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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


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.


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?


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?


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.


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.


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.


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.


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.


“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.


“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.


“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.”


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.


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.”


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.


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.


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.


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.”


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.


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.


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.”


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.



“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.


“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.”


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.


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.


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.


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.”


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: 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.


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.


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.


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.


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.


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.


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.


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.


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?


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.


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.


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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