Name: 【TED】Nicholas Christakis: How social networks predict epidemics (Nicholas Christakis: How social networks predict epidemics)
Uploaded: 2016-09-19T02:03:11.000Z
Duration: 18 min 26 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

For the last 10 years, I've been spending my time trying to figure out

Reflexive pronouns

assemble themselves into social networks.

And the kind of social network I'm talking about

that human beings have been assembling for hundreds of thousands of years,

ever since we emerged from the African savannah.

and sibling and relative relationships with other people

who in turn have similar relationships with other people.

And this spreads on out endlessly into a distance.

And you get a network that looks like this.

Every line between them is a relationship between two people --

And you can get this kind of vast fabric of humanity,

And my colleague, James Fowler and I have been studying for quite sometime

that govern how these networks are assembled

that govern how they operate, how they affect our lives.

whether it might be possible to take advantage of this insight,

to actually find ways to improve the world,

to actually fix things, not just understand things.

So one of the first things we thought we would tackle

would be how we go about predicting epidemics.

And the current state of the art in predicting an epidemic --

if you're the CDC or some other national body --

from physicians and laboratories in the field

that report the prevalence or the incidence of certain conditions.

So, so and so patients have been diagnosed with something,

and all these data are fed into a central repository, with some delay.

you'll know where the epidemic was today.

of the idea of Google Flu Trends, with respect to the flu,

where by looking at people's searching behavior today,

what the status of the epidemic was today,

what's the prevalence of the epidemic today.

not just rapid warning about an epidemic,

but also to predict epidemics of all sorts of kinds.

For example, anything that spreads by a form of social contagion

like patriotism, or altruism, or religion

like dieting behavior, or book purchasing,

or drinking, or bicycle-helmet [and] other safety practices,

anything in which there's kind of an interpersonal spread.

by the mechanism I'm going to show you now.

So here on the Y-axis, we have the percent of the people affected,

And at the very beginning, not too many people are affected,

And the reason for this shape is that at the very beginning,

and then they affect, or infect, two people,

who in turn affect four, eight, 16 and so forth,

and you get the epidemic growth phase of the curve.

And eventually, you saturate the population.

who are still available that you might infect,

and then you get the plateau of the curve,

and you get this classic sigmoidal curve.

But things don't just diffuse in human populations at random.

Because, as I said, we live our lives in networks,

and these networks have a particular kind of a structure.

Now if you look at a network like this --

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【TED】Nicholas Christakis: How social networks predict epidemics (Nicholas Christakis: How social networks predict epidemics)

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