Hans Rosling on HIV: New facts and stunning data visuals

251,855 views ใƒป 2009-05-13

TED


ืื ื ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ืœืžื˜ื” ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ.

ืžืชืจื’ื: nadav shalit ืžื‘ืงืจ: Gal Tabakman
00:12
(Applause)
0
12160
5000
(ืžื—ื™ืื•ืช ื›ืคื™ื™ื)
00:18
AIDS was discovered 1981; the virus, 1983.
1
18160
5000
ืžื—ืœืช ื”ืื™ื™ื“ืก ื”ืชื’ืœืชื” ื‘-1981, ื”ื ื’ื™ืฃ - ื‘-1983
00:23
These Gapminder bubbles show you
2
23160
2000
ื‘ื•ืขื•ืช ื’ืืคืžื™ื™ื ื“ืจ ืืœื• ืžืžื—ื™ืฉื•ืช
00:25
how the spread of the virus was in 1983 in the world,
3
25160
4000
ืืช ื”ืชืคืฉื˜ื•ืช ื”ื•ื™ืจื•ืก ื‘ืขื•ืœื ื‘-1983
00:29
or how we estimate that it was.
4
29160
2000
ืื• ืื™ืš ืื ื• ืžืขืจื™ื›ื™ื ืฉื”ื™ื ื”ื™ื™ืชื”
00:31
What we are showing here is --
5
31160
2000
ืžื” ืฉืื ื• ืžืฆื™ื’ื™ื ื›ืืŸ ื”ื•ื -
00:33
on this axis here, I'm showing percent of infected adults.
6
33160
7000
ืขืœ ื”ืฆื™ืจ ื”ื–ื” ื›ืืŸ, ืื ื™ ืžืจืื” ืืช ืื—ื•ื– ื”ืžื‘ื•ื’ืจื™ื ื”ืžื•ื“ื‘ืงื™ื.
00:40
And on this axis, I'm showing dollars per person in income.
7
40160
5000
ื•ืขืœ ื”ืฆื™ืจ ื”ื–ื”, ืื ื™ ืžืจืื” ื”ื›ื ืกื” ื‘ื“ื•ืœืจ ืœืื“ื.
00:45
And the size of these bubbles, the size of the bubbles here,
8
45160
4000
ื•ื’ื•ื“ืœ ื”ื‘ื•ืขื•ืช ื”ืืœื”, ื’ื•ื“ืœ ื”ื‘ื•ืขื•ืช ื›ืืŸ,
00:49
that shows how many are infected in each country,
9
49160
3000
ื–ื” ืžืจืื” ื›ืžื” ืื ืฉื™ื ื ื•ืฉืื™ื ืืช ื”ื ื’ื™ืฃ ื‘ื›ืœ ืžื“ื™ื ื”,
00:52
and the color is the continent.
10
52160
2000
ื•ื”ืฆื‘ืข ืžื™ื™ืฆื’ ืืช ื”ื™ื‘ืฉืช.
00:54
Now, you can see United States, in 1983,
11
54160
2000
ืขื›ืฉื™ื•, ื›ืืŸ ืืชื ื™ื›ื•ืœื™ื ืœืจืื•ืช ืฉื‘ืืจื”"ื‘, ื‘-1983,
00:56
had a very low percentage infected,
12
56160
3000
ื”ื™ื” ืื—ื•ื– ื ืžื•ืš ืžืื•ื“ ืฉืœ ื ืฉืื™ื,
00:59
but due to the big population, still a sizable bubble.
13
59160
4000
ืืš ื‘ืฉืœ ื”ืื•ื›ืœื•ืกื™ื” ื”ื’ื“ื•ืœื”, ืขื“ื™ื™ืŸ ื”ื‘ื•ืขื” ื’ื“ื•ืœื” ืœืžื“ื™
01:03
There were quite many people infected in the United States.
14
63160
3000
ื”ื™ื• ื“ื™ ื”ืจื‘ื” ื ืฉืื™ื ื‘ืืจื”"ื‘.
01:06
And, up there, you see Uganda.
15
66160
2000
ื•ื›ืืŸ ืœืžืขืœื”, ืืชื ืจื•ืื™ื ืืช ืื•ื’ื ื“ื”.
01:08
They had almost five percent infected,
16
68160
3000
ืฉื ื”ื™ื• ื›ืžืขื˜ ื—ืžื™ืฉื” ืื—ื•ื–ื™ื ื ืฉืื™ื,
01:11
and quite a big bubble in spite of being a small country, then.
17
71160
3000
ื•ื‘ื•ืขื” ื’ื“ื•ืœื” ืœืžื“ื™ ืœืžืจื•ืช ื”ื™ื•ืชื” ืื– ืžื“ื™ื ื” ืงื˜ื ื”.
01:14
And they were probably the most infected country in the world.
18
74160
5000
ื•ื–ื• ื”ื™ื™ืชื” ื”ืžื“ื™ื ื” ืขื ื”ื ืฉืื•ืช ื”ื’ื‘ื•ื”ื” ื‘ื™ื•ืชืจ ื‘ืขื•ืœื.
01:19
Now, what has happened?
19
79160
2000
ืขื›ืฉื™ื•, ืžื” ืงืจื”?
01:21
Now you have understood the graph
20
81160
2000
ืขื›ืฉื™ื• ื”ื‘ื ืชื ืืช ื”ืชืจืฉื™ื,
01:23
and now, in the next 60 seconds,
21
83160
3000
ื•ื›ืขืช, ื‘-60 ื”ืฉื ื™ื•ืช ื”ืงืจื•ื‘ื•ืช,
01:26
we will play the HIV epidemic in the world.
22
86160
3000
ื ืจื™ืฅ ืืช ืžื’ืคืช ื”-HIV ื‘ืขื•ืœื.
01:29
But first, I have a new invention here.
23
89160
3000
ืื‘ืœ ืงื•ื“ื ืœื›ืŸ, ื™ืฉ ืœื™ ื›ืืŸ ื”ืžืฆืื” ื—ื“ืฉื”.
01:34
(Laughter)
24
94160
3000
(ืฆื—ื•ืง)
01:39
I have solidified the beam of the laser pointer.
25
99160
4000
ืžื™ืฆืงืชื™ ืืช ืงืจืŸ ื”ืœื™ื™ื–ืจ
01:43
(Laughter)
26
103160
3000
(ืฆื—ื•ืง)
01:46
(Applause)
27
106160
3000
(ืžื—ื™ืื•ืช ื›ืคื™ื™ื)
01:52
So, ready, steady, go!
28
112160
4000
ืื–, ืœืžืงื•ืžื•ืช, ื”ื›ืŸ, ืฆื!
01:56
First, we have the fast rise in Uganda and Zimbabwe.
29
116160
4000
ืจืืฉื™ืช, ื™ืฉ ืœื ื• ืขืœื™ื” ืžื”ื™ืจื” ื‘ืื•ื’ื ื“ื” ื•ื‘ื–ื™ืžื‘ืื‘ื•ื•ื”.
02:00
They went upwards like this.
30
120160
2000
ื”ื ืขืœื• ื›ืš.
02:02
In Asia, the first country to be heavily infected was Thailand --
31
122160
4000
ื‘ืืกื™ื”, ื”ืžื“ื™ื ื” ื”ืจืืฉื•ื ื” ืขื ืื—ื•ื–ื™ ื ืฉืื•ืช ื’ื‘ื•ื”ื™ื ื”ื™ื™ืชื” ืชืื™ืœื ื“.
02:06
they reached one to two percent.
32
126160
2000
ื”ื ื”ื’ื™ืขื• ืœืื—ื•ื– ืื—ื“ ืขื“ ืฉื ื™ื™ื.
02:08
Then, Uganda started to turn back,
33
128160
2000
ืื–, ืื•ื’ื ื“ื” ื”ื—ืœื” ืœื—ื–ื•ืจ ื—ื–ืจื”,
02:10
whereas Zimbabwe skyrocketed,
34
130160
2000
ื‘ืขื•ื“ ื–ื™ืžื‘ืื‘ื•ื•ื” ื ืกืงื” ืžืขืœื”,
02:12
and some years later South Africa had a terrible rise of HIV frequency.
35
132160
4000
ื•ื›ืžื” ืฉื ื™ื ืžืื•ื—ืจ ื™ื•ืชืจ ื‘ื“ืจื•ื ืืคืจื™ืงื” ื”ื™ื™ืชื” ืขืœื™ื” ื ื•ืจืื™ืช ื‘ืชื“ื™ืจื•ืช HIV.
02:16
Look, India got many infected,
36
136160
2000
ืชืจืื•, ื‘ื”ื•ื“ื• ื™ืฉ ื ืฉืื™ื ืจื‘ื™ื,
02:18
but had a low level.
37
138160
2000
ืืš ืจืžื” ื ืžื•ื›ื”.
02:20
And almost the same happens here.
38
140160
2000
ื•ื›ืžืขื˜ ืื•ืชื• ื”ื“ื‘ืจ ืžืชืจื—ืฉ ื›ืืŸ.
02:22
See, Uganda coming down, Zimbabwe coming down,
39
142160
3000
ืจืื•, ืื•ื’ื ื“ื” ื™ื•ืจื“ืช, ื–ื™ืžื‘ืื‘ื•ื•ื” ื™ื•ืจื“ืช,
02:25
Russia went to one percent.
40
145160
2000
ืจื•ืกื™ื” ื”ื’ื™ืขื” ืœืื—ื•ื– ืื—ื“.
02:27
In the last two to three years,
41
147160
3000
ื‘ืฉื ืชื™ื™ื-ืฉืœื•ืฉ ื”ืื—ืจื•ื ื•ืช,
02:30
we have reached a steady state of HIV epidemic in the world.
42
150160
4000
ื”ื’ืขื ื• ืœื™ืฆื™ื‘ื•ืช ื‘ืžื’ืคืช ื”-HIV ื‘ืขื•ืœื.
02:34
25 years it took.
43
154160
3000
ืœืงื— ืœื–ื” 25 ืฉื ื”.
02:37
But, steady state doesn't mean that things are getting better,
44
157160
3000
ืื‘ืœ ื™ืฆื™ื‘ื•ืช ืื™ืŸ ืžืฉืžืขื•ืชื” ืฉื”ืžืฆื‘ ืžืฉืชืคืจ,
02:40
it's just that they have stopped getting worse.
45
160160
3000
ืืœื ืจืง ืฉื”ื•ื ืœื ื ืขืฉื” ื™ื•ืชืจ ื’ืจื•ืข.
02:43
And it has -- the steady state is, more or less,
46
163160
4000
ื”ืžืฆื‘ ื”ื™ืฆื™ื‘ ื”ื•ื ืฉื‘ืขืจืš,
02:47
one percent of the adult world population is HIV-infected.
47
167160
4000
ืื—ื•ื– ืื—ื“ ืžืื•ื›ืœื•ืกื™ื™ืช ื”ืขื•ืœื ื”ื‘ื•ื’ืจืช ื ื•ืฉื HIV.
02:51
It means 30 to 40 million people,
48
171160
3000
ื–ื” ืื•ืžืจ 30 ืขื“ 40 ืžื™ืœื™ื•ืŸ ืื ืฉื™ื,
02:54
the whole of California -- every person,
49
174160
2000
ื›ืœ ืชื•ืฉื‘ื™ ืงืœื™ืคื•ืจื ื™ื”,
02:56
that's more or less what we have today in the world.
50
176160
2000
ื–ื” ื‘ืขืจืš ืžื” ืฉื™ืฉ ืœื ื• ื‘ืขื•ืœื ื›ื™ื•ื.
02:58
Now, let me make a fast replay of Botswana.
51
178160
5000
ืขื›ืฉื™ื•, ื”ืจืฉื• ืœื™ ืœื”ืจื™ืฅ ื”ื™ืœื•ืš ืžื”ื™ืจ ืฉืœ ื‘ื•ื˜ืกื•ืื ื”.
03:03
Botswana -- upper middle-income country in southern Africa,
52
183160
4000
ื‘ื•ื˜ืกื•ื•ืื ื” - ืžื“ื™ื ื” ืขื ื”ื›ื ืกื” ื‘ื™ื ื•ื ื™ืช-ื’ื‘ื•ื”ื” ื‘ื“ืจื•ื ืืคืจื™ืงื”,
03:07
democratic government, good economy,
53
187160
3000
ืžืžืฉืœ ื“ืžื•ืงืจื˜ื™, ื›ืœื›ืœื” ื˜ื•ื‘ื”,
03:10
and this is what happened there.
54
190160
2000
ื•ื–ื” ืžื” ืฉืงืจื” ืฉื.
03:12
They started low, they skyrocketed,
55
192160
2000
ื”ื ื”ืชื—ื™ืœื• ื ืžื•ืš, ื”ื ื ืกืงื• ืžืขืœื”,
03:14
they peaked up there in 2003,
56
194160
3000
ื”ื ื”ื’ื™ืขื• ืœืฉื™ื ืฉื ื‘-2003,
03:17
and now they are down.
57
197160
2000
ื•ื›ืขืช ื”ื ื‘ื™ืจื™ื“ื”.
03:19
But they are falling only slowly,
58
199160
2000
ืืš ื”ื ื™ื•ืจื“ื™ื ื‘ืื™ื˜ื™ื•ืช,
03:21
because in Botswana, with good economy and governance,
59
201160
2000
ื›ื™ื•ื•ืŸ ืฉื‘ื‘ื•ื˜ืกื•ื•ืื ื”, ืขื ื›ืœื›ืœื” ื•ืžืžืฉืœ ื˜ื•ื‘ื™ื,
03:23
they can manage to treat people.
60
203160
3000
ื”ื ืžืฆืœื™ื—ื™ื ืœื˜ืคืœ ื‘ืื•ื›ืœื•ืกื™ื”.
03:26
And if people who are infected are treated, they don't die of AIDS.
61
206160
3000
ื•ืื ื ืฉืื™ื ืžืงื‘ืœื™ื ื˜ื™ืคื•ืœ, ื”ื ืœื ืžืชื™ื ืžืื™ื™ื“ืก.
03:29
These percentages won't come down
62
209160
3000
ื”ืื—ื•ื–ื™ื ื”ืืœื” ืœื ื™ืจื“ื•
03:32
because people can survive 10 to 20 years.
63
212160
2000
ื›ื™ื•ื•ืŸ ืฉืื ืฉื™ื ื™ื›ื•ืœื™ื ืœืฉืจื•ื“ 10 ืขื“ 20 ืฉื ื”.
03:34
So there's some problem with these metrics now.
64
214160
3000
ืื– ืขื›ืฉื™ื• ื™ืฉ ื‘ืขื™ื” ืžืกื•ื™ืžืช ืขื ื”ืžื“ื“ื™ื ื”ืืœื”.
03:37
But the poorer countries in Africa, the low-income countries down here,
65
217160
4000
ืื‘ืœ ื”ืžื“ื™ื ื•ืช ื”ืขื ื™ื•ืช ื™ื•ืชืจ ื‘ืืคืจื™ืงื”, ื”ืžื“ื™ื ื•ืช ืขื ื”ื”ื›ื ืกื” ื”ื ืžื•ื›ื” ื›ืืŸ ืœืžื˜ื”,
03:41
there the rates fall faster, of the percentage infected,
66
221160
6000
ืฉื ืื—ื•ื–ื™ ื”ื ืฉืื•ืช ื™ื•ืจื“ื™ื ืžื”ืจ ื™ื•ืชืจ
03:47
because people still die.
67
227160
2000
ื›ื™ื•ื•ืŸ ืฉืื ืฉื™ื ืขื“ื™ื™ืŸ ืžืชื™ื
03:49
In spite of PEPFAR, the generous PEPFAR,
68
229160
3000
ืœืžืจื•ืช ื”ืชื›ื ื™ืช ื”ื ืฉื™ืื•ืชื™ืช ื”ื ื“ื™ื‘ื” ืœื˜ื™ืคื•ืœ ื‘ืื™ื™ื“ืก
03:52
all people are not reached by treatment,
69
232160
3000
ืœื ื›ืœ ื”ืื ืฉื™ื ืžืงื‘ืœื™ื ื˜ื™ืคื•ืœ
03:55
and of those who are reached by treatment in the poor countries,
70
235160
2000
ื•ืžืชื•ืš ืืœื” ื”ืžืงื‘ืœื™ื ื˜ื™ืคื•ืœ ื‘ืืจืฆื•ืช ื”ืขื ื™ื•ืช,
03:57
only 60 percent are left on treatment after two years.
71
237160
3000
ืจืง 60 ืื—ื•ื–ื™ื ื ืฉืืจื™ื ืชื—ืช ื˜ื™ืคื•ืœ ืœืื—ืจ ืฉื ืชื™ื™ื.
04:00
It's not realistic with lifelong treatment
72
240160
4000
ื˜ื™ืคื•ืœ ืœื›ืœ ื”ื—ื™ื™ื ืื™ื ื• ืžืฆื™ืื•ืชื™
04:04
for everyone in the poorest countries.
73
244160
2000
ืœื›ืœ ืื“ื ื‘ืžื“ื™ื ื•ืช ื”ืขื ื™ื•ืช ื‘ื™ื•ืชืจ
04:06
But it's very good that what is done is being done.
74
246160
3000
ืื‘ืœ ื–ื” ื˜ื•ื‘ ืžืื•ื“ ืฉืžื” ืฉื ืขืฉื” ืื›ืŸ ื ืขืฉื”.
04:09
But focus now is back on prevention.
75
249160
4000
ืืš ื”ืžื™ืงื•ื“ ื—ื•ื–ืจ ืขื›ืฉื™ื• ืœืžื ื™ืขื”.
04:13
It is only by stopping the transmission
76
253160
3000
ืจืง ืข"ื™ ืžื ื™ืขืช ื”ื“ื‘ืงื”
04:16
that the world will be able to deal with it.
77
256160
3000
ื”ืขื•ืœื ื™ื•ื›ืœ ืœื”ืชืžื•ื“ื“ ืขื ื–ื”.
04:19
Drugs is too costly -- had we had the vaccine,
78
259160
2000
ืชืจื•ืคื•ืช ื”ืŸ ื™ืงืจื•ืช ืžื“ื™ - ืื ื”ื™ื” ืœื ื• ื—ื™ืกื•ืŸ,
04:21
or when we will get the vaccine, that's something more effective --
79
261160
3000
ืื• ื›ืืฉืจ ื™ื”ื™ื” ื—ื™ืกื•ืŸ, ื–ื” ืžืฉื”ื• ื™ื•ืชืจ ื™ืขื™ืœ -
04:24
but the drugs are very costly for the poor.
80
264160
2000
ืืš ื”ืชืจื•ืคื•ืช ื”ืŸ ื™ืงืจื•ืช ืžืื•ื“ ื‘ืฉื‘ื™ืœ ื”ืขื ื™ื™ื.
04:26
Not the drug in itself, but the treatment
81
266160
2000
ืœื ื”ืชืจื•ืคื” ืขืฆืžื”, ืืš ื”ื˜ื™ืคื•ืœ
04:28
and the care which is needed around it.
82
268160
2000
ื•ื”ื”ืฉื’ื—ื” ื”ื ื“ืจืฉืช ืกื‘ื™ื‘ื•.
04:32
So, when we look at the pattern,
83
272160
3000
ืื– ื›ืฉืื ื• ืžื‘ื™ื˜ื™ื ื‘ื“ืคื•ืก,
04:35
one thing comes out very clearly:
84
275160
2000
ื“ื‘ืจ ืื—ื“ ืžืชื‘ืœื˜:ืืชื
04:37
you see the blue bubbles
85
277160
2000
ืจื•ืื™ื ืืช ื”ื‘ื•ืขื•ืช ื”ื›ื—ื•ืœื•ืช
04:39
and people say HIV is very high in Africa.
86
279160
2000
ื•ืื ืฉื™ื ืื•ืžืจื™ื ืฉืจืžืช ื”-HIV ื‘ืืคืจื™ืงื” ื’ื‘ื•ื”ื” ืžืื•ื“.
04:41
I would say, HIV is very different in Africa.
87
281160
3000
ืื ื™ ืื•ืžืจ, HIV ื”ื•ื ืžืื•ื“ ืฉื•ื ื” ื‘ืืคืจื™ืงื”.
04:44
You'll find the highest HIV rate in the world
88
284160
4000
ืชืžืฆืื• ืืช ืจืžืช ื”-HIV ื”ื’ื‘ื•ื”ื” ื‘ื™ื•ืชืจ ื‘ืขื•ืœื
04:48
in African countries,
89
288160
2000
ื‘ืžื“ื™ื ื•ืช ืืคืจื™ืงืื™ื•ืช,
04:50
and yet you'll find Senegal, down here --
90
290160
2000
ืืš ืžื ื’ื“ ืชืžืฆืื• ื‘ืกื ื’ืœ, ื›ืืŸ ืœืžื˜ื”,
04:52
the same rate as United States.
91
292160
2000
ืืช ืื•ืชื” ืจืžื” ื›ืžื• ื‘ืืจื”"ื‘.
04:54
And you'll find Madagascar,
92
294160
2000
ื•ืชืžืฆืื• ืืช ืžื“ื’ืกืงืจ,
04:56
and you'll find a lot of African countries
93
296160
2000
ื•ืชืžืฆืื• ืžื“ื™ื ื•ืช ืืคืจื™ืงืื™ื•ืช ืจื‘ื•ืช
04:58
about as low as the rest of the world.
94
298160
3000
ื‘ืจืžื” ื ืžื•ื›ื” ื›ืžื• ื‘ืฉืืจ ื”ืขื•ืœื.
05:01
It's this terrible simplification that there's one Africa
95
301160
4000
ื–ื• ื”ื”ืคืฉื˜ื” ื”ื ื•ืจืื™ืช ืฉื™ืฉื ื” ืืคืจื™ืงื” ืื—ืช
05:05
and things go on in one way in Africa.
96
305160
2000
ื•ืฉื“ื‘ืจื™ื ืžืชืจื—ืฉื™ื ื‘ืื•ืคืŸ ืื—ื“ ื‘ืืคืจื™ืงื”.
05:07
We have to stop that.
97
307160
2000
ืขืœื™ื ื• ืœืžื ื•ืข ื–ืืช.
05:09
It's not respectful, and it's not very clever
98
309160
3000
ื–ื” ืœื ืžื›ื•ื‘ื“, ื•ื–ื” ืœื ืžืžืฉ ื ื‘ื•ืŸ
05:12
to think that way.
99
312160
2000
ืœื—ืฉื•ื‘ ื›ืš.
05:14
(Applause)
100
314160
4000
(ืžื—ื™ืื•ืช ื›ืคื™ื™ื)
05:18
I had the fortune to live and work for a time in the United States.
101
318160
3000
ื”ื™ื” ืœื™ ื”ืžื–ืœ ืœื—ื™ื•ืช ื•ืœืขื‘ื•ื“ ืœืžืฉืš ืชืงื•ืคื” ื‘ืืจื”"ื‘.
05:21
I found out that Salt Lake City and San Francisco were different.
102
321160
4000
ืžืฆืืชื™ ืฉืกื•ืœื˜ ืœื™ื™ืง ืกื™ื˜ื™ ื•ืกืŸ ืคืจื ืกื™ืกืงื• ื”ืŸ ืฉื•ื ื•ืช.
05:25
(Laughter)
103
325160
2000
(ืฆื—ื•ืง)
05:27
And so it is in Africa -- it's a lot of difference.
104
327160
3000
ื•ื›ืš ื”ื“ื‘ืจ ื‘ืืคืจื™ืงื” - ื™ืฉ ื”ื‘ื“ืœื™ื ืจื‘ื™ื.
05:30
So, why is it so high? Is it war?
105
330160
2000
ืื–, ืœืžื” ื–ื” ื›ืœ ื›ืš ื’ื‘ื•ื”? ื”ืื ื–ื” ื‘ืฉืœ ืžืœื—ืžื•ืช?
05:32
No, it's not. Look here.
106
332160
2000
ืœื, ื–ื” ืœื. ืจืื• ื›ืืŸ.
05:34
War-torn Congo is down there -- two, three, four percent.
107
334160
3000
ืงื•ื ื’ื• ืฉืกื•ืขืช ื”ืžืœื—ืžื•ืช ื›ืืŸ ืœืžื˜ื” - ืฉื ื™ื™ื, ืฉืœื•ืฉื”, ืืจื‘ืขื” ืื—ื•ื–ื™ื.
05:37
And this is peaceful Zambia, neighboring country -- 15 percent.
108
337160
4000
ื•ื–ื• ื–ืžื‘ื™ื” ื”ืžืฆื•ื™ื” ื‘ืžืฆื‘ ืฉืœื•ื, ืžื“ื™ื ื” ืฉื›ื ื” - 15 ืื—ื•ื–.
05:41
And there's good studies of the refugees coming out of Congo --
109
341160
3000
ื•ื™ืฉื ื ืžื—ืงืจื™ื ื˜ื•ื‘ื™ื ืœื’ื‘ื™ ื”ืคืœื™ื˜ื™ื ื”ืžื’ื™ืขื™ื ืžืงื•ื ื’ื• -
05:44
they have two, three percent infected,
110
344160
2000
ืืฆืœื”ื ืฉื ื™ื™ื, ืฉืœื•ืฉื” ืื—ื•ื–ื™ ื ืฉืื•ืช,
05:46
and peaceful Zambia -- much higher.
111
346160
2000
ื•ื‘ื–ืžื‘ื™ื” ื”ืฉืœื•ื•ื” - ืื—ื•ื–ื™ื ื’ื‘ื•ื”ื™ื ื‘ื”ืจื‘ื”.
05:48
There are now studies clearly showing
112
348160
2000
ืžื—ืงืจื™ื ื›ืขืช ืžืจืื™ื ื‘ื‘ื™ืจื•ืจ
05:50
that the wars are terrible, that rapes are terrible,
113
350160
3000
ืฉื”ืžืœื—ืžื•ืช ื”ืŸ ื ื•ืจืื™ื•ืช, ืžืงืจื™ ื”ืื•ื ืก ื”ื ื ื•ืจืื™ื.
05:53
but this is not the driving force for the high levels in Africa.
114
353160
3000
ืืš ืืœื• ืœื ื”ื›ื•ื—ื•ืช ื”ืžื ื™ืขื™ื ืžืื—ื•ืจื™ ืื—ื•ื–ื™ ื”ื ืฉืื•ืช ื”ื’ื‘ื•ื”ื™ื ื‘ืืคืจื™ืงื”.
05:56
So, is it poverty?
115
356160
2000
ืื–, ื”ืื ื–ื” ื”ืขื•ื ื™?
05:58
Well if you look at the macro level,
116
358160
2000
ืื ื ื‘ื™ื˜ ื‘ืจืžืช ื”ืžืืงืจื•,
06:00
it seems more money, more HIV.
117
360160
2000
ื ืจืื” ืฉื™ื•ืชืจ ื›ืกืฃ ืžืฉืžืขื• ื™ื•ืชืจ HIV.
06:02
But that's very simplistic,
118
362160
3000
ืืš ื–ื” ืžืื•ื“ ืคืฉื˜ื ื™,
06:05
so let's go down and look at Tanzania.
119
365160
2000
ืื– ื‘ื•ืื• ื ืจื“ ืœืžื˜ื” ื•ื ืจืื” ืืช ื˜ื ื–ื ื™ื”.
06:07
I will split Tanzania in five income groups,
120
367160
4000
ืื ื™ ืืคืฆืœ ืืช ื˜ื ื–ื ื™ื” ืœื—ืžืฉ ืงื‘ื•ืฆื•ืช ืข"ืค ื”ื›ื ืกื”,
06:11
from the highest income to the lowest income,
121
371160
2000
ืžื”ื”ื›ื ืกื” ื”ื’ื‘ื•ื”ื” ื‘ื™ื•ืชืจ ืœื ืžื•ื›ื” ื‘ื™ื•ืชืจ,
06:13
and here we go.
122
373160
2000
ื•ื”ื ื”.
06:15
The ones with the highest income, the better off -- I wouldn't say rich --
123
375160
3000
ืืœื• ืขื ื”ื”ื›ื ืกื” ื”ื’ื‘ื•ื”ื” ื‘ื™ื•ืชืจ, ืฉืžืฆื‘ื ื˜ื•ื‘ ื™ื•ืชืจ, ืœื ืื’ื™ื“ ืฉื”ื ืขืฉื™ืจื™ื,
06:18
they have higher HIV.
124
378160
2000
ื™ืฉ ืœื”ื ื™ื•ืชืจ HIV.
06:20
The difference goes from 11 percent down to four percent,
125
380160
3000
ื”ื”ื‘ื“ืœ ื ืข ื‘ื™ืŸ 11 ืื—ื•ื– ื•ืขื“ 4 ืื—ื•ื–,
06:23
and it is even bigger among women.
126
383160
2000
ื•ื”ืคืขืจ ืืคื™ืœื• ื’ื“ื•ืœ ื™ื•ืชืจ ื‘ืงืจื‘ ื ืฉื™ื.
06:25
There's a lot of things that we thought, that now, good research,
127
385160
4000
ื”ืจื‘ื” ื“ื‘ืจื™ื ืฉื—ืฉื‘ื ื•, ื›ืขืช, ืžื—ืงืจื™ื ื˜ื•ื‘ื™ื,
06:29
done by African institutions and researchers
128
389160
3000
ืฉื‘ื•ืฆืขื• ืข"ื™ ืžื›ื•ื ื™ื ื•ื—ื•ืงืจื™ื ืืคืจื™ืงืื™ื
06:32
together with the international researchers, show that that's not the case.
129
392160
3000
ื™ื—ื“ ืขื ื—ื•ืงืจื™ื ื‘ื™ืŸ-ืœืื•ืžื™ื™ื, ืžืจืื™ื ืฉืœื ื›ืš ื”ื“ื‘ืจ.
06:35
So, this is the difference within Tanzania.
130
395160
2000
ืื–, ื–ื” ื”ื”ื‘ื“ืœ ื‘ืชื•ืš ื˜ื ื–ื ื™ื”.
06:37
And, I can't avoid showing Kenya.
131
397160
2000
ื•ืื ื™ ืœื ื™ื›ื•ืœ ืœื”ืžื ืข ืžืœื”ืจืื•ืช ืืช ืงื ื™ื”.
06:39
Look here at Kenya.
132
399160
2000
ื”ื‘ื™ื˜ื• ื›ืืŸ ื‘ืงื ื™ื”.
06:41
I've split Kenya in its provinces.
133
401160
2000
ืคื™ืฆืœืชื™ ืืช ืงื ื™ื” ืœืžื—ื•ื–ื•ืชื™ื”.
06:43
Here it goes.
134
403160
2000
ื”ื ื”.
06:45
See the difference within one African country --
135
405160
3000
ืจืื• ืืช ื”ื”ื‘ื“ืœื™ื ื‘ืชื•ืš ืžื“ื™ื ื” ืืคืจื™ืงืื™ืช ืื—ืช -
06:48
it goes from very low level to very high level,
136
408160
3000
ื”ื ื ืขื™ื ืžืจืžื” ื ืžื•ื›ื” ืžืื•ื“ ืœืจืžื” ื’ื‘ื•ื”ื” ืžืื•ื“,
06:51
and most of the provinces in Kenya is quite modest.
137
411160
3000
ื•ื‘ืจื‘ ื”ืžื—ื•ื–ื•ืช ื‘ืงื ื™ื” ื”ืจืžื” ื“ื™ ื ืžื•ื›ื”.
06:54
So, what is it then?
138
414160
2000
ืื ื›ืš, ืžื”ื• ื”ื“ื‘ืจ?
06:56
Why do we see this extremely high levels in some countries?
139
416160
4000
ืžื“ื•ืข ืื ื• ืจื•ืื™ื ืจืžื•ืช ื’ื‘ื•ื”ื•ืช ื›ืœ ื›ืš ื‘ืžื“ื™ื ื•ืช ืžืกื•ื™ืžื•ืช?
07:00
Well, it is more common with multiple partners,
140
420160
3000
ื–ื” ื™ื•ืชืจ ืฉื›ื™ื— ืขื ืฉื•ืชืคื™ื ืžืจื•ื‘ื™ื,
07:03
there is less condom use,
141
423160
3000
ื™ืฉ ืคื—ื•ืช ืฉื™ืžื•ืฉ ื‘ืงื•ื ื“ื•ืžื™ื,
07:06
and there is age-disparate sex --
142
426160
3000
ื•ื™ืฉื ื ื™ื—ืกื™ ืžื™ืŸ ื‘ื™ืŸ ื‘ื ื™ ื’ื™ืœืื™ื ืฉื•ื ื™ื --
07:09
that is, older men tend to have sex with younger women.
143
429160
3000
ื›ืœื•ืžืจ, ื’ื‘ืจื™ื ืžื‘ื•ื’ืจื™ื ื ื•ื˜ื™ื ืœืงื™ื™ื ื™ื—ืกื™ื ืขื ื ืฉื™ื ืฆืขื™ืจื•ืช ืžื”ื.
07:12
We see higher rates in younger women than younger men
144
432160
3000
ืื ื• ืจื•ืื™ื ืจืžื•ืช ื’ื‘ื•ื”ื•ืช ื™ื•ืชืจ ื‘ื ืฉื™ื ืฆืขื™ืจื•ืช ืœืขื•ืžืช ื’ื‘ืจื™ื ืฆืขื™ืจื™ื
07:15
in many of these highly affected countries.
145
435160
2000
ื‘ืจื‘ื•ืช ืžื”ืžื“ื™ื ื•ืช ื‘ื”ืŸ ืื—ื•ื– ื”ื ืฉืื™ื ื’ื‘ื•ื”.
07:17
But where are they situated?
146
437160
2000
ืืš ื”ื™ื›ืŸ ื”ืŸ ืžืฆื•ื™ื•ืช?
07:19
I will swap the bubbles to a map.
147
439160
2000
ืื ื™ ืืขื‘ื™ืจ ืืช ื”ื‘ื•ืขื•ืช ืœืžืคื”.
07:21
Look, the highly infected are four percent of all population
148
441160
4000
ืจืื•, ื”ืžื“ื™ื ื•ืช ื”ื ื’ื•ืขื•ืช ื‘ื™ื•ืชืจ ืžื”ื•ื•ืช 4 ืื—ื•ื–ื™ื ืžื›ืœืœ ื”ืื•ื›ืœื•ืกื™ื”
07:25
and they hold 50 percent of the HIV-infected.
149
445160
3000
ื•ื™ืฉ ื‘ื”ืŸ 50 ืื—ื•ื– ืžื ืฉืื™ ื”-HIV.
07:28
HIV exists all over the world.
150
448160
3000
HIV ืงื™ื™ื ื‘ื›ืœ ืจื—ื‘ื™ ื”ืขื•ืœื.
07:31
Look, you have bubbles all over the world here.
151
451160
2000
ืจืื•, ื™ืฉ ื‘ื•ืขื•ืช ื‘ื›ืœ ืจื—ื‘ื™ ื”ืขื•ืœื ื›ืืŸ.
07:33
Brazil has many HIV-infected.
152
453160
3000
ื‘ื‘ืจื–ื™ืœ ื™ืฉ ื ืฉืื™ HIV ืจื‘ื™ื.
07:36
Arab countries not so much, but Iran is quite high.
153
456160
3000
ื‘ืžื“ื™ื ื•ืช ืขืจื‘ ืœื ื›ืœ ื›ืš, ืืš ื‘ืื™ืจืŸ ื”ืจืžื” ื“ื™ ื’ื‘ื•ื”ื”.
07:39
They have heroin addiction and also prostitution in Iran.
154
459160
4000
ื™ืฉ ื‘ืื™ืจืŸ ื”ืชืžื›ืจื•ืช ืœื”ืจื•ืื™ืŸ ื•ื–ื ื•ืช.
07:43
India has many because they are many.
155
463160
2000
ื‘ื”ื•ื“ื• ื™ืฉ ืจื‘ื™ื ื›ื™ื•ื•ืŸ ืฉื”ื ืจื‘ื™ื.
07:45
Southeast Asia, and so on.
156
465160
2000
ื“ืจื•ื-ืžื–ืจื— ืืกื™ื”, ื•ื›ืŸ ื”ืœืื”.
07:47
But, there is one part of Africa --
157
467160
2000
ืืš, ื™ืฉ ืื–ื•ืจ ืื—ื“ ื‘ืืคืจื™ืงื” --
07:49
and the difficult thing is, at the same time,
158
469160
2000
ื•ื‘ื• ื‘ืขืช, ื”ื“ื‘ืจ ื”ืงืฉื” ื”ื•ื,
07:51
not to make a uniform statement about Africa,
159
471160
4000
ืœื ืœืฆืืช ื‘ื”ืฆื”ืจื” ืื—ื™ื“ื” ืœื’ื‘ื™ ืืคืจื™ืงื”,
07:55
not to come to simple ideas of why it is like this, on one hand.
160
475160
4000
ืœื ืœื”ื’ื™ืข ืœืžืกืงื ื” ืคืฉื•ื˜ื” ืฉืœ ืœืžื” ื”ืžืฆื‘ ื”ื•ื ื›ื–ื”, ืžืฆื“ ืื—ื“.
07:59
On the other hand, try to say that this is not the case,
161
479160
3000
ืžืฆื“ ืฉื ื™, ืœื”ื•ื“ื•ืช ืฉื–ื”ื• ืžืฆื‘ ืงืฉื”,
08:02
because there is a scientific consensus about this pattern now.
162
482160
4000
ื›ื™ื•ื•ืŸ ืฉื™ืฉ ื”ืกื›ืžื” ืžื“ืขื™ืช ืœื’ื‘ื™ ื”ื“ืคื•ืก ื”ื–ื” ื›ืขืช.
08:06
UNAIDS have done good data available, finally,
163
486160
3000
UNAIDS ืฉื™ื—ืจืจื• ืžื™ื“ืข, ืกื•ืฃ ื›ืš ืกื•ืฃ,
08:09
about the spread of HIV.
164
489160
3000
ืœื’ื‘ื™ ื”ืชืคืฉื˜ื•ืช HIV.
08:12
It could be concurrency.
165
492160
3000
ื–ื• ื™ื›ื•ืœื” ืœื”ื™ื•ืช ื‘ื•-ื–ืžื ื™ื•ืช.
08:15
It could be some virus types.
166
495160
3000
ื–ื” ื™ื›ื•ืœ ืœื”ื™ื•ืช ืกื•ื’ ื•ื™ืจื•ืก ืžืกื•ื™ื.
08:18
It could be that there is other things
167
498160
4000
ื™ื›ื•ืœ ืœื”ื™ื•ืช ืฉื™ืฉื ื ื’ื•ืจืžื™ื ืื—ืจื™ื
08:22
which makes transmission occur in a higher frequency.
168
502160
3000
ืฉืžื•ื‘ื™ืœื™ื ืœื”ื“ื‘ืงื” ื‘ืชื“ื™ืจื•ืช ื’ื‘ื•ื”ื”.
08:25
After all, if you are completely healthy and you have heterosexual sex,
169
505160
3000
ืื—ืจื™ ื”ื›ืœ, ืื ืืชื” ื‘ืจื™ื ืœื—ืœื•ื˜ื™ืŸ ื•ืžืงื™ื™ื ื™ื—ืกื™ ืžื™ืŸ ื”ื˜ืจื•ืกืงืกื•ืืœื™ื™ื,
08:28
the risk of infection in one intercourse is one in 1,000.
170
508160
5000
ื”ืกื™ื›ื•ืŸ ืœื”ื“ื‘ืงื” ื‘ืžื”ืœืš ืžื’ืข ืžื™ื ื™ ืื—ื“ ื”ื™ื ืื—ื“ ืœ-1000.
08:33
Don't jump to conclusions now on how to
171
513160
2000
ืืœ ืชืงืคืฆื• ืœืžืกืงื ื•ืช ืขื›ืฉื™ื•;
08:35
behave tonight and so on.
172
515160
2000
ืชืชื ื”ื’ื• ื™ืคื” ื”ืขืจื‘ ื•ื›ื•'.
08:37
(Laughter)
173
517160
2000
(ืฆื—ื•ืง)
08:39
But -- and if you are in an unfavorable situation,
174
519160
3000
ืื‘ืœ -- ื•ืื ืืชื ื‘ืžืฆื‘ ืœื ืžื™ื˜ื‘ื™,
08:42
more sexually transmitted diseases, it can be one in 100.
175
522160
3000
ื™ื•ืชืจ ืžื—ืœื•ืช ืžื™ืŸ, ื–ื” ื™ื›ื•ืœ ืœื”ื™ื•ืช ืื—ื“ ืœ-100.
08:45
But what we think is that it could be concurrency.
176
525160
3000
ืื‘ืœ ืžื” ืฉืื ื—ื ื• ื—ื•ืฉื‘ื™ื ื”ื•ื ืฉื™ื›ื•ืœ ืœื”ื™ื•ืช ืฉื–ื• ื”ื‘ื•-ื–ืžื ื™ื•ืช.
08:48
And what is concurrency?
177
528160
2000
ื•ืžื”ื™ ืื•ืชื” ื‘ื•-ื–ืžื ื™ื•ืช?
08:50
In Sweden, we have no concurrency.
178
530160
2000
ื‘ืฉื‘ื“ื™ื”, ืื™ืŸ ืœื ื• ื‘ื•-ื–ืžื ื™ื•ืช.
08:52
We have serial monogamy.
179
532160
2000
ื™ืฉ ืœื ื• ืžื•ื ื•ื’ืžื™ื” ืกื“ืจืชื™ืช.
08:54
Vodka, New Year's Eve -- new partner for the spring.
180
534160
2000
ื•ื•ื“ืงื”, ืขืจื‘ ื”ืฉื ื” ื”ื—ื“ืฉื” -- ืคืจื˜ื ืจ ื—ื“ืฉ ืœืื‘ื™ื‘.
08:56
Vodka, Midsummer's Eve -- new partner for the fall.
181
536160
2000
ื•ื•ื“ืงื”, ืขืจื‘ ืืžืฆืข ื”ืงื™ืฅ -- ืคืจื˜ื ืจ ื—ื“ืฉ ืœืกืชื™ื•.
08:58
Vodka -- and it goes on like this, you know?
182
538160
2000
ื•ื•ื“ืงื” -- ื•ื›ืš ื–ื” ื ืžืฉืš, ืžื‘ื™ื ื™ื?
09:00
And you collect a big number of exes.
183
540160
3000
ื•ืืชื ืื•ืกืคื™ื ืžืกืคืจ ืืงืกื™ื ื’ื“ื•ืœ.
09:03
And we have a terrible chlamydia epidemic --
184
543160
2000
ื•ื™ืฉ ืœื ื• ืžื’ื™ืคืช ื›ืœืžื™ื“ื™ื” ื ื•ืจืื™ืช --
09:05
terrible chlamydia epidemic which sticks around for many years.
185
545160
4000
ืžื’ืคืช ื›ืœืžื™ื“ื™ื” ื ื•ืจืื™ืช ืฉื ืฉืืจืช ืœืžืฉืš ืฉื ื™ื.
09:09
HIV has a peak three to six weeks after infection
186
549160
3000
ืœ-HIV ื™ืฉ ืฉื™ื ืฉืœื•ืฉื” ืขื“ ืฉื™ืฉื” ืฉื‘ื•ืขื•ืช ืžื”ื“ื‘ืงื”
09:12
and therefore, having more than one partner in the same month
187
552160
3000
ื•ืœื›ืŸ, ืงื™ื•ื ื™ื•ืชืจ ืžืคืจื˜ื ืจ ืื—ื“ ื‘ืื•ืชื• ื—ื•ื“ืฉ
09:15
is much more dangerous for HIV than others.
188
555160
3000
ื”ื™ื ื”ืจื‘ื” ื™ื•ืชืจ ืžืกื•ื›ื ืช ื‘ื”ืขื‘ืจืช HIV ืžืืฉืจ ื‘ื–ื™ื”ื•ืžื™ื ืื—ืจื™ื.
09:18
Probably, it's a combination of this.
189
558160
2000
ื›ื ืจืื” ืฉืžื“ื•ื‘ืจ ื‘ืฉื™ืœื•ื‘ ืฉืœ ืืœื”.
09:20
And what makes me so happy is that we are moving now
190
560160
3000
ื•ืžื” ืฉืžืฉืžื— ืื•ืชื™ ื”ื•ื ืฉืื ื—ื ื• ื ืขื™ื ื›ื™ื•ื
09:23
towards fact when we look at this.
191
563160
2000
ืœืขื‘ืจ ืขื•ื‘ื“ื” ื›ืืฉืจ ืื ื—ื ื• ืžื‘ื™ื˜ื™ื ื‘ื–ื”.
09:25
You can get this chart, free.
192
565160
2000
ืืชื ื™ื›ื•ืœื™ื ืœื”ืฉื™ื’ ืืช ื”ื˜ื‘ืœื” ื”ื–ื• ื‘ื—ื™ื ื.
09:27
We have uploaded UNAIDS data on the Gapminder site.
193
567160
3000
ื”ืขืœื™ื ื• ืžื™ื“ืข ืž-UNAIDS ืœ-Gapminder.org.
09:30
And we hope that when we act on global problems in the future
194
570160
4000
ื•ืื ื• ืžืงื•ื•ื™ื ืฉื›ืืฉืจ ื ืคืขืœ ื‘ื ื•ืฉืื™ื ื’ืœื•ื‘ืืœื™ื™ื ื‘ืขืชื™ื“
09:34
we will not only have the heart,
195
574160
3000
ืœื ื™ื”ื™ื” ืœื ื• ืจืง ืืช ื”ืœื‘,
09:37
we will not only have the money,
196
577160
2000
ืœื ื™ื”ื™ื” ืœื ื• ืจืง ืืช ื”ื›ืกืฃ,
09:39
but we will also use the brain.
197
579160
3000
ืืœื ื™ื”ื™ื” ืœื ื• ื’ื ืืช ื”ืฉื›ืœ.
09:42
Thank you very much.
198
582160
2000
ืชื•ื“ื” ืจื‘ื” ืœื›ื.
09:44
(Applause)
199
584160
6000
(ืžื—ื™ืื•ืช ื›ืคื™ื™ื)
ืขืœ ืืชืจ ื–ื”

ืืชืจ ื–ื” ื™ืฆื™ื’ ื‘ืคื ื™ื›ื ืกืจื˜ื•ื ื™ YouTube ื”ืžื•ืขื™ืœื™ื ืœืœื™ืžื•ื“ ืื ื’ืœื™ืช. ืชื•ื›ืœื• ืœืจืื•ืช ืฉื™ืขื•ืจื™ ืื ื’ืœื™ืช ื”ืžื•ืขื‘ืจื™ื ืขืœ ื™ื“ื™ ืžื•ืจื™ื ืžื”ืฉื•ืจื” ื”ืจืืฉื•ื ื” ืžืจื—ื‘ื™ ื”ืขื•ืœื. ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ื”ืžื•ืฆื’ื•ืช ื‘ื›ืœ ื“ืฃ ื•ื™ื“ืื• ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ ืžืฉื. ื”ื›ืชื•ื‘ื™ื•ืช ื’ื•ืœืœื•ืช ื‘ืกื ื›ืจื•ืŸ ืขื ื”ืคืขืœืช ื”ื•ื•ื™ื“ืื•. ืื ื™ืฉ ืœืš ื”ืขืจื•ืช ืื• ื‘ืงืฉื•ืช, ืื ื ืฆื•ืจ ืื™ืชื ื• ืงืฉืจ ื‘ืืžืฆืขื•ืช ื˜ื•ืคืก ื™ืฆื™ืจืช ืงืฉืจ ื–ื”.

https://forms.gle/WvT1wiN1qDtmnspy7