Chances are your friends are more popular than you are. It is a basic feature of social networks that has been known about for some time. Consider both an enthusias- tic cocktail party hostess with hundreds of acquaintances and an ill-tempered guy, who may have one or two friends. Statistically speaking, the average person is much more likely to know the hostess simply because she has so many more friends. This, in essence, is what is called the "friendship paradox": the friends of any random individual are likely to be more central to the social web than the individual himself.
Now researchers think this seemingly depressing fact can be made to work as an early warning system to detect outbreaks of contagious diseases. By studying the friends of a randomly selected group of individuals, epidemic disease experts can isolate those people who are more connected to one another and are therefore more likely to catch diseases like the flu early. This could allow health authorities to spot outbreaks weeks in advance of current surveillance methods.
In a report which has been submitted to the Proceedings of the National Academy of Sciences, Nicholas Christakis from Harvard University and James Fowler from the University of California, San Diego put the friendship paradox to good use. In a trial carried out last autumn, they monitored the spread of both seasonal flu and H1N1, popularly known as swine flu, through students and their friends at Harvard University, and found that their social links were indeed causing them to get infected sooner.
As this result came with the benefit of hindsight, the researchers tried to come up with a real-time measure that could potentially provide an early warning sign of an outbreak as it began to spread. Currently, the methods used to assess an infection by America’s Centers for Disease Control and Prevention lag an outbreak by a week or two. Google’s Flu Trends is at best contemporaneous with an outbreak. Dr. Christakis and Dr. Fowler suggest that a hybrid method might be developed in which the search inquiries of a group of highly connected (ie, pop- ular) individuals could be scanned for signs of the flu.
Although the technique has so far only been demonstrated for the flu and in the social surroundings of a university, the researchers nevertheless think that it could help predict other infectious diseases and do so on a larger scale. Nor should it be difficult to implement. Public-health officials already conduct random sampling, so getting the participants to name a few friends too should not be onerous. When it comes to infectious diseases, your friends really do say a lot about you.
According to the "friendship paradox",

A:one’s friends are usually more popular than him. B:ill-tempered persons often have very few friends. C:people tend to befriend those more sociable than them. D:the hostess wins her acquaintances through parties.

Chances are your friends are more popular than you are. It is a basic feature of social networks that has been known about for some time. Consider both an enthusias- tic cocktail party hostess with hundreds of acquaintances and an ill-tempered guy, who may have one or two friends. Statistically speaking, the average person is much more likely to know the hostess simply because she has so many more friends. This, in essence, is what is called the "friendship paradox": the friends of any random individual are likely to be more central to the social web than the individual himself.
Now researchers think this seemingly depressing fact can be made to work as an early warning system to detect outbreaks of contagious diseases. By studying the friends of a randomly selected group of individuals, epidemic disease experts can isolate those people who are more connected to one another and are therefore more likely to catch diseases like the flu early. This could allow health authorities to spot outbreaks weeks in advance of current surveillance methods.
In a report which has been submitted to the Proceedings of the National Academy of Sciences, Nicholas Christakis from Harvard University and James Fowler from the University of California, San Diego put the friendship paradox to good use. In a trial carried out last autumn, they monitored the spread of both seasonal flu and H1N1, popularly known as swine flu, through students and their friends at Harvard University, and found that their social links were indeed causing them to get infected sooner.
As this result came with the benefit of hindsight, the researchers tried to come up with a real-time measure that could potentially provide an early warning sign of an outbreak as it began to spread. Currently, the methods used to assess an infection by America’s Centers for Disease Control and Prevention lag an outbreak by a week or two. Google’s Flu Trends is at best contemporaneous with an outbreak. Dr. Christakis and Dr. Fowler suggest that a hybrid method might be developed in which the search inquiries of a group of highly connected (ie, pop- ular) individuals could be scanned for signs of the flu.
Although the technique has so far only been demonstrated for the flu and in the social surroundings of a university, the researchers nevertheless think that it could help predict other infectious diseases and do so on a larger scale. Nor should it be difficult to implement. Public-health officials already conduct random sampling, so getting the participants to name a few friends too should not be onerous. When it comes to infectious diseases, your friends really do say a lot about you.
By using the "friendship paradox", people may

A:prevent outbreaks of contagious diseases. B:isolate people from each other to avoid flu. C:abandon the current surveillance methods. D:predict outbreaks of flu earlier than present.

Chances are your friends are more popular than you are. It is a basic feature of social networks that has been known about for some time. Consider both an enthusias- tic cocktail party hostess with hundreds of acquaintances and an ill-tempered guy, who may have one or two friends. Statistically speaking, the average person is much more likely to know the hostess simply because she has so many more friends. This, in essence, is what is called the "friendship paradox": the friends of any random individual are likely to be more central to the social web than the individual himself.
Now researchers think this seemingly depressing fact can be made to work as an early warning system to detect outbreaks of contagious diseases. By studying the friends of a randomly selected group of individuals, epidemic disease experts can isolate those people who are more connected to one another and are therefore more likely to catch diseases like the flu early. This could allow health authorities to spot outbreaks weeks in advance of current surveillance methods.
In a report which has been submitted to the Proceedings of the National Academy of Sciences, Nicholas Christakis from Harvard University and James Fowler from the University of California, San Diego put the friendship paradox to good use. In a trial carried out last autumn, they monitored the spread of both seasonal flu and H1N1, popularly known as swine flu, through students and their friends at Harvard University, and found that their social links were indeed causing them to get infected sooner.
As this result came with the benefit of hindsight, the researchers tried to come up with a real-time measure that could potentially provide an early warning sign of an outbreak as it began to spread. Currently, the methods used to assess an infection by America’s Centers for Disease Control and Prevention lag an outbreak by a week or two. Google’s Flu Trends is at best contemporaneous with an outbreak. Dr. Christakis and Dr. Fowler suggest that a hybrid method might be developed in which the search inquiries of a group of highly connected (ie, pop- ular) individuals could be scanned for signs of the flu.
Although the technique has so far only been demonstrated for the flu and in the social surroundings of a university, the researchers nevertheless think that it could help predict other infectious diseases and do so on a larger scale. Nor should it be difficult to implement. Public-health officials already conduct random sampling, so getting the participants to name a few friends too should not be onerous. When it comes to infectious diseases, your friends really do say a lot about you.
Which of the following is true of Nicholas Christakis and James Fowler’s research

A:It is a real-time measure. B:Its subjects are university students. C:It spots the spread of flu in advance. D:It discovers social links cause flu infection.

Chances are your friends are more popular than you are. It is a basic feature of social networks that has been known about for some time. Consider both an enthusias- tic cocktail party hostess with hundreds of acquaintances and an ill-tempered guy, who may have one or two friends. Statistically speaking, the average person is much more likely to know the hostess simply because she has so many more friends. This, in essence, is what is called the "friendship paradox": the friends of any random individual are likely to be more central to the social web than the individual himself.
Now researchers think this seemingly depressing fact can be made to work as an early warning system to detect outbreaks of contagious diseases. By studying the friends of a randomly selected group of individuals, epidemic disease experts can isolate those people who are more connected to one another and are therefore more likely to catch diseases like the flu early. This could allow health authorities to spot outbreaks weeks in advance of current surveillance methods.
In a report which has been submitted to the Proceedings of the National Academy of Sciences, Nicholas Christakis from Harvard University and James Fowler from the University of California, San Diego put the friendship paradox to good use. In a trial carried out last autumn, they monitored the spread of both seasonal flu and H1N1, popularly known as swine flu, through students and their friends at Harvard University, and found that their social links were indeed causing them to get infected sooner.
As this result came with the benefit of hindsight, the researchers tried to come up with a real-time measure that could potentially provide an early warning sign of an outbreak as it began to spread. Currently, the methods used to assess an infection by America’s Centers for Disease Control and Prevention lag an outbreak by a week or two. Google’s Flu Trends is at best contemporaneous with an outbreak. Dr. Christakis and Dr. Fowler suggest that a hybrid method might be developed in which the search inquiries of a group of highly connected (ie, pop- ular) individuals could be scanned for signs of the flu.
Although the technique has so far only been demonstrated for the flu and in the social surroundings of a university, the researchers nevertheless think that it could help predict other infectious diseases and do so on a larger scale. Nor should it be difficult to implement. Public-health officials already conduct random sampling, so getting the participants to name a few friends too should not be onerous. When it comes to infectious diseases, your friends really do say a lot about you.
From Paragraph 4, we can learn that Google’s Flu Trends

A:lags an outbreak. B:precedes an outbreak. C:accompanies an outbreak. D:predicts an outbreak.

Chances are your friends are more popular than you are. It is a basic feature of social networks that has been known about for some time. Consider both an enthusias- tic cocktail party hostess with hundreds of acquaintances and an ill-tempered guy, who may have one or two friends. Statistically speaking, the average person is much more likely to know the hostess simply because she has so many more friends. This, in essence, is what is called the "friendship paradox": the friends of any random individual are likely to be more central to the social web than the individual himself.
Now researchers think this seemingly depressing fact can be made to work as an early warning system to detect outbreaks of contagious diseases. By studying the friends of a randomly selected group of individuals, epidemic disease experts can isolate those people who are more connected to one another and are therefore more likely to catch diseases like the flu early. This could allow health authorities to spot outbreaks weeks in advance of current surveillance methods.
In a report which has been submitted to the Proceedings of the National Academy of Sciences, Nicholas Christakis from Harvard University and James Fowler from the University of California, San Diego put the friendship paradox to good use. In a trial carried out last autumn, they monitored the spread of both seasonal flu and H1N1, popularly known as swine flu, through students and their friends at Harvard University, and found that their social links were indeed causing them to get infected sooner.
As this result came with the benefit of hindsight, the researchers tried to come up with a real-time measure that could potentially provide an early warning sign of an outbreak as it began to spread. Currently, the methods used to assess an infection by America’s Centers for Disease Control and Prevention lag an outbreak by a week or two. Google’s Flu Trends is at best contemporaneous with an outbreak. Dr. Christakis and Dr. Fowler suggest that a hybrid method might be developed in which the search inquiries of a group of highly connected (ie, pop- ular) individuals could be scanned for signs of the flu.
Although the technique has so far only been demonstrated for the flu and in the social surroundings of a university, the researchers nevertheless think that it could help predict other infectious diseases and do so on a larger scale. Nor should it be difficult to implement. Public-health officials already conduct random sampling, so getting the participants to name a few friends too should not be onerous. When it comes to infectious diseases, your friends really do say a lot about you.
It can be inferred from the last paragraph that this new research

A:is limited in scale. B:is not easy to implement. C:has limited applications. D:conducts random samplin