 --> 00:27:05,920 Speaker 1: just a matter of the will. What we've been doing 454 00:27:05,920 --> 00:27:09,040 Speaker 1: at Blue Todd That memory is seered into my brain 455 00:27:09,600 --> 00:27:12,120 Speaker 1: and it is what gets me up every day out 456 00:27:12,119 --> 00:27:16,000 Speaker 1: of bed, day after day, thinking about the inevitable one 457 00:27:16,040 --> 00:27:18,560 Speaker 1: that is coming and how do we get ourselves ready 458 00:27:18,600 --> 00:27:23,320 Speaker 1: for it. So if we had been wired to pick 459 00:27:23,440 --> 00:27:27,640 Speaker 1: up on your December thirty first warning and we had 460 00:27:27,760 --> 00:27:32,200 Speaker 1: moved appropriately. How much different would the last four months 461 00:27:32,240 --> 00:27:35,480 Speaker 1: have been. I think it would have been very different. 462 00:27:35,640 --> 00:27:38,840 Speaker 1: I think the question there is does an insight lead 463 00:27:38,880 --> 00:27:41,280 Speaker 1: to an action? But if there was an early recognition 464 00:27:41,800 --> 00:27:46,440 Speaker 1: and then various steps taken, especially around things like early testing, 465 00:27:46,640 --> 00:27:50,360 Speaker 1: really just an understanding of what's happening in terms of cases, 466 00:27:50,400 --> 00:27:52,560 Speaker 1: are they going up or going down? I think a 467 00:27:52,600 --> 00:27:55,320 Speaker 1: lot of these things really could have mitigated and lessen 468 00:27:55,400 --> 00:27:58,879 Speaker 1: the impact. South Korea had a big outbreak centered around Dagu, 469 00:27:59,000 --> 00:28:01,080 Speaker 1: but they managed to use a lot of their resources 470 00:28:01,119 --> 00:28:03,640 Speaker 1: to try and get in front of this. Singapore learned 471 00:28:03,680 --> 00:28:06,400 Speaker 1: a lot of lessons from the Stars outbreak. They kept 472 00:28:06,440 --> 00:28:09,800 Speaker 1: their curve pretty flat almost for two months. So I 473 00:28:09,840 --> 00:28:12,359 Speaker 1: think there are lots of things that one could do 474 00:28:12,840 --> 00:28:17,479 Speaker 1: to use time to possibly prevent or failing that, at 475 00:28:17,560 --> 00:28:22,600 Speaker 1: least mitigate and blunt the consequences and impacts. I'm really 476 00:28:22,640 --> 00:28:24,439 Speaker 1: delighted who you would take the time to do this. 477 00:28:24,920 --> 00:28:27,199 Speaker 1: Thank you very much. Thank you for your interest in 478 00:28:27,240 --> 00:28:29,600 Speaker 1: the work that we're doing and for a chance to 479 00:28:29,640 --> 00:28:34,520 Speaker 1: talk about it. Thank you to my guest doctor Cameron Khan. 480 00:28:35,119 --> 00:28:37,639 Speaker 1: You can learn more about the virus spread from Wuhan 481 00:28:37,720 --> 00:28:40,120 Speaker 1: to the rest of the world and blue dot on 482 00:28:40,160 --> 00:28:43,760 Speaker 1: our show page at newtsworld dot com. News World is 483 00:28:43,800 --> 00:28:49,080 Speaker 1: produced by Gingwish, Sweet sixty and iHeartMedia. Our executive producer 484 00:28:49,440 --> 00:28:53,560 Speaker 1: is Debbie Myers and our producer is Garnsey Slump. The 485 00:28:53,680 --> 00:28:57,360 Speaker 1: artwork for the show was created by Steve Penny Special 486 00:28:57,400 --> 00:29:00,680 Speaker 1: thanks to the team at Gingwish tweet sixty. Please email 487 00:29:00,720 --> 00:29:04,760 Speaker 1: me with your comments at newt at newtsworld dot com. 488 00:29:04,840 --> 00:29:07,400 Speaker 1: If you've been enjoying Newtsworld, I hope you'll go to 489 00:29:07,440 --> 00:29:10,920 Speaker 1: Apple Podcasts and both rate us with five stars and 490 00:29:11,080 --> 00:29:13,640 Speaker 1: give us a review so others can learn what it's 491 00:29:13,640 --> 00:29:19,680 Speaker 1: all about. On the next episode of Newtsworld, to explore 492 00:29:19,760 --> 00:29:23,120 Speaker 1: with a real expert, the potential for a vaccine, what's 493 00:29:23,200 --> 00:29:26,320 Speaker 1: involved in the likelihood that in the near future we're 494 00:29:26,360 --> 00:29:28,640 Speaker 1: going to be in a better place with COVID nineteen. 495 00:29:29,200 --> 00:29:31,360 Speaker 1: I'm new Gingrich. This is Newtsworld.