S.

S.

Deaths from Cove in 19, a huge drop from a couple of weeks earlier, when the predictions were more like a couple million deaths.

科夫的死亡人數在19人，比幾周前大幅下降，當時的預測更像是幾百萬人的死亡。

This led to a lot of confusion about where the White House's numbers came from and why the predictions shifted.

這導致了很多人對白宮的數據從何而來，為什麼預測會發生變化感到困惑。

So let's break down where those numbers air coming from To make any prediction about how many people will die from a virus, you have to first know how many people will get it.

所以讓我們來分析一下這些數字是怎麼來的 要預測有多少人會死於病毒，你必須先知道有多少人會感染病毒。

And thats why this number keeps coming up.

這就是為什麼這個數字不斷出現的原因。

The basic reproductive number, or are not is in a totally susceptible population.

基本的繁殖數量，或不是在一個完全易感的人群。

How many people would one person go on to infect?

一個人會去感染多少人？

This is Emily Ricotta, a research fellow with the U.

我是Emily Ricotta，是美國大學的研究員。

S.

S.

National Institutes of Health.

國家衛生研究所。

So if you drop one infected person into a totally susceptible population, how many more cases are you going to see?

那麼，如果你把一個感染者丟到一個完全易感的人群中，你還能看到多少病例？

As you can probably imagine, are not, is pretty important for predicting how bad an outbreak will be if are not is less than one.

你可能可以想象，are not，是相當重要的預測有多糟糕的爆發將是如果are not是小於1。

Over time, you'll end up with fewer and fewer new cases on the disease will die out if are not is more than one.

隨著時間的推移，你會結束與越來越少的新病例上的疾病會死了，如果不是一個以上。

That's when you can start to run into some problems, depending on how severe the illness is.

這時候你就會開始遇到一些問題，這取決於疾病的嚴重程度。

Even an are not of two gets more than 1000 people infected Onley nine links down the chain, so the common cold, if it hasn't, are not of to.

即使是一個不的兩個得到超過1000人感染Onley九個環節的鏈，所以普通的感冒，如果它沒有，都不的到。

How does that change policy?

這如何改變政策？

It doesn't right.

這是不對的。

But if I have a pathogen that hasn't are not of two, and it's killing 1% 10% 50% of the people it infects, I'm going to respond to much differently are not should be pretty simple to calculate its based on three main things.

但是，如果我有一個病原體，沒有是兩個，它的殺1% 10% 50%的人，它感染的，我要去迴應很多不同的是不應該是很簡單的計算其基於三個主要的東西。

Transmissibility, or how likely it is that you'll be infected through contact with someone who has the disease average rate of contact or how many people the average infected person will come in contact with overtime and finally, duration of infectiousness, which is just how long the person spreading the disease is contagious.

傳染性，或者說你通過與患有這種疾病的人接觸而被感染的可能性有多大，平均接觸率或者說平均感染者會在多長時間內接觸到多少人，最後，傳染性持續時間，也就是傳播這種疾病的人傳染性有多長。

For getting those factors involves a whole bunch of calculus that takes into account things like how many people at any given time are susceptible to infection and how many are actually infected.

因為得到這些因素涉及到一大堆的微積分，要考慮到在任何給定時間有多少人容易被感染，以及有多少人實際被感染等問題。

This is what some of the simplest equations look like.

這就是一些最簡單的方程的樣子。

Of course, just having the equations isn't enough.

當然，光有方程是不夠的。

The thing that I want to emphasize about are not, is that it is very specific.

我想強調的是都不是，是它很具體。

Thio the time of the outbreak, the place the population so there's never really just one are not for a passages.

亥時的爆發，地方的人口所以從來沒有真正只是一個都不為通。

And those three factors, you just don't know that information with a new outbreak earlier that you are building these models in an outbreak.

而這三個因素，你只是不知道，資訊與新的爆發更早，你是建立這些模型在爆發。

The harder is because you have more educated guesses and data that's not as specific to the outbreak is.

更難的是，因為你有更多有學問的猜測和數據，沒有那麼具體的爆發是。

You'd want.

你想。

Normally, scientists can estimate things like transmissibility based on data from previous outbreaks.

通常情況下，科學家們可以根據以往爆發的數據來估計傳播性等問題。

But for the early predictions of the spread of cove in 19, all scientists had where the numbers from Wuhan, China, along with the data we've collected about other types of Corona viruses that infect humans.

但是，對於19年的隩的傳播的早期預測，所有的科學家都有來自中國武漢的數字，以及我們收集的關於感染人類的其他類型的電暈病毒的數據。

What we do with especially the early stages of an outbreak, is that we take data from the endemic coronaviruses and we take data from what we saw in stars we take data from Was on Mars, we say, Okay, let's see what happens if we make co vid, uh, you know, spread the same as an endemic coronavirus.

我們所做的，尤其是爆發的早期階段， 是我們從地方性冠狀病毒的數據， 我們從我們看到的星星的數據， 我們從火星上的數據， 我們說，好吧，讓我們看看會發生什麼，如果我們做共同的視頻， 呃，你知道，傳播相同的地方性冠狀病毒。

How many people does that, in fact, And then as we progress and we get more modern data, we start feeding that into the model and updating it as we go.

事實上，有多少人這樣做，然後隨著我們的進步，我們得到更多的現代數據，我們開始將這些數據輸入到模型中，並隨著我們的發展而更新它。

The early estimates for are not based on the initial outbreak in Wuhan were 2.2 to 2.7.

不根據武漢市的初發疫情，早期估計為2.2～2.7。

So more than the flu, which brings us to the more detailed models for Cove in 19 that we were talking about earlier are not is going to be different depending on where you are.

所以比起流感，這就給我們帶來了更詳細的科夫在19年的模型，我們前面說的是不是要根據你所在的地方而有所不同。

So if I drop an infected person into the middle of New York City and I drop it infected person into the middle of rural America, that they're gonna be two very different things because the number of people that are going to come in contact with each other are very different.

所以，如果我把一個受感染的人 到紐約市的中間 我把它感染的人 到美國農村的中間， 他們會是兩個非常不同的事情 因為數量的人 這將是接觸 與對方是非常不同的。

The report that predicted millions of deaths in the US alone, which was put together by researchers at the Imperial College London, used 2.4 as the average are not for the coronavirus.

預測僅在美國就有數百萬人死亡的報告，由倫敦帝國學院的研究人員彙總，使用2.4作為平均數都不是冠狀病毒。

That was based on transmission rates reported early on in China from other data, they estimated things like the percentage of cases where the patient needed to be hospitalized.

那是根據中國早期報告的傳播率從其他數據中得出的，他們估計了一些東西，比如病人需要住院的病例比例。

They predicted that 30% of those hospitalized would need critical care, like a ventilator, based on the rates among early cases.

他們預測，根據早期病例的比例，30%的住院患者需要重症監護，比如呼吸機。

Other factors needed more guesswork, for example, that half of the people who needed critical care would die a number they landed on based on input from clinical experts.

其他因素需要更多的猜測，例如，有一半需要重症監護的人將會死亡，這是他們根據臨床專家的意見降落的數字。

When they use those numbers to model the epidemic in the U.

當他們用這些數字來模擬美國的流行病。

S and Great Britain, they predicted that about 2.2 million people in the U.

S和英國，他們預測美國約有220萬人。

S.

S.

And 510,000 people in Great Britain would die without policies to slow the spread.

而如果沒有政策來減緩蔓延，英國將有51萬人死亡。

Those numbers aren't as relevant anymore, though, since most countries did adopt physical distancing measures.

不過這些數字已經不那麼重要了，因為大多數國家確實採取了物理距離措施。

One major problem with all this is that testing data might not reflect how many people actually have co vid.

所有這些的一個主要問題是，測試數據可能無法反映出有多少人真正擁有co vid。

19 tests haven't been widely available in many places, including the U.

19測試還沒有在很多地方廣泛使用，包括美國。

S.

S.

So plenty of people without symptoms or with mild symptoms aren't being tested, and that totally throws off the number for are not.

所以，很多沒有症狀或症狀輕微的人沒有被測試，這完全拋出的數字為不是。

If you don't know how many people have been infected, you can't really calculate transmissibility.

如果你不知道有多少人被感染，你就無法真正計算傳染率。

That's why at least one major group, the Institute for Health Metrics and Evaluation or I H.

這就是為什麼至少有一個主要的團體，衛生計量與評估研究所或I H。

M.

M.

E, makes its predictions based on reported deaths instead of are not.

E，根據報告的死亡人數進行預測，而不是不。

The group is based out of Washington, and we know that the U.

該集團總部設在華盛頓，我們知道，美。

S government at least is somewhat referencing its model, which might explain why the White House predictions were so different.

S政府至少在一定程度上參考了它的模式，這或許可以解釋為什麼白宮的預測如此不同。

The I H M E team figured that death rates, while not 100% perfect, would still be a more accurate statistic than the number of people who have co vid.

I H M E團隊認為，死亡率雖然不是百分之百的完美，但仍會是一個比co vid的人數更準確的統計數字。

19 people with the most severe cases usually have been getting tested, which means we have at least a semi accurate idea of how many people are dying specifically from the disease.

19個病例最嚴重的人通常已經得到了測試，這意味著我們至少有一個半準確的想法，有多少人正在死於具體的疾病。

By analyzing the pattern of death rates in Wuhan, they were able to come up with a mathematical formula for how different physical distancing measures like closing schools, affected the number of deaths.

通過分析武漢市的死亡率模式，他們能夠得出一個數學公式，說明不同的物理距離措施，如關閉學校，如何影響死亡人數。

Then they applied that model to hot spots in the U.

然後，他們將該模型應用於美國的熱點地區。

S.

S.

As well as the country as a whole, taking into account average death rates for different age groups since populations conditioner in that aspect, basing their model on death rates was also a useful way to predict the number of people who would need to be hospitalized.

以及整個國家，考慮到不同年齡段的平均死亡率，因為人口在這方面有條件，將他們的模型建立在死亡率的基礎上，也是預測需要住院的人數的有用方法。

If you're predicting 100 deaths and previous data is saying 10% of those hospitalized die, you can work backwards.

如果你預測的是100人死亡，而之前的數據是說有10%的住院者死亡，你可以倒推。

And guess that roughly 1000 people were probably hospitalized when the White House released its 100,000 to 240,000 range for the number of deaths in the U.

並猜測白宮公佈美國死亡人數10萬至24萬範圍時，大概有1000人住院。

S.

S.

It cited the I H M E model as a main source it was looking at like with the models based on Are Not.

它把I H M E模型作為主要來源，它正在尋找像基於 "不是 "的模型。

The Emmy predictions continue to change.

艾美獎的預測還在繼續變化。

Is the pandemic continues?

疫情是否還在繼續？

If anything is clear from all of these different models, it's that none of them could be perfectly accurate.

如果說從所有這些不同的模型中，有什麼是明確的，那就是沒有一個模型可以做到完全準確。

These numbers aren't static.

這些數字不是靜態的。

They're constantly evolving, especially as we get new data, especially as we are put into new situations.

它們在不斷地發展，特別是當我們獲得新的數據，特別是當我們被投入到新的環境中時。

Researchers don't really expect to get all of the hard and fast numbers that would allow them to calculate an exact are not, but for our models to be as accurate as possible, we need more reliable data, and the best way to get that data is by testing more people.

研究人員並不真的期望得到所有能讓他們計算出確切的數字都不是，但為了讓我們的模型儘可能準確，我們需要更多可靠的數據，而獲得這些數據的最好方法就是測試更多的人。

So we have better info on how many people catch the virus and when.

這樣我們就能更好地瞭解有多少人感染病毒以及何時感染。

That way, with some clever math, we can get the basic information we need to see what works for slowing Covad, 19 spread and then we can use those strategies to help keep people safe.

這樣一來，通過一些巧妙的數學，我們就可以得到我們所需要的基本資訊，看看什麼東西可以減緩科瓦德、19的傳播，然後我們就可以利用這些策略來幫助保護人們的安全。