Will there be another pandemic in your lifetime?

423,145 views ・ 2022-11-10

TED-Ed


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翻译人员: Grace Man 校对人员: Yip Yan Yeung
00:06
The Black Death.
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黑死病。
00:08
The 1918 Flu Pandemic.
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1918 年流感大流行。
00:10
COVID-19.
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2019 冠状病毒病 (COVID-19)。
00:11
We tend to think of these catastrophic, world-changing pandemics
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人们往往觉得这些改变世界的 灾难性疫情是极小概率事件。
00:15
as very unlikely events.
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00:18
But between 1980 and 2020,
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但是 1980 至 2020 年间,
00:20
at least three diseases emerged that caused global pandemics.
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至少出现了三次 造成全球性疫情的疾病。
00:24
COVID-19, yes, but also the 2009 swine flu and HIV/AIDS.
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没错,有 COVID-19,
还有 2009 年猪流感以及 艾滋病毒/艾滋病。
00:29
Disease outbreaks are surprisingly common.
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疾病爆发普遍得令人吃惊。
00:32
Over the past four centuries,
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在过去的四个世纪里,
00:34
the longest stretch of time without a documented outbreak
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没有任何致死量 超过一万人的疾病爆发记录
00:37
that killed at least 10,000 people was just four years.
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最长时间间隔仅为四年。
00:42
As bad as these smaller outbreaks are,
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虽然这些小规模的疾病爆发很糟糕,
00:44
they’re far less deadly than a COVID-19-level pandemic.
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但是它们的致死率远低于 COVID-19 级别的疫情。
00:47
In fact, many people born after the 1918 flu lived their entire lives
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事实上,很多出生在 1918 年流感大流行以后的人,
00:52
without experiencing a similar world-changing pandemic.
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一生中都没遇到过类似级别的 足以改变世界程度的的疫情。
00:55
What’s the probability that you do, too?
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那你遇到的概率是多大呢?
00:58
There are several ways to answer this question.
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可以从好几个角度回答这个问题。
01:00
You could look at history.
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可以回顾历史。
01:02
A team of scientists and engineers who took this approach
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一组采用了该方法的 科学家与工程师
01:05
catalogued all documented epidemics and pandemics between 1600 and 1950.
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将 1600 年至 1950 年间所有 被记录的流行病与疫情分类统计。
01:10
They used that data to do two things.
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他们用收集的数据做了两件事。
01:13
First, to graph the likelihood that an outbreak of any size
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首先,作图呈现不限规模的疾病 在一定时间段内
01:16
pops up somewhere in the world over a set period of time.
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在世界上某地爆发的可能性。
01:20
And second, to estimate the likelihood that that outbreak would get large enough
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第二,预测疾病爆发会扩大到
01:24
to kill a certain percentage of the world's population.
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导致一定比例的地球人口 死亡的可能性。
01:27
This graph shows that while huge pandemics are unlikely,
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从这幅图可以看出, 虽然大规模疫情是小概率事件,
01:31
they're not that unlikely.
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但是可能性其实也没有那么小。
01:34
The team used these two distributions to estimate that the risk
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研究团队用上述方法估算 COVID-19 级别的疫情
01:37
of a COVID-19-level pandemic is about 0.5% per year,
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发生的风险概率大约为 每年 0.5% ,
01:41
and could be as high as 1.4%
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如果新型疾病在未来 出现得愈加频繁,
01:44
if new diseases emerge more frequently in the future.
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那风险概率可高达 1.4% 。
01:48
And we’ll come back to those numbers,
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我们之后会再回来讨论这些数字。
01:49
but first, let’s look at another way to estimate the likelihood
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现在,我们来看估测 未来疫情可能性的另一种方法:
01:52
of a future pandemic:
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01:54
modeling one from the ground up.
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从底层开始建立模型。
01:56
For most pandemics to happen, a pathogen, which is a microbe that can cause disease,
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对于大部分疫情来说, 发生的必要条件是
病原体,也就是 一种可以导致疾病的微生物,
02:00
has to spill over from its normal host by making contact with and infecting a human.
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需要接触人体并且感染人类, 以达到从正常宿主溢出的目的。
02:06
Then, the pathogen has to spread widely,
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而后,病原体需要广泛蔓延,
02:09
crossing international boundaries and infecting lots of people.
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穿过国界与地域的限制, 感染很多人。
02:13
Many variables determine whether a given spillover event becomes a pandemic.
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很多变量决定了一个特定的溢出事件 是否最终会蔓延成为疫情。
02:18
For example, the type of pathogen, how often humans come into close contact
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例如,病原体种类,
人类与动物宿主 近距离接触的频繁程度,
02:23
with its animal reservoir, existing immunity, and so on.
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现有的免疫力,等等。
02:27
Viruses are prime candidates to cause the next big pandemic.
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病毒是导致下一次大疫情的 潜在罪魁祸首。
02:31
Scientists estimate that there are about 1.7 million as-yet-undiscovered viruses
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科学家预计有 1700 万 尚未被发现的病毒
02:37
that currently infect mammals and birds,
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正在感染着哺乳动物和鸟类,
02:40
and that roughly 40% of these have the potential to spill over and infect humans.
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这其中的 40% 有着 病毒溢出并感染人体的潜力。
02:46
A team of scientists built a model using this information,
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科学家团队进行建模分析, 输入模型的信息为病毒溢出概率
02:49
as well as data about the global population, air travel networks,
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以及以下数据: 全球人口,航空旅行网络,
02:52
how people move around in communities, country preparedness levels,
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人们在社区内的移动情况, 国家防灾备灾水平,
02:56
and how people might respond to pandemics.
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人们针对疫情采取的应对措施。
02:58
The model generated hundreds of thousands of virtual pandemics.
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模型生成了成百上千种模拟疫情。
03:02
The scientists then used this catalog to estimate
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科学家用这个样本估测
03:05
that the probability of another COVID-19-level pandemic
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发生另一次 COVID-19 级别 疫情的概率为
03:08
is 2.5 to 3.3% per year.
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每年 2.5% 至 3.3%。
03:12
To get a sense of how these risks play out over a lifetime,
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为了对这些概率在我们的一生中 会产生何种影响有更直观的认识,
03:15
let’s pick a value roughly in the middle of all these estimates: 2%.
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我们取一个这些概率 大约的中间值: 2% 。
03:19
Now let’s build what’s called a probability tree diagram
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现在画概率树状图
03:22
to model all possible scenarios.
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来模拟所有可能的情形。
03:25
The first branch of the tree represents the first year:
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第一个分支代表第一年:
03:28
there’s a 2% probability of experiencing a COVID-19-level pandemic,
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COVID-19 级别的疫情 有 2% 的可能性发生,
03:32
which means there’s a 98% probability of not experiencing one.
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也就是说 有 98% 的可能性不会发生。
03:36
Second branch, same thing,
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第二个分叉,情况相同。
03:38
Third branch, same.
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第三个分叉,也是一样的。
03:39
And so on, 72 more times.
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如此再继续 72 次。
03:42
There is only one path that results in a fully pandemic-free lifetime:
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只有一条路径通向 完全没有疫情的一生:
03:47
98%, or 0.98, multiplied by itself 75 times,
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98%, 或者是 0.98, 乘以它自己 75 次,
03:52
which comes out to roughly 22%.
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结果大约为 22%。
03:55
So the likelihood of living through at least one more COVID 19-level-pandemic
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所以在接下来的 75 年,遇到 至少一次 COVID-19 级别疫情的
03:59
in the next 75 years is 100 minus 22%, or 78%.
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概率为 100% 减去 22%, 或者是 78% 。
04:05
78%!
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78% !
04:07
If we use the most optimistic yearly estimate— 0.5%—
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如果我们使用最乐观的 全年估计 —— 0.5% ——
04:11
the lifetime probability drops to 31%.
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那么一生中的可能性 降到了 31% 。
04:15
If we use the most pessimistic one, it jumps to 92%.
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如果我们使用最悲观的全年估计, 那结果大幅增长至 92% 。
04:19
Even 31% is too high to ignore;
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即使是 31% , 也大到了我们不能忽视的程度。
04:22
even if we get lucky, future generations might not.
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即使我们够幸运, 后代们未必足够幸运。
04:26
Also, pandemics are usually random, independent events:
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况且,疫情通常是 随机且独立的事件:
04:29
so even if the yearly probability of a COVID-19-level pandemic is 1%,
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所以即使 COVID-19 级别疫情的 全年可能性概率为 1% ,
04:34
we could absolutely get another one in ten years.
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我们在未来的十年内 也很有可能会再遇到一次。
04:38
The good news is we now have tools that make pandemics less destructive.
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好消息是我们现在已经拥有工具 使疫情带来的伤害没那么严重。
04:43
Scientists estimated that early warning systems, contact tracing,
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科学家们估计 早期预警系统、接触者追踪、
04:46
social distancing, and other public health measures
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保持社交距离 以及其他公共卫生措施
04:49
saved over a million lives in just the first six months
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仅仅在美国 COVID-19 疫情 发生的前六个月,
04:52
of the COVID-19 pandemic in the US,
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就拯救了超过一百万人,
04:55
not to mention the millions of lives saved by vaccines.
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更不要说,还有疫苗 拯救了数百万条生命。
04:59
One day, another pandemic will sweep the globe.
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有朝一日,另一场疫情 会再次席卷全球。
05:02
But we can work to make that day less likely to be tomorrow.
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但我们可以努力让那一天不是明天。
05:06
We can reduce the risk of spillover events,
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我们可以降低病毒溢出事件 发生的风险,
05:08
and we can contain spillovers that do happen
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即使发生了病毒溢出, 我们也能有效遏制,
05:11
so they don’t become full-blown pandemics.
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使其不会蔓延成为全面爆发的疫情。
05:14
Imagine how the future might look if we interacted
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想象一个这样的未来,
05:17
with the animal world more carefully,
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人类与动物世界 接触得更加小心谨慎,
05:19
and if we had well-funded, open-access global disease monitoring programs,
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我们拥有资金充足的、开放式的 全球疾病监控项目,
05:23
AI-powered contact tracing and isolation measures,
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人工智能支持的 接触者追踪和隔离措施,
05:26
universal vaccines, next-generation antiviral drugs,
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通用的疫苗, 下一代抗病毒药物,
05:29
and other tech we haven't even thought of.
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以及其他我们想象不到的科技。
05:32
It’s in our power to change these probabilities.
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相信我们有 改变这些可能性的力量。
05:35
So, we have a choice: we could do nothing and hope we get lucky.
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所以我们要做出选择:什么都不做, 祈祷我们足够幸运。
05:38
Or we could take the threat seriously enough
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或者足够重视这些威胁,
05:40
that it becomes a self-defeating prophecy.
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让它们都成为不攻自破的预言。
05:43
Which future would you rather live in?
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你更想活在什么样的未来里?
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