1 00:00:00,900 --> 00:00:06,160 Viruses are some of the fastest-spreading organisms 2 00:00:06,160 --> 00:00:07,880 on this planet. 3 00:00:07,880 --> 00:00:13,080 And each time a virus spreads from one person to the next, 4 00:00:13,080 --> 00:00:18,280 the virus itself replicate in their billions. 5 00:00:18,280 --> 00:00:23,480 Just one infection can lead to a pandemic. 6 00:00:47,080 --> 00:00:50,040 APPLAUSE 7 00:01:03,000 --> 00:01:05,960 Would you like to see that again in slow motion? 8 00:01:05,960 --> 00:01:08,120 Can we run it again on the VT, please? 9 00:01:14,800 --> 00:01:18,080 There you go, that's a proper chain reaction. 10 00:01:18,080 --> 00:01:22,360 So welcome to the second Christmas lecture of 2021 11 00:01:22,360 --> 00:01:24,400 from the Royal Institution. 12 00:01:24,400 --> 00:01:27,200 My name is Professor Jonathan Van-Tam. 13 00:01:27,200 --> 00:01:29,760 I'm the Deputy Chief Medical Officer of England. 14 00:01:29,760 --> 00:01:31,680 But I'm not here tonight 15 00:01:31,680 --> 00:01:33,920 because I'm the Deputy Chief Medical Officer. 16 00:01:33,920 --> 00:01:36,240 I'm here because I've spent most of my life 17 00:01:36,240 --> 00:01:39,200 being a respiratory virus epidemiologist. 18 00:01:40,640 --> 00:01:45,600 Now, previously we delved into the micro world of viruses - 19 00:01:45,600 --> 00:01:50,760 how small they are, how they infect us, how they damage our bodies, 20 00:01:50,760 --> 00:01:54,120 and how we then fight back with our immune systems. 21 00:01:55,880 --> 00:01:59,960 But to understand how viruses spread, 22 00:01:59,960 --> 00:02:05,560 we're going to need to focus on person-to-person transmission, 23 00:02:05,560 --> 00:02:09,760 and on human behaviour in the wider population. 24 00:02:11,080 --> 00:02:15,560 So...I'm going to take you over to a map now. 25 00:02:15,560 --> 00:02:19,840 And before I play this map, I want you to look for 26 00:02:19,840 --> 00:02:24,680 red circles that are getting bigger, that show that an epidemic 27 00:02:24,680 --> 00:02:27,200 is growing in a certain place. 28 00:02:27,200 --> 00:02:30,800 And then blue circles that are getting smaller, 29 00:02:30,800 --> 00:02:34,240 that show that the epidemic is dying down again. 30 00:02:34,240 --> 00:02:38,280 And on the left-hand side there's a date there - 31 00:02:38,280 --> 00:02:41,840 26th of January 2020. 32 00:02:41,840 --> 00:02:46,440 And that's going to run really fast, forwards, as we play the map. 33 00:02:46,440 --> 00:02:48,040 Let's play it, please. 34 00:02:48,040 --> 00:02:51,120 So you can see our Covid pandemic has begun in China, 35 00:02:51,120 --> 00:02:54,960 and very quickly, it started to spread around the world, 36 00:02:54,960 --> 00:02:56,120 growing bigger. 37 00:02:56,120 --> 00:02:58,440 We're now in May, already. 38 00:02:58,440 --> 00:03:01,440 And you can see now South America's started to light up, 39 00:03:01,440 --> 00:03:04,400 South Africa's lighting up, and some of those circles 40 00:03:04,400 --> 00:03:05,800 are getting very big. 41 00:03:05,800 --> 00:03:09,520 But at the same time, some of them are cooling down between waves. 42 00:03:09,520 --> 00:03:11,520 And you can see that we're now in October, 43 00:03:11,520 --> 00:03:13,640 getting towards the northern hemisphere winter. 44 00:03:13,640 --> 00:03:16,240 You can see quite big circles appearing. 45 00:03:16,240 --> 00:03:22,160 Now, we know that Covid feeds off close contact. 46 00:03:23,400 --> 00:03:27,760 And that's why it can go worldwide in weeks. 47 00:03:27,760 --> 00:03:31,000 But scientists have a lot of experience of studying 48 00:03:31,000 --> 00:03:35,800 pandemics like flu pandemics and similar diseases. 49 00:03:35,800 --> 00:03:39,240 And scientists have been really racing for two years 50 00:03:39,240 --> 00:03:43,320 to understand the cause of spread of this pandemic. 51 00:03:43,320 --> 00:03:48,640 Why has it been so quick? And why has the scale been so massive? 52 00:03:49,880 --> 00:03:54,000 Now, to reveal some of those invisible forces 53 00:03:54,000 --> 00:03:55,640 which drive infection, 54 00:03:55,640 --> 00:03:59,240 we're going to welcome our first expert this evening. 55 00:03:59,240 --> 00:04:02,360 And I'm really pleased to say, she's an old friend of mine 56 00:04:02,360 --> 00:04:05,200 and we've done a lot of work together over the years 57 00:04:05,200 --> 00:04:09,360 studying how flu viruses pass from one person to another. 58 00:04:09,360 --> 00:04:12,400 Would you please welcome Professor Cath Noakes? 59 00:04:21,360 --> 00:04:23,320 So good evening, everybody. 60 00:04:23,320 --> 00:04:26,240 I'm a mechanical engineer and I'm an expert 61 00:04:26,240 --> 00:04:29,240 in airborne infection and ventilation. 62 00:04:29,240 --> 00:04:32,200 And I'm quite interested in knowing, in how does the virus 63 00:04:32,200 --> 00:04:35,040 get from one person to another person? 64 00:04:35,040 --> 00:04:37,800 And how does this happen in the different places where we go? 65 00:04:37,800 --> 00:04:41,960 So in our schools, our workplaces, our homes, the places we socialise. 66 00:04:43,160 --> 00:04:45,360 But before that, some of you might be thinking, 67 00:04:45,360 --> 00:04:48,200 "What's an engineer doing on stage looking at viruses? 68 00:04:48,200 --> 00:04:50,160 "Isn't that a medical thing?" 69 00:04:50,160 --> 00:04:53,080 So I study a field called fluid dynamics - 70 00:04:53,080 --> 00:04:55,400 how liquids and gases behave. 71 00:04:55,400 --> 00:04:58,880 And this is the same thing - the techniques we use will tell us 72 00:04:58,880 --> 00:05:02,520 how aeroplanes fly, can help us design wind turbines, 73 00:05:02,520 --> 00:05:05,560 how car engines work, all kinds of things. 74 00:05:05,560 --> 00:05:09,880 But we use the same technique to understand viruses 75 00:05:09,880 --> 00:05:12,240 and how they travel from person to person 76 00:05:12,240 --> 00:05:15,720 and how the air in buildings moves. 77 00:05:15,720 --> 00:05:18,800 So to start with, the first thing we're going to do 78 00:05:18,800 --> 00:05:20,080 is ask a question. 79 00:05:20,080 --> 00:05:22,680 How does a virus that's inside our bodies - 80 00:05:22,680 --> 00:05:26,360 it's in our respiratory system - get out and get to other people? 81 00:05:26,360 --> 00:05:29,560 Can anybody suggest any ideas how it's going to get out? 82 00:05:29,560 --> 00:05:31,320 Coughing. Coughing, brilliant. 83 00:05:31,320 --> 00:05:33,600 Any other thoughts? Sneezing. Sneezing. 84 00:05:33,600 --> 00:05:35,480 Air droplets. Air droplets and things - 85 00:05:35,480 --> 00:05:37,360 oh, you know quite a lot already. 86 00:05:37,360 --> 00:05:40,160 OK, we've got coughing and sneezing here - brilliant ways - 87 00:05:40,160 --> 00:05:42,000 and we're going to start with those. 88 00:05:42,000 --> 00:05:44,400 So to demonstrate coughing and sneezing, 89 00:05:44,400 --> 00:05:47,880 we've got our lovely sneeze machine here. 90 00:05:47,880 --> 00:05:51,560 OK. And to work this, I'm going to need two volunteers 91 00:05:51,560 --> 00:05:54,160 from the audience. Lots of hands up here. 92 00:05:54,160 --> 00:05:57,880 OK, so there, the black jacket and the grey sleeves? 93 00:05:57,880 --> 00:06:01,640 OK, and we'll have - there, with the pinkish shirt on. 94 00:06:01,640 --> 00:06:03,080 Yes, if you want to come down. 95 00:06:05,880 --> 00:06:08,120 Come and stand over there. OK. 96 00:06:10,120 --> 00:06:13,560 So can you tell me your name? My name is Amir. 97 00:06:13,560 --> 00:06:16,760 Amir - nice to have you here, Amir. And your name is? Milly. 98 00:06:16,760 --> 00:06:18,840 Milly. So I've got Amir and Millie. 99 00:06:18,840 --> 00:06:22,280 Right, OK. So, Amir, you're going to have a very important job. 100 00:06:22,280 --> 00:06:25,800 So you are going to work our sneeze machine, OK? So... 101 00:06:25,800 --> 00:06:28,920 And then, Milly, I'm afraid you're going to be 102 00:06:28,920 --> 00:06:30,880 our human target for this. 103 00:06:32,480 --> 00:06:34,960 Don't worry - it's not a real virus, OK? 104 00:06:34,960 --> 00:06:37,480 Can you go off with our team over there? 105 00:06:37,480 --> 00:06:40,560 They're going to get you kitted up so you don't get covered in it. 106 00:06:40,560 --> 00:06:43,040 Right, Amir, we need to get you doing 107 00:06:43,040 --> 00:06:44,800 a really important job over here. 108 00:06:44,800 --> 00:06:47,320 So, you see you've got a bicycle pump here? 109 00:06:47,320 --> 00:06:50,000 And then what you're going to do is you're going to pump it up. 110 00:06:50,000 --> 00:06:52,000 We've got a dial here and we're going to pump it up 111 00:06:52,000 --> 00:06:53,880 until it reaches that red line. 112 00:06:53,880 --> 00:06:57,120 You have to pump it right up, yeah? That's perfect. 113 00:06:57,120 --> 00:07:01,040 When it gets to the red line, just pump a little bit and hold it there. 114 00:07:02,280 --> 00:07:06,760 OK, so, JVT, why do we actually cough and sneeze? 115 00:07:06,760 --> 00:07:10,480 Well, coughing and sneezing is actually completely natural 116 00:07:10,480 --> 00:07:12,120 to a human. 117 00:07:12,120 --> 00:07:15,560 Because, as we walk around in our daily lives and we breathe, 118 00:07:15,560 --> 00:07:20,080 our lungs take in dust, dirt, bacteria, and they combine 119 00:07:20,080 --> 00:07:24,480 with the mucus that's in our lungs and we've got to get rid of it, 120 00:07:24,480 --> 00:07:26,760 and so we cough and we sneeze. 121 00:07:26,760 --> 00:07:30,840 And the classic example is putting pepper on your food and getting 122 00:07:30,840 --> 00:07:35,200 it up your nose and your body says, "I don't like that, it's irritating 123 00:07:35,200 --> 00:07:37,400 "my nose, I've got to get rid of it." 124 00:07:37,400 --> 00:07:39,960 And so out comes the sneeze. 125 00:07:39,960 --> 00:07:43,960 And if you're infected with a virus in your airways, 126 00:07:43,960 --> 00:07:49,960 the virus also upsets your airways and makes them sensitive 127 00:07:49,960 --> 00:07:53,440 and, again, can make you want to cough or sneeze. 128 00:07:53,440 --> 00:07:56,880 And if you're infected with a virus, then what comes out is, 129 00:07:56,880 --> 00:08:00,520 of course, also likely to contain the virus, as well. 130 00:08:01,880 --> 00:08:03,760 OK, thank you for that. 131 00:08:03,760 --> 00:08:06,360 So, OK, do you want to come back on here? 132 00:08:06,360 --> 00:08:09,200 OK, you'll probably want to pull your visor down. 133 00:08:11,720 --> 00:08:16,480 Perfect, right, now, where do we think Milly should stand? 134 00:08:16,480 --> 00:08:18,760 Over here, by JVT? No, not near me. 135 00:08:18,760 --> 00:08:20,480 LAUGHTER 136 00:08:20,480 --> 00:08:22,800 OK, closer? Closer. Closer, come on, then. 137 00:08:24,840 --> 00:08:27,360 That's going to be too easy, isn't it? Far too easy. 138 00:08:27,360 --> 00:08:30,160 OK, I think we can take you a little bit further away, Milly. 139 00:08:30,160 --> 00:08:34,000 So maybe about here, so you're now about two metres away. 140 00:08:34,000 --> 00:08:37,800 We tend to think two metres is a safe distance. 141 00:08:37,800 --> 00:08:40,880 OK, just stand still there, don't panic. 142 00:08:40,880 --> 00:08:44,160 Right, Amir, are we nice and pressurised? 143 00:08:44,160 --> 00:08:47,160 Perfect, so your next job - you see this trigger here? 144 00:08:47,160 --> 00:08:48,920 You're going to pull that trigger. 145 00:08:48,920 --> 00:08:50,800 So, are we ready, audience? 146 00:08:50,800 --> 00:08:54,640 ALL: Three, two, one... Sneeze. 147 00:08:54,640 --> 00:08:56,120 Oh! 148 00:08:56,120 --> 00:09:01,080 And you've even broken our sneeze machine - fantastic! 149 00:09:01,080 --> 00:09:02,720 Are you all right there, Milly? 150 00:09:02,720 --> 00:09:05,280 Yeah. Thank you for that, that was brilliant. 151 00:09:05,280 --> 00:09:07,080 Let's have a look at what happened. 152 00:09:07,080 --> 00:09:09,920 I think we saw quite a lot happen there, didn't we? 153 00:09:09,920 --> 00:09:14,600 So if we turn the lights down and we look on the floor, 154 00:09:14,600 --> 00:09:16,800 we should be able to see... 155 00:09:16,800 --> 00:09:18,560 Can you see this on the floor? 156 00:09:18,560 --> 00:09:21,320 Can you see all these tiny dots? 157 00:09:21,320 --> 00:09:24,200 Oh, look - some big splats over here. 158 00:09:24,200 --> 00:09:26,520 That was one big sneeze you did, Amir. 159 00:09:26,520 --> 00:09:30,040 And if we look at Milly, we can see we've got some 160 00:09:30,040 --> 00:09:32,000 on her overalls here. 161 00:09:32,000 --> 00:09:35,560 We've got some here, and we've got it on our face guard. 162 00:09:35,560 --> 00:09:38,840 So Milly got quite a face full of that then. 163 00:09:38,840 --> 00:09:42,720 So thank you very much to both of you, OK. 164 00:09:42,720 --> 00:09:45,760 Can we give a big round of applause to Milly and Amir? 165 00:09:51,360 --> 00:09:52,560 OK. 166 00:09:54,240 --> 00:09:56,800 So what we saw there were large droplets. 167 00:09:56,800 --> 00:09:59,840 And those droplets could carry many virus particles. 168 00:09:59,840 --> 00:10:03,360 They could theoretically carry probably over 100 virus particles. 169 00:10:03,360 --> 00:10:05,480 They're actually a little bit smaller in reality 170 00:10:05,480 --> 00:10:06,960 to what we saw there. 171 00:10:06,960 --> 00:10:10,200 But it's likely to be rare that these large droplets can infect you, 172 00:10:10,200 --> 00:10:12,160 and there's two reasons for this. 173 00:10:12,160 --> 00:10:15,360 One is that we produce quite small numbers of them, 174 00:10:15,360 --> 00:10:18,600 and when we do, we produce them quite infrequently. 175 00:10:18,600 --> 00:10:22,040 The second thing is that they travel ballistically. 176 00:10:22,040 --> 00:10:23,840 That means they fly like a ball. 177 00:10:25,480 --> 00:10:28,080 And they just hit the ground, OK? 178 00:10:28,080 --> 00:10:32,320 So to actually infect you, they'd have to make a direct hit. 179 00:10:32,320 --> 00:10:35,120 They'd have to hit your mucous membranes - so your eyes, 180 00:10:35,120 --> 00:10:37,120 your nose, or in your mouth - 181 00:10:37,120 --> 00:10:40,720 and that's quite hard unless you're right in front of somebody. 182 00:10:40,720 --> 00:10:44,960 But what we did see was just how easily an infectious person 183 00:10:44,960 --> 00:10:46,920 can contaminate surfaces. 184 00:10:46,920 --> 00:10:50,240 So, let's go over to JVT and look at how important 185 00:10:50,240 --> 00:10:51,920 this might be for transmission. 186 00:10:53,360 --> 00:10:57,320 So, Cath, you shone the light on the volunteer, but if you'd looked 187 00:10:57,320 --> 00:11:02,360 a bit more widely, then perhaps we might have seen more contamination. 188 00:11:02,360 --> 00:11:05,320 So can we just drop the lights? 189 00:11:05,320 --> 00:11:06,720 Let's have a look... 190 00:11:07,760 --> 00:11:11,000 ..on top of the nozzle itself. 191 00:11:11,000 --> 00:11:14,880 And you can see there's some pretty 192 00:11:14,880 --> 00:11:19,040 extensive contamination there, as well, 193 00:11:19,040 --> 00:11:21,400 on touched surfaces. 194 00:11:21,400 --> 00:11:23,680 Now what I want you to do is imagine 195 00:11:23,680 --> 00:11:27,440 if we'd had an infected person in this lecture theatre 196 00:11:27,440 --> 00:11:31,520 and, as they'd come in, they'd touched lots of things. 197 00:11:31,520 --> 00:11:34,040 What is the kind of thing we would see? 198 00:11:34,040 --> 00:11:35,960 Let's have a look. 199 00:11:35,960 --> 00:11:38,480 So let's start on this handrail. 200 00:11:38,480 --> 00:11:40,400 And look... 201 00:11:40,400 --> 00:11:42,760 Big hand prints. 202 00:11:44,320 --> 00:11:47,120 I think they're probably the producer's handprints 203 00:11:47,120 --> 00:11:50,640 and I think this is a bit of a wind-up, but there we are. 204 00:11:50,640 --> 00:11:53,640 So if we go down and have a look on here... 205 00:11:55,560 --> 00:11:59,320 ..there's a whole load more. 206 00:12:00,400 --> 00:12:06,280 And this is just the kind of snotty handprint that can happen 207 00:12:06,280 --> 00:12:10,520 very easily with somebody who's got a respiratory virus. 208 00:12:10,520 --> 00:12:12,080 And it probably goes... 209 00:12:12,080 --> 00:12:14,960 Oh, there's another one - we're following the trail here. 210 00:12:14,960 --> 00:12:18,680 And it goes all the way up... 211 00:12:18,680 --> 00:12:21,240 LAUGHTER Oh, er, no. 212 00:12:21,240 --> 00:12:22,960 Yeah... 213 00:12:22,960 --> 00:12:24,720 But you get the idea. 214 00:12:24,720 --> 00:12:26,360 So... 215 00:12:27,600 --> 00:12:31,280 There's lots of contamination, and it wouldn't matter 216 00:12:31,280 --> 00:12:36,360 at all to me how contaminated this is if I just stand here. 217 00:12:36,360 --> 00:12:39,400 Because the viruses can't jump to me, they can't jump. 218 00:12:39,400 --> 00:12:40,760 Viruses can't jump. 219 00:12:42,240 --> 00:12:47,760 But because I'm a human, as I go around, I touch things. 220 00:12:47,760 --> 00:12:50,200 And then I touch my face. 221 00:12:50,200 --> 00:12:52,400 And if I touch my nose, 222 00:12:52,400 --> 00:12:55,680 my eyes or my mouth, 223 00:12:55,680 --> 00:12:59,040 then the virus might get inside my airway. 224 00:12:59,040 --> 00:13:03,720 And it's hand-touch-face behaviour that gives the virus 225 00:13:03,720 --> 00:13:06,600 that link to get inside us. 226 00:13:06,600 --> 00:13:10,680 Hand washing and hand sanitisation are really important, 227 00:13:10,680 --> 00:13:14,000 but they're not just important for coronavirus - 228 00:13:14,000 --> 00:13:17,760 they're important for a whole load of different infections. 229 00:13:17,760 --> 00:13:21,800 Things like norovirus, which gives you diarrhoea and vomiting. 230 00:13:21,800 --> 00:13:25,280 And flu, which you'll know and recognise. 231 00:13:25,280 --> 00:13:30,160 So hand washing has a kind of multiplicity of uses. 232 00:13:30,160 --> 00:13:34,560 It's also very important before you eat food 233 00:13:34,560 --> 00:13:36,680 and before you prepare food. 234 00:13:38,400 --> 00:13:43,160 So, it is possible that viruses can spread in this way. 235 00:13:43,160 --> 00:13:46,120 But, actually, there's a far more direct way. 236 00:13:46,120 --> 00:13:49,840 So, we're going to go back to our sneeze machine. 237 00:13:49,840 --> 00:13:52,280 This time we're going to do something slightly different - 238 00:13:52,280 --> 00:13:54,720 we're going to turn the theatre lights down and we're going 239 00:13:54,720 --> 00:13:56,800 to put a UV light on, and Dan is going to operate 240 00:13:56,800 --> 00:13:59,360 our sneeze machine this time. 241 00:13:59,360 --> 00:14:02,240 And we're going to try and look for something different that you 242 00:14:02,240 --> 00:14:05,880 might not really have noticed the first time around. 243 00:14:05,880 --> 00:14:08,160 So, are we ready? 244 00:14:08,160 --> 00:14:12,880 Another countdown... ALL: Three, two, one... Sneeze. 245 00:14:12,880 --> 00:14:14,680 Whoa! 246 00:14:17,080 --> 00:14:20,440 So did you see it? Did you see a cloud come out? 247 00:14:20,440 --> 00:14:26,800 Yeah? That cloud, tiny liquid droplets called aerosols. 248 00:14:26,800 --> 00:14:29,200 Let's have another look in slow motion. 249 00:14:29,200 --> 00:14:31,160 So if we have a look on here. 250 00:14:31,160 --> 00:14:36,200 Here, we can see it. And let's play it again in slow motion. 251 00:14:36,200 --> 00:14:41,040 You saw that cloud. So it's a turbulent puff of air, 252 00:14:41,040 --> 00:14:47,080 it carries those small aerosols - really concentrated to start with 253 00:14:47,080 --> 00:14:52,000 and then, as it gets further away, it disperses into the air. 254 00:14:52,000 --> 00:14:55,040 So this is showing you something about aerosols. 255 00:14:55,040 --> 00:14:58,960 And aerosols come in a huge range of different sizes, 256 00:14:58,960 --> 00:15:00,960 and they behave in different ways. 257 00:15:00,960 --> 00:15:03,000 In fact, because they're liquids, some of them 258 00:15:03,000 --> 00:15:07,040 start to evaporate when they're released, and become smaller. 259 00:15:07,040 --> 00:15:11,720 And this all really matters because the size of an aerosol, 260 00:15:11,720 --> 00:15:14,320 it determines how much virus it could carry 261 00:15:14,320 --> 00:15:17,400 and it determines their behaviour and, therefore, 262 00:15:17,400 --> 00:15:20,520 it actually determines whether they might infect us 263 00:15:20,520 --> 00:15:24,440 if there's a virus hitching a ride in that aerosol. 264 00:15:24,440 --> 00:15:27,440 So, what we're going to do now is we're going to magnify some 265 00:15:27,440 --> 00:15:31,880 aerosols by around 10,000 times. So, are we ready up the top there? 266 00:15:31,880 --> 00:15:35,680 OK. So, these aerosols are much heavier than usual, 267 00:15:35,680 --> 00:15:39,080 so can you all put your hands up down the bottom of the audience? 268 00:15:39,080 --> 00:15:41,840 Just in case one of our aerosols hits you. 269 00:15:41,840 --> 00:15:46,160 OK. Let us go, then. So, we ready? 270 00:15:46,160 --> 00:15:49,880 Go, go, go, go! 271 00:15:49,880 --> 00:15:53,400 And there come our aerosols. Fantastic. 272 00:15:54,440 --> 00:15:59,000 So, this is our biggest aerosol 273 00:15:59,000 --> 00:16:02,520 and this is far too big for real aerosol. 274 00:16:02,520 --> 00:16:06,040 This represents one that's about 100 microns in diameter - 275 00:16:06,040 --> 00:16:08,640 that's one tenth of a millimetre - 276 00:16:08,640 --> 00:16:12,640 and it's a little bit bigger than the thickness of a human hair. 277 00:16:12,640 --> 00:16:16,360 So, you would actually be able to see this one, but what you saw is 278 00:16:16,360 --> 00:16:20,360 these fell down, crashed down to surfaces really quickly. 279 00:16:20,360 --> 00:16:22,960 One of these real aerosols would usually 280 00:16:22,960 --> 00:16:26,920 land on a surface in under about a minute. 281 00:16:26,920 --> 00:16:29,440 If you're close to somebody - you're stood, you know, 282 00:16:29,440 --> 00:16:30,920 a metre or so in front of them - 283 00:16:30,920 --> 00:16:33,640 you could breathe them in, but the chances are these big 284 00:16:33,640 --> 00:16:38,720 ones are more likely to contaminate surfaces like we saw earlier. 285 00:16:38,720 --> 00:16:41,240 But we can go smaller, OK? 286 00:16:41,240 --> 00:16:45,680 So we're now going to release some smaller aerosols. Are we ready? 287 00:16:45,680 --> 00:16:49,760 Go. OK. So, these ones... 288 00:16:49,760 --> 00:16:53,400 As you've all caught them! 289 00:16:53,400 --> 00:16:58,520 So these balloons represent an aerosol that's about 30 microns 290 00:16:58,520 --> 00:17:02,600 in diameter - that's 1/30 of a millimetre. 291 00:17:02,600 --> 00:17:06,760 These would actually be smaller than you can see, 292 00:17:06,760 --> 00:17:08,680 and they took a bit longer to come down. 293 00:17:08,680 --> 00:17:13,320 So for these real aerosols, they'd be suspended in the air usually 294 00:17:13,320 --> 00:17:17,240 for a few minutes, and they can stay in the air a bit longer, they can be 295 00:17:17,240 --> 00:17:23,120 carried by the movement of air in a room, or by heat rising in a room, 296 00:17:23,120 --> 00:17:25,680 and these aerosols are probably quite dangerous 297 00:17:25,680 --> 00:17:27,600 because they're quite easily inhaled, 298 00:17:27,600 --> 00:17:31,680 they can deposit in your nose and your throat - and particularly 299 00:17:31,680 --> 00:17:33,880 when you're fairly close to somebody. 300 00:17:33,880 --> 00:17:36,000 But we can get smaller than that. 301 00:17:36,000 --> 00:17:41,400 So let's have our next aerosols, please. Are you ready? 302 00:17:41,400 --> 00:17:47,120 And if we look up, we can see that our next aerosols are bubbles. 303 00:17:47,120 --> 00:17:52,720 So these are representing something that's about 5 microns in diameter. 304 00:17:52,720 --> 00:17:58,080 These are 200 times smaller than a millimetre and, as you can see, 305 00:17:58,080 --> 00:18:01,920 they're really easily carried in the air across the room 306 00:18:01,920 --> 00:18:05,920 and they can be suspended for some time - maybe an hour, 307 00:18:05,920 --> 00:18:09,480 maybe even more for these real aerosols. 308 00:18:09,480 --> 00:18:12,080 Let's have a look at a really small aerosol. 309 00:18:12,080 --> 00:18:18,120 So this aerosol here is 0.3 microns in diameter - that is 310 00:18:18,120 --> 00:18:22,360 really small - and you can see we've got a coronavirus here, 311 00:18:22,360 --> 00:18:25,840 and there's a coronavirus inside the aerosol, and what we've got 312 00:18:25,840 --> 00:18:30,600 is this yellowy stuff is the liquid fluid in your lungs. 313 00:18:30,600 --> 00:18:33,840 And then you can see green and red - that's proteins 314 00:18:33,840 --> 00:18:36,960 and fats that are within the respiratory fluid. 315 00:18:36,960 --> 00:18:39,000 So these really small ones - 316 00:18:39,000 --> 00:18:43,600 the ones that are usually under about 5 microns in diameter - 317 00:18:43,600 --> 00:18:48,440 these ones can actually carry all the way down into your lungs, 318 00:18:48,440 --> 00:18:51,880 and that potentially becomes a really direct way of infection. 319 00:18:51,880 --> 00:18:53,720 And at the beginning of the pandemic, 320 00:18:53,720 --> 00:18:55,840 we focused a lot on surfaces - 321 00:18:55,840 --> 00:19:00,840 we were all washing our hands - but now scientists have pulled together 322 00:19:00,840 --> 00:19:03,360 a lot of information over the past year, 323 00:19:03,360 --> 00:19:07,120 two years, done a lot of work on this, and think that most infection 324 00:19:07,120 --> 00:19:11,440 happens from inhaling these different sizes of aerosols. 325 00:19:13,080 --> 00:19:15,600 So we've already seen that coughing 326 00:19:15,600 --> 00:19:20,240 and sneezing makes aerosols, and we do this when we're sick 327 00:19:20,240 --> 00:19:23,920 or need to clear our airways, but what about other activities? 328 00:19:23,920 --> 00:19:26,760 Do we even produce aerosols when we breathe? 329 00:19:26,760 --> 00:19:29,600 So to understand more about this, I'd like to welcome 330 00:19:29,600 --> 00:19:32,560 Professor Jonathan Reid and his team, who are going to tell us 331 00:19:32,560 --> 00:19:34,680 something much more about aerosols. 332 00:19:39,760 --> 00:19:44,200 OK. So, welcome, Jonathan. Thank you, Cath. 333 00:19:44,200 --> 00:19:48,120 Why is aerosol science so important during this pandemic? 334 00:19:48,120 --> 00:19:52,240 OK. So you've been talking quite a bit about the different sizes of 335 00:19:52,240 --> 00:19:56,760 aerosol particles that are important here - aerosols and droplets. 336 00:19:56,760 --> 00:19:59,960 And aerosol science helps us understand how they're generated. 337 00:19:59,960 --> 00:20:02,040 You know, do they come from lower in the lung? 338 00:20:02,040 --> 00:20:04,240 Do they come from the mouth? 339 00:20:04,240 --> 00:20:08,640 And then, once we exhale them, what happens to them? 340 00:20:08,640 --> 00:20:11,680 How do they travel around? How do they get transported? 341 00:20:11,680 --> 00:20:16,080 How does the virus remain infectious while it's airborne? 342 00:20:16,080 --> 00:20:18,760 And then, when someone who's uninfected breathes it in, 343 00:20:18,760 --> 00:20:22,920 what happens - what happens during that inhalation process? 344 00:20:22,920 --> 00:20:25,960 Cath, I'm going to give you one interesting fact, OK? 345 00:20:25,960 --> 00:20:30,840 This is really surprising. If we take the entire global population 346 00:20:30,840 --> 00:20:33,120 and we work out how much of that aerosol 347 00:20:33,120 --> 00:20:37,440 they produce per second - those particles smaller than 5 microns - 348 00:20:37,440 --> 00:20:41,280 it's much less than a teaspoon across the global population. 349 00:20:41,280 --> 00:20:42,800 That's incredible. OK. 350 00:20:42,800 --> 00:20:44,440 So we're going to have a demonstration 351 00:20:44,440 --> 00:20:46,240 to look at aerosols now. 352 00:20:46,240 --> 00:20:48,480 And we had to pre-select a volunteer for this 353 00:20:48,480 --> 00:20:51,160 because this requires a little bit more practice. 354 00:20:51,160 --> 00:20:53,800 So, can we give Helena a round of applause, 355 00:20:53,800 --> 00:20:55,600 who's going to come and do this for us? 356 00:21:03,400 --> 00:21:08,040 So, welcome, Helena, and Andy here, who's an anaesthetist, is going 357 00:21:08,040 --> 00:21:11,560 to help set up Helena, while Vicky's going to drive our laptop here. 358 00:21:11,560 --> 00:21:14,800 Jonathan, how do we actually measure aerosols? 359 00:21:14,800 --> 00:21:16,840 Well, with such a small amount generated, 360 00:21:16,840 --> 00:21:18,360 it's really very difficult. 361 00:21:18,360 --> 00:21:22,000 It's like looking for a table tennis ball in Wembley Stadium - 362 00:21:22,000 --> 00:21:24,080 it's that big a challenge 363 00:21:24,080 --> 00:21:27,480 and so we have to do the measurements in a very clean space. 364 00:21:27,480 --> 00:21:30,360 In this space at the moment - just remember this number 365 00:21:30,360 --> 00:21:33,720 because it will be important - I'm recording 8,000, 366 00:21:33,720 --> 00:21:37,800 and that's 8,000 particles per centimetre cubed. 367 00:21:37,800 --> 00:21:41,400 When... We need to... If you breathe that in, 368 00:21:41,400 --> 00:21:44,840 you will just exhale those particles straight back out. 369 00:21:44,840 --> 00:21:47,880 So we need the person to breathe in clean air so that 370 00:21:47,880 --> 00:21:50,840 when they breathe out, we only see the particles that they're 371 00:21:50,840 --> 00:21:54,720 breathing out, and that's what we're going to try and measure now. 372 00:21:54,720 --> 00:21:57,280 This is, hopefully, going to work in practice here. 373 00:21:57,280 --> 00:21:59,280 So, Helena's now breathing into our machine. 374 00:21:59,280 --> 00:22:00,760 So tell us what we're seeing here. 375 00:22:00,760 --> 00:22:03,640 OK. What we're seeing at the moment in the red trace, as you 376 00:22:03,640 --> 00:22:05,840 come across here - both the top and the bottom - 377 00:22:05,840 --> 00:22:08,360 we're seeing, actually, Helena breathing, 378 00:22:08,360 --> 00:22:13,800 we're seeing her inhale and exhale. And so, she's exhaling... 379 00:22:13,800 --> 00:22:19,640 now she's inhaling and so that's breathing in and out that clean air. 380 00:22:19,640 --> 00:22:23,680 Now we're going to start looking at the particles that she's exhaling. 381 00:22:23,680 --> 00:22:25,600 Vicky, could you turn the particles on? 382 00:22:25,600 --> 00:22:29,840 So, now in the black trace, we see the particles she's emitting 383 00:22:29,840 --> 00:22:33,160 when she's breathing out, OK? So we're recording those now. 384 00:22:33,160 --> 00:22:35,440 If you look on the top left here, 385 00:22:35,440 --> 00:22:38,160 we're down 0.26 per centimetre cubed. 386 00:22:38,160 --> 00:22:40,640 Remember, in this room, we were 8,000, OK? 387 00:22:40,640 --> 00:22:43,160 So, very, very small number, OK? 388 00:22:43,160 --> 00:22:47,680 And we can see that tracking very nicely her exhalation. 389 00:22:47,680 --> 00:22:51,600 Now what we're going to do is ask her to just say "ah", 390 00:22:51,600 --> 00:22:53,240 to vocalise. 391 00:22:53,240 --> 00:22:55,760 OK. So, Helena, can you do an "ah"? 392 00:22:55,760 --> 00:22:57,920 Ahhh... 393 00:22:57,920 --> 00:23:01,840 That's it. Off the scale. Off the scale. Wow! OK, massive response. 394 00:23:01,840 --> 00:23:04,760 OK, I'll ask Vicky to rescale that, so that the breathing has 395 00:23:04,760 --> 00:23:08,160 gone down into the noise here, OK? And we're off the scale. 396 00:23:08,160 --> 00:23:11,640 She's probably generated about 50 times more mass - 397 00:23:11,640 --> 00:23:14,960 at least 50 times more mass when she's vocalising - 398 00:23:14,960 --> 00:23:17,280 and you also see there the particles, 399 00:23:17,280 --> 00:23:22,480 this bottom scale, have got larger, they've gone from 1 to 2 microns. 400 00:23:22,480 --> 00:23:24,200 So, can we try that again, Helena? 401 00:23:24,200 --> 00:23:27,840 But this time, do it really loudly, as loudly as you can on that "ah". 402 00:23:32,080 --> 00:23:35,600 Here we go again. You ready? See the aerosol from the breathing. 403 00:23:35,600 --> 00:23:38,320 Ahhh... Now she's vocalising, 404 00:23:38,320 --> 00:23:42,120 see it going up, up, up, up, up, off the scale again 405 00:23:42,120 --> 00:23:45,400 and even higher that time because she's louder. 406 00:23:45,400 --> 00:23:48,680 OK, so, thank you very much, Helena. That was brilliant. 407 00:23:48,680 --> 00:23:52,320 You can take that off now and can we give Helena 408 00:23:52,320 --> 00:23:53,720 a round of applause here? 409 00:23:59,960 --> 00:24:02,280 So, what we saw that is, the louder you were, 410 00:24:02,280 --> 00:24:04,200 the more aerosols you produce. 411 00:24:04,200 --> 00:24:06,920 So, why is this all just so important, then? 412 00:24:06,920 --> 00:24:09,440 It's so important because we need to know how much 413 00:24:09,440 --> 00:24:13,040 aerosol there is that you're emitting that could carry the virus. 414 00:24:13,040 --> 00:24:14,760 If you're generating a lot more - 415 00:24:14,760 --> 00:24:17,920 so when you speak very loudly or you sing very loudly - 416 00:24:17,920 --> 00:24:20,960 you can generate about 30 times more aerosol, 417 00:24:20,960 --> 00:24:25,080 and that could carry more virus, and someone could become infected. 418 00:24:25,080 --> 00:24:28,120 And in addition, when people breathe, 419 00:24:28,120 --> 00:24:31,360 people generate about a thousand-fold difference between 420 00:24:31,360 --> 00:24:34,480 those who are very low emitters and those who are very high emitters, 421 00:24:34,480 --> 00:24:36,880 and we're trying to understand what drives that. 422 00:24:36,880 --> 00:24:38,880 Thank you very much, Jonathan. 423 00:24:38,880 --> 00:24:41,360 So, thank you to Professor Jonathan Reid and to Andy 424 00:24:41,360 --> 00:24:42,960 and Vicky for helping with the demo. 425 00:24:42,960 --> 00:24:44,960 Let's give them a big round of applause. 426 00:24:44,960 --> 00:24:46,760 APPLAUSE 427 00:24:53,080 --> 00:24:59,160 So, you've learnt here about droplets and about aerosols 428 00:24:59,160 --> 00:25:03,440 and how they spread viruses around. 429 00:25:03,440 --> 00:25:05,840 And so the next big question is, 430 00:25:05,840 --> 00:25:09,560 what can we actually do to stop that? 431 00:25:09,560 --> 00:25:14,120 Can you think of a device that we could use to stop droplet 432 00:25:14,120 --> 00:25:16,240 and aerosol spread? Just shout it out. 433 00:25:16,240 --> 00:25:17,560 ALL: Masks. 434 00:25:17,560 --> 00:25:20,480 Exactly. Exactly. Masks. 435 00:25:20,480 --> 00:25:25,000 And masks and face coverings are something we've become very 436 00:25:25,000 --> 00:25:28,360 familiar with in the last two years. 437 00:25:28,360 --> 00:25:30,800 But they've been used for decades 438 00:25:30,800 --> 00:25:35,800 and decades by health care workers and by other workers who work 439 00:25:35,800 --> 00:25:41,240 in really dusty environments where they need to protect their lungs. 440 00:25:41,240 --> 00:25:45,120 And I'll tell you something really amazing - you can 441 00:25:45,120 --> 00:25:52,960 trace the idea of using filtering face masks in health care 442 00:25:52,960 --> 00:25:58,880 back to this very lecture theatre, this very spot, in 1870. 443 00:25:58,880 --> 00:26:06,160 And in 1870, an address was given by Professor John Tyndall 444 00:26:06,160 --> 00:26:10,560 and in the papers that he wrote up that go with that lecture, 445 00:26:10,560 --> 00:26:16,360 he said that he could see a role for cotton filtered masks to 446 00:26:16,360 --> 00:26:20,960 remove particles and prevent the spread of infection. 447 00:26:20,960 --> 00:26:26,160 And so filtering is a really simple idea that we still use today 448 00:26:26,160 --> 00:26:31,720 but, actually, in the modern mask, there are some hidden secrets. 449 00:26:33,320 --> 00:26:36,400 So let's bring on our mask demo 450 00:26:36,400 --> 00:26:40,120 and then we'll be able to see how masks work. 451 00:26:41,560 --> 00:26:44,440 OK, so we're going to start with something really basic. 452 00:26:44,440 --> 00:26:48,960 Imagine this net here is like a simple, single-layer cloth mask - 453 00:26:48,960 --> 00:26:53,920 a bit like a bandanna or a scarf - and it's regular fibres in here. 454 00:26:53,920 --> 00:26:57,600 So I'm going to ask JVT to help me with this. So, can you take 455 00:26:57,600 --> 00:27:02,400 our tray of aerosols, and can you pour them through our mask? 456 00:27:02,400 --> 00:27:06,320 So, as you can see, there are some large ones and some small ones 457 00:27:06,320 --> 00:27:08,840 here, and it's no accident at all 458 00:27:08,840 --> 00:27:11,880 that these are Boston United aerosols. 459 00:27:11,880 --> 00:27:13,760 LAUGHTER 460 00:27:21,560 --> 00:27:27,200 So, what you can see is that our mask has stopped the big aerosols, 461 00:27:27,200 --> 00:27:31,280 but a not very good game for Boston United today, 462 00:27:31,280 --> 00:27:34,320 because all the small aerosols have fallen through the mask, 463 00:27:34,320 --> 00:27:37,800 so it didn't stop those small aerosols. 464 00:27:37,800 --> 00:27:40,440 So let's try and solve this. 465 00:27:40,440 --> 00:27:43,320 We've got an option for how we can manage this 466 00:27:43,320 --> 00:27:49,800 and all we can do is we're now going to put a second layer on our mask. 467 00:27:49,800 --> 00:27:54,600 Now, this is a slightly different layer because this time, if you look 468 00:27:54,600 --> 00:27:58,760 at it, it's not just a nice regular layer, it's a bit more chaotic. 469 00:27:58,760 --> 00:28:02,400 We've got crisscrossing fibres this time. So, shall we have another go? 470 00:28:02,400 --> 00:28:04,440 Pour our aerosols on. 471 00:28:09,880 --> 00:28:12,760 And this time, you saw that the mask blocked nearly all of them - 472 00:28:12,760 --> 00:28:14,720 there were a few that escaped round the sides, 473 00:28:14,720 --> 00:28:19,680 but it blocked all of the small ones and all of the large ones here. 474 00:28:19,680 --> 00:28:24,920 And this process of blocking is something called inertial impaction. 475 00:28:24,920 --> 00:28:27,960 It's basically where the aerosols that are trying to get through 476 00:28:27,960 --> 00:28:31,640 the fibres of the mask essentially collide with the fibres, 477 00:28:31,640 --> 00:28:36,600 and even if the gaps in the mask are quite big compared to the size 478 00:28:36,600 --> 00:28:40,400 of the aerosol, they still collide as they try 479 00:28:40,400 --> 00:28:43,080 and weave their way through all this fabric. 480 00:28:43,080 --> 00:28:45,680 But we have a problem with one size of aerosols - 481 00:28:45,680 --> 00:28:49,320 the ones that are just less than a micron, the ones about 0.5 micron. 482 00:28:49,320 --> 00:28:51,200 They are harder to trap 483 00:28:51,200 --> 00:28:53,560 and they sometimes sneak through on air currents. 484 00:28:53,560 --> 00:28:57,400 So to solve this, some masks have a hidden force, 485 00:28:57,400 --> 00:29:00,320 and we're going to demonstrate this force. 486 00:29:00,320 --> 00:29:04,680 So what we have here is a Van de Graaf generator 487 00:29:04,680 --> 00:29:06,880 and this generates an electric field. 488 00:29:06,880 --> 00:29:09,680 So if you'd like to turn it on, JVT. 489 00:29:09,680 --> 00:29:11,960 So now we're going to generate an electric field. 490 00:29:11,960 --> 00:29:15,560 I've got some tiny aerosols here, polystyrene balls, 491 00:29:15,560 --> 00:29:19,840 and if I bring it close to the generator, you see them jumping? 492 00:29:19,840 --> 00:29:22,440 They jump onto the generator... 493 00:29:22,440 --> 00:29:25,680 as the electrostatic field grabs hold of them. 494 00:29:25,680 --> 00:29:28,720 OK. Let's do some round the front. 495 00:29:28,720 --> 00:29:29,960 OK. 496 00:29:29,960 --> 00:29:33,240 So that causes those aerosols to stick. 497 00:29:33,240 --> 00:29:37,880 Perhaps we'll turn it off now before we electrocute ourselves. OK. 498 00:29:37,880 --> 00:29:40,720 And this process is actually used in masks, 499 00:29:40,720 --> 00:29:44,320 so some of the medical-grade masks actually have 500 00:29:44,320 --> 00:29:48,160 fibres inside them that are charged, they carry an electrostatic charge, 501 00:29:48,160 --> 00:29:51,240 and they can cause those smallest aerosols to stick. 502 00:29:53,520 --> 00:29:57,760 So, as you can see, there's some quite amazing science 503 00:29:57,760 --> 00:30:02,280 going on inside a modern medical mask. 504 00:30:02,280 --> 00:30:07,400 And you may have heard of the N95 mask - but actually, in medicine, 505 00:30:07,400 --> 00:30:12,920 we call it a respirator - and the N95 mask, one like this... 506 00:30:16,840 --> 00:30:21,760 ..has multiple layers inside it, and electrostatic charging. 507 00:30:21,760 --> 00:30:25,600 So if we go over to my picture over here, 508 00:30:25,600 --> 00:30:30,760 we can have a look. And this is, essentially, a mesh sandwich, 509 00:30:30,760 --> 00:30:33,320 so you can see the two slices of bread - 510 00:30:33,320 --> 00:30:36,400 they're the kind of more normal mask layers - 511 00:30:36,400 --> 00:30:40,200 but in the middle, there is this purple filling, sandwich 512 00:30:40,200 --> 00:30:43,200 filling, and those purple fibres, as you can see, 513 00:30:43,200 --> 00:30:47,840 they're very random and very chaotic in how they're laid down, 514 00:30:47,840 --> 00:30:50,920 and they also have an electrostatic charge on them. 515 00:30:52,000 --> 00:30:56,440 And that's what's inside an N95 mask. 516 00:30:56,440 --> 00:31:00,960 Some larger studies in the population are beginning to show 517 00:31:00,960 --> 00:31:06,920 that when we all wear face coverings in crowded indoor settings, 518 00:31:06,920 --> 00:31:10,680 this does actually help to reduce the spread of many 519 00:31:10,680 --> 00:31:15,440 respiratory viruses, including Covid. 520 00:31:15,440 --> 00:31:20,120 Let's now go over to Cath, who should be on the grand staircase. 521 00:31:23,400 --> 00:31:26,400 So, masks are not the only solution - and, in fact, 522 00:31:26,400 --> 00:31:29,240 it's not really feasible that we wear them all the time, 523 00:31:29,240 --> 00:31:33,480 but there is something else we can do. We can use ventilation. 524 00:31:33,480 --> 00:31:36,480 So we've got quite used to, during the pandemic, having to 525 00:31:36,480 --> 00:31:40,000 ventilate buildings more, having to open our windows more, and what 526 00:31:40,000 --> 00:31:43,880 ventilation does is it deals with some of those really small aerosols, 527 00:31:43,880 --> 00:31:47,040 the ones that can stay suspended in the air for a long time, 528 00:31:47,040 --> 00:31:50,680 and it can flush them out of a space. But we know 529 00:31:50,680 --> 00:31:55,480 we have to do it, but actually what is the science behind ventilation? 530 00:31:55,480 --> 00:31:57,200 So, air is invisible, 531 00:31:57,200 --> 00:32:00,560 it's really hard to see how it moves in a building, 532 00:32:00,560 --> 00:32:04,280 but what we can do is use a scale model, and if we use a scale model 533 00:32:04,280 --> 00:32:08,880 and fill it with water, then the water behaves just like the air. 534 00:32:08,880 --> 00:32:11,240 So, here we've got an underwater model 535 00:32:11,240 --> 00:32:13,480 and this is a model of a lecture theatre, it's a 536 00:32:13,480 --> 00:32:17,680 little bit like the lecture theatre you're in, although much simpler. 537 00:32:17,680 --> 00:32:22,520 And inside it we've filled it with hot water and we've coloured it red, 538 00:32:22,520 --> 00:32:27,080 so that represents the fact you're in a warm indoor environment. 539 00:32:27,080 --> 00:32:30,680 And then outside, the clear water is cold, 540 00:32:30,680 --> 00:32:33,600 and that's like the cold air outside. 541 00:32:33,600 --> 00:32:36,480 Now, it's got some vents in it, so if I open some of these 542 00:32:36,480 --> 00:32:40,360 vents at the bottom and we take one out on this side.. 543 00:32:42,240 --> 00:32:45,800 ..and one out on the other side. OK. 544 00:32:45,800 --> 00:32:49,360 We can see not much is happening yet, but if I now take the vent 545 00:32:49,360 --> 00:32:54,080 out in the roof, what you should be able to see is you get a plume now 546 00:32:54,080 --> 00:32:59,160 moving out of it and the air of the cold water is going in at the bottom 547 00:32:59,160 --> 00:33:01,680 and it's displacing the red liquid, 548 00:33:01,680 --> 00:33:04,000 so it's pushing out the warm air. 549 00:33:04,000 --> 00:33:06,880 Now, if that warm air contained virus, it would be flushing 550 00:33:06,880 --> 00:33:09,640 out that virus in that environment. 551 00:33:09,640 --> 00:33:12,440 And this whole process is driven by heat, 552 00:33:12,440 --> 00:33:15,400 it's the temperature difference between inside 553 00:33:15,400 --> 00:33:17,720 and outside that makes that air flow, 554 00:33:17,720 --> 00:33:20,480 and the bigger that temperature difference, 555 00:33:20,480 --> 00:33:22,120 the more air flow we get. 556 00:33:22,120 --> 00:33:26,160 And so, actually, when it's cold outside, and, say, like at 557 00:33:26,160 --> 00:33:29,800 Christmas, when you're at home, if you open the windows, you'll get a 558 00:33:29,800 --> 00:33:33,760 blast of cold air through the house, it's a really good thing to do. 559 00:33:33,760 --> 00:33:37,160 Now, we can do this for much bigger buildings, too, 560 00:33:37,160 --> 00:33:41,480 and if we come over here, we can see a much bigger water model. 561 00:33:41,480 --> 00:33:45,320 In fact, this is the biggest water tank model in the world 562 00:33:45,320 --> 00:33:48,520 and we haven't got any water in it because we'd actually have to put it 563 00:33:48,520 --> 00:33:50,320 in a really big tank. So this is a model 564 00:33:50,320 --> 00:33:52,320 of the Bloomberg building - 565 00:33:52,320 --> 00:33:54,640 this is in London and this works... 566 00:33:54,640 --> 00:33:57,040 It's a naturally ventilated building 567 00:33:57,040 --> 00:34:00,600 and it works by this atrium here, which acts a bit like the big 568 00:34:00,600 --> 00:34:04,760 lecture theatre - it pulls air, hot air rises and it pulls clean, 569 00:34:04,760 --> 00:34:07,840 fresh air into the floors below. 570 00:34:07,840 --> 00:34:11,240 But this process doesn't work in every building, 571 00:34:11,240 --> 00:34:14,560 so in some buildings we need mechanical ventilation to help - 572 00:34:14,560 --> 00:34:18,760 that uses ducts and fans to push the air through. And, actually, 573 00:34:18,760 --> 00:34:21,200 the lecture theatre you're in at the moment 574 00:34:21,200 --> 00:34:23,680 has a mechanical ventilation system. 575 00:34:23,680 --> 00:34:27,320 It actually doesn't matter what type of system we have in a building - 576 00:34:27,320 --> 00:34:32,400 whether it's driven by heat or the wind or by mechanical means - 577 00:34:32,400 --> 00:34:35,640 what matters is that we have good ventilation to flush that 578 00:34:35,640 --> 00:34:37,760 virus out that might be in the air. 579 00:34:37,760 --> 00:34:39,880 Thank you, Cath. 580 00:34:39,880 --> 00:34:43,400 So, during this pandemic, 581 00:34:43,400 --> 00:34:49,600 we have learnt a huge amount about the spread of respiratory viruses. 582 00:34:49,600 --> 00:34:53,440 The science is explaining to us how we can be 583 00:34:53,440 --> 00:34:59,320 exposed through the behaviour of aerosols and droplets. 584 00:34:59,320 --> 00:35:05,160 And it also shows why ventilation and masks or face coverings 585 00:35:05,160 --> 00:35:07,720 are also pretty important. 586 00:35:09,720 --> 00:35:12,560 But there's no single magic bullet. 587 00:35:13,680 --> 00:35:17,600 Ventilation on its own is not going to work. 588 00:35:17,600 --> 00:35:20,440 Handwashing on its own, not going to work. 589 00:35:21,600 --> 00:35:24,800 Face masks or face coverings on their own, 590 00:35:24,800 --> 00:35:27,000 they're not going to work. 591 00:35:27,000 --> 00:35:30,280 But if you combine all of them together, 592 00:35:30,280 --> 00:35:33,080 you get a much greater effect. 593 00:35:33,080 --> 00:35:34,960 Cath - oh, you're back - 594 00:35:34,960 --> 00:35:39,680 how complete is our knowledge in this, your specialist area? 595 00:35:39,680 --> 00:35:41,480 So we've learnt an awful lot. 596 00:35:41,480 --> 00:35:44,040 We've pulled a lot of knowledge together really fast 597 00:35:44,040 --> 00:35:47,320 over the pandemic. And this knowledge is really important. 598 00:35:47,320 --> 00:35:49,200 It's not just knowledge for Covid-19 - 599 00:35:49,200 --> 00:35:52,880 it will matter beyond that for any future diseases, 600 00:35:52,880 --> 00:35:55,880 and to just develop healthier environments, 601 00:35:55,880 --> 00:35:59,200 give us cleaner air for future generations. 602 00:35:59,200 --> 00:36:01,760 Cath, it's been great to see you again, 603 00:36:01,760 --> 00:36:05,840 and thanks so much for joining us and telling us all about aerobiology 604 00:36:05,840 --> 00:36:08,320 this evening. Thank you. Thank you so much. Cath Noakes. 605 00:36:18,840 --> 00:36:23,680 So, some of you will have heard of the Three Cs - 606 00:36:23,680 --> 00:36:27,440 close contact, crowding 607 00:36:27,440 --> 00:36:31,840 and closed environments with low ventilation. 608 00:36:31,840 --> 00:36:36,360 Those are the perfect conditions to get a respiratory virus 609 00:36:36,360 --> 00:36:38,240 to spread quickly. 610 00:36:39,320 --> 00:36:41,200 And without vaccines, 611 00:36:41,200 --> 00:36:45,920 we have to rely on masks, distancing and ventilation. 612 00:36:47,240 --> 00:36:50,880 But if things start to move really fast, 613 00:36:50,880 --> 00:36:55,480 then we are going to have to stop contacts between people... 614 00:36:57,000 --> 00:37:02,200 ..and to reduce crowding, to stop the overall spread. 615 00:37:02,200 --> 00:37:07,000 Now, it's quite difficult to make sense of transmission 616 00:37:07,000 --> 00:37:09,680 when it's not just two people - 617 00:37:09,680 --> 00:37:12,760 the virus going from one person to the next - 618 00:37:12,760 --> 00:37:17,440 but instead a virus spreading around the whole of the UK population, 619 00:37:17,440 --> 00:37:20,840 which is just under 70 million people. 620 00:37:20,840 --> 00:37:25,440 And to do that, you need to combine two science disciplines - 621 00:37:25,440 --> 00:37:30,760 first of all, the science of aerosol spread - aerobiology - 622 00:37:30,760 --> 00:37:35,800 and secondly, the science of mathematical modelling. 623 00:37:35,800 --> 00:37:38,640 And if you combine them, you can begin to get 624 00:37:38,640 --> 00:37:44,680 a really good understanding of how a respiratory virus will spread. 625 00:37:44,680 --> 00:37:48,760 Now, maths is not my strongest subject, 626 00:37:48,760 --> 00:37:51,920 but I know someone who's really good at it. 627 00:37:51,920 --> 00:37:55,120 And our next expert is that person. 628 00:37:55,120 --> 00:37:59,600 She'll reveal the kind of wonders of the maths of epidemics. 629 00:37:59,600 --> 00:38:04,200 She's internationally renowned. Please welcome Professor Julia Gog. 630 00:38:13,400 --> 00:38:16,440 Thank you, JVT. And hello, everyone. 631 00:38:16,440 --> 00:38:20,040 I'm a mathematician from the University of Cambridge, 632 00:38:20,040 --> 00:38:23,360 and I use mathematics to understand how diseases spread 633 00:38:23,360 --> 00:38:25,680 through the population. 634 00:38:25,680 --> 00:38:28,960 Maybe it's not obvious right away why we need mathematics 635 00:38:28,960 --> 00:38:31,680 and why we can't just work out intuitively 636 00:38:31,680 --> 00:38:34,240 how infection will spread between us. 637 00:38:34,240 --> 00:38:37,640 We understand how it spreads from person to person, 638 00:38:37,640 --> 00:38:40,640 but at the population level, what happens? 639 00:38:40,640 --> 00:38:45,160 So if we start with one person initiating an epidemic - 640 00:38:45,160 --> 00:38:49,840 so we've got one person and they're infectious for one week, 641 00:38:49,840 --> 00:38:54,840 and during that time, on average, they infect one other person. 642 00:38:54,840 --> 00:38:58,360 We represent our first week with this one person here, 643 00:38:58,360 --> 00:39:00,320 this black band at the top. 644 00:39:00,320 --> 00:39:06,480 In week two, they infect someone, so week two is the white band. 645 00:39:06,480 --> 00:39:10,280 Then that week, they infect one more person, the black band. 646 00:39:10,280 --> 00:39:12,280 And you can see how this is going to go. 647 00:39:12,280 --> 00:39:14,840 I'm interested to know what happens after ten weeks. 648 00:39:17,800 --> 00:39:19,600 And there we are. 649 00:39:19,600 --> 00:39:24,880 Ten weeks of infection, we've got ten people, ten centimetres' ribbon. 650 00:39:24,880 --> 00:39:28,680 However, I'm going to make a very small change. 651 00:39:28,680 --> 00:39:32,120 Instead of each person just infecting one more person 652 00:39:32,120 --> 00:39:36,440 on average, what happens if they infect two on average? 653 00:39:36,440 --> 00:39:39,640 And we've got this over here. So we start with one person infected - 654 00:39:39,640 --> 00:39:41,200 again, black band at the top. 655 00:39:42,400 --> 00:39:45,400 And then two people infected. 656 00:39:45,400 --> 00:39:48,960 And then, week three, it's four people infected. 657 00:39:48,960 --> 00:39:50,160 Can we just guess, 658 00:39:50,160 --> 00:39:52,560 can we intuit what's going to happen after ten weeks? 659 00:39:52,560 --> 00:39:54,240 I wonder what it's going to be. 660 00:39:54,240 --> 00:39:58,000 Now, do you think it's going to be more or less than twice 661 00:39:58,000 --> 00:40:00,840 the size of this one? Higher or lower? 662 00:40:00,840 --> 00:40:02,280 AUDIENCE: Higher! 663 00:40:02,280 --> 00:40:06,720 Oh, OK. OK, what about higher than me? Higher or lower? 664 00:40:06,720 --> 00:40:08,160 AUDIENCE: Higher! 665 00:40:08,160 --> 00:40:10,320 Higher? I'm not very big, though. 666 00:40:10,320 --> 00:40:13,440 So what about the highest I can possibly reach? Higher or lower? 667 00:40:13,440 --> 00:40:15,560 AUDIENCE: Higher! Higher? OK. 668 00:40:15,560 --> 00:40:17,120 Are you going to keep saying higher? 669 00:40:17,120 --> 00:40:21,200 What about...? Will it get up to the balcony up there, to the adults? 670 00:40:21,200 --> 00:40:22,520 AUDIENCE: Higher! 671 00:40:22,520 --> 00:40:23,800 Higher? 672 00:40:23,800 --> 00:40:25,840 OK. Will it go out the building? 673 00:40:25,840 --> 00:40:27,440 AUDIENCE: No. No? OK. 674 00:40:27,440 --> 00:40:29,160 Well, let's find out. Let's see this. 675 00:40:29,160 --> 00:40:31,520 So that was one, two, 676 00:40:31,520 --> 00:40:33,280 four, 677 00:40:33,280 --> 00:40:34,560 eight, 678 00:40:34,560 --> 00:40:36,080 16, 679 00:40:36,080 --> 00:40:37,600 32, 680 00:40:37,600 --> 00:40:40,120 64... Oh, there it goes now! 681 00:40:40,120 --> 00:40:41,680 128. 682 00:40:42,920 --> 00:40:45,000 256. 683 00:40:45,000 --> 00:40:47,760 And then we're into the 512. 684 00:40:48,920 --> 00:40:51,840 Whoa! 685 00:40:51,840 --> 00:40:53,880 It's still going! 686 00:40:53,880 --> 00:40:57,160 Right, that's right the whole way up to the top of the ceiling there. 687 00:40:57,160 --> 00:40:59,480 Now, if you weren't surprised how tall this was - 688 00:40:59,480 --> 00:41:01,480 and it's over ten metres - 689 00:41:01,480 --> 00:41:04,560 then, probably, you were actually using mathematics to work it out. 690 00:41:04,560 --> 00:41:08,640 This is exponential growth. It's growing geometrically each time. 691 00:41:08,640 --> 00:41:13,000 Humans tend to think more in linear terms. We just add a bit each time. 692 00:41:13,000 --> 00:41:16,600 This is multiplicative. And this isn't always intuitive. 693 00:41:16,600 --> 00:41:19,920 What we've actually seen, also, here is R - 694 00:41:19,920 --> 00:41:22,960 the very famous capital R, the reproduction ratio 695 00:41:22,960 --> 00:41:25,560 which we've all heard about during this pandemic. 696 00:41:25,560 --> 00:41:30,000 On this one, we were infecting one person on average each generation - 697 00:41:30,000 --> 00:41:32,880 each infection, one more person. 698 00:41:32,880 --> 00:41:37,760 This one, each infected person was infecting two people on average. 699 00:41:37,760 --> 00:41:41,480 That seemed like a relatively small difference. 700 00:41:41,480 --> 00:41:45,360 But after ten weeks - these are both ten weeks, remember - 701 00:41:45,360 --> 00:41:46,680 that's a huge difference. 702 00:41:46,680 --> 00:41:51,040 This one's here and that one's gone way up to the ceiling. 703 00:41:51,040 --> 00:41:55,600 So, one of the things we did in the very early stages of this pandemic 704 00:41:55,600 --> 00:42:00,400 is we said to the modellers, "Can you calculate the R for us?" 705 00:42:00,400 --> 00:42:06,280 And very quickly, they came up with an R that was around 5 706 00:42:06,280 --> 00:42:10,840 and a generation time of about five days. 707 00:42:10,840 --> 00:42:16,400 And if you do the exponential mathematics, in ten generations, 708 00:42:16,400 --> 00:42:22,440 that creates just under ten million cases. 709 00:42:22,440 --> 00:42:29,000 And that is literally how quickly a virus that's infectious can spread. 710 00:42:30,240 --> 00:42:36,240 And some diseases are actually even more infectious than Covid. 711 00:42:36,240 --> 00:42:42,560 The big one is measles, where the R value is over 15. 712 00:42:42,560 --> 00:42:45,880 But we don't worry about measles any more 713 00:42:45,880 --> 00:42:49,760 because we have good, safe, effective vaccinations 714 00:42:49,760 --> 00:42:54,160 and we vaccinate young children, and so we're safe from that one. 715 00:42:58,280 --> 00:43:02,560 Now, real epidemics don't just carry on to some mathematical infinity. 716 00:43:02,560 --> 00:43:06,040 As the epidemic goes on, the whole process changes a little bit. 717 00:43:06,040 --> 00:43:08,560 The system is changing through time. 718 00:43:08,560 --> 00:43:11,280 And at this point, we need mathematical models to help us 719 00:43:11,280 --> 00:43:13,520 explore these different factors. 720 00:43:13,520 --> 00:43:15,880 But rather than showing you some equations, 721 00:43:15,880 --> 00:43:19,360 we've turned this into a game of lucky dip. 722 00:43:21,960 --> 00:43:25,440 To help me run this model, we have two audience members 723 00:43:25,440 --> 00:43:28,480 lined up to help us who are in a bubble together, so... 724 00:43:28,480 --> 00:43:32,280 I'm going to put my mask on before you come down, actually... 725 00:43:32,280 --> 00:43:34,680 ..because we're going to have to work together a little bit 726 00:43:34,680 --> 00:43:36,080 on this one to make this model run. 727 00:43:37,800 --> 00:43:40,240 So... Please come on down. 728 00:43:40,240 --> 00:43:42,880 APPLAUSE 729 00:43:48,680 --> 00:43:51,200 Thanks for helping me with this. What's your name? 730 00:43:51,200 --> 00:43:55,200 I'm Eden. Eden? Thank you. And...? Noah. Noah. Great. 731 00:43:55,200 --> 00:43:57,120 Right, I'm going to put this around here. 732 00:43:57,120 --> 00:43:59,840 Eden, why don't you come around here? 733 00:43:59,840 --> 00:44:03,240 And you're going to look after our population of 26. 734 00:44:03,240 --> 00:44:04,920 Just keep mixing them. 735 00:44:04,920 --> 00:44:07,160 And your main job is to keep the population well mixed, 736 00:44:07,160 --> 00:44:08,520 no-one hiding in the corners. 737 00:44:09,960 --> 00:44:14,000 And, Noah, I'm going to ask you to pick out people from the population 738 00:44:14,000 --> 00:44:15,920 to run our epidemic. 739 00:44:15,920 --> 00:44:19,280 We've got the help of JVT, as well. I'm going to come around here. 740 00:44:19,280 --> 00:44:22,840 Let's start this off. Start by picking one ball. 741 00:44:25,320 --> 00:44:28,040 This represents our first person who's infected. 742 00:44:28,040 --> 00:44:30,560 Each of these balls is a person. 743 00:44:30,560 --> 00:44:33,240 We've got a population of 26 in the bag. 744 00:44:33,240 --> 00:44:36,520 And this is our first infected. If you open it up... 745 00:44:39,080 --> 00:44:41,600 ..there's a red token inside. 746 00:44:41,600 --> 00:44:46,080 And I'm going to give these to JVT and we're going to stick these here. 747 00:44:48,080 --> 00:44:51,120 This person's now been infected... 748 00:44:51,120 --> 00:44:53,560 ..and they will recover from infection, 749 00:44:53,560 --> 00:44:55,800 and then they can't be infected again, 750 00:44:55,800 --> 00:44:57,440 so they're now going to be immune. 751 00:44:57,440 --> 00:44:59,920 So they can't be infected further on in the pandemic. 752 00:44:59,920 --> 00:45:03,280 So let's put them back into the population. 753 00:45:03,280 --> 00:45:06,920 And, Eden, do your thing of giving the population a really good mix. 754 00:45:06,920 --> 00:45:10,240 Now, audience, you're going to help me with this bit. 755 00:45:10,240 --> 00:45:15,240 Each person that's infected, we'll pick out two for each person 756 00:45:15,240 --> 00:45:18,960 for the next generation. OK? So we start with one here. 757 00:45:18,960 --> 00:45:21,560 So how many are we going to pick for Generation 2? 758 00:45:21,560 --> 00:45:22,920 Just shout out. AUDIENCE: Two! 759 00:45:22,920 --> 00:45:25,440 Great. Eden, please pass up two. 760 00:45:25,440 --> 00:45:28,480 Here's the next generation. There's two. Let's open them up. 761 00:45:30,320 --> 00:45:32,480 There's one. 762 00:45:32,480 --> 00:45:35,080 There's two. And the empty ones go back in. 763 00:45:35,080 --> 00:45:36,640 And give them a really good mix. 764 00:45:39,400 --> 00:45:41,840 Now, audience, we've got two infected now. 765 00:45:41,840 --> 00:45:43,800 How many are we going to pick, next generation? 766 00:45:43,800 --> 00:45:44,920 AUDIENCE: Four! 767 00:45:44,920 --> 00:45:46,280 Four? Good. 768 00:45:46,280 --> 00:45:49,400 Right, can you pass up four, please? There we go. One, two... 769 00:45:50,440 --> 00:45:52,880 ..three, four. Let's get those opened up. 770 00:45:52,880 --> 00:45:56,280 I'll give you a hand now. Empty ones back in the population. 771 00:45:56,280 --> 00:45:57,840 Well done. 772 00:45:57,840 --> 00:45:59,280 And give them a good mix. 773 00:46:01,840 --> 00:46:04,640 OK, audience, we've got four infected, so next generation, 774 00:46:04,640 --> 00:46:06,440 we need to pick...? AUDIENCE: Eight! 775 00:46:06,440 --> 00:46:07,800 Eight. Right, let's go eight. 776 00:46:09,000 --> 00:46:11,560 Six... 777 00:46:11,560 --> 00:46:14,360 Eight. Oh! Some of these are empty. 778 00:46:14,360 --> 00:46:16,200 They're not all full of tokens any more, 779 00:46:16,200 --> 00:46:18,400 so these are people who are immune. 780 00:46:18,400 --> 00:46:20,320 They're not going to catch this disease now. 781 00:46:20,320 --> 00:46:23,080 Let's get these open and find out how many tokens there are. 782 00:46:27,680 --> 00:46:28,760 There's some. 783 00:46:31,480 --> 00:46:34,080 OK, audience, we got six, so we're going to pick...? 784 00:46:34,080 --> 00:46:36,080 AUDIENCE: 12! Let's go for it. 785 00:46:37,160 --> 00:46:38,840 Ooh! 786 00:46:38,840 --> 00:46:41,160 There's only three there. 787 00:46:41,160 --> 00:46:44,240 OK. Oh, no, We've missed one, haven't we? Thank you, this side. 788 00:46:47,040 --> 00:46:49,480 OK, we got four this generation. How many next generation? 789 00:46:49,480 --> 00:46:50,720 AUDIENCE: Eight! 790 00:46:50,720 --> 00:46:53,360 So only picking eight this time. Let's get these out. 791 00:46:53,360 --> 00:46:54,600 There are some. 792 00:46:56,000 --> 00:46:58,880 It looks like our epidemic's turned over, doesn't it? 793 00:46:58,880 --> 00:47:00,800 So we've got three, so we're going to pick...? 794 00:47:00,800 --> 00:47:02,760 AUDIENCE: Six! Let's go. Ah! 795 00:47:02,760 --> 00:47:06,520 Just one. Now we've just got one infected, so we're going to pick... 796 00:47:06,520 --> 00:47:08,280 AUDIENCE: Two! Two. Let's go. 797 00:47:09,440 --> 00:47:11,760 One... Oh. 798 00:47:11,760 --> 00:47:14,720 Well, they're empty. So how many infecteds have we got this time? 799 00:47:14,720 --> 00:47:15,840 AUDIENCE: None. 800 00:47:15,840 --> 00:47:19,000 And the epidemic has ended. That was absolutely brilliant. 801 00:47:19,000 --> 00:47:21,840 Thank you very much for helping me run that epidemic. That was superb. 802 00:47:21,840 --> 00:47:22,920 APPLAUSE Thank you. 803 00:47:28,000 --> 00:47:30,360 Thank you, Noah and Eden. That was absolutely brilliant. 804 00:47:30,360 --> 00:47:34,480 So, usually, when you get to the end of the epidemic, 805 00:47:34,480 --> 00:47:37,760 it's not because we've run out of people to infect. 806 00:47:37,760 --> 00:47:39,120 In fact... 807 00:47:40,400 --> 00:47:46,000 ..I can see one, two, three, four capsules 808 00:47:46,000 --> 00:47:49,320 that actually had a token left in at the end. 809 00:47:49,320 --> 00:47:52,400 So...this was a perfectly normal capsule. 810 00:47:52,400 --> 00:47:54,760 It was in there the whole time. 811 00:47:54,760 --> 00:47:57,360 It could have been chosen, but it wasn't. 812 00:47:57,360 --> 00:47:59,640 The epidemic ended before it was chosen. 813 00:47:59,640 --> 00:48:03,760 There was nothing magic about this. So what protected it? 814 00:48:04,880 --> 00:48:06,320 Well, it was the herd immunity. 815 00:48:06,320 --> 00:48:09,480 It was all the other empty capsules around it. 816 00:48:09,480 --> 00:48:12,120 We were still picking at the end, but we were mostly picking out 817 00:48:12,120 --> 00:48:15,440 empty capsules, and that brought the epidemic down. 818 00:48:15,440 --> 00:48:19,080 Now, maybe you want to know, how does our lucky-dip model 819 00:48:19,080 --> 00:48:22,400 that we ran here compare with a real epidemic? 820 00:48:22,400 --> 00:48:25,960 And what we have here... This is a real epidemic. 821 00:48:25,960 --> 00:48:31,280 So, this was actually influenza at a boys' boarding school in 1978. 822 00:48:31,280 --> 00:48:32,680 This was an outbreak of flu. 823 00:48:34,560 --> 00:48:36,600 Along the horizontal axis here, we have time. 824 00:48:36,600 --> 00:48:40,760 You can see this was late January, running into February. 825 00:48:40,760 --> 00:48:43,720 And the vertical axis is the number of cases. 826 00:48:43,720 --> 00:48:47,520 The curve with the black dots says, "Confined to bed". 827 00:48:47,520 --> 00:48:49,920 So these are children who are ill with influenza, 828 00:48:49,920 --> 00:48:53,000 so they were confined to bed. And then "convalescent". 829 00:48:53,000 --> 00:48:56,320 These were the children who were recovering from flu at that time. 830 00:48:56,320 --> 00:48:58,960 And, actually, can you see...? They're not exactly the same, 831 00:48:58,960 --> 00:49:01,320 but there's a lot in common between them, right? 832 00:49:01,320 --> 00:49:05,560 However, in lucky dip, all the balls were the same as each other - 833 00:49:05,560 --> 00:49:09,600 they were all identical. Now, we are not all the same as each other. 834 00:49:09,600 --> 00:49:12,040 There's lots of variability between us. 835 00:49:12,040 --> 00:49:13,760 And probably, if I asked any of you, 836 00:49:13,760 --> 00:49:16,000 you can give me more and more details that we might want 837 00:49:16,000 --> 00:49:19,200 to include to make our models realistic. 838 00:49:19,200 --> 00:49:23,160 However, that does not always make the models more helpful. 839 00:49:23,160 --> 00:49:27,720 The key question is, which of these details are important? 840 00:49:27,720 --> 00:49:31,680 Which of them really change what's going to happen with the epidemic? 841 00:49:31,680 --> 00:49:33,760 And it might surprise you what matters. 842 00:49:35,320 --> 00:49:38,720 To explore this further, we're going to run another epidemic model, 843 00:49:38,720 --> 00:49:42,040 but this time, we're going to need the whole audience for that. 844 00:49:42,040 --> 00:49:45,120 And to do that, please welcome Rob and Steve from WiFi Wars. 845 00:49:45,120 --> 00:49:46,880 APPLAUSE 846 00:49:52,360 --> 00:49:54,600 Hi, Rob. Hi, Steve. Hello. Hello. 847 00:49:54,600 --> 00:49:56,840 Steve, can you tell us what you've set up here? 848 00:49:56,840 --> 00:49:59,080 Yeah, OK, so we've set up a Wi-Fi network in the room 849 00:49:59,080 --> 00:50:02,280 and we've got everybody here to connect to it using their phones, 850 00:50:02,280 --> 00:50:04,120 and then we've mapped where everybody is sat. 851 00:50:04,120 --> 00:50:06,800 So what that means, thanks to a clever bit of code that Rob's done, 852 00:50:06,800 --> 00:50:08,800 we can hopefully now let you use them all 853 00:50:08,800 --> 00:50:11,680 as a population to simulate epidemics. 854 00:50:11,680 --> 00:50:14,480 Uh-huh! Perfect. Mm-hm. Right. 855 00:50:14,480 --> 00:50:18,560 So, if you hold your phones up so we can see you... OK. 856 00:50:18,560 --> 00:50:20,360 We've got all white at the moment, 857 00:50:20,360 --> 00:50:24,440 but now let's set this up as a population with some variability. 858 00:50:24,440 --> 00:50:27,640 Ooh! Right, so some have yellow... 859 00:50:27,640 --> 00:50:29,000 STEVE LAUGHS 860 00:50:29,000 --> 00:50:30,840 So the colours denote different R numbers. 861 00:50:30,840 --> 00:50:32,600 OK, perfect. 862 00:50:32,600 --> 00:50:35,960 So we've got... About 40% of you have white. 863 00:50:35,960 --> 00:50:38,800 If you have white, your R equals 0. You're isolators. 864 00:50:38,800 --> 00:50:41,280 If you get infected, you're not going to infect anyone else. 865 00:50:41,280 --> 00:50:45,560 Yep, and yellow is an R of 1. That's another 40% of you. Mm-hm. 866 00:50:45,560 --> 00:50:50,120 And then 20%, unfortunately, have a R of 2. That's our blue ones. 867 00:50:50,120 --> 00:50:51,440 Right, so if you get infected, 868 00:50:51,440 --> 00:50:54,040 you'll infect two other people on average. Indeed. 869 00:50:54,040 --> 00:50:58,840 Right, so that's 40% 0, 40% 1, 20% 2... Correct. 870 00:50:58,840 --> 00:51:01,200 ..so that's an average of 0.8. 871 00:51:01,200 --> 00:51:05,360 So the population average R is 0.8. Bit less than one. 872 00:51:05,360 --> 00:51:09,000 So, Rob will now infect somebody in the room. 873 00:51:09,000 --> 00:51:11,760 Someone's infected. So, red means they're infected. Exactly. 874 00:51:11,760 --> 00:51:13,680 OK, so it's first infection. 875 00:51:13,680 --> 00:51:16,400 What happens with one more generation of infection? 876 00:51:16,400 --> 00:51:18,280 Let's see. 877 00:51:18,280 --> 00:51:20,920 Oh, oh! One over there. 878 00:51:20,920 --> 00:51:22,840 What happens with another generation? 879 00:51:24,240 --> 00:51:26,200 That's it. That's your lot, I'm afraid. 880 00:51:26,200 --> 00:51:29,080 OK. So, actually, that was a really limited outbreak, wasn't it? 881 00:51:29,080 --> 00:51:31,960 It was just two people infected. 882 00:51:31,960 --> 00:51:35,040 And, actually, perhaps we shouldn't have been surprised by that 883 00:51:35,040 --> 00:51:38,960 because that was R equals 0.8, so on average, 884 00:51:38,960 --> 00:51:42,640 the number of infections goes down each generation. 885 00:51:42,640 --> 00:51:44,080 Maybe we should do this again, 886 00:51:44,080 --> 00:51:46,040 but this time give it more of a push at the start. 887 00:51:46,040 --> 00:51:48,000 Well, Rob can infect as many people as you like, 888 00:51:48,000 --> 00:51:51,160 so how many would you like this time? Shall we try three? Three? 889 00:51:51,160 --> 00:51:55,240 Lovely. So we've reset. You've all got a fresh identity. 890 00:51:55,240 --> 00:51:57,080 OK, so we've shuffled you again. 891 00:51:57,080 --> 00:51:59,280 Same number. We've just shuffled you again. 892 00:51:59,280 --> 00:52:02,640 Let's try infecting three this time, to give it a real push at the start. 893 00:52:02,640 --> 00:52:03,680 Sure. 894 00:52:05,960 --> 00:52:07,400 Let's run this epidemic. Mm-hm. 895 00:52:08,600 --> 00:52:11,160 Oh, there we go! SHOUTING FROM AUDIENCE 896 00:52:12,160 --> 00:52:13,560 That one's taken off. 897 00:52:15,600 --> 00:52:17,440 OK, so we did get some outbreaks there, 898 00:52:17,440 --> 00:52:19,280 but it still didn't really get that far, did it? 899 00:52:19,280 --> 00:52:21,480 It was just little clusters. 900 00:52:21,480 --> 00:52:24,880 Now, one of the things we've done here is that we just shuffled 901 00:52:24,880 --> 00:52:29,520 initially what colour you were, but life is not like that, is it? 902 00:52:29,520 --> 00:52:31,200 We're not randomly linked to other people. 903 00:52:31,200 --> 00:52:33,640 We're just not a random state. 904 00:52:33,640 --> 00:52:34,680 At the end of tonight, 905 00:52:34,680 --> 00:52:37,560 you're not going to go home to a random house, are you? 906 00:52:37,560 --> 00:52:39,040 And when you go back to school, 907 00:52:39,040 --> 00:52:41,760 you're not going to go to a random school, right? 908 00:52:41,760 --> 00:52:44,000 We've got a lot more structure in what we do. 909 00:52:44,000 --> 00:52:47,600 And in particular, mixers - people who mix a lot - 910 00:52:47,600 --> 00:52:50,280 tend to hang out with other mixers. 911 00:52:50,280 --> 00:52:53,480 People with similar mixing rates cluster together. 912 00:52:53,480 --> 00:52:55,800 So...can we do that? We can. We can group them. 913 00:52:55,800 --> 00:52:57,640 So we'll leave a little bit of variability in, 914 00:52:57,640 --> 00:52:59,640 cos it wouldn't be completely uniform, but... 915 00:52:59,640 --> 00:53:00,920 Can you give us the other one? 916 00:53:00,920 --> 00:53:03,280 Right, let's pick someone to be infected initially. 917 00:53:03,280 --> 00:53:06,720 So, one infection again. One. One. 918 00:53:06,720 --> 00:53:09,320 OK. Let's run this epidemic. 919 00:53:09,320 --> 00:53:11,120 OK. 920 00:53:12,520 --> 00:53:14,680 SHOUTING FROM AUDIENCE 921 00:53:17,520 --> 00:53:20,040 Thank you. That was a lot more dramatic, wasn't it? 922 00:53:21,360 --> 00:53:24,400 I could hear the shrieks as the epidemic moved across the room! 923 00:53:24,400 --> 00:53:26,280 But, remember, this was the same distribution 924 00:53:26,280 --> 00:53:28,800 of yellow, white and blue, wasn't it? We didn't change that. 925 00:53:28,800 --> 00:53:30,680 Exactly the same ratios between the three. 926 00:53:30,680 --> 00:53:34,480 But we just plumped them together. It was the same average R - 927 00:53:34,480 --> 00:53:37,280 it was still R equals 0.8 - 928 00:53:37,280 --> 00:53:39,800 but we got a really dramatically different outcome. 929 00:53:39,800 --> 00:53:42,320 And that was brilliantly demonstrated in this epidemic here, 930 00:53:42,320 --> 00:53:44,720 so, Steve and Rob, thank you very much. 931 00:53:44,720 --> 00:53:47,480 Thank you. APPLAUSE 932 00:53:50,040 --> 00:53:51,360 That was well done, audience. 933 00:53:51,360 --> 00:53:54,520 Reality is really a very complex network 934 00:53:54,520 --> 00:53:57,320 or a web of interactions between us, 935 00:53:57,320 --> 00:53:59,240 the ways we're interconnected with each other, 936 00:53:59,240 --> 00:54:01,760 how that changes over time. 937 00:54:01,760 --> 00:54:04,440 But when we start to think about mixing 938 00:54:04,440 --> 00:54:07,280 and the contact rates between us, 939 00:54:07,280 --> 00:54:11,640 we might wonder about special events that bring us together - things like 940 00:54:11,640 --> 00:54:17,520 sporting events or concerts that result in more contact between us. 941 00:54:17,520 --> 00:54:19,800 What we've actually found is these sorts of events 942 00:54:19,800 --> 00:54:23,640 actually have a very limited effect on the epidemic overall, 943 00:54:23,640 --> 00:54:26,280 but what really matters here is timing. 944 00:54:26,280 --> 00:54:28,840 POPPING 945 00:54:28,840 --> 00:54:30,440 Sorry, was that a bit early? 946 00:54:30,440 --> 00:54:33,000 Brilliant timing, JVT! 947 00:54:33,000 --> 00:54:36,600 That was like a small event that only hit one or two people, but... 948 00:54:36,600 --> 00:54:38,840 Actually, that was really quite a small event, and... 949 00:54:38,840 --> 00:54:40,280 I can do better than that! 950 00:54:40,280 --> 00:54:42,600 Oh, dear! 951 00:54:42,600 --> 00:54:43,680 Uh-oh! 952 00:54:48,120 --> 00:54:49,800 LAUGHTER 953 00:54:51,160 --> 00:54:53,400 Do we want a big event? 954 00:54:53,400 --> 00:54:55,560 SHOUTS FROM AUDIENCE 955 00:54:55,560 --> 00:54:57,600 JULIA LAUGHS 956 00:54:57,600 --> 00:54:59,560 That looks like a much bigger event! 957 00:54:59,560 --> 00:55:01,920 It's a much bigger event. 958 00:55:01,920 --> 00:55:03,800 Let's count down. 959 00:55:03,800 --> 00:55:07,560 Three... AUDIENCE: ..two, one! 960 00:55:07,560 --> 00:55:09,240 LOUD POPPING, CHEERS 961 00:55:11,120 --> 00:55:15,800 Actually, so that large party popper was like a large event. 962 00:55:15,800 --> 00:55:19,800 And super-spreader events do get talked about on the media a lot, 963 00:55:19,800 --> 00:55:22,600 but actually... They might be significant where they are, 964 00:55:22,600 --> 00:55:26,040 but they don't really have a longer impact or more major impact 965 00:55:26,040 --> 00:55:28,640 at the population level when we're thinking about the epidemic 966 00:55:28,640 --> 00:55:29,680 as a whole. 967 00:55:30,840 --> 00:55:33,680 Let's think about, for example, the Euro Football Championships 968 00:55:33,680 --> 00:55:35,960 which happened earlier this year. 969 00:55:35,960 --> 00:55:38,240 It's very easy to focus on the stadiums 970 00:55:38,240 --> 00:55:41,520 and the matches themselves - everyone going there. 971 00:55:41,520 --> 00:55:44,920 But, actually, for the individuals, that might have been risky, 972 00:55:44,920 --> 00:55:47,600 but what was happening in the rest of the country? 973 00:55:47,600 --> 00:55:50,520 Well, we were meeting up in smaller groups, 974 00:55:50,520 --> 00:55:53,240 perhaps to watch the matches together on the TV - 975 00:55:53,240 --> 00:55:56,360 so maybe smaller groups or medium-size groups - 976 00:55:56,360 --> 00:56:00,240 and what we found was the Euros clearly did have an impact 977 00:56:00,240 --> 00:56:02,880 on the Covid-19 pandemic, 978 00:56:02,880 --> 00:56:06,160 but we don't think it was because so much the stadiums, 979 00:56:06,160 --> 00:56:09,560 but it was because the small boosts everywhere else. 980 00:56:09,560 --> 00:56:14,440 Sometimes, lots of small boosts makes a much bigger difference 981 00:56:14,440 --> 00:56:17,280 if they're all at the same time. 982 00:56:17,280 --> 00:56:19,520 We're going to demonstrate this together now. 983 00:56:19,520 --> 00:56:21,920 So hopefully you've got a party popper under your seat, 984 00:56:21,920 --> 00:56:23,880 so if you can get hold of it... 985 00:56:23,880 --> 00:56:27,000 Hold it up high. Don't point at anyone. 986 00:56:27,000 --> 00:56:29,400 I'm going to count down. Ready? 987 00:56:29,400 --> 00:56:32,080 Three, two, one! 988 00:56:32,080 --> 00:56:34,200 LOUD POPPING 989 00:56:38,920 --> 00:56:41,160 That was actually really dramatic! 990 00:56:41,160 --> 00:56:44,360 Simultaneous events are sometimes really important 991 00:56:44,360 --> 00:56:46,680 and we need to factor those into models. 992 00:56:46,680 --> 00:56:52,400 But, actually, football games and big mass events can be safe 993 00:56:52,400 --> 00:56:57,000 if they're properly controlled, with testing and other measures 994 00:56:57,000 --> 00:57:01,720 such as good ventilation or masks and face coverings. 995 00:57:01,720 --> 00:57:08,000 But we need to concentrate much more on events that take place 996 00:57:08,000 --> 00:57:13,240 right across the whole nation where everyone does something smaller 997 00:57:13,240 --> 00:57:16,880 but at exactly the same time. 998 00:57:16,880 --> 00:57:20,840 So we've learned a lot during this pandemic about transmission, 999 00:57:20,840 --> 00:57:23,000 how the virus spreads between us, 1000 00:57:23,000 --> 00:57:27,800 how important the differences and the variability between us is, 1001 00:57:27,800 --> 00:57:31,640 and how that's organised, how we're interconnected to each other. 1002 00:57:31,640 --> 00:57:34,440 And, as we've just seen, timing is really important 1003 00:57:34,440 --> 00:57:38,440 in terms of when transmission and when contact happens. 1004 00:57:38,440 --> 00:57:41,760 Thank you, Julia. That was absolutely brilliant. 1005 00:57:41,760 --> 00:57:45,440 You've made the complexity of mathematical modelling 1006 00:57:45,440 --> 00:57:47,880 really very simple for us tonight, 1007 00:57:47,880 --> 00:57:50,600 so thank you for joining us once again. 1008 00:57:50,600 --> 00:57:52,800 APPLAUSE 1009 00:57:57,240 --> 00:58:01,720 So, tonight, we've seen the amazing science of aerobiology, 1010 00:58:01,720 --> 00:58:06,040 how ventilation works, and the wonders of mathematical modelling. 1011 00:58:07,080 --> 00:58:12,400 But our biggest weapon in fighting viruses is still vaccines. 1012 00:58:13,560 --> 00:58:17,640 In the next lecture, we'll look at the extraordinary breakthroughs 1013 00:58:17,640 --> 00:58:22,800 in vaccine technology, and the advances in genetic sequencing 1014 00:58:22,800 --> 00:58:25,440 that are helping us to fight back. 1015 00:58:25,440 --> 00:58:27,680 APPLAUSE