I'm about to break some life-or-death news to
the people that are gathered here.
Now, I may look or even feel perfectly normal,
but I'm actually carrying a potentially lethal disease that
I have been spreading around this normally quiet, leafy little town.
And I've got to say, they all look remarkably happy about it.
That's probably because it's part of the biggest experiment of its kind.
With the help of thousands of volunteers, we are
about to simulate the outbreak of a fatal contagion throughout the UK.
That might seem like a funny thing to want to do, but if I can
succeed, this will save lives when - not if - a real pandemic hits.
Our pandemic might be simulated,
but global outbreaks of deadly flu are a very real threat.
In the last hundred years, lethal strains of flu have
swept around the planet four times, killing untold millions.
The UK Government puts pandemic flu at the top of its risk register.
The reason for that is it WILL happen,
there WILL be another pandemic.
The next virulent outbreak could happen at any time.
To predict the devastation it could cause,
researchers need to understand how much we move around
and how many people we meet, and for that, they need good data.
So I've joined forces with Ebola doctor Javid Abdelmoneim,
and we've launched a smartphone app that will reveal how a lethal
pandemic virus could strike the UK.
Now, the infection started here, in Haslemere.
The pandemic started last night, at midnight.
I probably have contaminated quite a few people now.
I think there's a really strong chance
that I have caught the pandemic.
We will find out how quickly it spreads.
Are some parts of the UK safer than others
when it comes to the spread of an infectious disease?
Will it reach all parts of the UK?
Our BBC Pandemic experiment will find out.
And we'll discover how many of us could die.
My word! That's an enormous number. That IS an enormous number.
The pandemic that we've started here in Haslemere might well be
simulated, but the information that it gives us
will help to answer very real life-and-death questions.
This will be the first ever life-saving pandemic.
It seems like there's no shortage of things to
worry about at the moment - global terrorism, the devastating
effects of climate change and volatile world leaders.
So I hate to break it to you, but according to the UK Government's
official list of risks to this country, we actually have
another, much bigger threat that we also need to worry about.
Now, this isn't just a hypothetical disaster.
This is something that has happened
several times in the past hundred years.
It's also something that we seem to have forgotten about.
Could I ask you one question on camera? Sure.
Do you know what was the disaster that killed the most
people in the last hundred years? No, I don't.
Could you have a guess? Oh, no, don't put me on the spot!
HANNAH LAUGHS
Ooh... That's a question. I've no idea! I don't know.
Oh, God, that's a tricky one, isn't it? Er...
Is it CO2 emissions?
I would imagine the most recent tsunami.
Tsunami? Well, yes, the 2005 tsunami
did kill a lot of people, tens of thousands of people.
Something much bigger, that killed tens of millions of people.
Probably World War II.
First and Second World War. Yeah, the First, Second World War.
War and natural disasters. That's all I can think of. Generally war.
Yeah, generally war. OK. So, the actual answer is Spanish flu,
which killed... What?! ..up to 100 million people...
I had no idea. ..just 100 years ago. Sicknesses. Oh, wow.
..and killed up to a hundred million people,
so more than World War I and World War II combined.
That's incredible.
Spanish flu was a truly global contagion, a pandemic,
a virus 20 times more deadly than ordinary seasonal flu.
It was a new strain of flu
that overwhelmed victims' natural immunity.
The startling fact is that another unknown
and potentially deadly flu virus could emerge at any time,
which is why our Government needs to be prepared.
Dotted around the country are secret depots housing vast stockpiles,
all poised for the next big outbreak -
protective kit so that health workers
can carry out their jobs safely, antiviral
drugs that can limit symptoms if they're taken early enough.
It is critical that the right supplies are in the right
place at the right time.
But those calculations depend on mathematical models that
predict how quickly a virus will spread
and how many of us will become infected.
With this kind of information, you can make really important decisions,
things like, have you got enough hospital beds,
do you need to close the airports, have you got enough
antivirals or how many body bags are you going to need?
Now, these are real life-or-death questions,
but you don't get to have a trial run. But with mathematical
simulations and computer models, you can
try out a few different scenarios so that when the time comes
you can make the right decision and save thousands of lives.
To create the models they need,
mathematicians use a set of rules, basically a series of equations.
These equations predict how a particular disease moves,
from person to person and then from place to place.
But to do that, mathematicians need information about us -
how we move and who we meet.
The more accurate the data,
the better the prediction that emerges from the calculations.
But how good is the data we already have?
Are we as prepared as we can be for the next deadly outbreak?
modelling for pandemics, Dr Adam Kucharski.
If you want to build a model of how infectious diseases spread,
what do you need to know?
There are a couple of bits of information that are really useful.
One is the contacts that people have.
For an infection like flu, for example,
it depends on who you interact with, who you meet.
So that's one kind of data we really need.
The second thing is where those contacts are made -
how much you interact with your community,
how much you travel to other locations,
and that can really help us understand how much
a disease will spread more broadly, outside a single community.
Don't you have all this data already? We're in the 21st century,
people are being tracked all the time. Don't you already know this?
There is information out there, but it's really not
designed for this specific question about how diseases spread.
So we might have data about where people are
but no idea of who those people are, how many contacts they're making.
On a much smaller scale,
there's been surveys of what interactions people
have in a given day, but that's not linked to their movements.
It's important to link those two together,
to have the movements AND the contacts in one data set.
Those surveys, do they cover a big portion of the population?
Generally, because of the effort that's required to survey people...
If you imagine writing a diary of all your contacts,
it's quite an intensive effort,
and the data sets are generally quite small.
We're talking maybe a few hundred, a thousand people. Wow.
OK, so, in a population of millions,
that's a tiny fraction of the people you might want to capture. Yeah.
We can say something general about the whole country,
but we really can't understand how different regions,
for example, interact with each other.
And this is the big problem. Pandemic planning relies on
limited data sets from the UK,
samples of just hundreds or a few thousand people,
which means epidemiologists have to make their best
assumptions about how we behave.
But if the assumptions are wrong, the results could be, too.
And that could have fatal consequences
unless we can improve the current data.
So, what can I do to help? What's the ideal situation here?
What would you like? What would be fantastic to have
is information on how people move,
and ideally not just where they think they move
but actually some way of verifying where they go,
and information on who they are - what's their age, what's their
professional status? And who they interact with -
what kind of contacts do they have,
how many people are they meeting, have they met these people before?
And if we had all those things, that would be a really useful data set.
And that would help you work out
how a disease might spread in the country.
That could give us a much better understanding of these things.
OK, how many people do you need me to get on board here?
I think if we could get about 10,000 people in this kind of data set,
that would be fantastic.
OK.
No problem!
Getting data about the travel
and social habits of 10,000 volunteers would clearly be
a very dramatic improvement,
a step change in the detail available to pandemic researchers.
It IS a challenge,
but the technology that can make it happen is already out there.
Smartphones are the perfect tool for pandemic research.
Forget the phone bit,
it's the combination of a built-in GPS tracker
and specially designed apps that make them a data gatherer's dream.
OK, so here's the plan.
Adam and his fellow mathematicians need GPS data
and social contact data to be able to predict the spread
of a pandemic, and we're going to help him, using a smartphone.
We have created the BBC Pandemic app, and here's how it works.
Users who download the app
instantly catch our harmless virtual pandemic virus.
To get the detailed data we need,
the app will track their locations anonymously
once an hour for 24 hours.
Users will also be asked their age and gender,
and the app asks them to record the details of every person
they encounter during that 24-hour period.
The combined contacts, GPS and age information will be
sent to our maths team at Cambridge University.
Real data from 10,000 people will fundamentally improve
the accuracy and detail of the current assumptions
and so predict with greater confidence than
ever before how devastating the next pandemic could be.
This really is the very first time that GPS data has been
combined with data about how many people you come into contact with
and data about your demographics.
This is an incredibly powerful tool for predicting
the spread of pandemics.
Now, I don't know about you,
but I think that's reason enough to download it.
Now all I need to do is get a few thousand people involved.
It is now 24 minutes past eight.
This morning, our campaign to get 10,000 app users nationwide begins.
If a pandemic comes, you can't stop it.
The only thing you can do is be as prepared as possible. Right.
It's a difference between having 1,000 patients a day in the NHS
or 10,000 patients a day in the NHS.
We've built this smartphone app
that you can download on all different platforms.
It's called the BBC Pandemic.
Our national experiment is under way.
But that's not all we're setting out to do.
HANDBELL RINGS
Oyez!
We're recruiting for a second experiment
here in Haslemere in Surrey.
Good citizens of Haslemere!
We want this small town to give us a close-up view of an outbreak,
watch how a virus spreads from one person to the next.
Take part today and sign up for...
If we can spread a virtual contagion through Haslemere, this
unprecedented detail can be used
to test the best ways to stop an outbreak.
Who should be vaccinated? Should schools be closed
or places where people gather?
We've persuaded nearly 500 people from Haslemere to take part,
and they show up as blue dots here on our map.
The Haslemere app is different.
Unlike the national experiment, the people here start uninfected
and will only become infected
if they come into contact with another infected person.
This app works by pinpointing
their location every five minutes for three days.
The GPS tracks created by each user are sent
to our mathematicians in Cambridge.
The team will combine all of the tracks to identify the moments
when our volunteers overlap.
The equations then calculate whether someone becomes infected
based on how long they remain close to an infected person.
Once the calculations are done,
the results will be sent back to the app, informing users
whether or not they have been infected
and, if so, how many people they went on to infect.
The app collects data anonymously,
but some users have started filming their daily
lives in the moments before the BBC Pandemic hits their town.
In a real outbreak, any one of them could become a victim.
Hi, my name's Lynette, I'm 61 years old. My name's Ed. I'm 37.
I'm a schoolteacher here in Haslemenre.
My name is Faye Walton. I'm 41 years old.
My name's Pippa Hollins. I'm a nurse in Haslemere.
I'm just about to have my breakfast with John, my husband.
There's John. Say good morning, John. Good morning!
My name is Justine Charman. I live and work in Haslemere.
So, it's a nice, quiet morning in the Apple Tree.
Everyone with the app in Haslemere is susceptible to getting infected,
but our outbreak can only start
when someone who is already infected comes to town, a patient zero.
The term "patient zero" is commonly used to describe an individual
who first introduces a contagious disease to a community.
Now, sometimes things really are that simple,
one person who somehow or other starts everything off.
In 2014, when Ebola hit West Africa, epidemiologists tracked
patient zero back to a two-year-old toddler in Guinea.
And in 2009, when swine flu hit the UK,
it was a plane with six infected people travelling
back from Mexico that brought it to this country.
Eventually, from that handful of cases,
half a million people would go on to be affected.
So, let's imagine something similar to that last flu pandemic.
I've just come back from my holiday, and I'm feeling a bit tired,
but it's probably just jet lag.
And I've got a few errands that I need to run about town.
Let the BBC Pandemic begin.
I'm now actively infectious, and the app is tracking my every move today.
First, to the local gym for a spot of yoga.
Anyone else with the app is also being tracked.
In Cambridge, our maths team enter all the GPS tracks into their model.
To create a detailed map
that will reveal how the virus actually spreads,
we want to infect as many people as possible, so the team has set
mathematical rules that mean no-one is immune to the virus.
And it's highly infectious...
..which means I could infect someone up to 20 metres away.
But the longer and closer we are to each other,
the higher the probability of infection.
All I need to do now is to come in contact with as many
people as possible.
So, time to move on.
A spot of shopping, I think.
I don't want to attract too much attention to myself today
and risk affecting other people's behaviour,
so I'm wearing a small camera as I go shopping.
Can you recommend me something? What do you like?
Hmm, science!
Where next?
I work in a shop just over the road from where I live.
I've worked there for about six years in all now.
Have you got washers in here? Yes. Where are they?
Our pandemic is just a simulation, so obviously I don't feel sick,
but it is entirely possible that someone with a genuine
virus would be well enough to walk around like this.
All viruses are different.
It's almost as though they've got different personalities which
will affect how they spread.
So something like HIV will lurk around inside your body
for years before finally making itself known when you become ill.
And even something like flu, you can be infectious for a full day without
feeling ill, meaning that you can carry on about your normal,
everyday business without realising that you're spreading a disease.
So, today I popped in for a coffee with my friend,
and that was really nice.
Thank you very much. And your food order's been taken, yeah? Thank you.
We're on Haslemere High Street.
We've just left the studio for the day. Everything's all cleared up.
Hello, I'm Mary. This is Mary. OK, and we run the checkouts together.
Right, time for a pint, I think, and my last stop here for the day.
Hopefully, there'll be a few people in here with the app.
Thanking you!
But it's going to take a few days to be sure of how far the virtual
BBC virus has spread.
And all the while, our volunteers are blithely
carrying on with their normal routine.
It is the end of the day for me, and it's my favourite
part of the day, because we're going to go and pick Daisy up.
It's been a really, really long day. It's about half past nine.
I've come into contact with loads of kids today -
a big assembly, I've taken all of Year 7 out.
So, it's 20 past 11, Thursday night.
I've just finished work after a very, very long day.
Over the next 72 hours,
our maths team in Cambridge will be working on the data day and night,
calculating how many of the good people of Haslemere
- or their phones, at least -
have caught the BBC Pandemic from my patient zero walk.
JAVID: Spanish flu was by far
the deadliest pandemic in the last century.
At its peak, around 15,000 people died in England
and Wales in a single week.
It was so virulent, it made people's lungs bleed.
Death followed within hours.
It was a terrifying variant of the seasonal flu we see each winter.
But what makes the radical difference between seasonal
and pandemic flu?
I'm meeting one of the country's leading experts to find out
how a flu virus can transform into a killer that can
threaten our nation at any moment.
If we think about what one of these viruses
looks like to our body, here it comes.
It's basically a bag with all its genetic material in the middle,
and on the outside it's got all these little spikes.
Now, all these spikes are what's sticking out
and being presented to our immune system, and it makes antibodies.
The antibodies we can draw like little Y-shaped things, and they
perfectly recognise the exact shape of the spike of this influenza
virus, and the person is protected against that invading virus.
These antibodies are very specific to that flu virus,
so we're immune to flu, surely.
Yes, that would be the case
if everything just stayed the same year on year, but it doesn't,
and the reason for that is viruses create thousands
and millions of copies of themselves in a person as they replicate.
Now, in copying themselves, they sometimes make mistakes,
so if this virus is replicating and it makes a mistake in the gene
that encodes this spike, now the spike looks different.
It might be a different shape.
I'm going to actually draw it as a different colour here.
So now the spikes are all coloured yellow. OK?
It's a bit like you go outside in your coat
and then you change it and put on a different-coloured coat.
You now look different to everybody outside.
This virus looks different to all the antibodies that had been
induced in the first place to the white virus.
So even though you had all those white antibodies, they don't
do you any good whatsoever against this yellow virus.
It's this ability of the flu virus to transform itself that can
make it so dangerous.
When the outer coat of proteins changes in only a small way,
that's called antigenic drift. It's still a familiar virus.
That's what happens with seasonal flu.
These relatively minor changes in the coat of seasonal flu
viruses mean our immune system gives us some protection.
But the real danger is that a new, completely unrecognisable
flu virus will emerge, a virus with pandemic potential
which our bodies have no immunity to fight off
and can cause far more damage.
Pandemic flu viruses emerge
because humans are not the only creatures that get flu.
Pandemics are influenza viruses that come across from other species
of animals into humans, usually from birds,
wild birds like ducks, geese, swans.
And the thing about those viruses is
although they're also still flu viruses -
they look just the same on the inside - their coats,
their spikes that are on the outside are completely different.
Because these viruses have got genes from the animal population,
it can mean that their coat is dramatically different.
That's called antigenic shift,
and it can mean the human immune system is entirely unprepared.
Therefore everybody gets infected, the outbreak is explosive
because everybody is susceptible,
and we see it ripping round the world in a matter of days and weeks.
Right now, there are several particularly worrying flu
viruses circulating in bird populations.
So far, those viruses haven't mutated enough
to transmit easily between humans.
But the fear is they can, which is
why we are conducting our experiment.
We need as much data as possible before the next pandemic erupts.
In Haslemere, we've been using our local app to track
nearly 500 people for three days.
The aim here is to see in exceptional detail how
a virus moves from person to person as an outbreak unfolds.
If we can see what drives an infection through this community,
it could help save the lives of these people come a real pandemic.
Hi! It's Friday, the shop is super-busy.
We've had an absolutely mental day.
I know a lot of people are using
this pandemic app that are in the pub.
I probably have contaminated quite a few people now.
Or, if any of them have got it, they probably have given it to me.
This is my Friday evening. My son's got a friend coming round,
and I'm about to cook them both dinner.
So if I am contaminated, that will pass on.
I hope this information is helpful to you. Have a good evening. Cheers!
All the volunteers in Haslemere are about to discover their fate.
Only the computer model knows which, if any,
of the people turning up here are infected.
I need to give them a PIN number to reveal their individual results.
There's been no dry run. Nothing like this has been attempted before.
I'm about to find out if my walk around town had any effect at all.
To be honest, it's all a bit nerve-racking.
To infect you all, there had to first be a patient zero,
which you may have guessed was actually me.
We will see if I actually managed to successfully infect any of you.
OK, so if you could get your phones out, I'm going
to give two different PIN numbers.
When you put in that PIN number, it will tell you
whether or not you've been infected.
If you have been infected, please move over to this side,
and if you haven't been infected, move over to that side.
OK, so the two PIN numbers are...
Drum roll!
..for Android, please type in 8165...
..and for Apple, type in 8195.
OK, if you could move to this side if you've been infected
and that side if you have not been infected...
Look at that! OK.
I have to confess, I think we've got a good number of you...
We managed to infect a good number of you.
VOICEOVER: This is a great result.
72 people in this small gathering alone have been infected.
Yes, I was infected. I'm really surprised at the results.
I've infected one person.
I know exactly where I got infected and what time I got infected,
just purely because I'm on annual leave
so I haven't done much, just pottered around the house.
So the one time I did go out I actually got infected.
But this is just a fraction of all our volunteers in Haslemere.
To see how the infection moved from person to person,
we need to look at the bigger picture.
Our mathematicians from the University of Cambridge,
led by Professor Julia Gog,
have finished crunching the data.
The first thing I want to show you is the patient zero track,
which is you!
That's the High Street there. Mm-hm. The gym there.
That's where I go first, that's where I go to yoga. Here I come.
The blue dot there is one of the users, the other users,
and they're uninfected, because everyone else starts uninfected. OK.
It's only you that started infected.
And let's see what happens when you cross paths with them.
Ooh...! And here I come, ready to infect them.
So, I'm looping round, because I'm doing some filming. And...
Ah! So that's them getting infected. Now they're infected.
And they're off up the High Street.
So if they cross paths with someone else,
there's a chance that other person will get infected. Exactly.
So it's a chance. And the closer they are, the longer they are,
infection could happen.
That's one person, then. That's one infection.
And what happens next? Ah, well, let's have a look.
So, this is looking at, zoomed out, on the High Street. This is still me
as I'm wandering round. Yeah.
And now there's loads of dots, loads of people who are infected.
The blue dots are people who haven't been infected yet,
and you can see the orange or red with the circle around.
And you can see you because you've got the extra dotted halo around.
And actually, often I come into contact with people or people
are coming into contact with each other and not getting infected.
So it's not every single time. No.
It depends on proximity, length of time.
So that's the whole of Haslemere.
So now this is when I went off to the supermarket.
And in the pub, of course, at the end of the day!
A vital way to end a day!
We're now at six o'clock in the evening.
I got there about half ten or so.
How many people by this point
are infected overall?
There were 77. 77?! Yes. Just in one day? That's right.
Just from me arriving. OK. How many people did I infect, then?
more got infected, more got infected... Mm-hm. Wow.
With no immunity to our virus, by Day 3 the infection has
spread out of control through our volunteers.
Loads of people have this all over the map. Oh, my goodness!
What have we done?! SHE LAUGHS
Wow, it's everywhere. It's not even...
You don't even just have little clusters any more,
it's literally everywhere.
And people are still getting infected. Oh, my goodness.
That is an extraordinary number of people to be infected from just
one person walking down the High Street. Yeah.
VOICEOVER: In just three days,
86% of all people who took part were infected.
Our pandemic virus spread like wildfire because no-one was immune.
If this had been a real virus,
the town would have come to a standstill.
Instead, it's created an extraordinary
simulation of how an infection would spread from person to person.
Even better, our app also reveals information that will help
test how we can stop an infection spreading.
For those of you who were infected,
your apps should also tell you how many people you went on to infect.
How many people infected no-one else?
How many people infected one or more?
Two or more?
Three or more?
Four or more?
Five or more?
How many did you get, sir?
Six. Six. And, madam?
Five. Five. OK. These are our super-spreaders, then!
You got six people. I got six people.
I'm feeling very guilty about it!
And I already know I need to apologise to
a lot of people at the coffee shop!
Oh, is that where you think it was, where you think it happened?
Friday morning. You were sitting in the coffee shop? I was.
But actually, I've just noticed,
I spoke to you on my patient zero walk.
You were in the hardware shop.
That's right. Is that where you work? I do. Did I infect you?
Yeah, I assume so! And how many people did you then go on to infect?
Eight. Eight people? That makes you the champion.
And it's not a great championship to win. No.
Do you think it was just people that you were speaking to in the shop?
I think it's just the high volume
of customers that we have, really, in the shop.
I'm quite impressed, actually!
I don't really know! It's quite shocking, really, eight.
As our data reveals, when it comes to infectious diseases,
clearly we aren't all equal.
Pandemic researchers call people like Justine
social super-spreaders, and they're thought to be key players
in the spread of a real infection.
It may seem obvious that someone whose job or lifestyle
brings them into contact with a lot of people could spread more disease,
but this is the first time they've actually been identified like this.
Thanks to the app, we can now test a potentially life-saving idea.
If we can understand who the super-spreaders are,
then it might help with control,
for example a situation where we've got a vaccine
but either it's in short supply or we've got a very limited time
to deploy it so we can't just vaccinate everyone.
You've got to choose who you're going to vaccinate. Be selective.
Exactly, and rather than choosing at random, you can be really
strategic and choose those who are going to infect a lot of other
people or would have infected other people.
So on the left is going to be our original Haslemere epidemic,
and on the right is going to be our simulation of what would have
happened if we vaccinate the super-spreaders.
How do you define that?
Yeah, so, for this one we chose the people who actually infected
more than three other people...three or more.
So if you take those out... Yeah.
..vaccinate them so they're immune from the virus... Yep.
..and see how it changes. Yes. OK. And that was only 46 people,
so it's a pretty small vaccine roll-out.
And what you see is the dots are in the same place. Mm-hm.
But... So, this is me, my little walk on the first day.
A few infected. Quite a lot going on on this one.
This one we've seen before. Much less, actually.
We're in Day 2 now. Far less! Oh, my gosh!
Oh, my gosh. So just vaccinating 10% of the population.
That's a much slower epidemic.
We vaccinated 46. Stephen, you ran this one.
How many fewer people were there infected in the super-spreaders?
There were...
Sorry... 112 fewer. OK. 112.
So that's people who weren't vaccinated
but ended up not getting infected just by virtue
of the fact that the super-spreaders were protected away from the virus.
That's exactly right. Gosh, it does make quite a big difference, then.
We simulated the vaccination of only 10% of our app users,
but targeting these super-spreaders
reduced the number of infections by 40%.
Our app allowed us to identify super-spreaders in Haslemere, but in
the real world, people who work in busy places like schools
could also be super-spreaders.
Deciding who to vaccinate first with greatest effect is problematic,
but there could be a way of identifying individual
super-spreaders BEFORE a contagion,
and that could mean a revolution in pandemic prevention.
That's the theory being tested in a unique experiment.
Researchers at Imperial College want to know if some
people are biological super-spreaders.
Is there something about their genes
that makes them become highly infectious?
If there is a biological reason, there could be a test to identify
super-spreaders before an outbreak hits, and that's
the revolution in pandemic control that could be starting here today.
It's about 7am, and this looks like any ordinary hospital wing.
In fact, it's a research facility.
The patients through there are perfectly healthy...
..for the time being.
Technically, Joydeep Dutta isn't a patient.
He's one of ten healthy volunteers being quarantined in this ward
about to be infected with a large dose of purified swine flu,
because researchers are hoping they can pinpoint what makes
people react differently to the same virus.
Dr Chris Chiu is running the experiment.
There's so much that we don't know about flu infection -
why some people get more sick than others.
And in fact, we don't really know what contagiousness is.
To find out what might make someone particularly contagious,
the team will dose Joydeep and the other volunteers with flu
then compare how they respond.
Each of these vials is packed with
five million swine flu virus particles,
the same strain that caused the 2009 pandemic.
Meanwhile, Joydeep is calmly waiting next door.
What made you want to do this?
I had a family member who had the swine flu in 2009, and she was
quite affected by it, and it changed her life, to a certain extent.
What happens next is down to a battle between the five million
virus particles and Joydeep's personal biology.
At the moment, there's no way of predicting how he will react.
Over the following days, his samples, along with those from
the other volunteers, are tested to see how infected they've become.
Everyone was inoculated with the same dose,
which makes the difference in their results astonishing.
Chris first shows me a sample from one of the other volunteers.
What you can see here is that every spot that's green is a cell
that's infected with flu.
So you're physically seeing swine flu virus right there,
and it's showing up green.
Exactly.
VOICEOVER: But Joydeep's samples look radically different.
Joydeep has made a lot more virus in his nose than the other person.
We can zoom in on one of those areas where it's particularly dense, and
here there's one which looks like it's swollen up and falling apart.
Oh, wow! That literally does look like a sort of...
..almost a puff of smoke.
So that's a cell that's bursting open in death, so to speak,
overwhelmed by virus. That's right, yeah.
That's exactly what's happening. Gosh.
It's a very early result, but it suggests there is something specific
about Joydeep's biology that could make him particularly infectious.
Do you think you actually might be able to find a genetic test
to predetermine who might become really contagious with flu?
Yeah, so, that would be the ultimate hope. Wow.
There is a way to go before this research turns into a reality,
but once we have the ability to identify super-spreaders,
our data shows vaccinating them could be
an effective way of reducing a contagion.
What's so exciting about the data we have gathered is that
mathematicians can now use it to test a range of interventions.
What if we closed schools?
How would that impact the number of people infected?
And shutting down places where people gather?
Or asking them to stay at home?
Now we have this data, there are all manner of possibilities
that could be tested to reveal how
communities like Haslemere could be saved...
..but for one snag.
Haslemere is not closed off from the world.
I've been down to Christchurch in Dorset.
I'm now down on the Bakerloo line, at Marylebone.
I'm just going to take a trip on the Tube and head back to Waterloo.
Viruses travel wherever we take them,
from town to town or village or city...
..which is why three weeks ago we also launched our national
experiment in the hope of getting 10,000 people to take part.
The second phase of our experiment is going to use the
BBC Pandemic app to collect data from volunteers
all around the country, and our mathematicians are going to
calculate how quickly a Spanish-flu-like virus might spread.
We'll find out how many of us could get infected
and how many of us could die.
In the national experiment, users will
"catch" our harmless virtual virus as soon as they start the app.
This time, they will be tracked once an hour for 24 hours.
The aim now is to track journeys around the entire country,
across cities and between towns and countryside.
The app also asks users for their age and gender and to report
the age and number of people they encounter during that 24 hours.
The aim is to collect data that will improve our understanding
of how much we actually move and mix.
For example, do some age groups travel more than others?
How isolated are older people?
If we can recruit 10,000 users, it will be the largest,
most detailed UK data set ever collected and it will predict how
far and how fast a lethal pandemic could spread across the country.
If a virus that was carrying a very high case fatality became
transmissible through the air between people,
we could have a devastating pandemic on our hands.
Millions of people could die around the world.
We know that a pandemic will come. There's no doubt about that.
These are regular occurrences.
What we saw from the last flu pandemic in 2009, which was
a relatively mild disease,
was that our health service was almost unable to cope.
In my mission to get the best data, I need to involve people from
all ages but especially the ones
we know least about - young people.
Which is why I'm bringing in some big guns to help spread
word about the BBC Pandemic app...
..masters of new media, social media.
These guys are a new breed of marketers.
They've got Twitter, Facebook
and Instagram wrapped around their little finger,
and hopefully they're going to help me make the BBC Pandemic go viral.
CHATTER
If everyone in this room looks a bit on the young side, it's because,
I'm told, the average age of people here is 23.
Now, that means that a lot of these people were born after 1995 and
really are the first generation that was raised in the smartphone era.
Now, given that this is the group that I need to target,
who better to ask for help?
The team here are fabulously well connected on Facebook,
with about three million young adult followers in the UK alone.
Hi, guys. We're now here in the city centre today trying out
the BBC's new Pandemic app, which could potentially...
A specially crafted video targeting this group has been
sitting on their Facebook page for the last five hours.
But has anyone bitten?
So, on Facebook we've reached 1,015,750 people.
OK, so what does that mean by "reached"?
Does that mean it came up in their timelines?
That means that 1,015,750 people have actually seen that video.
Not necessarily watched the whole video,
but they have seen that video on their timeline.
So, how many people watched it? How many people watched it?
So, how many people actually watched it?
That is 263,885 video views.
VOICEOVER: Watching the video is one thing,
but how many people actually followed the link to the app?
So, just on Android devices... OK.
..we've got 1,473. But then on Apple... Mm-hm...
..we've got...
..a lot higher, 4,340.
That's amazing! So we have got pushing 6,000 people on there...
Pushing 6,000. Yeah, yeah. ..in five hours. Five hours, yeah.
I am actually deeply impressed. SHE LAUGHS
I'm deeply impressed. That is genuinely amazing.
ON VIDEO: It sounds amazing! That's a gamechanger.
The more people that download it and use it, the more effective it is.
Social media and maths are unlikely weapons,
but they are essential in the fight against a pandemic.
Despite all the wonders of modern medicine,
another Spanish-flu-like virus could still bring society to a halt.
You may have thought that the huge number of fatalities seen
during the Spanish flu pandemic is something that we could
relegate to a bygone era.
And that's partially true,
with modern medicines such as antibiotics.
Antibiotics would be useful for the secondary bacterial
infections that flu victims often suffer and that often kill them,
ones like bacterial pneumonia.
But antibiotics would not be the cure-all,
because they do not work against viruses.
Antibiotics treat bacterial infections by interfering
with the processes that keep bacteria alive.
But viruses aren't alive.
They're just bags containing genetic material...
..which means antibiotics are powerless against viruses.
What you need are vaccines.
Every autumn, thousands of us
line up to get a seasonal flu jab that's tailor-made each year.
The vaccine is designed to produce antibodies that block
the spikes of the specific flu viruses circulating that winter.
But the problem with a pandemic flu virus is you only know about it
when it's started making people ill.
And designing a vaccine takes time.
I need to find out how much time so we can factor
that into our predictions about the spread of the BBC Pandemic.
So I've come to one of only two factories in the UK that
produce flu vaccine.
At the moment, it's busy pumping out millions of doses of seasonal
flu vaccine needed each year.
So, I'm going to take you down this area here.
All of them rely on these.
This is where the eggs come in from the farms.
Dr Laura O'Brien is in charge of this site.
Laura, why are we using eggs to make vaccines?
So, we've used eggs for decades to manufacture influenza vaccine.
Viruses need living cells to grow. And if you think about a single egg,
there's lots of living cells in there,
it's protected by a shell, so it's almost a clean environment,
and it's just the perfect place for a virus to propagate and grow.
There are a huge number of eggs here.
How many do you handle in a day?
Every single day, you know, Monday through to Sunday,
here in the plant we're putting
over half a million eggs through the factory.
So, the eggs have been incubated...
Right now, this factory is making only seasonal flu vaccine.
Each fertilised egg is inoculated with flu virus,
which then replicates.
After just a few days, the eggs are packed with the virus particles.
Then all those virus particles are inactivated to make the vaccine,
so they're powerless to cause flu
but can still fool the human body into making antibodies.
We put them in different incubators
so not all the one day's batch goes the same.
When it comes to making flu vaccine, you first need to
know which strain of the flu virus to grow inside the eggs.
A global monitoring system run by the World Health Organization
constantly watches out for animal flu viruses
that have hopped into humans.
It's the WHO that declares
if one of these pandemic viruses is on the loose.
Laura, presumably when a pandemic is declared, production changes.
When the strain's declared, that's the Big Bang.
When the pandemic's declared, here at the site
we have a pandemic response plan that we immediately enact.
It's a race against time to isolate and identify the pandemic
virus so that a vaccine can be created and put into production.
It really is all hands on deck, then. There's urgency.
How long can you expect from that get-go moment
to produce your first batch of vaccines?
What we see is, for a pandemic strain,
generally in industry it takes about four months from that strain to
actually getting those first doses out there to patients worldwide.
At least four months before a pandemic vaccine
is tested and available. That's a sobering thought.
So just how many of us could become infected
before a vaccine is available?
We can use the results of our national experiment to find out.
We launched the nationwide BBC Pandemic app a little over
two months ago.
We needed thousands of people of different ages in different
locations to catch our virus
to get real detail on how people move around the country
and who they come into contact with.
So, how did we do?
Adam, when we met a couple of months ago, you set me a challenge
to go out and get a data set for you.
Just remind us of what it was you were looking for.
The aim was to get data on social interactions and movements.
We know these kind of things are important for diseases,
but, really, to have those things combined and have a large number
of participants would be really valuable for predicting epidemics.
And, Julia, you've got the results, I know,
of how many we actually managed. We needed 10,000, right?
That was the number that you set. Are you ready? Yeah.
He-hey! That's brilliant! So, almost 29,000. Wow, that is impressive.
And that's proper, full, rich,
glorious data for every single one of those people.
Yep, these are all users who've used the app, and we've got
information on movements, locations, the contact survey,
all the bits of it together.
Is that data spread across different age groups and different areas?
Yeah. This is one of the important things you wanted, wasn't it?
So, I can show you how it's distributed by age.
And here we go.
This is fantastic. We've got the younger age groups, we've got
the kind of young adults, right up to much older people.
The previous data set was mainly people over 35, is that right? Yeah.
One of the challenges with particularly survey data is
you get much more participation in older age groups.
To have these young groups, young professionals, is just fantastic.
We don't have this in this scale anywhere else.
Our app has generated a massive set of data about the movements
of men and women of different ages, living in cities, towns
and rural areas all over the country
and how much they mix together. It's revealed new information.
We now know the over-60s make more, longer journeys
than people under 24.
And country folk spend more time
further away from home than city dwellers.
All this information improves the calculations
and the accuracy of pandemic predictions.
But before we predict the spread of a deadly virus through the UK,
we still need one more piece of information for the model -
how easily the flu virus can spread.
One of the ways we can tailor models to a specific infection is
to use a value known as R0, and this measures, for a specific
infectious person, on average how many infections they generate.
So how many people one person goes on to infect.
Exactly. If you drop one person in a susceptible population,
how many new infections will they create?
And that's different for lots of different diseases.
So, you know, measles, Ebola are ones I've got in my head, but...
Exactly. So for Ebola in West Africa in the early stages,
it was around two in some areas.
So each case on average was generating two additional cases.
But for measles it's much higher. It can be 15 or 20 in some...
So one person can infect 20 people before...?
If it hits a susceptible population, exactly, yeah.
That's why vaccination's important in that one.
So, what number did you actually use for flu?
We looked at past pandemics, and the typical range is 1.5 to two.
And what we've done for the BBC Pandemic is we've got an R0 of 1.8.
OK. And that's not massively high.
That's not crazy high for a pandemic.
It's a reasonable scenario to be planning for.
This is what we've been waiting for.
Thanks to nearly 30,000 app users,
we've now got the best data ever available for our team
to use in their calculations.
How many people in the UK does the mathematical model predict
could become infected
if a Spanish-flu-like pandemic began in Haslemere today?
So, Haslemere is glowing in red here,
just so we can see where it is. As well as seeing the spread,
which will be the dots going towards red as the cases increase,
we'll also see a bar with the total number of infections. OK. Ready?
So it hits London very quickly, then.
And then we need to zoom out. And we're already zooming out.
Look at it, literally sweeping through.
We had the Glasgow and Belfast area go off before Edinburgh.
43 million people?!
And that number is extraordinary.
43.3 million, which is about just over two thirds of the country.
Based on data from thousands of app users,
the model predicts very few parts of the country will escape infection.
Only the most remote corners remain blue.
But the prediction of the number of deaths is even more shocking.
By the end of May 1919, the death toll from Spanish flu
had risen to over 150,000 in England and Wales alone.
Globally, it's thought at least 2% of people affected died.
Based on the worst-case scenario of 43 million infected nationwide and
then falling sick, a 2% death rate today would be utterly catastrophic.
My word! That's an enormous number. That IS an enormous number.
So, in the worst, worst case, that's the number
of deaths that we could expect based on our data in a future pandemic.
VOICEOVER: Over 800,000 dead, millions more infected.
And there's one more piece of shocking information that
our calculations can reveal - how quickly those deaths will happen.
Over what period are all these deaths going to be seen?
Yeah, so, we can have a look at the timescale,
and I can show you that here.
We're going to go through, and we're
going to see with the time up there, so you've got the day up there.
So we're back to Day 1, back to Haslemere.
So, this is one month in. Gosh. London.
Two months in, you can see
it's made it up to Manchester, Edinburgh, Belfast.
You can kind of see it moving. But there's a few long-distance numbers.
Yeah, it jumps over to Northern Ireland.
Yeah, I mean, that was just by about Day 60, two months, wasn't it,
even well before it got to Aberdeen there?
You don't have very long, do you? No. That's really frightening.
But presumably... I mean, you've got a timescale there,
but presumably pandemic flu isn't going to arise for the first
time on this planet... it's not likely to be Haslemere.
It'll be across the globe somewhere.
Yeah, chances are it will start somewhere else in the world.
Based on what we know about these kind of viruses,
it may well be somewhere in rural South-East Asia, for example.
And what sort of lead time might we have on that,
that sort of knowledge coming to us?
Potentially, if it emerges there, we'd get, say,
two to four weeks before it actually arrived in the UK.
OK, let's call that a month.
And then we've just seen it takes just about two months to get
from right near the south coast all the way up to Aberdeen and Belfast.
That's three months. And I learnt that it takes at least
four months to get pandemic flu vaccine out.
So there's a real gap there.
Our data revealed an alarming reality.
We forecast over 800,000 could die
if a pandemic spreads unchecked throughout the UK.
43 million could become infected before a vaccine is available.
Until the precise nature of the virus shows itself,
we won't know how many will actually fall sick.
But government plans assume half of us will be affected.
It is truly frightening. What would any of us do faced with
the prospect of a pandemic virus sweeping towards our hometown?
The gravity of the situation has made our volunteers reflective.
So, if we caught a deadly virus, what would we do differently?
Do you think we should all come to this place? No. What would we do?
Stay at home? Stay at home? Why would we stay at home?
So no-one else catches the virus.
John and I are just driving up to north London.
We were just thinking about whether...
..if there was a pandemic, whether we'd be doing this, and the answer,
we both agree, is no. We'd be staying at home.
If you thought it was that infectious that it could
travel really quickly, you'd want to stay put,
because you wouldn't want to give it to anyone else.
But you wouldn't want to be around anyone that might have it.
If you found out now, it's happened, it's happening now,
was the first thing to do to get back to your kids?
Yes, on the one hand, but then, turn that on the head,
if there's a risk that you're infected and they're not...
Yeah, so you're like patient zero. ..do you go home? You tell me. Mm.
When a pandemic arrives in the UK, the NHS will be on the front line.
Dr Chloe Sellwood leads pandemic planning for NHS England.
We do plan for the reasonable worst-case scenario,
so we look at what 1918 was like and we look at those numbers,
and we translate them across to now and we think about how
that could impact on the NHS, and that's where modelling comes in.
That gives us some numbers.
And that helps us to think about how the NHS might be affected
and how the NHS might need to respond differently.
So if you think about 50% of the population being
affected in the reasonable worst-case scenario,
if all of those cases happened in one week, that's a really big,
short, sharp peak, lots of cases.
If we can slow the spread so they happen over a much longer period,
that reduces the impact on the NHS and makes it much more manageable.
Yes, we're seeing a pandemic for a longer period of time,
but it's fewer patients at any one time.
And of course, therefore, then increase the likelihood
of the NHS being able to cope. Yes, exactly.
We need ways to slow down a pandemic when one inevitably hits,
and this is where mathematical modelling can come in.
It can provide hard numbers to help guide decisions,
because the long-term closure of schools or public transport
would have an enormous impact on society.
Interventions like that can't be taken lightly.
But our data can be used to test a far simpler idea, too.
The Government have a key message during any major outbreak.
They tell us to wash our hands.
But what difference would it make if we did?
Could something as simple as hand-washing make any real
dent on the death toll?
We've asked Julia to try something that has never been done before.
She's used our data to simulate
the impact hand-washing could have in a major flu outbreak.
What we can model here is the effect of EVERYBODY - and again, we're
assuming the best-case scenario - everybody takes extra precautions.
That means washing hands more often,
certainly after meeting infectious people, but maybe washing your
hands, like, five to ten times more than normal per day,
and everyone does it for the full period.
And we can look at the effect that has on transmission. OK.
So, on the left is the one we've seen before. Right.
There's nothing on the right yet!
Look at the numbers!
So now, by two months, actually,
only London is really affected.
Do you know... Do you think
it's going to reach the final same number but it's just slower?
No? Overall. Well, it doesn't look like...
I mean, it's slowing down. That's a good question.
So, two things happen there.
It's definitely slower in spread,
and the total number is...what?
Lower. It's only...three quarters?
Yeah, three quarters of what you had before.
That's 13 million people who don't have the virus.
I mean, that's quite a big, persuasive message there, isn't it,
just washing your hands?
But, I mean, we're talking about genuinely millions of people
and potentially hundreds of thousands of lives.
That's right. There's 13 million cases averted.
That's a lot of lives.
If we all commit to changing our behaviour,
there ARE ways to slow the spread of a pandemic.
But the real hope for the future is prevention,
and that's something that's being worked on in labs around the world.
One of those labs is at University College London.
It's run by biochemical engineer Dr Tarit Mukhapadhyay.
Researchers like Tarit are working on radically new ways for making
enormous quantities of flu vaccine quickly.
Instead of eggs, Tarit is growing vaccine in yeast cells
in a steel vat called a bioreactor.
The bigger the vat, the more quickly
mass doses of vaccine can be produced.
But there's an even bigger revolution in the offing.
Well, the Holy Grail is actually try and create a universal flu vaccine.
Right. And so this is a vaccine you don't need to constantly update
every year, or indeed wait for a pandemic to emerge,
before you can create a vaccine.
The coats of flu viruses constantly change,
meaning a new vaccine is needed for each one.
But we now know some bits of the flu virus don't change so easily.
Make a vaccine that targets those stable bits of the virus
and you've got some protection ready and waiting
when a pandemic virus emerges.
Rather than constantly having to update the vaccine,
a vaccine that is based upon the universal system may be good
enough for five years, and I guess the Holy Grail is making
a vaccine that's good enough for ten years.
I mean, we will have to update it at some stage,
but it gives us a little more breathing space and a little
more flexibility in trying to deal with future pandemics.
That really does sound incredible to achieve,
but how close are we to doing that?
Well, I guess in terms of getting a universal vaccine out there, I mean,
we're still at least one decade if not more years away.
I think getting a commercial universal flu vaccine out there
is still a long way away.
Until we get there, our data will put pandemic researchers
in the best position possible.
As well as their long-term forecasts, good GPS and contact data
will be essential for them to model accurately during a pandemic.
This would be incredibly useful,
just having this scale of data but also having it by different
age group, by different geographical locations.
It means we can really look at things in much more detail
than has been possible before.
To have this kind of data for, what, 20,000 people,
that's just incredible. We dream of that kind of data!
You're welcome!
It began by recruiting a community.
..and that side, if you have not been infected.
It's turned into the biggest
citizen science experiment in pandemic research.
Yes, you heard that right,
an app could help save lives. And here's how.
It's brought some harsh realities into sharp focus.
My word! That's the number of deaths that we could expect,
based on our data, in a future pandemic.
The BBC Four Pandemic experiment has created an invaluable legacy.
Thanks to tens of thousands of you across the country,
we now have a new gold-standard data set for pandemic research,
a vast amount of information that will be shared with mathematicians
from the Department of Health and pandemic researchers nationwide.
But the experiment isn't over yet.
The app is still available, if you would like to join in.
And we still need your movement and contact data,
which will be passed anonymously to our maths team in Cambridge.
Better data equals better maths, which equals better models,
and better models mean better health care for all of us.
So, with that in mind, the maths team have come up with a few
areas of our data set that could
still do with a little bit of improvement.
They want more teenagers and over-60s to download the app,
so please spread the word.
We also need to see how our behaviour at weekends
compared to weekdays changes the predictions,
so you can feel free to use the app as many times as you like,
weekends AND weekdays, no matter how mundane your plans are.
In fact, you can all still take part in the biggest citizen science
experiment of its kind, unleashing the life-saving power of mathematics
on what the Government considers to be the greatest risk to our society.