The Vital Data You Flush Down the Toilet | Newsha Ghaeli | TED

61,281 views ・ 2024-01-05

TED


μ•„λž˜ μ˜λ¬Έμžλ§‰μ„ λ”λΈ”ν΄λ¦­ν•˜μ‹œλ©΄ μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€.

λ²ˆμ—­: Jenny Led κ²€ν† : JY Kang
00:04
Has it ever occurred to you, as you walk down the street,
0
4251
3503
길을 κ±·λ‹€κ°€ 문득 이런 생각 ν•˜μ‹  적 μžˆμœΌμ‹ κ°€μš”?
00:07
just how much data is flowing beneath your feet?
1
7796
3628
μ—¬λŸ¬λΆ„ 발 밑에 μ–Όλ§ˆλ‚˜ λ§Žμ€ 정보가 흐λ₯΄κ³  μžˆλŠ”μ§€ 생각해 λ³Έ 적이 μžˆλ‚˜μš”?
00:12
A wealth of information on our health and our well-being
2
12175
2920
우리 건강과 μ‚Άμ˜ μ§ˆμ— λŒ€ν•œ μˆ˜λ§Žμ€ 정보가 λ„μ‹œμ˜ ν•˜μˆ˜κ΄€μ„ 톡해 흐λ₯΄κ³  μžˆμŠ΅λ‹ˆλ‹€.
00:15
is running through our city sewers,
3
15136
2294
00:17
and we're all contributing to it every single time we use the toilet.
4
17472
3879
그리고 μš°λ¦¬κ°€ ν™”μž₯싀을 μ‚¬μš©ν•  λ•Œλ§ˆλ‹€ κ·Έ 정보듀을 λ§Œλ“€μ–΄λ‚΄κ³  있죠.
00:22
Think about it.
5
22561
1167
생각해 λ³΄μ„Έμš”.
00:23
Everybody pees and poops,
6
23770
2461
λˆ„κ΅¬λ‚˜ μ˜€μ€Œμ„ μ‹Έκ³  λ˜₯을 μ‹Έμ£ .
00:26
and we know that urine and stool contain a rich source of information
7
26273
3878
κ±°κΈ°μ—λŠ” 우리의 건강과 μ‚Άμ˜ μ§ˆμ— λŒ€ν•œ λ‹€μ–‘ν•œ 정보가 담겨 μžˆμŠ΅λ‹ˆλ‹€.
00:30
on our health and our well-being.
8
30151
1585
00:32
Our doctors look at it all the time to analyze for a variety of things.
9
32195
4588
우리 μ˜μ‚¬λ“€μ€ μ΄λŸ¬ν•œ 배섀물을 톡해 λ‹€μ–‘ν•œ 것듀을 λΆ„μ„ν•©λ‹ˆλ‹€.
00:37
Now, every time you flush,
10
37158
1627
자, λ³€κΈ°μ˜ 물을 내리면
00:38
you're sending this valuable information down into our sewers,
11
38785
3754
μ—¬λŸ¬λΆ„μ˜ κ·Έ κ·€μ€‘ν•œ 정보가 ν•˜μˆ˜κ΄€μœΌλ‘œ ν˜λŸ¬κ°‘λ‹ˆλ‹€.
00:42
where it's mixing with waste from hundreds of thousands of other people.
12
42539
3837
그리고 λ‹€λ₯Έ μˆ˜μ‹­λ§Œ λͺ…μ˜ λ°°μ„€λ¬Όκ³Ό ν•˜μˆ˜κ΄€μ—μ„œ ν•¨κ»˜ λ’€μ„žμ΄κ²Œ 되죠.
00:46
Once collected, it looks something like this.
13
46418
2961
κ·Έλ ‡κ²Œ ν•΄μ„œ λͺ¨μΈ 게 λ°”λ‘œ μ΄κ²ƒμž…λ‹ˆλ‹€.
00:49
This tiny sample
14
49379
2044
이 μž‘μ€ μƒ˜ν”Œμ€
00:51
comes from a wastewater treatment plant
15
51423
2043
백만 λͺ… μ΄μƒμ˜ νμˆ˜κ°€ λͺ¨μΈ 폐수 처리μž₯μ—μ„œ κ°€μ Έμ˜¨ κ²ƒμž…λ‹ˆλ‹€.
00:53
that represents more than one million people.
16
53508
2711
00:56
And from it, we can detect all sorts of things about that community:
17
56219
4171
이λ₯Ό 톡해 κ·Έ 곡동체에 κ΄€ν•œ μ˜¨κ°– μ’…λ₯˜μ˜ 정보λ₯Ό μ•Œμ•„λ‚Ό 수 있죠.
01:01
the infectious disease viruses that are circulating in our bodies,
18
61141
4421
우리 λͺΈμ—μ„œ μœ ν–‰ν•˜λŠ” 전염병 λ°”μ΄λŸ¬μŠ€λΌλ“ μ§€,
01:05
chemical markers for the drugs that are most commonly consumed.
19
65562
4171
κ°€μž₯ 일반적으둜 μ†ŒλΉ„λ˜λŠ” μ•½λ¬Όμ˜ 흔적 같은 것듀을 μ•Œ 수 있죠.
01:09
And we can analyze for all the bacteria that live in our collective microbiomes.
20
69733
5130
그리고 집단 미생물 ꡰ집에 μ„œμ‹ν•˜λŠ” λͺ¨λ“  λ°•ν…Œλ¦¬μ•„λ₯Ό 뢄석할 수 μžˆμŠ΅λ‹ˆλ‹€.
01:15
Now, if this sounds too close for comfort,
21
75488
2044
자, 이게 잘 와닿지 μ•ŠλŠ”λ‹€λ©΄
01:17
just consider all the personalized data that you're parting with every day
22
77532
3754
개인 맞좀 데이터λ₯Ό μƒκ°ν•˜μ‹œλ©΄ λ©λ‹ˆλ‹€.
μŠ€λ§ˆνŠΈν°μ΄λ‚˜ 슀마트 μ›ŒμΉ˜ 같은 기기둜 맀일 μˆ˜μ§‘λ˜λŠ” 정보 같은 κ±°μ£ .
01:21
when you use gadgets like your smartphone or your smart watch.
23
81286
3628
01:24
What's amazing about sewage
24
84956
2086
μƒν™œ ν•˜μˆ˜μ˜ λ†€λΌμš΄ 점은
01:27
is that it's naturally aggregated and anonymized.
25
87042
3169
μžμ—°μ μœΌλ‘œ μ§‘κ³„λ˜κ³  읡λͺ…ν™”λœλ‹€λŠ” κ²ƒμž…λ‹ˆλ‹€.
01:30
Once flushed,
26
90879
1418
일단 물을 내리면,
01:32
your waste is mixing with that of thousands and thousands of people,
27
92339
3378
μ—¬λŸ¬λΆ„μ˜ μƒν™œ ν•˜μˆ˜λŠ” λ‹€λ₯Έ 수천 λͺ…μ˜ μ‚¬λžŒλ“€μ˜ 것과 μ„žμ΄κ²Œ λ©λ‹ˆλ‹€.
01:35
so there's actually no way to tie any information from here
28
95717
3378
κ·Έλž˜μ„œ μ—¬κΈ°μ„œ λ‚˜μ˜¨ μ–΄λ–€ 정보도 λˆ„κ΅¬μ˜ 것인지 μ•Œμ•„λ‚Ό 방법이 μ—†μŠ΅λ‹ˆλ‹€.
01:39
back to a specific person.
29
99095
1877
01:41
Put differently, it's the perfect data dump.
30
101264
3379
λ°”κΏ” λ§ν•˜λ©΄, 정말 μ™„λ²½ν•œ 정보 폐기 방법이죠.
01:44
(Laughter)
31
104684
2503
(μ›ƒμŒ)
01:47
The thoughtful collection and analysis of sewage
32
107187
3003
μƒν™œ ν•˜μˆ˜λ₯Ό μ‹ μ€‘ν•˜κ²Œ μˆ˜μ§‘, λΆ„μ„ν•˜λ©΄
01:50
has the potential to radically improve health outcomes
33
110231
3170
μ „ 세계 λ„μ‹œμ˜ 보건 μƒνƒœλ₯Ό 근본적으둜 κ°œμ„ ν•  수 μžˆμŠ΅λ‹ˆλ‹€.
01:53
in cities around the world,
34
113401
1877
01:55
and it's a growing field called "wastewater epidemiology."
35
115278
3712
κ·Έλž˜μ„œ 이λ₯Έλ°” β€œνμˆ˜ μ—­ν•™β€μ΄λΌλŠ” λΆ„μ•Όκ°€ μ£Όλͺ©λ°›κ³  μžˆλŠ” 것이죠.
01:59
And wastewater epidemiology is but one example
36
119324
2753
폐수 역학은 μ˜€λŠ˜λ‚  λ„μ‹œμ—μ„œ μƒμ„±λ˜λŠ” μ—¬λŸ¬ λΉ… 데이터 μ€‘μ˜ ν•˜λ‚˜μΌ λΏμž…λ‹ˆλ‹€.
02:02
of all the big data that we're generating in our cities today.
37
122118
3879
02:07
Consider all the data that you generate with every phone call, package delivered,
38
127082
4004
μ—¬λŸ¬λΆ„μ΄ λ§Œλ“€μ–΄λ‚΄λŠ” μ˜¨κ°– 데이터듀을 μƒκ°ν•΄λ³΄μ„Έμš”.
μ „ν™”λ₯Ό ν•˜κ³ , 택배λ₯Ό 보내고, μš΄μ „μ„ ν•  λ•Œμ˜ 정보듀.
02:11
mile driven.
39
131086
1418
02:12
It's data from cameras, sensors, drones,
40
132837
3170
카메라, μ„Όμ„œ, λ“œλ‘ μœΌλ‘œ μ–»λŠ” 데이터,
02:16
air quality, water quality monitoring,
41
136007
2753
곡기질, 수질 λͺ¨λ‹ˆν„°λ§,
02:18
and the vast amounts of information generated by our health care
42
138802
3461
그리고 우리의 의료 및 ꡐ윑 μ‹œμŠ€ν…œμ—μ„œ λ°©λŒ€ν•œ μ–‘μ˜ 데이터가 μƒμ„±λ©λ‹ˆλ‹€.
02:22
and our educational systems.
43
142263
1961
02:24
All of this information, these digital breadcrumbs,
44
144891
3212
이 λͺ¨λ“  정보, 이 디지털 정보듀은
02:28
tell us unique stories about our cities and the way that we live our lives.
45
148103
4629
우리 λ„μ‹œμ™€ 우리 μƒν™œ 방식에 λŒ€ν•œ λ…νŠΉν•œ 이야기λ₯Ό λ“€λ €μ€λ‹ˆλ‹€.
02:33
The thoughtful collection and analysis of this information
46
153149
4296
이 정보듀을 μ‹ μ€‘ν•˜κ²Œ μˆ˜μ§‘ν•˜κ³  λΆ„μ„ν•˜λ©΄
02:37
has the power to inform real-time improvements
47
157445
3337
문제λ₯Ό μ¦‰μ‹œ κ°œμ„ ν•  수 있게 λ©λ‹ˆλ‹€.
02:40
to things like social policy,
48
160824
1835
μ‚¬νšŒ μ •μ±…, ν™˜κ²½ 관리, 보건 ν˜•ν‰μ„± 같은 것듀 말이죠.
02:42
environmental management, health equity and more.
49
162701
2669
02:45
As an architect, I believe that we need to harness
50
165745
2962
κ±΄μΆ•κ°€λ‘œμ„œ μ €λŠ” μš°λ¦¬κ°€ 맀일 λ„μ‹œμ—μ„œ λ§Œλ“€μ–΄λ‚΄λŠ”
02:48
the hundreds of millions of terabytes of data
51
168707
2877
μˆ˜μ–΅ ν…ŒλΌλ°”μ΄νŠΈμ˜ 데이터λ₯Ό
02:51
that we're generating in our cities each and every day.
52
171626
3045
ν™œμš©ν•΄μ•Ό ν•œλ‹€κ³  μƒκ°ν•©λ‹ˆλ‹€.
02:54
And this is important now more than ever,
53
174671
2377
그리고 μ§€κΈˆμ€ κ·Έ μ–΄λŠ λ•Œλ³΄λ‹€ μ€‘μš”ν•©λ‹ˆλ‹€.
02:57
because for the first time in human history,
54
177090
2502
인λ₯˜ 역사상 처음으둜
02:59
more than half of all people live in cities.
55
179592
3379
전체 인ꡬ의 절반 이상이 λ„μ‹œμ— μ‚΄κ³  있기 λ•Œλ¬Έμ΄μ£ .
03:02
By 2050,
56
182971
1293
2050년이 되면
03:04
this number will grow to nearly seven in 10 people.
57
184264
3628
이 μˆ«μžλŠ” 더 λŠ˜μ–΄λ‚˜ 10λͺ… 쀑 거의 7λͺ…이 될 κ±°μ˜ˆμš”.
03:07
Now just think about what that means for a second.
58
187934
2961
이제 이것이 무엇을 μ˜λ―Έν•˜λŠ”μ§€ 잠깐 생각해 λ³΄μ„Έμš”.
03:10
It means our biggest crises,
59
190937
1960
μš°λ¦¬μ—κ²Œ κ°€μž₯ 큰 μœ„κΈ°κ°€ 될 κ²ƒμž…λ‹ˆλ‹€.
03:12
from climate change to pandemics to growing inequality,
60
192939
3921
κΈ°ν›„ λ³€ν™”λΆ€ν„° 팬데믹, λΆˆν‰λ“± μ¦λŒ€μ— 이λ₯΄κΈ°κΉŒμ§€
03:16
are going to hit cities first and hardest.
61
196901
3003
무엇보닀 우리 λ„μ‹œμ— κ°€μž₯ 큰 타격을 쀄 κ²ƒμž…λ‹ˆλ‹€.
03:21
But the era of big data offers an opportunity
62
201072
2961
ν•˜μ§€λ§Œ λΉ… 데이터 μ‹œλŒ€λŠ” μ΄λŸ¬ν•œ 문제λ₯Ό ν•΄κ²°ν•˜κΈ° μœ„ν•œ
03:24
for new and creative solutions to tackle these problems.
63
204075
3337
μƒˆλ‘­κ³  창의적인 해결책을 찾을 수 μžˆλŠ” 기회λ₯Ό μ œκ³΅ν•©λ‹ˆλ‹€.
03:29
So let's dive into the opportunity presented by wastewater epidemiology.
64
209038
4088
이제 폐수 역학이 μ œμ‹œν•˜λŠ” κΈ°νšŒμ— λŒ€ν•΄ μžμ„Ένžˆ μ•Œμ•„λ³΄κ² μŠ΅λ‹ˆλ‹€.
03:34
Some of you may have heard of it as it gained a lot of popularity
65
214335
3170
μ½”λ‘œλ‚˜ μ‹œκΈ°μ— λ§Žμ€ 관심을 λ°›μ•˜κΈ°μ— 이에 λŒ€ν•΄ 듀어보셨을 κ±°μ˜ˆμš”.
03:37
and attention during the COVID-19 pandemic.
66
217505
2795
03:41
In 2020, research groups from around the world
67
221009
3503
2020λ…„, μ „ μ„Έκ³„μ˜ 연ꡬ 그룹듀이
03:44
began detecting SARS-CoV-2 RNA,
68
224554
2836
ν•˜μˆ˜ μƒ˜ν”Œμ—μ„œ SARS-CoV-2λ₯Ό κ²€μΆœν–ˆμŠ΅λ‹ˆλ‹€.
03:47
the virus that causes COVID-19, in sewage samples.
69
227432
3086
μ½”λ‘œλ‚˜μ˜ 원인이 λ˜λŠ” λ°”μ΄λŸ¬μŠ€μ£ .
03:51
I was on one of those teams.
70
231102
1919
저도 κ·Έ νŒ€ 쀑 ν•œ λͺ…μ΄μ—ˆμ–΄μš”.
03:53
We and others showed that you can actually use sewage
71
233605
3545
저희와 λ‹€λ₯Έ νŒ€μ—μ„œλŠ” μ‹€μ œλ‘œ ν•˜μˆ˜λ₯Ό ν™œμš©ν•˜λ©΄
03:57
as an accurate representation of COVID activity in our communities.
72
237150
3503
ν•΄λ‹Ή μ§€μ—­μ˜ μ½”λ‘œλ‚˜ ν™œλ™μ„ μ •ν™•νžˆ 보여쀄 수 μžˆμŒμ„ μž…μ¦ν–ˆμŠ΅λ‹ˆλ‹€.
04:01
Let me show you what I mean.
73
241070
1502
무슨 말인지 λ³΄μ—¬λ“œλ¦΄κ²Œμš”.
04:02
Here we're looking at a time series over the course of the pandemic.
74
242572
4046
팬데믹 κΈ°κ°„ λ™μ•ˆμ˜ μ‹œκ³„μ—΄μ„ μ‚΄νŽ΄λ³΄κ² μŠ΅λ‹ˆλ‹€.
04:06
So from March 2020 through just last week.
75
246951
3128
2020λ…„ 3μ›”λΆ€ν„° λ°”λ‘œ μ§€λ‚œ μ£ΌκΉŒμ§€μ˜ μžλ£Œμž…λ‹ˆλ‹€.
04:10
The blue line represents COVID virus concentrations in sewage samples
76
250079
4922
νŒŒλž€μƒ‰μ€ λ―Έκ΅­ μ „μ—­μ˜ ν•˜μˆ˜ μƒ˜ν”Œμ— μžˆλŠ” μ½”λ‘œλ‚˜ λ°”μ΄λŸ¬μŠ€ 농도λ₯Ό λ‚˜νƒ€λƒ…λ‹ˆλ‹€.
04:15
from across the United States.
77
255043
1877
04:16
In yellow, we see COVID clinical case data.
78
256961
3587
λ…Έλž€μƒ‰ 선은 μ½”λ‘œλ‚˜ ν™˜μžμ˜ μž„μƒ 사둀 λ°μ΄ν„°μž…λ‹ˆλ‹€.
04:21
For the first two years of the pandemic, case data was very reliable.
79
261132
3921
팬데믹 초기 2λ…„ λ™μ•ˆμ€ μž„μƒ λ°μ΄ν„°λŠ” 맀우 μ •ν™•ν–ˆμŠ΅λ‹ˆλ‹€.
04:25
People were getting PCR-tested all the time.
80
265053
2836
μ‚¬λžŒλ“€μ€ 항상 PCR 검사λ₯Ό λ°›μ•˜κΈ° λ•Œλ¬Έμ΄μ£ .
04:27
During those two years,
81
267889
1209
κ·Έ 2λ…„ λ™μ•ˆ 두 데이터 μ„ΈνŠΈλŠ” 맀우 잘 μΆ”μ λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
04:29
the two data sets tracked very well.
82
269140
1960
04:31
That was great.
83
271142
1168
정말 μ’‹μ•˜μ–΄μš”.
04:32
It meant that sewage was also reliable
84
272310
2169
ν•˜μˆ˜ 데이터도 믿을 수 있고
04:34
and an accurate representation of disease burden.
85
274521
2585
μ§ˆλ³‘ λΆ€λ‹΄ 상황을 μ •ν™•ν•˜κ²Œ 보여주고 μžˆμ—ˆμ£ .
04:37
However, over the past year and a half to two years,
86
277857
2836
ν•˜μ§€λ§Œ μ§€λ‚œ 1λ…„ λ°˜μ—μ„œ 2λ…„ λ™μ•ˆ
04:40
we've seen a divergence in those data sets.
87
280693
2920
μ΄λŸ¬ν•œ 데이터 μ§‘ν•©μ—μ„œ 차이가 λ‚˜νƒ€λ‚¬μŠ΅λ‹ˆλ‹€.
04:43
People just aren't getting COVID-tested nearly as often.
88
283613
3212
μ‚¬λžŒλ“€μ΄ μ½”λ‘œλ‚˜ 검사λ₯Ό 훨씬 덜 λ°›μ•˜κ³ μš”.
04:46
Sewage, on the other hand,
89
286825
1918
반면 ν•˜μˆ˜λ„ λ•Œλ¬Έμ— 의료 μ„œλΉ„μŠ€λ₯Ό μ΄μš©ν•  ν•„μš”κ°€ μ—†μ—ˆκΈ° λ•Œλ¬Έμ΄μ£ .
04:48
doesn't require us to access health care services.
90
288785
3378
우리 λͺ¨λ‘λŠ” 배변물을 톡해 λ‚˜νƒ€λ‚΄μ–΄ 질 λΏμž…λ‹ˆλ‹€.
04:52
We're all represented just by peeing and pooping.
91
292163
3671
04:55
Throughout the pandemic, we and others also showed that sewage is predictive
92
295834
4295
팬데믹 κΈ°κ°„ λ™μ•ˆ 우리 연ꡬ진듀은 ν•˜μˆ˜κ°€ 예츑 κ°€λŠ₯ν•˜λ©°
05:00
and a leading indicator of new COVID clinical cases.
93
300129
4463
μƒˆλ‘œμš΄ μ½”λ‘œλ‚˜ μž„μƒ μ‚¬λ‘€μ˜ μ„ ν–‰ μ§€ν‘œλΌλŠ” 것도 λ³΄μ—¬μ£Όμ—ˆμŠ΅λ‹ˆλ‹€.
05:04
This is because infectious disease viruses incubate in our bodies
94
304634
4129
μ΄λŠ” 전염병 λ°”μ΄λŸ¬μŠ€κ°€ 우리 λͺΈμ— μž λ³΅ν•˜κ³  μžˆλ‹€κ°€
05:08
before we develop symptoms or go get tested.
95
308763
3128
λ‚˜μ€‘μ— 증상이 λ‚˜νƒ€λ‚˜κ±°λ‚˜ 검사λ₯Ό 받은 후에 μ•ŒκΈ° λ•Œλ¬Έμ΄μ£ .
05:12
Meanwhile, we've been excreting the virus for days.
96
312308
3295
잠볡기 λ™μ•ˆ, μš°λ¦¬λŠ” λ©°μΉ  λ™μ•ˆ λ°”μ΄λŸ¬μŠ€λ₯Ό λ°°μ„€ν•΄ μ™”λ˜ κ±°μ˜ˆμš”.
05:16
During COVID,
97
316271
1167
μ½”λ‘œλ‚˜ κΈ°κ°„ λ™μ•ˆ
05:17
it was shown that sewage was anywhere between one to three weeks
98
317438
4130
ν•˜μ£Όκ°€ 1μ£Όμ—μ„œ 3μ£Ό λ™μ•ˆμ€
05:21
leading indicator for clinical cases.
99
321568
2460
μž„μƒ μ‚¬λ‘€μ˜ μ„ ν–‰ μ§€ν‘œλ‘œ λ‚˜νƒ€λ‚¬μŠ΅λ‹ˆλ‹€.
05:25
Now I'm going to show you an example
100
325280
1751
이제 이것이 μ§€μ—­μ‚¬νšŒμ— μ€‘λŒ€ν•œ 영ν–₯을 미친 사둀λ₯Ό λ³΄μ—¬λ“œλ¦΄κ²Œμš”.
05:27
of one time that this led to a big community-impacting decision.
101
327073
3629
05:31
Here, we're looking at data from the Boston area
102
331244
3253
이것은 였미크둠 μœ ν–‰ λ‹Ήμ‹œ λ³΄μŠ€ν„΄ μ§€μ—­μ˜ λ°μ΄ν„°μž…λ‹ˆλ‹€.
05:34
during the Omicron wave.
103
334539
1793
05:36
In December 2021, towards the end of the month,
104
336332
3128
2021λ…„ 12μ›” 말에 μ ‘μ–΄λ“€λ©΄μ„œ
05:39
COVID cases began to skyrocket across the country
105
339460
3045
μ½”λ‘œλ‚˜ 사둀가 μ „κ΅­μ μœΌλ‘œ κΈ‰μ¦ν•˜κΈ° μ‹œμž‘ν–ˆκ³ 
05:42
and didn't slow until the end of January.
106
342547
2794
1μ›” λ§κΉŒμ§€ 쀄어듀지 μ•Šμ•˜μŠ΅λ‹ˆλ‹€.
05:45
Boston Children's Hospital, though, was ready.
107
345341
2419
ν•˜μ§€λ§Œ λ³΄μŠ€ν„΄ 어린이 병원은 μ€€λΉ„κ°€ λ˜μ–΄ μžˆμ—ˆμŠ΅λ‹ˆλ‹€.
05:47
They had been looking at Boston area sewage
108
347802
2211
그듀은 λ³΄μŠ€ν„΄ μ§€μ—­μ˜ ν•˜μˆ˜ 데이터λ₯Ό μ‚΄νŽ΄λ³΄λ‹€κ°€
05:50
and saw the sewage levels go up weeks earlier,
109
350054
3712
λͺ‡ μ£Ό μ „λΆ€ν„° ν•˜μˆ˜μ˜ μˆ˜μΉ˜κ°€ μ˜¬λΌκ°€λŠ” 것을 μ•Œμ•˜μ£ .
05:53
so they proactively postponed all non-emergency medical procedures.
110
353808
4671
κ·Έλž˜μ„œ λͺ¨λ“  비응급 의료 절차λ₯Ό μ„ μ œμ μœΌλ‘œ μ—°κΈ°ν–ˆμŠ΅λ‹ˆλ‹€.
05:59
They wanted to free up resources so that they could adequately respond
111
359522
3879
λ°€λ €μ˜€λŠ” μž…μ› μˆ˜μš”μ— 적절히 λŒ€μ‘ν•  수 μžˆλ„λ‘
06:03
to the incoming wave of hospitalizations.
112
363443
2419
μžμ›μ„ ν™•λ³΄ν•˜κΈ° μœ„ν•΄μ„œμ˜€μ£ .
06:07
Now wastewater epidemiology has been used
113
367238
2419
이제 폐수 역학은 λ‹€λ₯Έ μ‹œκΈ‰ν•œ 보건 λ¬Έμ œμ—λ„ ν™œμš©λ˜κ³  μžˆμŠ΅λ‹ˆλ‹€.
06:09
to tackle other pressing health issues as well.
114
369657
2878
06:12
Before the pandemic,
115
372994
1209
팬데믹 이전에 λ―Έκ΅­μ—μ„œ κ°€μž₯ μ‹¬κ°ν•œ 곡쀑보건 μœ„κΈ°λŠ”
06:14
the biggest public health crisis in the United States
116
374203
2920
점점 μ»€μ Έκ°€λŠ” μ•½λ¬Ό μœ ν–‰μ΄μ—ˆμŠ΅λ‹ˆλ‹€.
06:17
was our growing drug epidemic.
117
377123
2211
06:20
Drug overdoses were growing year over year
118
380001
2753
μ•½λ¬Ό κ³Όλ‹€ λ³΅μš©μ€ ν•΄λ§ˆλ‹€ μ¦κ°€ν•˜κ³  μžˆμ—ˆμœΌλ©°
06:22
and had become the leading cause of accidental death
119
382795
3254
50μ„Έ 미만 미ꡭ인의 사고 μ‚¬λ§μ˜ μ£Όμš” 원인이 λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
06:26
for Americans under the age of 50.
120
386049
2544
06:28
In 2018, a small town in North Carolina had seen overdoses go up,
121
388927
5171
2018λ…„ λ…ΈμŠ€μΊλ‘€λΌμ΄λ‚˜μ˜ μ†Œλ„μ‹œμ—μ„œ μ•½λ¬Ό κ³Όλ‹€ 볡용이 κΈ‰μ¦ν–ˆμŠ΅λ‹ˆλ‹€.
06:34
and they wanted better information,
122
394098
1961
μ‚¬λžŒλ“€μ€ 이λ₯Ό ν•΄κ²°ν•  수 μžˆλŠ” 더 λ‚˜μ€ 정보와 데이터가 ν•„μš”ν–ˆκ³ ,
06:36
better data to know what to do about it,
123
396059
2085
06:38
what was driving this trend and how to respond.
124
398186
2878
μ΄λŸ¬ν•œ μΆ”μ„Έλ₯Ό μ£Όλ„ν•˜λŠ” μš”μΈκ³Ό λŒ€μ‘ λ°©μ•ˆμ„ μ°Ύκ³  μžˆμ—ˆμŠ΅λ‹ˆλ‹€.
06:41
So we turned to the sewers, and together with the mayor's office,
125
401064
4087
κ·Έλž˜μ„œ μš°λ¦¬λŠ” ν•˜μˆ˜λ„λ‘œ λˆˆμ„ 돌렸고
μ‹œ 곡무원과 ν•¨κ»˜
06:45
we began to analyze sewage samples from several sites across the city
126
405193
4838
λ„μ‹œ μ „μ—­μ˜ μ—¬λŸ¬ μž₯μ†Œμ—μ„œ λ‚˜μ˜¨ ν•˜μˆ˜ μƒ˜ν”Œμ„ λΆ„μ„ν•˜κΈ° μ‹œμž‘ν–ˆμŠ΅λ‹ˆλ‹€.
06:50
and were able to show that prescription opioids
127
410073
3879
κ·Έ κ²°κ³Ό μ²˜λ°©ν˜• λ§ˆμ•½μ„± μ§„ν†΅μ œκ°€ 주둜 μ‚¬μš©λ˜κ³  μžˆμŒμ„ λ°ν˜€λƒˆμ£ .
06:53
were the drug most commonly consumed, not injectable opioids.
128
413952
3795
λ§ˆμ•½ 주사가 μ•„λ‹ˆκ³ μš”.
06:58
Equipped with this data,
129
418748
1919
이 데이터λ₯Ό λ°”νƒ•μœΌλ‘œ
07:00
the city diverted resources from needle exchange sites
130
420667
3420
μ‹œ 당ꡭ은 주사 λ°”λŠ˜ κ΅ν™˜μ†Œμ˜ μ˜ˆμ‚°μ„ μ „μš©ν•˜μ—¬
07:04
and put that money into medication takeback programs instead.
131
424128
3629
λŒ€μ‹  μ•½λ¬Ό 회수 ν”„λ‘œκ·Έλž¨μ— νŽΈμ„±ν•˜μ˜€μŠ΅λ‹ˆλ‹€.
07:08
They advertised and held dozens of town halls
132
428174
2503
그듀은 μˆ˜μ‹­ 개의 μ§€μ—­μ‚¬νšŒ μ„€λͺ…νšŒλ₯Ό μ—΄μ–΄
07:10
where they talked about the adverse effects of prescription painkillers.
133
430718
4004
처방용 μ§„ν†΅μ œμ˜ λΆ€μž‘μš©μ— λŒ€ν•΄ μ•Œλ ΈμŠ΅λ‹ˆλ‹€.
07:15
That year,
134
435264
1460
κ·Έ ν•΄
07:16
the city saw a 40 percent reduction in overdoses,
135
436766
4630
이 λ„μ‹œμ—μ„œλŠ” κ³Όλ‹€ 볡용이 40%λ‚˜ κ°μ†Œν–ˆκ³ 
07:21
and for the first time,
136
441437
1293
처음으둜,
07:22
they had engaged their community in a dialogue around drugs,
137
442730
3462
지역 μ‚¬νšŒμ™€ ν•¨κ»˜ μ•½λ¬Ό, 쀑독 그리고 κ³Όλ‹€λ³΅μš©μ— κ΄€ν•œ
07:26
addiction and overdose.
138
446234
2002
μ˜κ²¬μ„ λ‚˜λˆ„κ²Œ λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
07:28
Now imagine if every city around the world had access to this sort of information.
139
448736
5714
이제 μ „ 세계 λͺ¨λ“  λ„μ‹œκ°€ 이런 μ’…λ₯˜μ˜ 정보λ₯Ό μ΄μš©ν•  수 μžˆλ‹€κ³  상상해 λ³΄μ„Έμš”.
07:34
Before the pandemic,
140
454951
1168
팬데믹 이전에 폐수 역학은 μž‘μ€ λΆ„μ•Όμ˜€μŠ΅λ‹ˆλ‹€.
07:36
wastewater epidemiology was a tiny field
141
456160
2920
07:39
with no more than a dozen experts worldwide.
142
459122
3211
μ „ μ„Έκ³„μ μœΌλ‘œ μ „λ¬Έκ°€κ°€ μ‹­μ—¬ λͺ…에 λΆˆκ³Όν–ˆμ£ .
07:42
Today, 72 countries
143
462333
3420
μ˜€λŠ˜λ‚ , 72κ°œκ΅­μ—μ„œ
07:45
have used wastewater monitoring to understand COVID-19.
144
465753
4255
폐수 λͺ¨λ‹ˆν„°λ§μ„ ν™œμš©ν•΄μ„œ μ½”λ‘œλ‚˜λ₯Ό μ—°κ΅¬ν•˜κ³  μžˆμŠ΅λ‹ˆλ‹€.
07:50
And it's time that we leverage these investments
145
470675
2419
이제 μ΄λŸ¬ν•œ 투자λ₯Ό ν™œμš©ν•˜μ—¬ λ‹€λ₯Έ λ¬Έμ œλ“€λ„ μ‚΄νŽ΄λ΄μ•Ό ν•  λ•Œμž…λ‹ˆλ‹€.
07:53
to monitor for all sorts of other things as well.
146
473094
2502
07:56
Imagine knowing when influenza and RSV are going to peak every year
147
476305
4505
맀년 μΈν”Œλ£¨μ—”μžμ™€ 호흑기 λ°”μ΄λŸ¬μŠ€κ°€ μ΅œκ³ μ‘°μ— 달할 μ‹œκΈ°λ₯Ό μ•Œκ³ 
08:00
so that our hospitals can prepare.
148
480810
2044
병원이 미리 λŒ€λΉ„ν•˜λŠ” κ±Έ 상상해 λ³΄μ„Έμš”.
08:03
Imagine mapping nutrition in our cities
149
483229
2669
λ„μ‹œμ˜ μ˜μ–‘ 지도λ₯Ό λ§Œλ“€μ–΄μ„œ
08:05
so that we can identify food deserts
150
485940
2169
μ‹λŸ‰μ΄ λΆ€μ‘±ν•œ 지역을 μ•Œμ•„λ‚΄κ³ 
08:08
and understand social determinants of health.
151
488109
2419
건강에 λŒ€ν•œ μ‚¬νšŒμ  μš”μΈμ„ 이해할 μˆ˜λ„ 있겠죠.
08:11
Imagine identifying superbugs and antibiotic resistant genes
152
491154
4713
슈퍼 λ°•ν…Œλ¦¬μ•„λ₯Ό μ°Ύμ•„λ‚΄κ±°λ‚˜
ν•­μƒμ œ λ‚΄μ„± μœ μ „μžμ˜ μΆœν˜„μ„ λ°ν˜€λ‚Ό μˆ˜λ„ μžˆμŠ΅λ‹ˆλ‹€.
08:15
as they emerge in our communities.
153
495908
2002
08:19
Imagine preventing the next pandemic before it happens.
154
499120
3503
λ‹€μŒ 팬데믹이 λ°œμƒν•˜κΈ° 전에 미리 μ˜ˆλ°©ν•  μˆ˜λ„ μžˆμ„ κ±°μ˜ˆμš”.
08:23
In the way that cholera prompted London to build modern-day sewer systems,
155
503499
5005
콜레라둜 인해 λŸ°λ˜μ€ ν˜„λŒ€μ‹ ν•˜μˆ˜λ„ μ‹œμŠ€ν…œμ„ κ±΄μ„€ν–ˆκ³ 
08:28
and poor health in the tenements of New York City
156
508546
2503
λ‰΄μš• μ‹œ 곡동 μ£Όνƒμ˜ μ—΄μ•…ν•œ 건강 λ¬Έμ œκ°€
08:31
were one of the catalysts behind the building of Central Park,
157
511049
4045
μ„ΌνŠΈλŸ΄ 파크 κ±΄μ„€μ˜ μ΄‰λ§€μ œ 쀑 ν•˜λ‚˜μ˜€λ˜ κ²ƒμ²˜λŸΌ,
08:35
this is how our cities can learn from COVID-19.
158
515136
3170
이것이 우리 λ„μ‹œκ°€ μ½”λ‘œλ‚˜λ‘œλΆ€ν„° 얻은 κ΅ν›ˆμΈ κ²ƒμž…λ‹ˆλ‹€.
08:38
And this is precisely how we can foster a new, intelligent kind of urbanization.
159
518806
5589
이것이 λ°”λ‘œ μš°λ¦¬κ°€ μƒˆλ‘­κ³  지λŠ₯적인 λ„μ‹œν™”λ₯Ό 촉진할 수 μžˆλŠ” λ°©λ²•μž…λ‹ˆλ‹€.
08:45
For years now, scientists, policymakers,
160
525354
3212
μˆ˜λ…„ μ „λΆ€ν„° κ³Όν•™μž, μ •μ±… μž…μ•ˆμž, 건좕가, λ„μ‹œ κ³„νšκ°€λ“€μ€
08:48
architects and urban planners
161
528566
2169
08:50
have been harnessing the power of technology and big data
162
530735
3420
기술과 λΉ… λ°μ΄ν„°μ˜ νž˜μ„ ν™œμš©ν•˜μ—¬
08:54
to future-proof our cities.
163
534197
2085
λ„μ‹œμ˜ 미래λ₯Ό 보μž₯ν•΄ μ™”μŠ΅λ‹ˆλ‹€.
08:57
Over the last decade,
164
537200
1543
μ§€λ‚œ 10λ…„ λ™μ•ˆ
08:58
chief technology officers have been appointed in cities
165
538743
3170
μ „ 세계 λ„μ‹œμ— 기술 λ‹΄λ‹Ή κ³ μœ„μ§λ“€μ΄ μž„λͺ…λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
09:01
around the world.
166
541954
1502
09:04
Roles once reserved for the boardrooms
167
544415
2127
ν•œλ•Œ μ‹€λ¦¬μ½˜ 밸리의 μ΄μ‚¬νšŒμ—μ„œλ‚˜ ν•„μš”λ‘œ ν–ˆλ˜ μžλ¦¬κ°€
09:06
and hallways of Silicon Valley
168
546584
1460
09:08
are now finally open in city hall.
169
548044
2794
이제 μ‹œμ²­μ—μ„œλ„ ν•„μš”ν•˜κ²Œ λ˜μ—ˆμ£ .
09:12
So next time you swipe your credit card,
170
552006
3128
κ·ΈλŸ¬λ‹ˆ λ‹€μŒμ— μ‹ μš©μΉ΄λ“œλ₯Ό κΈκ±°λ‚˜
09:15
take a ride in a taxi or tap your MetroCard,
171
555176
3462
νƒμ‹œλ₯Ό νƒ€κ±°λ‚˜ μ§€ν•˜μ²  μΉ΄λ“œλ₯Ό μ‚¬μš©ν•  λ•ŒλŠ”
09:18
just consider how you're contributing
172
558679
1961
μ—¬λŸ¬λΆ„μ΄ λ„μ‹œμ˜ 디지털 인프라 μ„±μž₯에 μ–Όλ§ˆλ‚˜ κΈ°μ—¬ν•˜λŠ”μ§€ 잘 μƒκ°ν•΄λ³΄μ„Έμš”.
09:20
to your city's ever-growing digital infrastructure.
173
560681
2878
09:24
And next time you use the toilet,
174
564685
2795
그리고 λ‹€μŒμ— ν™”μž₯싀을 μ΄μš©ν•  λ•ŒλŠ”
09:27
just remember, you're doing your civic duty.
175
567480
3128
μ‹œλ―Όμ˜ 의무λ₯Ό λ‹€ν•˜κ³  μžˆλ‹€λŠ” 것을 κΈ°μ–΅ν•˜μ‹œκΈΈ λ°”λžλ‹ˆλ‹€.
09:30
(Laughter)
176
570650
1543
(μ›ƒμŒ)
09:32
Thank you.
177
572235
1167
κ°μ‚¬ν•©λ‹ˆλ‹€.
09:33
(Applause)
178
573402
3921
(λ°•μˆ˜)
이 μ›Ήμ‚¬μ΄νŠΈ 정보

이 μ‚¬μ΄νŠΈλŠ” μ˜μ–΄ ν•™μŠ΅μ— μœ μš©ν•œ YouTube λ™μ˜μƒμ„ μ†Œκ°œν•©λ‹ˆλ‹€. μ „ 세계 졜고의 μ„ μƒλ‹˜λ“€μ΄ κ°€λ₯΄μΉ˜λŠ” μ˜μ–΄ μˆ˜μ—…μ„ 보게 될 κ²ƒμž…λ‹ˆλ‹€. 각 λ™μ˜μƒ νŽ˜μ΄μ§€μ— ν‘œμ‹œλ˜λŠ” μ˜μ–΄ μžλ§‰μ„ 더블 ν΄λ¦­ν•˜λ©΄ κ·Έκ³³μ—μ„œ λ™μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€. λΉ„λ””μ˜€ μž¬μƒμ— 맞좰 μžλ§‰μ΄ μŠ€ν¬λ‘€λ©λ‹ˆλ‹€. μ˜κ²¬μ΄λ‚˜ μš”μ²­μ΄ μžˆλŠ” 경우 이 문의 양식을 μ‚¬μš©ν•˜μ—¬ λ¬Έμ˜ν•˜μ‹­μ‹œμ˜€.

https://forms.gle/WvT1wiN1qDtmnspy7