The 1918 Flu Pandemic.

H1N1。

COVID-19。

新冠肺炎。

We tend to think of these catastrophic, world-changing pandemics as very unlikely events,

我們傾向於認為這些改變世界的災難性大流行病是不太可能發生的事件。

But between 1980 and 2020, at least three diseases emerged that caused global pandemics.

但在1980年和2020年之間，至少出現了三種引起全球大流行的疾病。

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

COVID-19，是的，但是還有H1N1跟愛滋病。

Disease outbreaks are surprisingly common.

疾病的爆發出乎意料地常見。

Over the past four centuries, the longest stretch of time without a documented outbreak that killed at least 10,000 people was just four years.

在過去的四個世紀，沒有記錄中造成至少萬人死亡的疫情爆發的時期，最長的一段僅有四年。

As bad as these small outbreaks are, they're far less deadly than a COVID-19-level pandemic.

儘管這些小規模的爆發很糟糕，但它們的致命性遠遠低於COVID-19級別的大流行病。

In fact, many people born after the 1918 flu lived their entire lives without experiencing a similar world-changing pandemic.

事實上，許多在H1N1之後出生的人，一生都沒有經歷過類似這種改變世界的大流行病。

What's the probability that you do, too?

你也是如此的機率有多大？

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.

你可以閱覽一下歷史。

A team of scientists and engineers who took this approach catalogued all documented epidemics and pandemics between 1600 and 1950.

一個由科學家和工程師組成的團隊採取這種方法，對1600年至1950年間所有記錄在案的流行病和大流行病進行了分類。

They used that data to do two things.

他們利用這些數據做了兩件事。

First, to graph the likelihood that an outbreak of any size pops up somewhere in the world over a set period of time.

首先，繪製一段時間內世界上某個地方、爆發任何規模的疫情。

And second, to estimate the likelihood that that outbreak would get large enough to kill a certain percentage of the world's population.

第二，估計疫情大到足以殺死世界上一定人口的可能性。

This graph shows that while huge pandemics are unlikely, they're not that unlikely.

這張圖顯示，雖然巨大的流行病不太可能發生。但也並不是那麼不可能。

The team used these two distributions to estimate that the risk of a COVID-19-level pandemic is about 0.5% per year,

該小組使用這兩個分佈來估計，每年發生COVID-19級別的大流行病的機率約為0.5%，

and could be as high as 1.4% if new diseases emerge more frequently in the future.

如果未來更頻繁地出現新的疾病，則可能高達 1.4%。

And we'll come back to those numbers,

我們再來回看看這些數字。

but first, let's look at another way to estimate the likelihood of a future pandemic: modeling one from the ground up.

但首先，讓我們看看用另一種方式來估計未來大流行病的可能性：從頭開始模擬一個大流行病。

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

對於大多數大流行病的發生，病原體（一種能夠引起疾病的微生物）

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

必須透過接觸和感染人類，而從其正常宿主那裡擴散。

Then, the pathogen has to spread widely, crossing international boundaries and infecting lots of people.

然後，病原體必須廣泛傳播，並跨越國界感染許多人。

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

許多變量決定了一個特定的擴散事件是否成為大流行。

For example, the type of pathogen, how often humans come into close contact with its animal reservoir, existing immunity, and so on.

例如，病原體的種類、人類密切接觸其動物宿主的頻率，及現有的免疫力等等。

Viruses are prime candidates to cause the next big pandemic.

病毒是引發下一次大流行的主要可能因素。

Scientists estimate that there are about 1.7 million as-yet-undiscovered viruses that currently infect mammals and birds,

科學家們估計大約有170萬種尚未被發現的病毒，正在感染哺乳動物和鳥類，

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

而其中約40%的病毒有可能擴散並感染人類。

A team of scientists built a model using this information, as well as data about the global population, air travel networks, how people move around in communities,

一個科學家小組利用這些資訊，以及有關全球人口、航空網、人們如何在社區內活動、

country preparedness levels, and how people might respond to pandemics.

國家的準備水準，和人們可能如何應對大流行病的數據來建立了一個模型。

The model generated hundreds of thousands of virtual pandemics.

該模型產生了數十萬個虛擬的大流行病。

The scientists then used this catalog to estimate that the probability of another COVID-19-level pandemic is 2.5 to 3.3% per year.

然後，科學家們利用這個目錄來估計每年發生另一場COVID-19級大流行的機率是2.5至3.3%。

To get a sense of how these risks play out over a lifetime, let's pick a value roughly in the middle of all these estimates: 2%.

為了瞭解我們一生中會有多少風險遇到這些大流行病，讓我們在這些所有估計值中大致選擇一個中間值—2%。

Now let's build what's called "a probability tree diagram" to model all possible scenarios.

現在我們來建立一個所謂的「樹狀圖」來模擬所有可能的情況。

The first branch of the tree represents the first year:

樹的第一個分支代表第一年：

There's a 2% probability of experiencing a COVID-19-level pandemic, which means there's a 98% probability of not experiencing one.

發生COVID-19級大流行病的機率為2%，這意味著有98%的概率我們不會經歷過。

Second branch, same thing,

第二支部，機率相同。

Third branch, same.

第三支部，一樣。

And so on, 72 more times.

以此類推，再進行72次。

There is only one path that results in a fully pandemic-free lifetime: 98%, or 0.98, multiplied by itself 75 times, which comes out to roughly 22%.

只有一條路徑可以使人在一生中完全不會經歷大流行病—98%，即0.98，它的75次方大約是22%。

So the likelihood of living through at least one more COVID 19-level-pandemic in the next 75 years is a hundred minus 22% or 78%.

因此，在接下來的75年內至少再經歷一次COVID-19級大流行的可能性，是100減去22%，即78%。

78%！

78%！

If we use the most optimistic yearly estimate— 0.5%—, the lifetime probability drops to 31%.

如果我們使用最樂觀的年度估計值—0.5%，那麼一生再經歷一次COVID-19級大流行的機率將下降到31%。

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

如果我們採用最悲觀的機率，它就會增加到92%。

Even 31% is too high to ignore;

即使是31%，也是高得不能再高了。

even if we get lucky, future generations might not.

即便我們運氣好，我們的後代也未必如此幸運。

Also, pandemics are usually random, independent events:

另外，大流行病通常是隨機的、獨立的事件。

So even if the yearly probability of a COVID-19-level pandemic is 1%, we could absolutely get another one in ten years.

因此，即使每年發生COVID-19級大流行的概率為1%，也絕對有可能在十年內再爆發另一場大流行。

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

好消息是我們現在有工具能讓大流行病的破壞性降低。

Scientists estimated that early warning systems, contact tracing, social distancing, and other public health measures saved over a million lives

科學家們估計，早期預警系統、接觸者追蹤、社交距離、以及其他公共衛生措施，

in just the first six months of the COVID-19 pandemic in the US,

在美國的COVID-19大流行的情況下，僅僅在頭六個月就拯救了超過一百萬條生命。

not to mention the millions of lives saved by vaccines.

更不用說疫苗所拯救了數百萬人的生命。

One day, another pandemic will sweep the globe.

有一天，另一種大流行病將席捲全球。

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

但我們可以努力使這一天不要太快發生。

We can reduce the risk of spillover events, and we can contain spillovers that do happen so they don't become full-blown pandemics.

我們可以減少擴散事件的風險，而且我們可以控制可能會發生的擴散，所以它們就不會成為全面的大流行病。

Imagine how the future might look if we interacted with the animal world more carefully,

想象一下，如果我們與動物世界的互動更加謹慎，未來會是什麼樣子？

and if we had well-funded, open-access global disease monitoring programs, AI-powered contact tracing and isolation measures,

以及如果我們有資金充足、可公開取用的全球疾病監測程式、由人工智能驅動的接觸者追蹤系統和隔離措施、

universal vaccines, next-generation antiviral drugs, and other tech we haven't even thought of.

普及的疫苗、次世代抗病毒藥物，以及其他我們甚至沒有想到的技術。

It's in our power to change these probabilities.

我們有能力改變這些機率。

So, we have a choice: we could do nothing and hope we get lucky, or we could take the threat seriously enough that it becomes a self-defeating prophecy.

因此，我們可以選擇什麼都不做並期望能幸運逃過一劫；或者我們可以認真對待這個威脅，讓它變成一個自我挫敗的預言。

Which future would you rather live in?

你想要生活在哪個未來？

Luckily for us, the virus that cause pandemic can and do go extinct.

幸運的是，引起大流行病的病毒能夠被滅絕，也確實曾被滅絕。

Find out what it takes to make that happen and how long with this video.

請觀看這支影片來了解是什麼讓它發生，以及需要多久時間。

Or watch this video to learn more about the one sickness that we can never seem to get rid of—the common cold.

或是觀看這支影片來更了解一個我們永遠無法擺脫的疾病—普通感冒。