Flu surveillance | Infectious diseases | Health & Medicine | Khan Academy

Let’s take a step back and
think about the entire planet– the planet we live on, Earth. We’ve got lots of little
continents and water, right? I’m going to draw that out here. And, if I was to ask you, how
much flu affects this planet– all the people that inhabit
this planet– in one year, probably the best
place to go to answer that question would be the WHO. And that’s exactly what I did. I went to the World
Health Organization, and I wanted to know how many
people on our planet, Earth, in one year– let
me write that out here so we know that we’re
not talking about many, many years, just one year– how
many people are hospitalized from the flu? So this is actually a
pretty mind-numbing number. Look at this huge
number, 5 million– 3 million to 5 million– end
up going to the hospital, because they have severe
complications of flu. This could be anything from
like, pneumonia to bronchitis, to having a horrible asthma
attack, something like that. So this is not how many
people get sick with the flu, but how many actually
end up in the hospital or have severe disease
from influenza. And then, another number
I wanted to look up is how many actually
die of having the flu? And you know, a lot
of people will say, well, you know, the
flu is not a big deal. It doesn’t really
affect you much, and you just get kind of sick. And if that were true, then we
wouldn’t be having a quarter million to a half a million
deaths each year from the flu. This is a really kind of a sad
commentary, when you consider the fact that this is
something that we actually do have a vaccine for. So in the world, we have a huge
number of deaths happening. Now, whenever I hear
statistics like this, my mind always kind
of takes comfort. Like, I always think, well, I’m
living in a developed country, and I have health insurance,
and I can go to the doctor if I need to. And so, what are the
numbers like in the US? I mean, I’m sure that they’re
obviously not as high as that. And what I wanted
to prove to you is, actually, the numbers
are not insignificant. So in the US, we have the CDC. And the Centers
for Disease Control tells us that we have
about 200,000 people going to the hospital each
year because of the flu. Now you have millions of
people getting the flu, right? That’s another number. But this is just how
many people end up going to the hospital
because of it. And then this is probably
the scariest thing, we have 3,000 to 49,000
people dying of the flu every single year. And I wanted to see why
they had such a big range. So I actually
looked at the study. And it turns out,
between 1976 and 2007, they actually kept
track of how many people had died of the flu. And this is not,
obviously, an exact graph. But I just wanted to show you
that they said, well, one year, the number was as
high as 49,000. This is number of
people that died because of the flu in the United States. And one year, the number
was as low as 3,000. And I say as low as
3,000, but, I mean, 3,000 deaths is still
a lot of deaths. So we have thousands
of people dying of flu, and we have hundreds
of thousands of people going to
the hospital for flu. So if someone ever
tells you that it’s not a serious problem in the US
or in developed countries, that is definitely not true. And it’s a huge problem
internationally. Now I want to show you
some interesting data. This actually
comes from the CDC. They actually put
this on their website, and you can check this out. It’s actually pretty
neat and helpful to understand exactly how we
gather information about flu. So the key word here
is surveillance. You see this influenza
surveillance report here. And surveillance
basically means, how do we gather data
around a disease, or gather data around
anything, really? So let’s talk
through that process. The traditional way we do it was
we say, OK, we have a person. Let’s say this person is me. Let’s say I’m feeling
pretty lousy from the flu. I’ve got a case of sore throat,
and I’ve got some runny nose, and maybe I’ve got some
fevers and body aches. So I’m going to go to my doctor. And my doctor is going
to be over here, in blue. And my doctor’s going to be
pretty smart, pretty savvy. And they’re going to figure
out pretty quickly that I’ve got the flu. They’re smiling because
they figured it out. And so, then they’re going
to take that information, and they’re going to say, OK,
well, I have a person here by the name of Rishi,
and he has the flu. And they’re going to send
that information where? It’s going to go to the
hospital that they work in– or the clinic, let’s say. So that clinic now has a
record of all the people that walk through
and have the flu. So now that clinic
or that hospital is going to also take
that list of people– and, you know, it’s protected,
confidential information, so it may, at this point, not
even have your name on it, maybe they just have the total
number of people with the flu– and they’re going to send that
over, let’s say, to the county. And I live in San Francisco. So let’s say this is
San Francisco County. So they send that information
to my county, San Francisco County. And then, that county is
going to get that information. They’re going to say,
well, thank you, hospital, for sending it over. And they’re going to
take that whole list, and they’re going to add it to
all the other hospitals that sent them information. And they’re going to
get a bigger number. And they’re going to
get that bigger number, and they’re going to send it
to the state of California. The state of California gathers
information about the flu. And they’re going to
say, thank you so much, county, for sending it over. This is California. And California is going to
gather up all the information about who’s got flu in
their entire state– all the different counties
that send them information. And they’re going to send
that information, finally, to the United States’ kind
of public health authority, and it’s based over
here in Atlanta. So eventually that information
goes to the Centers for Disease Control. So this is kind of the
chain of information that we traditionally use. And that’s why,
over here, it says, this is estimates
reported by the state and territorial
epidemiologists, so all the different
territories and states that are encompassed
by the United States. So that’s what that means. And this graph is
telling us that we’re seeing regional and widespread
flu in almost every state at the end of 2012– so,
just a couple weeks ago, since, today, the
date is January 10th. Now some really smart people
got together, and they said, is this the only way
to actually gather information about the flu? Maybe there’s another way. So some folks at
Google got in touch with some folks at the CDC. And they said, let’s
put our brains together, and let’s figure out
if there are other ways that we can actually
gather information. Now think about me. Now, I had the flu, right? What else might I do? Well, I might jump
on my computer, because I’m a
computer kind of guy, and I like to learn
about what’s going on. And so I might jump
on my computer. And I’ll say, OK, let
me search in Google. Maybe I’ll search in Google
to find out what I might have. So I’ll type into
Google and say, hey Google, tell me what
I need to worry about. And I’ll go through, and I
might find that Google tells me that if you type
in sore throat– let’s say, I type in the
word sore throat here– it might give me
some search results. It’ll say, well,
maybe you should take this medicine
or that medicine. Or I might type in the
word cough or fever. These are all words that
I might type in the day that I get sick. And I might also go to the
hospital, or I might not. Maybe I’m not that sick. So I think, let me just
type in these words. And what Google gets is
they get all the searches that Rishi did that day,
as well as all the searches that other people in
my community are doing. So maybe there are
other people searching. Maybe Mr. Red is searching, and
maybe Mrs. Blue is searching. So maybe all these other
people are searching as well for the same kind of words. And what are these words? These are basically all
flu searches, right? Kind of searches
related to the flu. So I’m going to call
them flu searches. And there are many
other terms as well, but I’m not listing all of
them, just kind of some of them, so you get a sense
for what this means. So Google, what they could do
is they could actually tally up the total number of searches
related to the flu that are happening in a community,
let’s say in San Francisco, in one day. And that total I’m calling this
total right here, flu searches. That’s the total searches
for flu-related terms in a day out of San Francisco. Now, over here, we’ve
got other searches. So let’s say we’ve got
searches for weather in Nepal. Maybe I’m going on
a trip to Nepal, or somebody in my
community is going, and they search for that. Or maybe someone is searching
for basketball news. They want to know which team
won and which team lost. They want to know that. And maybe a third person is
searching for cell phones. So really, these are
all the other kind of searches that are
happening on Google. And there are probably
thousands and thousands of them. And you could tally
all these searches up, and this would be the
total searches in Google. And this is, again,
the searches happening in one day in one community. This could be all the searches
happening in San Francisco. And maybe there’s a person over
here, a little girl in yellow, and maybe this is a man
in purple who’s searching, and maybe this is
a person in green. And these aren’t necessarily
different people, right? It could be, maybe Mr. Red
searched for sore throat, and then later he was
interested in basketball news. So he actually was
in both groups. So really, we’re
not counting people, we’re counting total searches. That’s the key idea here. Total searches is what matters,
in a day, in some community. Now you could actually
take these numbers and make some sense out of them. This is where the folks at
Google and the folks at the CDC did something very clever. They said, OK, let’s put this
number here and this number here, and let’s divide by them. So let’s do flu searches
divided by total searches. And if you divide
the two, you’re going to get some
fraction, some percentage. And it’s going to be pretty
small, because flu searches is going to be a small fraction
of the total searches that are happening. And then you could
take that fraction– this is where it gets
really interesting– and you could say, OK,
let’s look at a whole year. This fraction we
got for a given day, but you could do this every
single day for a whole year. And you could say, January,
February, March, April. You could go through the entire
year, the entire calendar. This is June. And let’s say July. This is August, September,
October, November, December. And if you did
this 365 times, you know, each day you
did this, let’s say, or you could do
this weekly, however you want, you would get
some fraction, some percent. And maybe the percentages
would be small, but you could graph them
out if you wanted to. And you’d probably
notice something like this– you’d notice
that in the winter months, the numbers get bigger,
and in the summer months, the numbers get smaller. Because, in the summertime,
fewer people are probably searching for cough,
runny nose, things like that, because those things
usually happen in the winter. So you might get
something like this– this is percent, over here. So this is kind of a
trend that you might see. Now, as I said, the
people at the CDC and the people at Google were
very clever to think of this. And they actually compared data. They said, OK, let’s compare
data from Google to CDC data. Let’s see how they
actually look side-by-side. Here, in my graph,
I had done one year. This is one year,
from here to here. And you can actually see
that this is basically the same thing. This is from January
through December. So we’re seeing peaks
in the wintertime, and that’s understandable. But the great thing
about this graph is you can see that Google
flu trends actually line up really nice with the US data. And the US data, if
you look down here, comes from, as we said, the CDC. So this is actually
information from the CDC about influenza-like illness. And they’re seeing,
or we’re seeing, that there’s a
fantastic correlation, both in the timing, because the
peaks are happening basically at the same time, and
also in the magnitude, so some years are smaller
and some years are bigger. So it’s actually
pretty impressive that the data from searches
that are happening on Google actually lines up really well
with data, the traditional way that we get data,
through surveillance in our public health system. Let me show you
one more thing now. So if you look currently– so
the last graph was six years. I just want to quickly
point that out. This is six years of
data, 2004 to 2009. But, if you look
currently, we actually have 20– and, where’s the
date here– 2012 and 2013. And here we are between
December and January. In fact, today’s date is
January 10th, and it’s 2013. So here we are, and you can see
that the searches are really peaking out. This is looking
at national data, but you could also change it. You could say, well,
instead of the US, I’d like to look
at Mexico, or I’d like to look at Canada,
or some other country. Or you could say, instead
of looking nationally, I want to look at some
city or some state. So you could change it,
and this is actually something I encourage you to
play with if you’re interested. Go to google.org
and play around. Check out your own country,
your own community, and you could see how
many people in your area are searching for
flu-related words. And then, finally,
if you actually look at the
international level, you can actually see the map that’s
happening internationally, globally. And here some interesting
trends also appear. You can see all this activity
in the northern hemisphere, and a lot less in the southern
hemisphere, which makes sense, because it’s our winter
season, and flu is definitely a virus that affects us
more in the wintertime. And you can see that
some countries have really intense levels, like
the US, and high levels, like Canada, and some
of these countries have more moderate
levels, or low levels, like Europe and
Russia and Japan.

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