Epidemics and the end of humankind | Rosalind Eggo | TEDxThessaloniki
-
0:16 - 0:19One hundred years ago,
a new influenza virus emerged, -
0:19 - 0:23spread around the world
and killed 50 to 100 million people. -
0:24 - 0:27For every 40 people that got
this influenza infection, -
0:27 - 0:29one of them died.
-
0:29 - 0:31And you think, maybe
that's not that bad odds, -
0:31 - 0:34but for the most recent
influenza pandemic, -
0:34 - 0:38for each person that died
there were probably 10,000 cases. -
0:38 - 0:41Which means that
this 1918 influenza pandemic -
0:41 - 0:43was the worst pandemic in history.
-
0:43 - 0:47Here's a graph showing the weekly deaths
at the time of the pandemic -
0:47 - 0:49in New York, London, Paris and Berlin.
-
0:50 - 0:54You can quite clearly see in the middle,
the major wave of the pandemic. -
0:54 - 0:57And so all the way
from North America to Europe, -
0:57 - 1:00this pandemic was happening
at the same time. -
1:00 - 1:05And this synchronicity, this is
a common feature of influenza pandemics. -
1:05 - 1:09So not only was there this major
influenza pandemic in 1918, -
1:09 - 1:12but it was also the tail end
of the First World War. -
1:12 - 1:14And I've marked here the Armistice,
-
1:14 - 1:17so the official end
of the First World War, in white. -
1:18 - 1:22So you can see here that not only
was this a terrible time for Europe, -
1:22 - 1:25but data were being collected on deaths.
-
1:25 - 1:29And this really showed
that infectious diseases are a priority -
1:29 - 1:31and that we need
to collect these kind of data -
1:31 - 1:35to understand how and why
these epidemics happen. -
1:35 - 1:40So computational and mathematical tools
can be used on data like these -
1:40 - 1:44to understand the transmission processes
and how the epidemic is occurring -
1:44 - 1:49with the ultimate aim of trying to develop
interventions, so control methods, -
1:49 - 1:53to curtail the epidemic
and to slow down transmission. -
1:54 - 1:58So, the difference between epidemics
and pandemics is one of scale. -
1:58 - 2:01Since they're Greek words,
you probably already know them, -
2:01 - 2:04but for those who aren't,
I'll just briefly explain. -
2:04 - 2:07An epidemic is geographically
localized to one place. -
2:07 - 2:13So for instance, the recent Ebola epidemic
in West Africa was confined to West Africa -
2:13 - 2:15and is therefore an epidemic.
-
2:15 - 2:19The 1918 influenza pandemic,
that spread around the world. -
2:19 - 2:22And spreading around the world
is what defines a pandemic. -
2:22 - 2:25When we get any new epidemic,
one thing that we're really interested in -
2:25 - 2:28is how quickly it's spreading
from person to person. -
2:28 - 2:31And we define this
as the reproduction number. -
2:31 - 2:35So the reproduction number
is the average number of new cases -
2:35 - 2:38that each infectious person
causes at the start. -
2:38 - 2:40So if you were the first person
-
2:40 - 2:43that got an epidemic,
or got a new virus or a new pathogen, -
2:43 - 2:47and nobody else had had it,
how many people would you infect? -
2:47 - 2:51So let's take, for example, that
one infectious person walks into the room. -
2:51 - 2:56And if the reproduction number is two,
we expect two new cases from that person. -
2:57 - 3:01And if those two people go off
and infect two more of their friends, -
3:01 - 3:04well, they might not have
two friends anymore, -
3:04 - 3:06but we now have four cases.
-
3:06 - 3:11And then if those four infect
two more each and so on and so forth, -
3:11 - 3:13you can see that the epidemic will grow.
-
3:13 - 3:15So the reproduction number,
-
3:15 - 3:19the average number of people
that each infectious person infects, -
3:19 - 3:23really determines how quickly
the epidemic grows. -
3:23 - 3:26OK, well, this is true,
especially in the beginning. -
3:26 - 3:30But, if you carried on like this
with each person infecting two, -
3:30 - 3:33step by step, as we've shown here,
-
3:33 - 3:38by the 33rd step, you would have
infected everybody on earth. -
3:38 - 3:40And we know that that doesn't happen.
-
3:40 - 3:43So, why is it that that doesn't happen?
-
3:43 - 3:47Well, this is because you start
to run out of susceptible people, -
3:47 - 3:49so people who haven't had the infection,
-
3:49 - 3:52and this is called
depletion of susceptibles. -
3:52 - 3:55So, to demonstrate this,
let's imagine that this person here, -
3:55 - 3:57we'll call her Christina,
-
3:57 - 3:59Christina was infected in the second step,
-
3:59 - 4:02which seems like pretty bad luck.
-
4:02 - 4:04Christina happens to be
friends with Spyros. -
4:05 - 4:10So when Spyros gets infected later,
and he tries to infect two more, -
4:10 - 4:13one of the people
he tries to infect is Christina. -
4:14 - 4:15But she's already had it.
-
4:15 - 4:20So here she is colored in blue because
she's has got immunity to infection -
4:20 - 4:22now that she's recovered.
-
4:22 - 4:24So when Spyros tries
to infect her, he can't, -
4:24 - 4:28and that means that
the number infected slows down. -
4:28 - 4:32And if this is true for other people
in the population, like this, -
4:32 - 4:36then you start to see a slow down
in the number of people infected. -
4:36 - 4:38So this is depletion of susceptibles.
-
4:38 - 4:42And I'll show you how we incorporate
these kind of processes -
4:42 - 4:44into models of transmission.
-
4:45 - 4:47If we were going to model
something like flu, -
4:47 - 4:49the first thing we would do
is divide the population -
4:49 - 4:51into three disease groups.
-
4:52 - 4:55So here you can see people
who are susceptible to infection, -
4:55 - 4:57so they're able to get infected.
-
4:57 - 5:00You can see infectious people
who have got the infection -
5:00 - 5:02and are spreading it to other people.
-
5:02 - 5:06And then you've got in blue
the recovered or died group. -
5:06 - 5:09So normally we assume
that when people recover from infection, -
5:09 - 5:10they are protected.
-
5:10 - 5:14But if it's a very severe infection,
they may also have died. -
5:14 - 5:17And everybody in the population
has to be one of these groups. -
5:17 - 5:23And we determine the rates
of transition between each group. -
5:23 - 5:27So when you get infected,
this happens at the rate of transmission, -
5:27 - 5:31and then when people recover,
this happens at the rate of recovery. -
5:31 - 5:34So this rate of transmission
is the most important one -
5:34 - 5:37when we're thinking about
how quickly epidemics grow. -
5:37 - 5:42What we want to define is when you have
an infectious person in the population -
5:42 - 5:46and they go out and they make
contacts with the people that they know, -
5:46 - 5:50how likely are they to pass
that infection on to their contacts? -
5:51 - 5:55And so, what we do when we mathematically
define the rate of transmission -
5:55 - 5:57is we're going
to divide it into four parts. -
5:57 - 6:00So first of all, we have
our rate of transmission -
6:00 - 6:03is equal to the number
of infectious people. -
6:03 - 6:05So the more infectious people there are,
-
6:05 - 6:07the higher the rate
of transmission will be -
6:07 - 6:11because there's a lot of people
around infecting people. -
6:12 - 6:16Then we multiply it by the number of
contacts that each person has on average. -
6:16 - 6:21So you can see here that the infectious
people make those contacts at random -
6:21 - 6:25with susceptible, infectious
or recovered people. -
6:25 - 6:29Then we include the probability
of infection on a contact. -
6:29 - 6:30So what is the chance
-
6:30 - 6:33that when an infectious person
meets a susceptible person -
6:33 - 6:35they give them the infection?
-
6:35 - 6:39For flu, this is probably around 10%,
something like that. -
6:39 - 6:42And then finally, we include
the proportion of the population -
6:42 - 6:44who are susceptible.
-
6:44 - 6:48So at the beginning of an epidemic,
when most people are susceptible, -
6:48 - 6:49so they haven't had it,
-
6:49 - 6:54the probability that you meet
a susceptible person is quite high. -
6:54 - 6:59But later, as this pool is depleted,
so you run out of susceptible people, -
6:59 - 7:03it becomes less likely
that you'll meet a susceptible individual. -
7:03 - 7:07So let's see how this
is incorporated into our models. -
7:08 - 7:10So this is what an epidemic looks like -
-
7:10 - 7:13a simulated epidemic in 5,000 people.
-
7:13 - 7:17You can see the grey bar
marks the susceptible group, -
7:17 - 7:19and it starts at 5,000,
which is everybody, -
7:19 - 7:22apart from one infectious person
at the beginning. -
7:22 - 7:25In red you can the infectious epidemic,
-
7:25 - 7:28and then in blue,
the recovered group at the end. -
7:29 - 7:31So what you might notice
is that at this point, -
7:31 - 7:35when half of the susceptible
individuals have been infected, -
7:35 - 7:39this part of the equation,
the proportion of the susceptible, -
7:39 - 7:40is also halved,
-
7:40 - 7:44which really pushes down
the rate of transmission. -
7:44 - 7:47And that's important, because
it's this depletion of susceptibles, -
7:47 - 7:49so running out of susceptible people,
-
7:49 - 7:53that causes the epidemic
to peak and then decline. -
7:53 - 7:57Now, the eagle-eyed among you
might have also noticed -
7:57 - 8:02that if you draw a horizontal line
at 5,000, which is the total population, -
8:02 - 8:06that by the end of the epidemic
there's a small gap. -
8:06 - 8:09There's a gap between
the total number of susceptible people -
8:09 - 8:12and the number of people
that were infected in total. -
8:12 - 8:16And that's because some people
don't get infected. -
8:16 - 8:18The lucky ones.
-
8:18 - 8:23So this total number of people infected
and the size of the gap -
8:23 - 8:27is determined by the reproduction number,
by how infectious the pathogen is. -
8:28 - 8:31So let's explore
how that relationship looks. -
8:31 - 8:33So what I'm showing you here,
-
8:33 - 8:39on the horizontal axis you can see
reproduction numbers from zero to five. -
8:39 - 8:42And on the vertical axis you can see
the percent of the population -
8:42 - 8:45that are infected in total.
-
8:45 - 8:48So let's take a look at some pathogens
that you might have heard of -
8:48 - 8:50and see what their
reproduction numbers are. -
8:51 - 8:57So here, for example, seasonal influenza,
probably around 1.4-1.5. -
8:57 - 9:01Ebola, that's around 2.
-
9:01 - 9:04Pandemic flu, maybe 2.5.
-
9:04 - 9:07SARS, around 3.
-
9:07 - 9:09And then smallpox, around 5.
-
9:09 - 9:15So for every case of smallpox
that we could see in the population, -
9:15 - 9:18we would expect to see
five more smallpox cases. -
9:18 - 9:21So, what's the relationship?
-
9:21 - 9:25Here you can see that from zero to one,
-
9:25 - 9:27when the reproduction number
is less than one, -
9:27 - 9:29nobody is infected.
-
9:29 - 9:32And that's because if you infect
less than one person -
9:32 - 9:35for each infectious person,
there's no epidemic. -
9:35 - 9:37And then it takes off rapidly,
-
9:37 - 9:40and it appears to approach 100%.
-
9:40 - 9:41But it doesn't quite.
-
9:41 - 9:44That line doesn't quite reach 100%.
-
9:44 - 9:48And to show you that, let's take a look
at even higher reproduction numbers. -
9:49 - 9:51So here you can see the same graph,
-
9:51 - 9:55but now the horizontal axis
starts at five and runs till 10, -
9:55 - 9:58and the vertical axis is much higher.
-
9:58 - 10:03So some pathogens in this region are
pertussis, which causes whooping cough, -
10:03 - 10:07and polio and diphtheria
are also around here. -
10:07 - 10:12So again you see the line increases
as the reproduction number gets higher. -
10:12 - 10:17But it still doesn't reach 100%
even though it looks like it. -
10:17 - 10:22OK, so what about if
it's even, even higher than that? -
10:22 - 10:24So let's take a look now, the same graph,
-
10:24 - 10:29but now the horizontal axis
starts at 10 and runs till 15. -
10:29 - 10:34So some pathogens that are this infectious
are things like norovirus. -
10:34 - 10:38If you don't do any hygienic measures,
then it's around 14. -
10:38 - 10:41And measles, in
the absence of vaccination, -
10:41 - 10:44the reproduction number
is between 12 and 18. -
10:44 - 10:47So if nobody is vaccinated
and there was one measles case, -
10:47 - 10:51we would expect to see
about 15 more measles cases. -
10:51 - 10:55And these are some of the most
infectious pathogens that we've got. -
10:56 - 11:01And so here, the line, it really, really
is not going to reach 100%. -
11:01 - 11:05It's really not going to get there,
no matter how infectious the pathogen, -
11:05 - 11:07which is great news, really good news.
-
11:08 - 11:13So, if there was a pathogen
that was so infectious like this, -
11:13 - 11:16very infectious,
we didn't do anything about it, -
11:16 - 11:21so there were no control measures,
there were no interventions, no vaccine, -
11:21 - 11:25and it happened to kill everyone,
which is extremely unlikely, -
11:25 - 11:29even then we wouldn't manage
to wipe out humanity. -
11:29 - 11:34So to answer that question, no, a pathogen
is not going to wipe out humanity. -
11:34 - 11:39Which is really good news for our species,
providing of course that the survivors, -
11:39 - 11:43the people who are left over
like the look of each other enough -
11:43 - 11:46to repopulate the planet.
-
11:46 - 11:48(Laughter)
-
11:48 - 11:49So that's good news.
-
11:49 - 11:52But normally, and what I do in my work,
-
11:52 - 11:55is we don't just try
and leave epidemics to happen. -
11:55 - 11:59The goal of my work is to try
and understand transmission enough -
11:59 - 12:02in order to develop
and evaluate control measures. -
12:02 - 12:04So control measures are things like
-
12:04 - 12:08closing schools or encouraging people
not to go to work when they're sick -
12:08 - 12:10or vaccinating people.
-
12:10 - 12:15And the aim of these control measures
is to push that reproduction number, -
12:15 - 12:18the average number of secondary
cases, down below one. -
12:19 - 12:23And that's because if each infectious
person infects less than one other person, -
12:23 - 12:25the epidemic will decline.
-
12:25 - 12:28So that's the goal of my work.
-
12:28 - 12:32Now, I do need to tell you
about the one exception. -
12:32 - 12:35Because there is always a but to this.
-
12:35 - 12:40There is one infection
that could be a bit of a problem. -
12:40 - 12:43And it's something that people
like to think a lot about, -
12:43 - 12:45and they've even made some movies about.
-
12:45 - 12:48And that's zombie infection.
-
12:48 - 12:49(Laughter)
-
12:49 - 12:51So although it's a bit more light-hearted,
-
12:51 - 12:53it's interesting to look
at zombie infection -
12:53 - 12:55and figure out why it is
-
12:55 - 12:59that this is something that
could wipe out everyone on earth. -
12:59 - 13:02So what we'll do is take
the same model that we had before. -
13:02 - 13:05We have our susceptible, infectious
and recovered groups -
13:05 - 13:07and our rates of transmission.
-
13:07 - 13:11And then we have that rate of transmission
divided into four parts. -
13:12 - 13:18So why is it that zombie infection
could wipe out everybody? -
13:18 - 13:21Well, first of all,
zombies break this first rule. -
13:22 - 13:26So, in our model we assume
that people recover from infection. -
13:26 - 13:30And as I understand it,
nobody recovers from zombie infection. -
13:31 - 13:33There's no films about people
who felt sick on the weekend -
13:33 - 13:35but showed up for work on Monday.
-
13:35 - 13:36(Laughter)
-
13:36 - 13:40The other thing that we assume is
that if people die from infection, -
13:40 - 13:43then they stay dead,
and zombies don't do that. -
13:43 - 13:44(Laughter)
-
13:44 - 13:47So that breaks that rule of our model.
-
13:47 - 13:48The other thing is
-
13:48 - 13:54that the probability of infection
on contact for zombies is very high. -
13:54 - 13:56I gather it is 100%.
-
13:56 - 14:00So for something like flu, if you meet
an infectious person, it's maybe 10%, -
14:00 - 14:04but for zombies you never see
somebody with just a skin wound -
14:04 - 14:06who doesn't get it.
-
14:06 - 14:07So it breaks that rule.
-
14:07 - 14:10And then finally, remember I told you
-
14:10 - 14:13that we assume that people
make contacts at random? -
14:13 - 14:17Well, zombies go looking
for susceptible people. -
14:18 - 14:20So that breaks that rule.
-
14:20 - 14:23And that means that the only epidemic
that could really infect everybody -
14:23 - 14:27and wipe out humanity
would be a zombie apocalypse. -
14:27 - 14:31And that's really, really good news
because zombies are not real. -
14:31 - 14:33Thank you very much.
-
14:33 - 14:36(Applause)
- Title:
- Epidemics and the end of humankind | Rosalind Eggo | TEDxThessaloniki
- Description:
-
From the 1918 Spanish flu pandemic to the 2014 Ebola outbreak in West Africa, humankind has lived in fear of a potent infectious disease that would mark its demise. Dr Rosalind Eggo is a mathematical modeller who tracks the spread of deadly viruses, in an attempt to stop them. In this talk, she combines science with humour and answers the question we all want to ask: “Will a pandemic mark the end of humankind?”
Rosalind Eggo is an Assistant Professor at the London School of Hygiene & Tropical Medicine in the UK. She received her PhD in the dynamics of the 1918 influenza pandemic from Imperial College London, and then worked at The University of Texas at Austin, USA. Rosalind works in mathematical modelling of infectious diseases. This means she uses computational and mathematical methods to understand the transmission of pathogens through populations. The aim of infectious disease modelling is to understand the routes and mechanisms that drive the spread of infections so that we can ultimately design interventions to prevent them. Rosalind has worked on analysis of pandemic influenza, Ebola, Zika, cholera, and other pathogens.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDxTalks
- Duration:
- 14:39
Rhonda Jacobs approved English subtitles for Epidemics and the end of humankind | Rosalind Eggo | TEDxThessaloniki | ||
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Chryssa R. Takahashi edited English subtitles for Epidemics and the end of humankind | Rosalind Eggo | TEDxThessaloniki | ||
Chryssa R. Takahashi edited English subtitles for Epidemics and the end of humankind | Rosalind Eggo | TEDxThessaloniki | ||
Chryssa R. Takahashi edited English subtitles for Epidemics and the end of humankind | Rosalind Eggo | TEDxThessaloniki | ||
Chryssa R. Takahashi edited English subtitles for Epidemics and the end of humankind | Rosalind Eggo | TEDxThessaloniki |