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科学美国人(翻译):COVID-19预测路径和分析免疫 2020.03.17

COVID-19: Predicting the Path and Analyzing Immunity

COVID-19:预测路径和分析免疫

SM: This is part two of this episode in our series of coronavirus editions of Scientific American’s Science Talk, posted on March 24, 2020. I’m Steve Mirsky.

这是我们发布于2020年3月24日的《科学美国人》关于冠状病毒的科学谈话系列的第二部分。我是史蒂夫·米尔斯基。

In part one of this episode, which we posted yesterday, contributing editor W. Wayt Gibbs reported on research showing that it’s likely that most people are catching COVID-19 from others who feel fine or think their mild symptoms are due to allergies or a cold. And you can probably also pick up the infection from virus deposited many hours earlier on inanimate objects like a gas pump or a credit card or a pen.

在我们昨天发布的这一集的第一部分中,特约编辑W. Wayt Gibbs报道了一项研究,表明大多数人很可能从感觉良好或认为自己的轻微症状是由过敏或感冒引起的其他人那里感染了covid19。你可能也会感染病毒,这些病毒在几个小时前就已经附着在无生命的物体上了,比如气泵、信用卡或笔。

Now, in part two, Gibbs talks with computational epidemiologist Lauren Ancel Meyers of the University of Texas about how scientists are building highly detailed computer models of the pandemic to predict when we’ll be able to ease up on social distancing. He also updates us on tests for immunity to the virus that are now coming out of research labs. Society, he warns, could face some tough ethical questions if we start treating people who are immune differently from those who are not.

现在,在第二部分中,Gibbs与德克萨斯大学的计算流行病学家Lauren Ancel Meyers讨论了科学家如何建立高度详细的流感大流行计算机模型来预测我们何时能够缓和社会距离。他还向我们提供了最新的病毒免疫测试结果,这些结果目前正从研究实验室中出来。他警告说,如果我们开始区别对待那些免疫的人和那些免疫的人,社会可能会面临一些棘手的道德问题。

Wayt recorded this episode on March 23.WWG: I posed four crucial questions about the coronavirus pandemic in part one of this episode. Let’s pick up now where we left off, with question three:What combination of shutdowns, closures and quarantines will most effectively reduce the death toll from COVID-19?

Wayt在3月23日录制了这段视频。《华尔街日报》:在本集的第一部分中,我提出了关于冠状病毒大流行的四个关键问题。让我们接着上一个问题:关闭、关闭和隔离哪些措施能最有效地减少COVID-19的死亡人数?

And what does the end game look like for this pandemic? When can we responsibly start reopening our office buildings, schools, restaurants, theaters and stadiums?

这场大流行的结局会是怎样的呢?我们何时才能负责任地重新开放办公楼、学校、餐馆、剧院和体育场?

There are no certain answers to these questions yet. But we aren’t completely in the dark.For decades, scientists have studied how infectious epidemics spread and eventually end. They’ve built and tested mathematical models and software that can replicate that behavior in a computer.

这些问题还没有确定的答案。但我们并非完全一无所知。几十年来,科学家们一直在研究传染病是如何传播并最终结束的。他们已经建立并测试了数学模型和软件,这些模型和软件可以在计算机中复制这种行为。

Of course, no model can capture the full complexity of the real world. And there is quite a bit about COVID-19 that we still don’t know. So the predictions these models make will never be 100 percent accurate.

当然,没有一个模型能够完全捕捉现实世界的复杂性。关于COVID-19还有很多我们不知道的地方。因此,这些模型做出的预测永远不会百分之百准确。

That said, when a wide range of models all start to point in the same direction—as, to take a different example, global warming models now do—then we need to pay attention.

也就是说,当各种各样的模型都开始指向同一个方向时——举个不同的例子,现在的全球变暖模型——那么我们需要注意了。

Last week, a large team of epidemiologists at the [MRC] Center for Global Infectious Disease Analysis at Imperial College London released a report with predictions from one of the most sophisticated models of this kind. They took software they had already developed and validated for pandemic influenza and then adapted it to make forecasts of the COVID-19 pandemic for Great Britain and the United States. The results in that report make for pretty sobering reading.

I’ll talk about those in a minute.

上周,伦敦帝国理工学院(Imperial College London) [MRC]全球传染病分析中心(Center for Global Infectious Disease Analysis)的一个大型流行病学家团队发布了一份报告,其中的预测来自最复杂的此类模型之一。他们将已经开发并验证的用于大流行性流感的软件用于英国和美国的COVID-19大流行预测。那份报告的结果读起来相当发人深省。我一会儿会讲到这些。

But first it’s important to understand a few key numbers that these models use to predict how the disease will spread. Numbers like how many others get infected by each COVID-19 case on average—that’s called the reproduction number, R. Another is the average number of days between someone catching the virus and passing it on to someone else—that’s called the serial interval. Hospitalization and mortality rates for each age group are also important. I spoke with a computational epidemiologist who has been working to improve estimates of these numbers so that model predictions can be more accurate.

但首先,重要的是要了解这些模型用来预测疾病如何传播的几个关键数字。像每个病毒感染了多少人这样的数字——平均19个病例——叫做繁殖数,r。另一个是感染病毒的人传染给其他人的平均天数——叫做连续时间间隔。每个年龄组的住院和死亡率也很重要。我与一位计算流行病学家进行了交谈,他一直在努力改进对这些数字的估计,以便使模型预测更准确。

LAM: “My name is Lauren Ancel Meyers. I'm a professor at the University of Texas at Austin. And I am a pandemic modeler. My background is in mathematical modeling of infectious disease dynamics.

LAM:“我的名字叫Lauren Ancel Meyers。我是德克萨斯大学奥斯汀分校的教授。我是一个流行病模型师。我的专业是传染病动力学的数学建模。

“Estimates for the basic reproduction number have ranged a little bit, but many have been in the range of two to three, meaning that the average infected person early in an uncontrolled epidemic is infecting maybe two to three other people, and that's an average.

“对基本繁殖数量的估计略有不同,但很多都在2到3之间,这意味着在一场无法控制的传染病的早期,平均感染人数可能是2到3人,这是平均水平。”

“The variation in the number of secondary cases can be quite wide. You can have some individuals who don't spread disease to anyone—or maybe to just one other person—and then other individuals who are super spreaders, people who are spreading to an unusually high number of other people.

“继发性病例数量的变化可能相当大。有些人不会传播疾病给任何人,或者可能只传播给一个人,而另一些人则是超级传播者,他们会传播给数量异常多的其他人。

“If you think about the reproduction number, there are really two parts to it. There is how intrinsically contagious you are. Is it a disease that leads to coughing, sneezing—causes you to be ill in a way that spreads virus? And then the other part of it is how many people you actually come in contact with.

“如果你考虑复制的数量,实际上有两个部分。这就是你的内在感染力。它是一种会导致咳嗽、打喷嚏的疾病吗?它会导致你生病并传播病毒吗?另一部分是你实际接触了多少人。

“And you can reduce the reproduction number by changing either one of those. And what social distancing interventions do is they're reducing the opportunities for transmission. They’re reducing the contacts that take place between infected and susceptible people.”

你可以通过改变其中任何一个来减少繁殖数量。社会距离干预所做的就是减少传播的机会。他们减少了感染者和易感人群之间的接触。”

WWG: In a paper published on March 19 in the journal Emerging Infectious Diseases, Meyers and her colleagues in Texas, France and China used case histories of more than 400 COVID-19 patients to determine how quickly the virus hopped from one person to the next.

WWG:在3月19日发表在《新发传染病》杂志上的一篇论文中,Meyers和她来自德克萨斯州、法国和中国的同事使用了400多例19例covid19患者的病史来确定病毒从一个人传播到另一个人的速度。

LAM: “So that’s called the serial interval. We estimated that the average length of the serial interval was around four days. So that’s pretty fast. And moreover, we found that over 10 percent of those—the secondary case actually reported feeling symptoms before the primary case, meaning that the infector, the primary case, probably was contagious and spread disease before they even knew they were sick, before they started feeling ill.”

林:“这就是所谓的序列间隔。我们估计序列间隔的平均长度为4天左右。这是非常快的。而且,我们发现超过10%的人在主诉之前就已经有了症状,这意味着主诉的感染者,可能在他们知道自己生病之前,在他们开始感觉生病之前,就已经有了传染性和传染性疾病了。”

WWG: The model Neil Ferguson and his team at Imperial College developed actually used a slower serial interval of six and a half days, and it explored the effects of reproductive numbers ranging between 2 and 2.6.

WWG:帝国理工学院的Neil Ferguson和他的团队开发的模型实际上使用了一个较慢的连续6天半的间隔,并且探索了2到2.6之间的生殖数的影响。

The program essentially creates a virtual country in a computer, complete with virtual cities, schools, workplaces and hospitals, all based on official statistics for Great Britain. As it runs, it simulates all the interactions that govern how an infectious disease spreads over months and years.

To estimate how effective social distancing and other control measure will likely be, they first simulated an imaginary, worst-case scenario in which we did none of those things and simply let COVID-19 rampage through the U.S. unchecked. The results from that simulation show why no country should contemplate taking a hands-off approach to this pandemic.

该程序基本上是在计算机中创建一个虚拟国家,包括虚拟城市、学校、工作场所和医院,所有这些都是基于英国的官方统计数据。当它运行时,它模拟了所有控制传染病如何在数月或数年内传播的相互作用。为了估计社会疏远和其他控制措施的效果,他们首先模拟了一个假想的最坏的情况,在这种情况下,我们什么都不做,任由covid19在美国横行。模拟的结果表明,为什么没有一个国家应该考虑对这一流行病采取不干涉的态度。

The model predicts that without quarantines or social distancing, mortality from the pandemic would skyrocket for the new few months, peak in June or July, and then decline through September, in a steep bell curve. If we took no measures to flatten that bell curve, the model predicts that over 80 percent of the U.S. population would be infected, and fatalities would number in the millions.

And that is why the U.S. and governments around the world have already taken big steps to flatten the curve, and the model predicts that they can be very effective. In fact, the main point of this study was not to scare people but to try to understand which combinations of control measures are likely to be most effective.

该模型预测,如果不进行隔离或与社会保持距离,甲型h1n1流感的死亡率在未来几个月将大幅上升,在6月或7月达到峰值,然后在整个9月呈陡峭的钟形曲线下降。如果我们不采取措施使钟形曲线变平,该模型预测超过80%的美国人口将会受到感染,死亡人数将达数百万。这就是为什么美国和世界各国政府已经采取了很大的措施来拉平这条曲线,而且该模型预测这些措施会非常有效。事实上,这项研究的主要目的不是吓唬人们,而是试图了解哪些控制措施的组合可能是最有效的。

The epidemiologists looked at various options combining five different control measures, including restrictions many of us are now living with: school and university closures, voluntary quarantines of the sick and their contacts for two weeks, and prohibition of social gatherings that are restrictive enough to reduce contacts among individuals by 75 percent outside of work and the home.

流行病学家看着各种选项结合五个不同的控制措施,包括限制我们中的许多人现在住:学校和大学闭包,自愿检疫的病人和他们的联系了两个星期,和禁止限制的社交聚会足以减少75%的个体之间的联系工作和家庭之外。

There’s some good news in these simulation results. It looks like closing schools, isolating confirmed cases and getting everyone to practice social distancing should drive the number of critical COVID-19 cases down to a level our health care systems can handle. The authors write that because it remains to be seen how people will respond to these controls, “it is difficult to be definitive about the likely initial duration of measures that will be required, except that it will be several months.”

这些模拟结果中有一些好消息。看来关闭学校,隔离确诊病例,让每个人都进行社交活动,应该会将19例关键病例的数量降低到我们的医疗系统能够处理的水平。作者写道,由于人们对这些控制措施的反应还有待观察,“很难确定可能需要的初步措施持续时间,除了几个月之外。”

But there’s also some bad news. In these simulations, infections start to shoot up quickly as soon as we let people congregate once again. The model predicts that within a few weeks of lifting the restrictions, we’re pretty much back where we started.

但也有一些坏消息。在这些模拟中,一旦我们让人们再次聚集,感染就会迅速增加。该模型预测,在取消限制的几周内,我们基本上回到了起点。

So until immunity to the disease becomes common, flattening the curve is like stepping on a balloon. You can change the shape of it by a lot, but the volume—the total number of people who will ultimately catch this virus—that is likely to remain about the same. And that’s because the epidemic can be reseeded and grow again by anyone who still has it, anywhere in the world—unless you can chase down every infected person and isolate them before they infect someone else.

因此,在对这种疾病的免疫力变得普遍之前,平坦的曲线就像踩在气球上一样。你可以改变它的形状很多,但体积——最终感染这种病毒的总人数——很可能保持不变。这是因为在世界上任何地方,这种流行病都可以被任何仍在感染它的人重新播种和繁殖——除非你能追踪每一个感染者,并在他们感染其他人之前隔离他们。

The researchers write: “To avoid a rebound in transmission, these policies will need to be maintained until large stocks of vaccine are available to immunise the population—which could be 18 months or more.”

研究人员写道:“为了避免传播的反弹,这些政策将需要维持到大量疫苗可用于人群免疫——可能需要18个月或更长时间。”

Other pandemic experts have come to similar conclusions. Here’s Ira Longini of the University of Florida:IL: “It’s probably only a vaccine that can really control the epidemic to a large extent by reducing the susceptibility of vaccinated people and also, if they do get infected, reducing the transmissibility to others.”

其他流行病专家也得出了类似的结论。佛罗里达大学的Ira Longini说:“这可能只是一种能够在很大程度上通过减少接种人群的易感性来控制疫情的疫苗,而且,如果他们真的被感染了,也会减少传染给其他人的可能性。”

WWG: After reading the report from Imperial College, Lauren Meyers had this to say:

LAM: “I think that their study is very plausible. I think there's a lot of uncertainty about when and how we will be able to relax some of these measures and yet keep this threat contained.

《华尔街日报》:读了帝国理工学院的报告后,劳伦·迈耶斯说:

LAM:“我认为他们的研究很有道理。我认为,我们何时以及如何能够放松其中一些措施,同时又能控制住这种威胁,这方面存在很多不确定性。

“I think that as the modeling community and public health community coalesce around this understanding that this is going to be a very challenging virus to control going forward, I think we are turning our attention to really thinking in a quantitative way and an innovative way about how we can, in the long run, continue to contain the virus while not having such a devastating social and economic cost on our societies.

“我认为作为建模社区和公共卫生界借鉴这种理解,这将是一个非常具有挑战性的病毒控制前进,我想我们会把注意力转移到想以量化方式和一种创新的方式如何,从长远来看,继续包含病毒而没有这种毁灭性的社会和经济成本在我们的社会。

“And I don't think we have the answers yet, but part of the answer will probably be maybe slowly relaxing social distancing measures, allowing us to get back to life as normal slowly, being very vigilant about testing in isolation and quarantine of cases and their contacts if, and when, disease is reintroduced into a community that has succeeded in controlling it.”

“我不认为我们有答案,但是答案的一部分可能会也许慢慢放松社会距离的措施,让我们慢慢回到正常生活,非常警惕测试在隔离检疫的情况下和他们联系,如果和当疾病重新进入一个社区,成功地控制它。”

WWG: China is now testing that approach by gradually loosening the intense lockdown it has imposed since January on much of its population. Let’s hope that what happens next in China proves that the modelers missed something important and that it is actually possible to end the epidemic in a large country even though the vast majority of the population still lacks immunity to the disease.

WWG:中国现在正在测试这种方法,逐步放松自1月份以来对其大部分人口实施的严密封锁。让我们希望接下来在中国发生的事情能够证明建模者忽略了一些重要的东西,并且在一个大国中,即使绝大多数人仍然对这种疾病缺乏免疫力,也有可能结束这种流行病。

It’s frustrating that no one can predict when a vaccine will be available. Numerous labs around the world are working at a breakneck pace to create and test vaccine. But no one has succeeded yet in making vaccines against the two other deadly coronaviruses that cause SARS and MERS.

令人沮丧的是,没有人能预测疫苗何时可用。世界各地的许多实验室正在以极快的速度研制和试验疫苗。但迄今为止,还没有人成功研制出针对另外两种致命的冠状病毒的疫苗,这两种病毒会导致非典和中东呼吸综合症。

Meyers says that, as we wait for a vaccine, a kind of herd immunity may well build naturally in the population, as more and more people become infected and then recover.

迈耶斯说,在我们等待疫苗的同时,随着越来越多的人受到感染,然后康复,群体免疫很可能在人群中自然形成。

LAM: “As immunity builds up in a population, the reproduction number starts to decline. There are just not as many options for transmission. And when the reproduction number gets to a point that is below one, that means that every infected case is expected to infect fewer than one other person. And because of that, the epidemic will start to peter out and eventually disappear.”

LAM:“随着种群免疫力的增强,繁殖数量开始下降。只是没有那么多的传播选择。当繁殖数量低于1时,就意味着每个感染病例的感染人数将少于1人。正因为如此,这种流行病将开始逐渐消失,并最终消失。”

WWG: But how will we know how much herd immunity has built up? A serologic test—one that looks for antibodies for the disease inside the blood—might help with that. Here’s Longini again:

IL: “We need a serologic test that’s specific to this virus—and we don’t have one right now—so we could find out what proportion of the population was infected and how much herd immunity there may be out there, keeping in mind the caveat that immunity may not be durable.”

WWG:但是我们怎么知道群体免疫已经建立了多少?一项血清学测试——一项在血液中寻找疾病抗体的测试——可能有助于解决这个问题。Longini再次:

IL:“我们需要对这种病毒进行专门的血清学检测,但我们现在还没有这样的检测,这样我们就可以知道有多少人感染了这种病毒,有多少人具有群体免疫力,要记住,这种免疫力可能不会持久。”

WWG: So now we’ve arrived to the last of those crucial questions I posed at the beginning—how each of us will know when we have immunity and no longer need to worry about catching COVID-19 or giving it to someone else—and also the question of what we will do with that information once we have it.

WWG:现在我们已经到了最后的关键问题我提出开始,我们每个人都知道当我们有免疫力,不再需要担心捕捉COVID-19或者给别人走近你的问题我们将怎么处理这些信息一旦我们拥有它。

Researchers have already shown that people who are infected with this coronavirus do develop antibodies to it. BioMedomics, a company in North Carolina, has even developed a fast test for those antibodies. It works much like a pee-on-a-stick pregnancy test, except the technician places a small drop of blood, along with a few drops of buffer solution, onto the paper strip. Fifteen minutes later, lines appear that tell you whether you have the IgG antibodies that reveal you were exposed to a coronavirus at some point in the past.

研究人员已经证明,感染这种冠状病毒的人确实会产生抗体。北卡罗来纳州的生物医学组学公司甚至开发了一种针对这些抗体的快速测试。它的工作原理很像验孕棒上的尿,只是技术人员在试纸上滴了一小滴血和几滴缓冲液。15分钟后,会出现一些线条,告诉你是否有IgG抗体,这些抗体表明你曾经接触过冠状病毒。

The current version of this test will read positive even if you’ve been exposed to any coronavirus, including the four mostly harmless human coronaviruses that cause the common cold. But in the near future, it’s likely that rapid diagnostics like this will be able to prove that someone has some immunity to COVID-19.

目前的检测结果为阳性,即使你接触过任何冠状病毒,包括导致普通感冒的四种基本无害的人类冠状病毒。但在不久的将来,像这样的快速诊断将有可能证明某人对COVID-19有一定的免疫力。

But we don’t know yet whether people who have antibodies against SARS-CoV-2 could still get reinfected by the virus and possibly give it to others for a day or two before their immune system wipes it out.

但是我们还不知道那些有SARS-CoV-2抗体的人是否仍然会被病毒再次感染,并可能在他们的免疫系统将其消灭之前将其传染给其他人一两天。

Researchers in China reported this week some encouraging results from a study on rhesus monkeys. Two weeks after the monkeys were infected with COVID-19, became sick and recovered, the scientists tried to reinfect the animals with the virus. The infection didn’t take.

中国的研究人员本周报告了一项猕猴研究的令人鼓舞的结果。在猴子感染了COVID-19两周后,它们生病并痊愈了,科学家们试图用病毒重新感染这些动物。感染没有发生。

That was a small and short study, but it gives some reason to hope that the same will be true for people as well. We’ll know it isn’t if people who have recovered from COVID-19 start coming down with the disease a second time.

这是一个小而短的研究,但它给了我们一些理由,希望同样的情况也会发生在人类身上。我们知道,从COVID-19恢复过来的人,不会再次患上这种疾病。

But if immunity to COVID-19 is long-lasting and does prevent people who have antibodies to the virus from infecting others, then we should think very carefully about how we use these antibody tests.

但是,如果对COVID-19的免疫力是持久的,并且确实能够防止具有该病毒抗体的人感染他人,那么我们应该非常仔细地考虑如何使用这些抗体测试。

A paper in the Journal of Medical Virology last month, written by doctors in China affiliated with BioMedomics, suggested that the 15-minute, paper-strip tests could be administered at train stations and airports to screen passengers.

上个月,《医学病毒学杂志》(Journal of Medical Virology)上刊登了一篇由生物医学组(BioMedomics)的中国医生撰写的论文。文中建议,可以在火车站和机场对乘客进行15分钟的纸带测试。

But consider the ripple effects of policies that allow only those who can demonstrate immunity to COVID-19 to resume travel, to go to theaters and restaurants, to work closely with others—while those who have yet to catch the virus cannot do any of those things.

但是考虑到政策的连锁反应,这些政策只允许那些对毒品有免疫力的人恢复旅行,去剧院和餐馆,与他人密切合作,而那些尚未感染病毒的人却不能做这些事情。

Will some nations effectively set up two-tiered societies, creating powerful incentives for some people—young people in particular—to infect themselves in order to develop immunity and escape the economic and psychological burdens of social distancing?

一些国家是否会有效地建立起两级社会,为一些人——尤其是年轻人——创造强大的激励,让他们感染自己,以发展免疫力,并逃避社会疏远带来的经济和心理负担?

Throughout the history of public health, there’s been a tension between the inequity of individual quarantines and the protection of society at large from greater harm. Now many states have taken the hard and unprecedented decision to effectively put everyone under quarantine. But if these restrictions stretch on for month after month after month—as the models suggest they might need to—governments will also face hard decisions about how to lift those quarantines—selectively or universally, temporarily or permanently. Now is the time to start plotting a smart—and equitable—end-game strategy for this pandemic.

纵观公共卫生的历史,在个体隔离的不公平和保护整个社会免受更大伤害之间一直存在着紧张关系。现在,许多州采取了前所未有的艰难决定,有效地隔离每个人。但是,如果这些限制一个月接着一个月地延续下去——正如模型所显示的那样——政府也将面临艰难的决定,即如何解除这些隔离——有选择地、普遍地、暂时地或永久地。现在是时候为这场大流行制定一个明智而公平的结局策略了。

For Scientific American’s Science Talk, I’m Wayt Gibbs.

SM: And I’m Steve Mirsky. Just wanted to remind you that all of our coronavirus coverage is available for free on our Web site: www.ScientificAmerican.com.

这就是科学美国人的科学谈话,我是韦特·吉布斯。

我是Steve Mirsky。只是想提醒你,我们所有关于冠状病毒的报道都可以在我们的网站上免费获得:www.ScientificAmerican.com。

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