@kknnaabb @_mbdr_ All else equal, a place with few or no poor people, low density single family housing, few or no nursing homes is going to do much better than the opposite. One you count for those differences, the NPI relationship is strong.

@kknnaabb @_mbdr_ 在其他条件相同的情况下，一个很少或没有穷人的地方、低密度的单户住宅、很少或没有疗养院的地方会比相反的情况好得多。考虑到这些差异，NPI 关系很牢固。

@kknnaabb @_mbdr_ I just showed it to you. "Stringency" = NPIs, restrictions, mandates. p<0.001.

@kknnaabb @_mbdr_ 我刚刚给你看了。 “严格”= NPI、限制、授权。 p

@kknnaabb @_mbdr_ You know the answer. Compare Miami to hard right FL counties in terms of density, inequality, nursing home residents, etc.

@kknnaabb @_mbdr_ 你知道答案。在密度、不平等、疗养院居民等方面将迈阿密与右翼佛罗里达州进行比较。

@kknnaabb @_mbdr_ Two standard deviation increase vs. decrease in stringency gets you to about the most and least locked down states. That's a difference of about 300k deaths.

@kknnaabb @_mbdr_ 两个标准差的增加与严格性的减少使您进入最多和最少的锁定状态。这是大约30万人死亡的差异。

@kknnaabb @_mbdr_ On stringency, one standard deviation increase in stringency (68 percentile, hardly the most locked down state) is associated with a reduction at a rate of 78k deaths nationally during the study period (600k total). That's a LOT.

@kknnaabb @_mbdr_ 在严格性方面，严格性的一个标准偏差增加（68 个百分位，几乎不是最封闭的状态）与研究期间全国死亡人数减少 7.8 万（总计 60 万）有关。好多啊。

@kknnaabb @_mbdr_ And obesity, racial makeup, and age don't show as significant factors once the others are accounted for, read the paper. These are likely embedded in some of the others of course (inequity tied to racial makeup, age tied to nursing home residents, etc.)

@kknnaabb @_mbdr_ 一旦考虑到其他因素，肥胖、种族构成和年龄就不会成为重要因素，请阅读本文。当然，这些可能嵌入在其他一些中（与种族构成相关的不平等，与疗养院居民相关的年龄等）

@kknnaabb @_mbdr_ That's just factually wrong. The evidence is right there. ("Evidence" isn't conclusive, of course, but this is a careful, well-designed study which does show strong *extremely* strong evidence of effect p<0.001.)

@kknnaabb @_mbdr_ 这实际上是错误的。证据就在那里。 （当然，“证据”不是决定性的，但这是一项精心设计的精心设计的研究，确实显示了强有力的*非常*强有力的证据证明了 p

@DeItaone https://t.co/TUnbEPvV0J

@DeItaone https://t.co/TUnbEPvV0J

@kknnaabb @_mbdr_ It would be interesting if someone would redo this work extending to the post-vax era. The paper is very clear on methods, so it isn't hard, just effort to collect the data and analyze. I haven't seen this, just a lot of stupidity like raw state vs. state comparisons.

@kknnaabb @_mbdr_ 如果有人将这项工作重做延伸到后vax 时代，那将会很有趣。论文的方法很清楚，所以不难，只是努力收集数据和分析。我还没有看到这个，只是像原始状态与状态比较这样的愚蠢。

@kknnaabb @_mbdr_ Right, it's not a preventative measure but it is one that you have to correct for if you want to evaluate preventative measures. Once you do that, the effect of preventive measures becomes strong, at least during the study period pre-vax (vax obviously important factor after).

@kknnaabb @_mbdr_ 是的，这不是一种预防措施，但如果你想评估预防措施，你必须纠正它。一旦你这样做了，预防措施的效果就会变得很强，至少在学习前的vax期间（vax显然是重要的因素之后）。

@kknnaabb @_mbdr_ It is inequality that has >0.5 correlation, making it enormously important. This means any analysis that doesn't correct for inequality (but also should look at the other identified factors: humidity, density, and nursing home population) is likely to be very wrong.

@kknnaabb @_mbdr_ 是不等式

@0minus_Prime @GennadySimanovs @DrEricDing @JPWeiland It's hilarious that you think Ferguson's models weren't broadly accurate. They predicted almost everything that happened. Multiple waves of infection (total 80% infected) with intermittent NPIs instituted over 18 months, 1 million dead in the US, 250K in UK w/NPIs.

@0minus_Prime @GennadySimanovs @DrEricDing @JPWeiland 有趣的是，您认为弗格森的模型并不准确。他们几乎预测了发生的一切。多波感染（总共 80% 感染），在 18 个月内采用间歇性 NPI，美国有 100 万人死亡，英国有 25 万人死亡。

@kknnaabb @_mbdr_ Typo: *Closing playgrounds probably does nothing

@kknnaabb @_mbdr_ Typo：*关闭游乐场可能无济于事

@kknnaabb @_mbdr_ Understanding inequity, humidity, etc. is important in part because it can confound examination of policy. If you have one state with stronger policies but more inequity, it may have worse outcomes. That does NOT mean the policies didn't work.

@kknnaabb @_mbdr_ 了解不平等、湿度等很重要，部分原因是它会混淆对政策的审查。如果你有一个州的政策更强，但不平等程度更高，它可能会产生更糟糕的结果。这并不意味着这些政策没有奏效。

@kknnaabb @_mbdr_ Read the thread or the paper, or just look at the picture. Stringency is that. The correlation is strong.
Of course not ALL of stringency works. Closing playgrounds probably does not nothing, but that's irrelevant.

@kknnaabb @_mbdr_ 阅读主题或论文，或者只看图片。严谨就是这样。相关性很强。
当然，并不是所有的严格性都有效。关闭游乐场可能没有什么，但这无关紧要。

@GidMK "you have to have gotten sick, had a positive test, and had that test properly reported — all things that have been, in many places, quite hard to do."
The "getting sick" part hasn't been hard to do!

@GidMK “你必须生病，检测呈阳性，并正确报告检测结果——所有这些在很多地方都很难做到。”
“生病”的部分并不难做到！

@Liberalism2021 @paulalexlewis @DonaldWelsh16 @ironandwine11 Not vaxed old is true. That's exactly what I said earlier!

@Liberalism2021 @paulalexlewis @DonaldWelsh16 @ironandwine11 不老是真的。这正是我之前所说的！

@Liberalism2021 @paulalexlewis @DonaldWelsh16 @ironandwine11 How many of those "sources" are actual studies of three doses and how many are people just repeating what "everyone knows"?

@Liberalism2021 @paulalexlewis @DonaldWelsh16 @ironandwine11 这些“来源”中有多少是对三剂的实际研究，有多少人只是在重复“每个人都知道”的内容？

@DerekParady @_mbdr_ I don't particularly. Incompetence is rampant.

@DerekParady @_mbdr_ 我不是特别喜欢。无能横行。

@DerekParady @_mbdr_ If you want to consider the Fed as "policy" then I'd agree, but it's a bit of a stretch. You don't have high rents, high gas prices, high food prices, etc. now because bars were closed in 2020.

@DerekParady @_mbdr_ 如果您想将美联储视为“政策”，那么我同意，但这有点牵强。你现在没有高租金、高汽油价格、高食品价格等，因为酒吧在 2020 年关闭。