[Script Info]
Title: 
[Events]
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
Dialogue: 0,0:00:15.68,0:00:19.38,Default,,0000,0000,0000,,One hundred years ago,\Na new influenza virus emerged,
Dialogue: 0,0:00:19.38,0:00:23.25,Default,,0000,0000,0000,,spread around the world\Nand killed 50 to 100 million people.
Dialogue: 0,0:00:23.75,0:00:26.99,Default,,0000,0000,0000,,For every 40 people that got\Nthis influenza infection,
Dialogue: 0,0:00:26.99,0:00:28.59,Default,,0000,0000,0000,,one of them died.
Dialogue: 0,0:00:28.59,0:00:31.33,Default,,0000,0000,0000,,And you think, maybe\Nthat's not that bad odds,
Dialogue: 0,0:00:31.33,0:00:33.77,Default,,0000,0000,0000,,but for the most recent\Ninfluenza pandemic,
Dialogue: 0,0:00:33.77,0:00:37.56,Default,,0000,0000,0000,,for each person that died\Nthere were probably 10,000 cases.
Dialogue: 0,0:00:37.95,0:00:40.94,Default,,0000,0000,0000,,Which means that\Nthis 1918 influenza pandemic
Dialogue: 0,0:00:40.94,0:00:43.26,Default,,0000,0000,0000,,was the worst pandemic in history.
Dialogue: 0,0:00:43.46,0:00:47.02,Default,,0000,0000,0000,,Here's a graph showing the weekly deaths\Nat the time of the pandemic
Dialogue: 0,0:00:47.02,0:00:49.49,Default,,0000,0000,0000,,in New York, London, Paris and Berlin.
Dialogue: 0,0:00:49.87,0:00:54.34,Default,,0000,0000,0000,,You can quite clearly see in the middle,\Nthe major wave of the pandemic.
Dialogue: 0,0:00:54.47,0:00:57.15,Default,,0000,0000,0000,,And so all the way\Nfrom North America to Europe,
Dialogue: 0,0:00:57.15,0:00:59.97,Default,,0000,0000,0000,,this pandemic was happening\Nat the same time.
Dialogue: 0,0:00:59.97,0:01:04.54,Default,,0000,0000,0000,,And this synchronicity, this is \Na common feature of influenza pandemics.
Dialogue: 0,0:01:05.27,0:01:08.100,Default,,0000,0000,0000,,So not only was there this major\Ninfluenza pandemic in 1918,
Dialogue: 0,0:01:08.100,0:01:11.99,Default,,0000,0000,0000,,but it was also the tail end\Nof the First World War.
Dialogue: 0,0:01:12.25,0:01:14.27,Default,,0000,0000,0000,,And I've marked here the Armistice,
Dialogue: 0,0:01:14.27,0:01:17.33,Default,,0000,0000,0000,,so the official end\Nof the First World War, in white.
Dialogue: 0,0:01:17.62,0:01:22.45,Default,,0000,0000,0000,,So you can see here that not only\Nwas this a terrible time for Europe,
Dialogue: 0,0:01:22.45,0:01:25.35,Default,,0000,0000,0000,,but data were being collected on deaths.
Dialogue: 0,0:01:25.35,0:01:28.88,Default,,0000,0000,0000,,And this really showed\Nthat infectious diseases are a priority
Dialogue: 0,0:01:28.88,0:01:31.09,Default,,0000,0000,0000,,and that we need\Nto collect these kind of data
Dialogue: 0,0:01:31.09,0:01:34.70,Default,,0000,0000,0000,,to understand how and why\Nthese epidemics happen.
Dialogue: 0,0:01:35.21,0:01:39.63,Default,,0000,0000,0000,,So computational and mathematical tools\Ncan be used on data like these
Dialogue: 0,0:01:39.63,0:01:43.74,Default,,0000,0000,0000,,to understand the transmission processes\Nand how the epidemic is occurring
Dialogue: 0,0:01:43.74,0:01:49.22,Default,,0000,0000,0000,,with the ultimate aim of trying to develop\Ninterventions, so control methods,
Dialogue: 0,0:01:49.22,0:01:53.24,Default,,0000,0000,0000,,to curtail the epidemic\Nand to slow down transmission.
Dialogue: 0,0:01:53.89,0:01:57.93,Default,,0000,0000,0000,,So, the difference between epidemics\Nand pandemics is one of scale.
Dialogue: 0,0:01:57.93,0:02:00.74,Default,,0000,0000,0000,,Since they're Greek words,\Nyou probably already know them,
Dialogue: 0,0:02:00.74,0:02:03.83,Default,,0000,0000,0000,,but for those who aren't,\NI'll just briefly explain.
Dialogue: 0,0:02:03.93,0:02:07.36,Default,,0000,0000,0000,,An epidemic is geographically\Nlocalized to one place.
Dialogue: 0,0:02:07.36,0:02:13.33,Default,,0000,0000,0000,,So for instance, the recent Ebola epidemic\Nin West Africa was confined to West Africa
Dialogue: 0,0:02:13.33,0:02:15.35,Default,,0000,0000,0000,,and is therefore an epidemic.
Dialogue: 0,0:02:15.35,0:02:18.91,Default,,0000,0000,0000,,The 1918 influenza pandemic,\Nthat spread around the world.
Dialogue: 0,0:02:18.91,0:02:21.88,Default,,0000,0000,0000,,And spreading around the world\Nis what defines a pandemic.
Dialogue: 0,0:02:21.88,0:02:25.23,Default,,0000,0000,0000,,When we get any new epidemic,\None thing that we're really interested in
Dialogue: 0,0:02:25.23,0:02:27.98,Default,,0000,0000,0000,,is how quickly it's spreading\Nfrom person to person.
Dialogue: 0,0:02:27.98,0:02:31.24,Default,,0000,0000,0000,,And we define this\Nas the reproduction number.
Dialogue: 0,0:02:31.24,0:02:35.05,Default,,0000,0000,0000,,So the reproduction number\Nis the average number of new cases
Dialogue: 0,0:02:35.05,0:02:38.31,Default,,0000,0000,0000,,that each infectious person\Ncauses at the start.
Dialogue: 0,0:02:38.31,0:02:39.80,Default,,0000,0000,0000,,So if you were the first person
Dialogue: 0,0:02:39.80,0:02:43.12,Default,,0000,0000,0000,,that got an epidemic,\Nor got a new virus or a new pathogen,
Dialogue: 0,0:02:43.12,0:02:46.68,Default,,0000,0000,0000,,and nobody else had had it,\Nhow many people would you infect?
Dialogue: 0,0:02:46.68,0:02:50.97,Default,,0000,0000,0000,,So let's take, for example, that\None infectious person walks into the room.
Dialogue: 0,0:02:51.07,0:02:56.40,Default,,0000,0000,0000,,And if the reproduction number is two,\Nwe expect two new cases from that person.
Dialogue: 0,0:02:56.67,0:03:00.58,Default,,0000,0000,0000,,And if those two people go off\Nand infect two more of their friends,
Dialogue: 0,0:03:00.58,0:03:03.53,Default,,0000,0000,0000,,well, they might not have\Ntwo friends anymore,
Dialogue: 0,0:03:03.53,0:03:05.84,Default,,0000,0000,0000,,but we now have four cases.
Dialogue: 0,0:03:05.84,0:03:10.66,Default,,0000,0000,0000,,And then if those four infect\Ntwo more each and so on and so forth,
Dialogue: 0,0:03:10.66,0:03:13.06,Default,,0000,0000,0000,,you can see that the epidemic will grow.
Dialogue: 0,0:03:13.45,0:03:15.05,Default,,0000,0000,0000,,So the reproduction number,
Dialogue: 0,0:03:15.05,0:03:18.66,Default,,0000,0000,0000,,the average number of people\Nthat each infectious person infects,
Dialogue: 0,0:03:18.66,0:03:22.60,Default,,0000,0000,0000,,really determines how quickly\Nthe epidemic grows.
Dialogue: 0,0:03:22.60,0:03:25.73,Default,,0000,0000,0000,,OK, well, this is true,\Nespecially in the beginning.
Dialogue: 0,0:03:25.73,0:03:30.12,Default,,0000,0000,0000,,But, if you carried on like this\Nwith each person infecting two,
Dialogue: 0,0:03:30.12,0:03:32.91,Default,,0000,0000,0000,,step by step, as we've shown here,
Dialogue: 0,0:03:32.91,0:03:37.64,Default,,0000,0000,0000,,by the 33rd step, you would have\Ninfected everybody on earth.
Dialogue: 0,0:03:37.64,0:03:39.55,Default,,0000,0000,0000,,And we know that that doesn't happen.
Dialogue: 0,0:03:39.55,0:03:43.31,Default,,0000,0000,0000,,So, why is it that that doesn't happen?
Dialogue: 0,0:03:43.49,0:03:46.73,Default,,0000,0000,0000,,Well, this is because you start\Nto run out of susceptible people,
Dialogue: 0,0:03:46.73,0:03:48.77,Default,,0000,0000,0000,,so people who haven't had the infection,
Dialogue: 0,0:03:48.77,0:03:51.78,Default,,0000,0000,0000,,and this is called\Ndepletion of susceptibles.
Dialogue: 0,0:03:51.78,0:03:55.31,Default,,0000,0000,0000,,So, to demonstrate this,\Nlet's imagine that this person here,
Dialogue: 0,0:03:55.31,0:03:57.06,Default,,0000,0000,0000,,we'll call her Christina,
Dialogue: 0,0:03:57.06,0:03:59.49,Default,,0000,0000,0000,,Christina was infected in the second step,
Dialogue: 0,0:03:59.49,0:04:01.59,Default,,0000,0000,0000,,which seems like pretty bad luck.
Dialogue: 0,0:04:01.59,0:04:04.27,Default,,0000,0000,0000,,Christina happens to be\Nfriends with Spyros.
Dialogue: 0,0:04:04.77,0:04:10.06,Default,,0000,0000,0000,,So when Spyros gets infected later,\Nand he tries to infect two more,
Dialogue: 0,0:04:10.06,0:04:12.91,Default,,0000,0000,0000,,one of the people\Nhe tries to infect is Christina.
Dialogue: 0,0:04:13.51,0:04:15.18,Default,,0000,0000,0000,,But she's already had it.
Dialogue: 0,0:04:15.18,0:04:20.20,Default,,0000,0000,0000,,So here she is colored in blue because\Nshe's has got immunity to infection
Dialogue: 0,0:04:20.20,0:04:21.89,Default,,0000,0000,0000,,now that she's recovered.
Dialogue: 0,0:04:21.89,0:04:24.45,Default,,0000,0000,0000,,So when Spyros tries\Nto infect her, he can't,
Dialogue: 0,0:04:24.45,0:04:28.18,Default,,0000,0000,0000,,and that means that\Nthe number infected slows down.
Dialogue: 0,0:04:28.18,0:04:32.14,Default,,0000,0000,0000,,And if this is true for other people\Nin the population, like this,
Dialogue: 0,0:04:32.14,0:04:35.98,Default,,0000,0000,0000,,then you start to see a slow down\Nin the number of people infected.
Dialogue: 0,0:04:35.98,0:04:38.08,Default,,0000,0000,0000,,So this is depletion of susceptibles.
Dialogue: 0,0:04:38.08,0:04:41.79,Default,,0000,0000,0000,,And I'll show you how we incorporate\Nthese kind of processes
Dialogue: 0,0:04:41.79,0:04:43.66,Default,,0000,0000,0000,,into models of transmission.
Dialogue: 0,0:04:44.78,0:04:46.93,Default,,0000,0000,0000,,If we were going to model\Nsomething like flu,
Dialogue: 0,0:04:46.93,0:04:49.41,Default,,0000,0000,0000,,the first thing we would do\Nis divide the population
Dialogue: 0,0:04:49.41,0:04:51.43,Default,,0000,0000,0000,,into three disease groups.
Dialogue: 0,0:04:51.58,0:04:54.81,Default,,0000,0000,0000,,So here you can see people\Nwho are susceptible to infection,
Dialogue: 0,0:04:54.81,0:04:56.63,Default,,0000,0000,0000,,so they're able to get infected.
Dialogue: 0,0:04:56.71,0:05:00.24,Default,,0000,0000,0000,,You can see infectious people\Nwho have got the infection
Dialogue: 0,0:05:00.24,0:05:02.25,Default,,0000,0000,0000,,and are spreading it to other people.
Dialogue: 0,0:05:02.25,0:05:06.13,Default,,0000,0000,0000,,And then you've got in blue\Nthe recovered or died group.
Dialogue: 0,0:05:06.13,0:05:09.05,Default,,0000,0000,0000,,So normally we assume\Nthat when people recover from infection,
Dialogue: 0,0:05:09.05,0:05:10.21,Default,,0000,0000,0000,,they are protected.
Dialogue: 0,0:05:10.21,0:05:14.18,Default,,0000,0000,0000,,But if it's a very severe infection,\Nthey may also have died.
Dialogue: 0,0:05:14.18,0:05:17.40,Default,,0000,0000,0000,,And everybody in the population\Nhas to be one of these groups.
Dialogue: 0,0:05:17.40,0:05:23.40,Default,,0000,0000,0000,,And we determine the rates\Nof transition between each group.
Dialogue: 0,0:05:23.40,0:05:27.15,Default,,0000,0000,0000,,So when you get infected,\Nthis happens at the rate of transmission,
Dialogue: 0,0:05:27.15,0:05:30.97,Default,,0000,0000,0000,,and then when people recover,\Nthis happens at the rate of recovery.
Dialogue: 0,0:05:30.97,0:05:33.91,Default,,0000,0000,0000,,So this rate of transmission\Nis the most important one
Dialogue: 0,0:05:33.91,0:05:36.99,Default,,0000,0000,0000,,when we're thinking about\Nhow quickly epidemics grow.
Dialogue: 0,0:05:36.99,0:05:41.78,Default,,0000,0000,0000,,What we want to define is when you have\Nan infectious person in the population
Dialogue: 0,0:05:41.78,0:05:45.82,Default,,0000,0000,0000,,and they go out and they make\Ncontacts with the people that they know,
Dialogue: 0,0:05:45.82,0:05:50.48,Default,,0000,0000,0000,,how likely are they to pass\Nthat infection on to their contacts?
Dialogue: 0,0:05:50.80,0:05:54.64,Default,,0000,0000,0000,,And so, what we do when we mathematically\Ndefine the rate of transmission
Dialogue: 0,0:05:54.64,0:05:57.05,Default,,0000,0000,0000,,is we're going\Nto divide it into four parts.
Dialogue: 0,0:05:57.05,0:05:59.92,Default,,0000,0000,0000,,So first of all, we have\Nour rate of transmission
Dialogue: 0,0:05:59.92,0:06:03.42,Default,,0000,0000,0000,,is equal to the number\Nof infectious people.
Dialogue: 0,0:06:03.42,0:06:05.34,Default,,0000,0000,0000,,So the more infectious people there are,
Dialogue: 0,0:06:05.34,0:06:07.35,Default,,0000,0000,0000,,the higher the rate\Nof transmission will be
Dialogue: 0,0:06:07.35,0:06:11.03,Default,,0000,0000,0000,,because there's a lot of people\Naround infecting people.
Dialogue: 0,0:06:11.63,0:06:16.30,Default,,0000,0000,0000,,Then we multiply it by the number of\Ncontacts that each person has on average.
Dialogue: 0,0:06:16.30,0:06:21.02,Default,,0000,0000,0000,,So you can see here that the infectious\Npeople make those contacts at random
Dialogue: 0,0:06:21.02,0:06:25.01,Default,,0000,0000,0000,,with susceptible, infectious\Nor recovered people.
Dialogue: 0,0:06:25.26,0:06:28.85,Default,,0000,0000,0000,,Then we include the probability\Nof infection on a contact.
Dialogue: 0,0:06:28.85,0:06:30.04,Default,,0000,0000,0000,,So what is the chance
Dialogue: 0,0:06:30.04,0:06:32.78,Default,,0000,0000,0000,,that when an infectious person\Nmeets a susceptible person
Dialogue: 0,0:06:32.78,0:06:34.74,Default,,0000,0000,0000,,they give them the infection?
Dialogue: 0,0:06:34.74,0:06:38.64,Default,,0000,0000,0000,,For flu, this is probably around 10%,\Nsomething like that.
Dialogue: 0,0:06:39.14,0:06:41.100,Default,,0000,0000,0000,,And then finally, we include\Nthe proportion of the population
Dialogue: 0,0:06:41.100,0:06:43.57,Default,,0000,0000,0000,,who are susceptible.
Dialogue: 0,0:06:43.57,0:06:47.65,Default,,0000,0000,0000,,So at the beginning of an epidemic,\Nwhen most people are susceptible,
Dialogue: 0,0:06:47.65,0:06:49.36,Default,,0000,0000,0000,,so they haven't had it,
Dialogue: 0,0:06:49.36,0:06:53.76,Default,,0000,0000,0000,,the probability that you meet\Na susceptible person is quite high.
Dialogue: 0,0:06:53.76,0:06:58.95,Default,,0000,0000,0000,,But later, as this pool is depleted,\Nso you run out of susceptible people,
Dialogue: 0,0:06:58.95,0:07:02.82,Default,,0000,0000,0000,,it becomes less likely\Nthat you'll meet a susceptible individual.
Dialogue: 0,0:07:02.93,0:07:07.48,Default,,0000,0000,0000,,So let's see how this\Nis incorporated into our models.
Dialogue: 0,0:07:07.95,0:07:10.19,Default,,0000,0000,0000,,So this is what an epidemic looks like -
Dialogue: 0,0:07:10.19,0:07:13.17,Default,,0000,0000,0000,,a simulated epidemic in 5,000 people.
Dialogue: 0,0:07:13.28,0:07:16.71,Default,,0000,0000,0000,,You can see the grey bar\Nmarks the susceptible group,
Dialogue: 0,0:07:16.71,0:07:19.17,Default,,0000,0000,0000,,and it starts at 5,000,\Nwhich is everybody,
Dialogue: 0,0:07:19.17,0:07:21.98,Default,,0000,0000,0000,,apart from one infectious person\Nat the beginning.
Dialogue: 0,0:07:22.25,0:07:24.89,Default,,0000,0000,0000,,In red you can the infectious epidemic,
Dialogue: 0,0:07:24.89,0:07:28.14,Default,,0000,0000,0000,,and then in blue,\Nthe recovered group at the end.
Dialogue: 0,0:07:28.56,0:07:31.43,Default,,0000,0000,0000,,So what you might notice\Nis that at this point,
Dialogue: 0,0:07:31.43,0:07:35.18,Default,,0000,0000,0000,,when half of the susceptible\Nindividuals have been infected,
Dialogue: 0,0:07:35.18,0:07:38.74,Default,,0000,0000,0000,,this part of the equation,\Nthe proportion of the susceptible,
Dialogue: 0,0:07:38.74,0:07:40.32,Default,,0000,0000,0000,,is also halved,
Dialogue: 0,0:07:40.45,0:07:43.74,Default,,0000,0000,0000,,which really pushes down\Nthe rate of transmission.
Dialogue: 0,0:07:43.74,0:07:46.90,Default,,0000,0000,0000,,And that's important, because\Nit's this depletion of susceptibles,
Dialogue: 0,0:07:46.90,0:07:48.95,Default,,0000,0000,0000,,so running out of susceptible people,
Dialogue: 0,0:07:48.95,0:07:52.58,Default,,0000,0000,0000,,that causes the epidemic\Nto peak and then decline.
Dialogue: 0,0:07:53.11,0:07:57.09,Default,,0000,0000,0000,,Now, the eagle-eyed among you\Nmight have also noticed
Dialogue: 0,0:07:57.09,0:08:02.33,Default,,0000,0000,0000,,that if you draw a horizontal line\Nat 5,000, which is the total population,
Dialogue: 0,0:08:02.33,0:08:05.82,Default,,0000,0000,0000,,that by the end of the epidemic\Nthere's a small gap.
Dialogue: 0,0:08:05.96,0:08:09.13,Default,,0000,0000,0000,,There's a gap between\Nthe total number of susceptible people
Dialogue: 0,0:08:09.13,0:08:12.37,Default,,0000,0000,0000,,and the number of people\Nthat were infected in total.
Dialogue: 0,0:08:12.48,0:08:16.03,Default,,0000,0000,0000,,And that's because some people\Ndon't get infected.
Dialogue: 0,0:08:16.03,0:08:17.58,Default,,0000,0000,0000,,The lucky ones.
Dialogue: 0,0:08:17.58,0:08:22.58,Default,,0000,0000,0000,,So this total number of people infected\Nand the size of the gap
Dialogue: 0,0:08:22.58,0:08:27.46,Default,,0000,0000,0000,,is determined by the reproduction number,\Nby how infectious the pathogen is.
Dialogue: 0,0:08:27.95,0:08:31.48,Default,,0000,0000,0000,,So let's explore\Nhow that relationship looks.
Dialogue: 0,0:08:31.48,0:08:33.44,Default,,0000,0000,0000,,So what I'm showing you here,
Dialogue: 0,0:08:33.44,0:08:39.08,Default,,0000,0000,0000,,on the horizontal axis you can see\Nreproduction numbers from zero to five.
Dialogue: 0,0:08:39.08,0:08:42.28,Default,,0000,0000,0000,,And on the vertical axis you can see\Nthe percent of the population
Dialogue: 0,0:08:42.28,0:08:44.67,Default,,0000,0000,0000,,that are infected in total.
Dialogue: 0,0:08:44.67,0:08:47.82,Default,,0000,0000,0000,,So let's take a look at some pathogens\Nthat you might have heard of
Dialogue: 0,0:08:47.82,0:08:50.38,Default,,0000,0000,0000,,and see what their\Nreproduction numbers are.
Dialogue: 0,0:08:50.77,0:08:56.64,Default,,0000,0000,0000,,So here, for example, seasonal influenza,\Nprobably around 1.4-1.5.
Dialogue: 0,0:08:57.11,0:09:00.54,Default,,0000,0000,0000,,Ebola, that's around 2.
Dialogue: 0,0:09:01.08,0:09:03.93,Default,,0000,0000,0000,,Pandemic flu, maybe 2.5.
Dialogue: 0,0:09:03.93,0:09:06.75,Default,,0000,0000,0000,,SARS, around 3.
Dialogue: 0,0:09:06.75,0:09:09.24,Default,,0000,0000,0000,,And then smallpox, around 5.
Dialogue: 0,0:09:09.24,0:09:14.65,Default,,0000,0000,0000,,So for every case of smallpox\Nthat we could see in the population,
Dialogue: 0,0:09:14.65,0:09:18.24,Default,,0000,0000,0000,,we would expect to see\Nfive more smallpox cases.
Dialogue: 0,0:09:18.24,0:09:20.79,Default,,0000,0000,0000,,So, what's the relationship?
Dialogue: 0,0:09:20.79,0:09:24.64,Default,,0000,0000,0000,,Here you can see that from zero to one,
Dialogue: 0,0:09:24.64,0:09:27.08,Default,,0000,0000,0000,,when the reproduction number\Nis less than one,
Dialogue: 0,0:09:27.08,0:09:28.84,Default,,0000,0000,0000,,nobody is infected.
Dialogue: 0,0:09:28.84,0:09:31.60,Default,,0000,0000,0000,,And that's because if you infect\Nless than one person
Dialogue: 0,0:09:31.60,0:09:34.84,Default,,0000,0000,0000,,for each infectious person,\Nthere's no epidemic.
Dialogue: 0,0:09:34.84,0:09:36.65,Default,,0000,0000,0000,,And then it takes off rapidly,
Dialogue: 0,0:09:36.65,0:09:39.69,Default,,0000,0000,0000,,and it appears to approach 100%.
Dialogue: 0,0:09:39.69,0:09:41.26,Default,,0000,0000,0000,,But it doesn't quite.
Dialogue: 0,0:09:41.26,0:09:44.04,Default,,0000,0000,0000,,That line doesn't quite reach 100%.
Dialogue: 0,0:09:44.04,0:09:48.39,Default,,0000,0000,0000,,And to show you that, let's take a look\Nat even higher reproduction numbers.
Dialogue: 0,0:09:48.77,0:09:50.85,Default,,0000,0000,0000,,So here you can see the same graph,
Dialogue: 0,0:09:50.85,0:09:54.91,Default,,0000,0000,0000,,but now the horizontal axis\Nstarts at five and runs till 10,
Dialogue: 0,0:09:54.91,0:09:57.94,Default,,0000,0000,0000,,and the vertical axis is much higher.
Dialogue: 0,0:09:57.94,0:10:03.25,Default,,0000,0000,0000,,So some pathogens in this region are\Npertussis, which causes whooping cough,
Dialogue: 0,0:10:03.25,0:10:06.57,Default,,0000,0000,0000,,and polio and diphtheria\Nare also around here.
Dialogue: 0,0:10:06.57,0:10:12.14,Default,,0000,0000,0000,,So again you see the line increases\Nas the reproduction number gets higher.
Dialogue: 0,0:10:12.14,0:10:16.77,Default,,0000,0000,0000,,But it still doesn't reach 100%\Neven though it looks like it.
Dialogue: 0,0:10:16.89,0:10:21.54,Default,,0000,0000,0000,,OK, so what about if\Nit's even, even higher than that?
Dialogue: 0,0:10:21.79,0:10:23.93,Default,,0000,0000,0000,,So let's take a look now, the same graph,
Dialogue: 0,0:10:23.93,0:10:28.90,Default,,0000,0000,0000,,but now the horizontal axis\Nstarts at 10 and runs till 15.
Dialogue: 0,0:10:28.90,0:10:33.56,Default,,0000,0000,0000,,So some pathogens that are this infectious\Nare things like norovirus.
Dialogue: 0,0:10:33.56,0:10:37.67,Default,,0000,0000,0000,,If you don't do any hygienic measures,\Nthen it's around 14.
Dialogue: 0,0:10:37.67,0:10:40.67,Default,,0000,0000,0000,,And measles, in\Nthe absence of vaccination,
Dialogue: 0,0:10:40.67,0:10:44.06,Default,,0000,0000,0000,,the reproduction number\Nis between 12 and 18.
Dialogue: 0,0:10:44.06,0:10:47.21,Default,,0000,0000,0000,,So if nobody is vaccinated\Nand there was one measles case,
Dialogue: 0,0:10:47.21,0:10:51.10,Default,,0000,0000,0000,,we would expect to see\Nabout 15 more measles cases.
Dialogue: 0,0:10:51.10,0:10:55.19,Default,,0000,0000,0000,,And these are some of the most\Ninfectious pathogens that we've got.
Dialogue: 0,0:10:56.01,0:11:00.52,Default,,0000,0000,0000,,And so here, the line, it really, really\Nis not going to reach 100%.
Dialogue: 0,0:11:00.83,0:11:04.53,Default,,0000,0000,0000,,It's really not going to get there,\Nno matter how infectious the pathogen,
Dialogue: 0,0:11:04.53,0:11:07.38,Default,,0000,0000,0000,,which is great news, really good news.
Dialogue: 0,0:11:07.66,0:11:12.72,Default,,0000,0000,0000,,So, if there was a pathogen\Nthat was so infectious like this,
Dialogue: 0,0:11:12.72,0:11:15.71,Default,,0000,0000,0000,,very infectious,\Nwe didn't do anything about it,
Dialogue: 0,0:11:15.71,0:11:20.55,Default,,0000,0000,0000,,so there were no control measures,\Nthere were no interventions, no vaccine,
Dialogue: 0,0:11:20.55,0:11:25.49,Default,,0000,0000,0000,,and it happened to kill everyone,\Nwhich is extremely unlikely,
Dialogue: 0,0:11:25.49,0:11:28.84,Default,,0000,0000,0000,,even then we wouldn't manage\Nto wipe out humanity.
Dialogue: 0,0:11:28.84,0:11:33.66,Default,,0000,0000,0000,,So to answer that question, no, a pathogen\Nis not going to wipe out humanity.
Dialogue: 0,0:11:33.66,0:11:39.30,Default,,0000,0000,0000,,Which is really good news for our species,\Nproviding of course that the survivors,
Dialogue: 0,0:11:39.30,0:11:43.15,Default,,0000,0000,0000,,the people who are left over\Nlike the look of each other enough
Dialogue: 0,0:11:43.15,0:11:46.09,Default,,0000,0000,0000,,to repopulate the planet.
Dialogue: 0,0:11:46.09,0:11:48.02,Default,,0000,0000,0000,,(Laughter)
Dialogue: 0,0:11:48.02,0:11:49.41,Default,,0000,0000,0000,,So that's good news.
Dialogue: 0,0:11:49.41,0:11:52.15,Default,,0000,0000,0000,,But normally, and what I do in my work,
Dialogue: 0,0:11:52.15,0:11:55.24,Default,,0000,0000,0000,,is we don't just try\Nand leave epidemics to happen.
Dialogue: 0,0:11:55.24,0:11:58.60,Default,,0000,0000,0000,,The goal of my work is to try\Nand understand transmission enough
Dialogue: 0,0:11:58.60,0:12:01.99,Default,,0000,0000,0000,,in order to develop\Nand evaluate control measures.
Dialogue: 0,0:12:01.99,0:12:03.89,Default,,0000,0000,0000,,So control measures are things like
Dialogue: 0,0:12:03.89,0:12:08.10,Default,,0000,0000,0000,,closing schools or encouraging people\Nnot to go to work when they're sick
Dialogue: 0,0:12:08.10,0:12:09.98,Default,,0000,0000,0000,,or vaccinating people.
Dialogue: 0,0:12:09.98,0:12:14.87,Default,,0000,0000,0000,,And the aim of these control measures\Nis to push that reproduction number,
Dialogue: 0,0:12:14.87,0:12:18.45,Default,,0000,0000,0000,,the average number of secondary\Ncases, down below one.
Dialogue: 0,0:12:18.59,0:12:23.08,Default,,0000,0000,0000,,And that's because if each infectious\Nperson infects less than one other person,
Dialogue: 0,0:12:23.08,0:12:24.96,Default,,0000,0000,0000,,the epidemic will decline.
Dialogue: 0,0:12:25.29,0:12:27.72,Default,,0000,0000,0000,,So that's the goal of my work.
Dialogue: 0,0:12:28.01,0:12:32.33,Default,,0000,0000,0000,,Now, I do need to tell you\Nabout the one exception.
Dialogue: 0,0:12:32.33,0:12:34.84,Default,,0000,0000,0000,,Because there is always a but to this.
Dialogue: 0,0:12:35.05,0:12:39.92,Default,,0000,0000,0000,,There is one infection\Nthat could be a bit of a problem.
Dialogue: 0,0:12:40.19,0:12:42.87,Default,,0000,0000,0000,,And it's something that people\Nlike to think a lot about,
Dialogue: 0,0:12:42.87,0:12:45.39,Default,,0000,0000,0000,,and they've even made some movies about.
Dialogue: 0,0:12:45.39,0:12:47.71,Default,,0000,0000,0000,,And that's zombie infection.
Dialogue: 0,0:12:47.71,0:12:49.03,Default,,0000,0000,0000,,(Laughter)
Dialogue: 0,0:12:49.03,0:12:51.21,Default,,0000,0000,0000,,So although it's a bit more light-hearted,
Dialogue: 0,0:12:51.21,0:12:53.29,Default,,0000,0000,0000,,it's interesting to look\Nat zombie infection
Dialogue: 0,0:12:53.29,0:12:54.66,Default,,0000,0000,0000,,and figure out why it is
Dialogue: 0,0:12:54.66,0:12:58.55,Default,,0000,0000,0000,,that this is something that\Ncould wipe out everyone on earth.
Dialogue: 0,0:12:58.85,0:13:02.10,Default,,0000,0000,0000,,So what we'll do is take\Nthe same model that we had before.
Dialogue: 0,0:13:02.10,0:13:04.83,Default,,0000,0000,0000,,We have our susceptible, infectious\Nand recovered groups
Dialogue: 0,0:13:04.83,0:13:06.96,Default,,0000,0000,0000,,and our rates of transmission.
Dialogue: 0,0:13:06.96,0:13:11.07,Default,,0000,0000,0000,,And then we have that rate of transmission\Ndivided into four parts.
Dialogue: 0,0:13:11.50,0:13:17.95,Default,,0000,0000,0000,,So why is it that zombie infection\Ncould wipe out everybody?
Dialogue: 0,0:13:18.26,0:13:21.30,Default,,0000,0000,0000,,Well, first of all,\Nzombies break this first rule.
Dialogue: 0,0:13:21.57,0:13:26.13,Default,,0000,0000,0000,,So, in our model we assume\Nthat people recover from infection.
Dialogue: 0,0:13:26.13,0:13:30.24,Default,,0000,0000,0000,,And as I understand it,\Nnobody recovers from zombie infection.
Dialogue: 0,0:13:30.72,0:13:33.43,Default,,0000,0000,0000,,There's no films about people\Nwho felt sick on the weekend
Dialogue: 0,0:13:33.43,0:13:35.19,Default,,0000,0000,0000,,but showed up for work on Monday.
Dialogue: 0,0:13:35.19,0:13:36.20,Default,,0000,0000,0000,,(Laughter)
Dialogue: 0,0:13:36.20,0:13:39.54,Default,,0000,0000,0000,,The other thing that we assume is\Nthat if people die from infection,
Dialogue: 0,0:13:39.54,0:13:43.41,Default,,0000,0000,0000,,then they stay dead,\Nand zombies don't do that.
Dialogue: 0,0:13:43.41,0:13:44.42,Default,,0000,0000,0000,,(Laughter)
Dialogue: 0,0:13:44.42,0:13:46.75,Default,,0000,0000,0000,,So that breaks that rule of our model.
Dialogue: 0,0:13:46.75,0:13:47.75,Default,,0000,0000,0000,,The other thing is
Dialogue: 0,0:13:47.75,0:13:53.97,Default,,0000,0000,0000,,that the probability of infection\Non contact for zombies is very high.
Dialogue: 0,0:13:53.97,0:13:56.48,Default,,0000,0000,0000,,I gather it is 100%.
Dialogue: 0,0:13:56.48,0:14:00.39,Default,,0000,0000,0000,,So for something like flu, if you meet\Nan infectious person, it's maybe 10%,
Dialogue: 0,0:14:00.39,0:14:03.88,Default,,0000,0000,0000,,but for zombies you never see\Nsomebody with just a skin wound
Dialogue: 0,0:14:03.88,0:14:05.59,Default,,0000,0000,0000,,who doesn't get it.
Dialogue: 0,0:14:05.59,0:14:07.31,Default,,0000,0000,0000,,So it breaks that rule.
Dialogue: 0,0:14:07.31,0:14:09.62,Default,,0000,0000,0000,,And then finally, remember I told you
Dialogue: 0,0:14:09.62,0:14:13.28,Default,,0000,0000,0000,,that we assume that people\Nmake contacts at random?
Dialogue: 0,0:14:13.28,0:14:16.85,Default,,0000,0000,0000,,Well, zombies go looking\Nfor susceptible people.
Dialogue: 0,0:14:17.74,0:14:19.69,Default,,0000,0000,0000,,So that breaks that rule.
Dialogue: 0,0:14:19.69,0:14:23.42,Default,,0000,0000,0000,,And that means that the only epidemic\Nthat could really infect everybody
Dialogue: 0,0:14:23.42,0:14:26.63,Default,,0000,0000,0000,,and wipe out humanity\Nwould be a zombie apocalypse.
Dialogue: 0,0:14:26.63,0:14:31.22,Default,,0000,0000,0000,,And that's really, really good news\Nbecause zombies are not real.
Dialogue: 0,0:14:31.44,0:14:32.72,Default,,0000,0000,0000,,Thank you very much.
Dialogue: 0,0:14:32.72,0:14:35.66,Default,,0000,0000,0000,,(Applause)