full transcript

From the Ted Talk by Nicholas Christakis: How social networks predict epidemics

Unscramble the Blue Letters


I should say that how far advanced a notice one might get about something depends on a host of factors. It could depend on the nature of the pathogen — different ptahonges, using this technique, you'd get different warning — or other pmhoneena that are sdnriepag, or frankly, on the structure of the human network. Now in our case, although it wasn't necessary, we could also actually map the network of the students.

So, this is a map of 714 students and their friendship ties. And in a minute now, I'm going to put this map into motion. We're going to take daily cuts through the network for 120 days. The red dots are going to be cases of the flu, and the ylleow dots are going to be friends of the people with the flu. And the size of the dots is going to be proportional to how many of their fnrieds have the flu. So bigger dots mean more of your friends have the flu. And if you look at this image — here we are now in September the 13th — you're going to see a few cases light up. You're going to see kind of boiolmng of the flu in the middle. Here we are on October the 19th. The sople of the epidemic curve is approaching now, in November. Bang, bang, bang, bang, bang — you're going to see lots of blooming in the middle, and then you're going to see a sort of leveling off, fewer and fewer cases towards the end of dceember. And this type of a vzaluaiitsion can show that epidemics like this take root and affect caetnrl iidavulinds first, before they acefft others.

Open Cloze


I should say that how far advanced a notice one might get about something depends on a host of factors. It could depend on the nature of the pathogen — different _________, using this technique, you'd get different warning — or other _________ that are _________, or frankly, on the structure of the human network. Now in our case, although it wasn't necessary, we could also actually map the network of the students.

So, this is a map of 714 students and their friendship ties. And in a minute now, I'm going to put this map into motion. We're going to take daily cuts through the network for 120 days. The red dots are going to be cases of the flu, and the ______ dots are going to be friends of the people with the flu. And the size of the dots is going to be proportional to how many of their _______ have the flu. So bigger dots mean more of your friends have the flu. And if you look at this image — here we are now in September the 13th — you're going to see a few cases light up. You're going to see kind of ________ of the flu in the middle. Here we are on October the 19th. The _____ of the epidemic curve is approaching now, in November. Bang, bang, bang, bang, bang — you're going to see lots of blooming in the middle, and then you're going to see a sort of leveling off, fewer and fewer cases towards the end of ________. And this type of a _____________ can show that epidemics like this take root and affect _______ ___________ first, before they ______ others.

Solution


  1. central
  2. spreading
  3. friends
  4. individuals
  5. slope
  6. phenomena
  7. visualization
  8. blooming
  9. affect
  10. december
  11. pathogens
  12. yellow

Original Text


I should say that how far advanced a notice one might get about something depends on a host of factors. It could depend on the nature of the pathogen — different pathogens, using this technique, you'd get different warning — or other phenomena that are spreading, or frankly, on the structure of the human network. Now in our case, although it wasn't necessary, we could also actually map the network of the students.

So, this is a map of 714 students and their friendship ties. And in a minute now, I'm going to put this map into motion. We're going to take daily cuts through the network for 120 days. The red dots are going to be cases of the flu, and the yellow dots are going to be friends of the people with the flu. And the size of the dots is going to be proportional to how many of their friends have the flu. So bigger dots mean more of your friends have the flu. And if you look at this image — here we are now in September the 13th — you're going to see a few cases light up. You're going to see kind of blooming of the flu in the middle. Here we are on October the 19th. The slope of the epidemic curve is approaching now, in November. Bang, bang, bang, bang, bang — you're going to see lots of blooming in the middle, and then you're going to see a sort of leveling off, fewer and fewer cases towards the end of December. And this type of a visualization can show that epidemics like this take root and affect central individuals first, before they affect others.

Frequently Occurring Word Combinations


ngrams of length 2

collocation frequency
social networks 4
central individuals 4
impending epidemic 4
party host 4
early detection 3
random sample 3
human beings 2
social network 2
predict epidemics 2
human populations 2
lines represent 2
randomly chosen 2
early warning 2
human population 2
friendship paradox 2
flu epidemic 2
interpersonal influence 2
herd immunity 2
understand social 2
social processes 2
social phenomena 2

ngrams of length 3

collocation frequency
understand social processes 2

Important Words


  1. advanced
  2. affect
  3. approaching
  4. bang
  5. bigger
  6. blooming
  7. case
  8. cases
  9. central
  10. curve
  11. cuts
  12. daily
  13. days
  14. december
  15. depend
  16. depends
  17. dots
  18. epidemic
  19. epidemics
  20. factors
  21. flu
  22. frankly
  23. friends
  24. friendship
  25. host
  26. human
  27. image
  28. individuals
  29. kind
  30. leveling
  31. light
  32. lots
  33. map
  34. middle
  35. minute
  36. motion
  37. nature
  38. network
  39. notice
  40. november
  41. october
  42. pathogen
  43. pathogens
  44. people
  45. phenomena
  46. proportional
  47. put
  48. red
  49. root
  50. september
  51. show
  52. size
  53. slope
  54. sort
  55. spreading
  56. structure
  57. students
  58. technique
  59. ties
  60. type
  61. visualization
  62. warning
  63. yellow