The Black Death. The 1918 Flu Pandemic. COVID-19.
黑死病、 1918 年流感疫情、 COVID-19。
We tend to think of these catastrophic, world-changing pandemics as very unlikely events.
我們傾向於將這些災難性的、 改變世界的流行病 視為不太可能發生的事件。
But between 1980 and 2020, at least three diseases emerged that caused global pandemics. COVID-19, yes, but also the 2009 swine flu and HIV/AIDS.
但從 1980 至 2020 年, 出現至少三次全球大流行的疾病: 除了 COVID-19,還有 2009 年的豬流感和愛滋病。
Disease outbreaks are surprisingly common. 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.
疫情爆發令人驚訝地普遍。 過去這四個世紀中, 沒有發生致死率 達一萬人的疫情的時間── 最長不過四年。
As bad as these smaller outbreaks are, they’re far less deadly than a COVID-19-level pandemic. In fact, many people born after the 1918 flu lived their entire lives without experiencing a similar world-changing pandemic. What’s the probability that you do, too?
這些小規模的疫情再怎麼糟, 殺傷力也比不上 COVID-19 等級的疫情。 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。 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. A team of scientists and engineers who took this approach catalogued all documented epidemics and pandemics between 1600 and 1950. They used that data to do two things. First, to graph the likelihood that an outbreak of any size pops up somewhere in the world over a set period of time. And second, to estimate the likelihood that that outbreak would get large enough to kill a certain percentage of the world's population. This graph shows that while huge pandemics are unlikely, they're not that unlikely. The team used these two distributions to estimate that the risk of a COVID-19-level pandemic is about 0.5% per year, and could be as high as 1.4% if new diseases emerge more frequently in the future.
鑒往知來。 有個科學家和工程師團隊用這個方法, 整理出 1600 至 1950 年 所有的區域流行和跨界的傳染病, 用那些數據去做兩件事。 首先是用圖表畫出在某段時間內 世界各地爆發大小疫情的可能性。 其次,估計疫情爆發的規模 大到足以造成相當比例 世界人口死亡的可能性。 圖表顯示,爆發大規模 疫情的可能性不高, 但也並非不可能。 該團隊使用這兩個機率分布估計出 每年發生 COVID-19 等級的 疫情風險大約 0.5%, 但這數字也可高達 1.4%, 如果有愈來愈多的新型疾病出現。
And we’ll come back to those numbers, but first, let’s look at another way to estimate the likelihood of a future pandemic: modeling one from the ground up.
我們會再回頭看這些數字, 現在先看另一種 預測爆發疫情大流行的方法: 疫情的基礎理論。
For most pandemics to happen, a pathogen, which is a microbe that can cause disease, has to spill over from its normal host by making contact with and infecting a human. Then, the pathogen has to spread widely, crossing international boundaries and infecting lots of people. Many variables determine whether a given spillover event becomes a pandemic. For example, the type of pathogen, how often humans come into close contact with its animal reservoir, existing immunity, and so on.
大多數流行病的病原體── 某種致病的微生物── 必須經過接觸才能 由原宿主轉移到人類身上。 然後,病原體必須廣泛傳播, 跨越邊界去感染很多人。 從區域流行病變成全球疫情 取決於許多變數。 例如,病原體的類型、 人類與原動物宿主密切接觸的頻率、 現有的免疫力等等。
Viruses are prime candidates to cause the next big pandemic. Scientists estimate that there are about 1.7 million as-yet-undiscovered viruses that currently infect mammals and birds, and that roughly 40% of these have the potential to spill over and infect humans.
病毒會是導致下一次 疫情大流行的可能主因。 科學家估計約有 170 萬種病毒尚未被發現, 主要感染哺乳動物和鳥類, 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, as well as data about the global population, air travel networks, how people move around in communities, country preparedness levels, and how people might respond to pandemics. The model generated hundreds of thousands of virtual pandemics. 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.
一班科學家使用這些資訊、 加上全球人口、航空旅行路線、 區域交通方式、各國的防疫水準、 及人們如何因應疫情等資訊 建立了一個模型。 該模型模擬了數十萬次流行病疫情, 科學家們再用該數據去推算出 再次發生 COVID-19 等級疫情的機率是── 每年 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%. Now let’s build what’s called a probability tree diagram to model all possible scenarios. The first branch of the tree represents the first year: there’s a 2% probability of experiencing a COVID-19-level pandemic, which means there’s a 98% probability of not experiencing one. Second branch, same thing, Third branch, same. And so on, 72 more times. 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%. So the likelihood of living through at least one more COVID 19-level-pandemic in the next 75 years is 100 minus 22%, or 78%.
要知道一生中碰到疫情的風險有多高, 我們先從這些預測數字中 取一個中間值: 2%。 再來,我們來畫一個概率樹狀圖, 模擬所有可能發生的情況。 這棵樹的第一個分枝代表第一年, 碰到 COVID-19 等級疫情的概率為 2%, 也就是說不會碰到疫情 的概率為 98%。 第二個分枝,一樣的假設。 第三個分枝,也是一樣。 如此類推,重複 72 次。 結果,一輩子完全不會 碰上疫情的路徑只有一條: 98% 或是 0.98 自身相乘 75 次, 結果大約是 22%。 所以未來的 75 年中碰到 COVID-19 等級疫情的機率是: 100% - 22% = 78%
78%!
78%!
If we use the most optimistic yearly estimate— 0.5%— the lifetime probability drops to 31%. If we use the most pessimistic one, it jumps to 92%.
如果我們採用最樂觀的 每年 0.5% 的機率, 有生之年再碰上 疫情的機率降到 31%。 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; even if we get lucky, future generations might not. Also, pandemics are usually random, independent events: so even if the yearly probability of a COVID-19-level pandemic is 1%, we could absolutely get another one in ten years.
即使是 31%,也高到不容忽視, 就算我們無事, 未來幾代人未必如此幸運。 此外,大流行通常是 隨機的、獨立的事件, 因此,即使發生COVID-19 等級疫情的年機率為 1%, 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. Scientists estimated that early warning systems, contact tracing, social distancing, and other public health measures saved over a million lives in just the first six months of the COVID-19 pandemic in the US, not to mention the millions of lives saved by vaccines.
好消息是我們現在擁有 降低疫情破壞力的工具。 科學家統計顯示出 早期預警系統、接觸者追踪、 社交距離及其他公共衛生措施, 在美國一開始遭受 COVID-19 的前六個月, 拯救了超過 100 萬人的性命, 更別提保護了數百萬人生命的疫苗。
One day, another pandemic will sweep the globe. But we can work to make that day less likely to be tomorrow. We can reduce the risk of spillover events, and we can contain spillovers that do happen so they don’t become full-blown pandemics. Imagine how the future might look if we interacted with the animal world more carefully, and if we had well-funded, open-access global disease monitoring programs, AI-powered contact tracing and isolation measures, universal vaccines, next-generation antiviral drugs, and other tech we haven't even thought of.
有一天,另一場疫情或將席捲全球, 但我們可以盡量延後那一天的到來。 我們可以降低擴散的風險, 針對已經擴散的, 我們也可以將之控制, 免於發展成全面性的疫情。 想像一下未來會是什麼樣子? 如果人類與動物間的互動能更為謹慎、 如果有一個資金充足、 開放的全球疾病監督計劃、 以人工智慧驅動的 接觸史追踪和隔離措施、 全球普及的疫苗、 新一代的抗病毒藥物, 甚至其他我們尚未想到的技術?
It’s in our power to change these probabilities. 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.
能否扭轉這些機率取決於我們。 我們可以選擇甚麼都不做,倚靠運氣, 或者,我們可以正視威脅, 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?