Will there be another pandemic in your lifetime?

譯者: Anna Lee 審譯者: Zoe Walmsley

The Black Death.

黑死病、

The 1918 Flu Pandemic.

1918 年流感疫情、

COVID-19.

COVID-19。

We tend to think of these catastrophic, world-changing pandemics

我們傾向於將這些災難性的、 改變世界的流行病

as very unlikely events.

視為不太可能發生的事件。

But between 1980 and 2020,

但從 1980 至 2020 年，

at least three diseases emerged that caused global pandemics.

出現至少三次全球大流行的疾病：

COVID-19, yes, but also the 2009 swine flu and HIV/AIDS.

除了 COVID-19，還有 2009 年的豬流感和愛滋病。

Disease outbreaks are surprisingly common.

疫情爆發令人驚訝地普遍。

Over the past four centuries,

過去這四個世紀中，

the longest stretch of time without a documented outbreak

沒有發生致死率 達一萬人的疫情的時間──

that killed at least 10,000 people was just four years.

最長不過四年。

As bad as these smaller outbreaks are,

這些小規模的疫情再怎麼糟，

they’re far less deadly than a COVID-19-level pandemic.

殺傷力也比不上 COVID-19 等級的疫情。

In fact, many people born after the 1918 flu lived their entire lives

事實上，許多出生於 1918 年流感之後的人，

從沒經歷過這類改變世界的疫情。

without experiencing a similar world-changing pandemic.

What’s the probability that you do, too?

你覺得這發生在你身上的 可能性有多大？

There are several ways to answer this question.

有幾種方法可以回答這個問題。

You could look at history.

鑒往知來。

A team of scientists and engineers who took this approach

有個科學家和工程師團隊用這個方法，

catalogued all documented epidemics and pandemics between 1600 and 1950.

整理出 1600 至 1950 年 所有的區域流行和跨界的傳染病，

They used that data to do two things.

用那些數據去做兩件事。

First, to graph the likelihood that an outbreak of any size

首先是用圖表畫出在某段時間內

pops up somewhere in the world over a set period of time.

世界各地爆發大小疫情的可能性。

And second, to estimate the likelihood that that outbreak would get large enough

其次，估計疫情爆發的規模

to kill a certain percentage of the world's population.

大到足以造成相當比例 世界人口死亡的可能性。

This graph shows that while huge pandemics are unlikely,

圖表顯示，爆發大規模 疫情的可能性不高，

they're not that unlikely.

但也並非不可能。

The team used these two distributions to estimate that the risk

該團隊使用這兩個機率分布估計出

of a COVID-19-level pandemic is about 0.5% per year,

每年發生 COVID-19 等級的 疫情風險大約 0.5%，

and could be as high as 1.4%

但這數字也可高達 1.4%，

if new diseases emerge more frequently in the future.

如果有愈來愈多的新型疾病出現。

And we’ll come back to those numbers,

我們會再回頭看這些數字，

but first, let’s look at another way to estimate the likelihood

現在先看另一種 預測爆發疫情大流行的方法：

of a future pandemic:

modeling one from the ground up.

疫情的基礎理論。

For most pandemics to happen, a pathogen, which is a microbe that can cause disease,

大多數流行病的病原體── 某種致病的微生物──

has to spill over from its normal host by making contact with and infecting a human.

必須經過接觸才能 由原宿主轉移到人類身上。

Then, the pathogen has to spread widely,

然後，病原體必須廣泛傳播，

crossing international boundaries and infecting lots of people.

跨越邊界去感染很多人。

Many variables determine whether a given spillover event becomes a pandemic.

從區域流行病變成全球疫情 取決於許多變數。

For example, the type of pathogen, how often humans come into close contact

例如，病原體的類型、 人類與原動物宿主密切接觸的頻率、

with its animal reservoir, existing immunity, and so on.

現有的免疫力等等。

Viruses are prime candidates to cause the next big pandemic.

病毒會是導致下一次 疫情大流行的可能主因。

Scientists estimate that there are about 1.7 million as-yet-undiscovered viruses

科學家估計約有 170 萬種病毒尚未被發現，

that currently infect mammals and birds,

主要感染哺乳動物和鳥類，

and that roughly 40% of these have the potential to spill over and infect humans.

其中大約 40% 有可能轉移到人類身上。

A team of scientists built a model using this information,

一班科學家使用這些資訊、

as well as data about the global population, air travel networks,

加上全球人口、航空旅行路線、

how people move around in communities, country preparedness levels,

區域交通方式、各國的防疫水準、

and how people might respond to pandemics.

及人們如何因應疫情等資訊 建立了一個模型。

The model generated hundreds of thousands of virtual pandemics.

該模型模擬了數十萬次流行病疫情，

The scientists then used this catalog to estimate

科學家們再用該數據去推算出

that the probability of another COVID-19-level pandemic

再次發生 COVID-19 等級疫情的機率是──

is 2.5 to 3.3% per year.

每年 2.5% 至 3.3%。

To get a sense of how these risks play out over a lifetime,

要知道一生中碰到疫情的風險有多高，

let’s pick a value roughly in the middle of all these estimates: 2%.

我們先從這些預測數字中 取一個中間值： 2%。

Now let’s build what’s called a probability tree diagram

再來，我們來畫一個概率樹狀圖，

to model all possible scenarios.

模擬所有可能發生的情況。

The first branch of the tree represents the first year:

這棵樹的第一個分枝代表第一年，

there’s a 2% probability of experiencing a COVID-19-level pandemic,

碰到 COVID-19 等級疫情的概率為 2%，

which means there’s a 98% probability of not experiencing one.

也就是說不會碰到疫情 的概率為 98%。

Second branch, same thing,

第二個分枝，一樣的假設。

Third branch, same.

第三個分枝，也是一樣。

And so on, 72 more times.

如此類推，重複 72 次。

There is only one path that results in a fully pandemic-free lifetime:

結果，一輩子完全不會 碰上疫情的路徑只有一條：

98%, or 0.98, multiplied by itself 75 times,

98% 或是 0.98 自身相乘 75 次，

which comes out to roughly 22%.

結果大約是 22%。

So the likelihood of living through at least one more COVID 19-level-pandemic

所以未來的 75 年中碰到 COVID-19 等級疫情的機率是：

in the next 75 years is 100 minus 22%, or 78%.

100% - 22% = 78%

78%!

78%！

If we use the most optimistic yearly estimate— 0.5%—

如果我們採用最樂觀的 每年 0.5% 的機率，

the lifetime probability drops to 31%.

有生之年再碰上 疫情的機率降到 31%。

If we use the most pessimistic one, it jumps to 92%.

如果採用用最悲觀的 3.3%， 機率則躍升至 92%。

Even 31% is too high to ignore;

即使是 31%，也高到不容忽視，

even if we get lucky, future generations might not.

就算我們無事， 未來幾代人未必如此幸運。

Also, pandemics are usually random, independent events:

此外，大流行通常是 隨機的、獨立的事件，

so even if the yearly probability of a COVID-19-level pandemic is 1%,

因此，即使發生COVID-19 等級疫情的年機率為 1%，

we could absolutely get another one in ten years.

幾乎可以肯定十年內會再次發生。

The good news is we now have tools that make pandemics less destructive.

好消息是我們現在擁有 降低疫情破壞力的工具。

Scientists estimated that early warning systems, contact tracing,

科學家統計顯示出 早期預警系統、接觸者追踪、

social distancing, and other public health measures

社交距離及其他公共衛生措施，

saved over a million lives in just the first six months

在美國一開始遭受 COVID-19 的前六個月，

of the COVID-19 pandemic in the US,

拯救了超過 100 萬人的性命，

not to mention the millions of lives saved by vaccines.

更別提保護了數百萬人生命的疫苗。

One day, another pandemic will sweep the globe.

有一天，另一場疫情或將席捲全球，

But we can work to make that day less likely to be tomorrow.

但我們可以盡量延後那一天的到來。

We can reduce the risk of spillover events,

我們可以降低擴散的風險，

and we can contain spillovers that do happen

針對已經擴散的， 我們也可以將之控制，

so they don’t become full-blown pandemics.

免於發展成全面性的疫情。

Imagine how the future might look if we interacted

想像一下未來會是什麼樣子？

with the animal world more carefully,

如果人類與動物間的互動能更為謹慎、

and if we had well-funded, open-access global disease monitoring programs,

如果有一個資金充足、 開放的全球疾病監督計劃、

AI-powered contact tracing and isolation measures,

以人工智慧驅動的 接觸史追踪和隔離措施、

universal vaccines, next-generation antiviral drugs,

全球普及的疫苗、 新一代的抗病毒藥物，

and other tech we haven't even thought of.

甚至其他我們尚未想到的技術？

It’s in our power to change these probabilities.

能否扭轉這些機率取決於我們。

So, we have a choice: we could do nothing and hope we get lucky.

我們可以選擇甚麼都不做，倚靠運氣，

Or we could take the threat seriously enough

或者，我們可以正視威脅，

that it becomes a self-defeating prophecy.

讓它成為一個不攻自破的預言。

Which future would you rather live in?

哪一個未來是你所樂見的呢?