@BenK78457034 @Nutmeg031992 @AbraarKaran Probably others too, that are easy adjustments people make to "be safe", like going to the store when it is less crowded. Those are harder to measure without real time data.

@BenK78457034@Nutmag031992@AbraarKaran可能还有其他人，这是人们为了“安全”而做的简单调整，比如在不太拥挤的时候去商店。如果没有实时数据，这些数据更难测量。

@BenK78457034 @Nutmeg031992 @AbraarKaran "Avoiding crowds" encompasses a lot. Do you work from home or in a crowded office? Do you eat in restaurants (and which ones)? Do you take transit (and how crowded is it). These are the changes that people demonstrably make.

@BenK78457034@Nutmag031992@AbraarKaran“避开人群”包含很多内容。你是在家工作还是在拥挤的办公室工作？你在餐馆吃饭吗（哪些餐馆）？你搭公交吗（有多拥挤）。这些都是人们明显做出的改变。

@Nutmeg031992 @BenK78457034 @AbraarKaran That's all that is needed to change behavior. The difference between 500 local cases or 400 is immaterial.

@肉豆蔻031992@BenK78457034@AbraarKaran这就是改变行为所需的一切。500例本地病例与400例之间的差异无关紧要。

@Nutmeg031992 @AbraarKaran They don't know daily cases. They know whether a large surge is building or peaked/receeding.

@肉豆蔻031992@AbraarKaran他们不知道日常病例。他们知道巨浪是正在形成还是正在达到顶峰/消退。

@SandwichExplor1 @carita_paige @Pemcdonald11 @SolliesSurgery @ASlavitt NY was not relevant to the thread at all. It was extraneous data on the graphic he used, instead of generating a new one for just California and Florida, which is what he was talking about.

@SandwichExplor1@carita_paige@Pemcdonald11@SolliesUrgery@ASlavitt NY与线程完全无关。这是他使用的图形上的无关数据，而不是他所说的仅针对加利福尼亚州和佛罗里达州生成的新数据。

@OSHA_Guy @ColinDotNet @DailyJLee There's NO trend here whatsoever. Wuhan->Alpha->Delta got continually more severe. Omicron was ONE step less severe. Next step? Who knows.

@OSHA_Guy@ColinDotNet@DailyJLee这里没有任何趋势。武汉->；α->；德尔塔的情况越来越严重。奥米克龙的病情轻了一步。下一步呢？谁知道呢。

@wsbstakepool @EricTopol @DanRather @CDCgov The reduction in 'from' is going to be higher than 'with' because the vaccines are relatively ineffective in preventing incidental infections.

@wsbstakepool@EricTopol@danrest@CDCgov由于疫苗在预防偶发感染方面相对无效，“from”的减少量将高于“with”。

@BenK78457034 @privatelynda @AbraarKaran Accumulatiom of infection-based immunity would result in a decline at a predicable exponential rate. Since the declines almost always slow down, there are behavior changes too.

@BenK78457034@privatelynda@AbraarKaran基于感染的免疫累积将以可预测的指数速度导致下降。由于下跌几乎总是放缓，行为也会发生变化。

@Nutmeg031992 @AbraarKaran Probably reported case numbers (and other reporting on things like a new variant taking hold) to an extent but people they know getting sick likely has an influence too.

@肉豆蔻031992@AbraarKaran可能在一定程度上报告了病例数（以及其他关于新变种的报告），但他们知道生病的人可能也有影响。

@RandyBensleyPGA @Pemcdonald11 @carita_paige @SolliesSurgery @ASlavitt Only a little. Without Miami, Florida has about 58k deaths (rather than 68k) and 19 million people rather than 21.5. Still about 300 on the chart.

@RandyBensleyPGA@Pemcdonald11@carita_paige@SolliesSurgery@ASlavitt只有一点点。如果没有迈阿密，佛罗里达州大约有5.8万人死亡（而不是6.8万人），1900万人死亡，而不是215万人。图表上仍有大约300个。

@Pemcdonald11 @carita_paige @SolliesSurgery @ASlavitt HE NEVER EVEN MENTIONED NY.
The thread was about California. He compared with Florida. He happened to use a graphic with the largest states by CDC data. Way to miss the point completely.

@Pemcdonald11@carita_paige@SolliesSurgery@ASlavitt他甚至从未提到过纽约。
这条线是关于加利福尼亚的。他拿佛罗里达州作比较。他碰巧使用了CDC数据显示的最大州的图表。完全没有抓住重点。

@AbraarKaran Cases have been declining very rapidly i.e. R is low (some estimates 0.5 or lower). In that context you can increase transmission *some* and not increase, just decrease more slowly.
This is likely why most surges decline slowly, people loosen up past the peak, but before bottom.

@AbraarKaran病例一直在快速下降，即R较低（一些估计为0.5或更低）。在这种情况下，你可以增加传输*一些*而不是增加，只是减少得更慢。
这可能就是为什么大多数涌浪都会缓慢下降，人们会在高峰过后，但在谷底之前放松下来。

@ScottHarrisID @fitterhappierAJ "No one saw it coming"

@ScottHarrisID@fitterhappierAJ“没人看到它来了”

@gxsingularity @xgoogoogoox @semi_i_photo @MEDIUMZA9 @BNODesk Including data back to 6/1 (before even delta FFS) is meaningless to the current situation.

@Gx奇异性@Xgoogoox@semi_i_photo@MEDIUMZA9@BNODesk，包括返回到6/1（甚至在delta FFS之前）的数据，对当前情况来说毫无意义。

@SolliesSurgery @ASlavitt There are excellent treatments. Both remdesivir and paxlovid are about 90% effective if given early. Monoclonal antibodies are in a constant arms race with variants but there is one that still works now.
What is needed is making them more available, exactly what is in that plan.

@SolliesUrgery@ASlavitt有很好的治疗方法。如果及早服用，雷姆德西韦和帕克洛维德的有效率都在90%左右。单克隆抗体一直在与变种进行军备竞赛，但现在还有一种仍然有效。
我们需要的是让他们更容易获得，这正是计划中的内容。

@semi_i_photo @gxsingularity @xgoogoogoox @MEDIUMZA9 @BNODesk Yeah, well that's not happening without dart guns.

@semi_i_photo@gxsingularity@xgoogoox@MEDIUMZA9@BNODesk是的，没有飞镖枪是不可能的。

@HilNorth @ashishkjha The spread numbers are very unreliable given poor testing in both places (Florida may be worse). Wouldn't read too much into that.
https://twitter.com/wsbgnl/sta...

@Hilnoth@ashishkjha鉴于两地的检测都很差（佛罗里达州可能更糟），传播数据非常不可靠。我不会读太多。
https://twitter.com/wsbgnl/sta...

@itosettiMD_MBA @GYamey Term apparently expired
https://twitter.com/EFudman/st...

@itosettiMD_MBA@GYamey的任期显然已经到期
https://twitter.com/EFudman/st...

@MyBoomerLife @tippet523 @ASlavitt That's 30% more, just as I said. 30% of 14.8 is 4.4% = 19.2%, close enough.

@MyBoomerLife@tippet523@ASlavitt这比我说的多了30%。14.8的30%等于4.4%=19.2%，足够接近了。

@rbgal5 @tippet523 @ASlavitt It does, there are lots of different factors everywhere.
Really an area implementing its own policy should know best where its vulnerabilities lie and address them.

@rbgal5@tippet523@ASlavitt确实如此，到处都有很多不同的因素。
实际上，一个执行自己政策的地区应该最清楚自己的弱点所在并加以解决。