COVID-19 Megathread 5: The Trumps catch COVID-19

[Green Line]

Here's what the Politifact says:

So what these posts did was take the 900 figure, which referred to cases in recent years where pneumonia is the underlying cause of death, and compare that to a broader category of cases this year where pneumonia is mentioned on a death certificate, data which is provisional at this point.

It seems the way to settle this would be to go back to previous years' provisional data and see what they looked like. Because even if we assume all the Covid-19 deaths were also assigned to pneumonia, and exclude those, what we have is:
Florida: 5,872 - 2,200 = 3,672 pneumonia deaths (vs 972 avg)
Texas 5,344 - 1,420 = 3,924 pneumonia deaths (vs 1,168 avg)

Still way above the average for previous years.

Except its not just Covid vs Pneumonia.  If pneumonia is listed on the death certificate at all it would be included in those figures, not necessarily as the primary cause of death (provisionally).

I don't see any evidence that Florida and Texas hospitals are engaged in some huge conspiracy to cover up their dead Covid patients.

I know. That's why I said the proper way to do it is to look back at previous years' provisional data and see what the average drop-off is.

An undercount doesn't require a massive conspiracy. All it requires is that people who died in their homes, or otherwise died before being tested and confirmed, or those who tested a false negative, not be counted as Covid patients.

Yes agreed, there is probably an undercount of Covid deaths nationwide, especially since we weren't testing for it until March for the most part.  There is no evidence of a specific undercount in FL or TX relative to the rest of the nation, which was clearly the intention of this tweet that you shared here.

Anyways, great news that there was another massive drop in deaths week-over-week.  Praise the Lord.

[Fmr. Gov. NickG]

Here’s an article published in the NY Times today on the potential for human challenge trials...I was beginning to lose hope about this, but the article was more optimistic than I expected.

https://www.nytimes.com/2020/07/01/health/coronavirus-vaccine-trials.html

Sign up at 1DaySooner.org!

[Dr. Arch]

The updated numbers for COVID-19 in the U.S. are in for 7/1 per: https://www.worldometers.info/coronavirus/country/us/

I'm keeping track of these updates daily and updating at the end of the day, whenever all states finish reporting for that day.

Δ Change: Day-by-day Growth or Decline or COVID-19 Spread/Deaths.

• IE: Are we flattening the curve enough?

Σ Increase: A day's contribution to overall percentage growth of COVID-19 cases/deaths.

• IE: What's the overall change in the total?

<Last Numbers for 3/26-3/28 in this Post>
<Last Numbers for 3/29-4/4 in this Post>
<Last Numbers for 4/5-4/11 in this Post>
<Last Numbers for 4/12-4/18 in this Post>
<Last Numbers for 4/19-4/25 in this post>
<Last Numbers for 4/26-5/2 in this post>
<Last Numbers for 5/3-5/9 in this post>
<Last Numbers for 5/10-5/16 in this post>
<Last Numbers for 5/17-5/23 in this post>
<Last Numbers for 5/24-5/30 in this post>
<Last Numbers for 5/31-6/6 in this post>
<Last Numbers for 6/7-6/13 in this post>

6/14: <Sunday>

• Cases: 2,162,144 (+19,920 | Δ Change: ↓21.27% | Σ Increase: ↑0.93%)
• Deaths: 117,853 (+326 | Δ Change: ↓53.56% | Σ Increase: ↑0.28%)

6/15:

• Cases: 2,182,950 (+20,806 | Δ Change: ↑4.45% | Σ Increase: ↑0.96%)
• Deaths: 118,283 (+430 | Δ Change: ↑31.90% | Σ Increase: ↑0.36%)

6/16:

• Cases: 2,208,400 (+25,450 | Δ Change: ↑22.32% | Σ Increase: ↑1.17%)
• Deaths: 119,132 (+849 | Δ Change: ↑97.44% | Σ Increase: ↑0.72%)

6/17:

• Cases: 2,234,471 (+26,071 | Δ Change: ↑2.44% | Σ Increase: ↑1.18%)
• Deaths: 119,941 (+809 | Δ Change: ↓4.71% | Σ Increase: ↑0.68%)

6/18:

• Cases: 2,263,651 (+29,180 | Δ Change: ↑11.93% | Σ Increase: ↑1.31%)
• Deaths: 120,688 (+747 | Δ Change: ↓7.66% | Σ Increase: ↑0.62%)

6/19:

• Cases: 2,297,190 (+33,539 | Δ Change: ↑14.94% | Σ Increase: ↑1.48%)
• Deaths: 121,407 (+719 | Δ Change: ↓3.75% | Σ Increase: ↑0.60%)

6/20:

• Cases: 2,330,578 (+33,388 | Δ Change: ↓0.45% | Σ Increase: ↑1.45%)
• Deaths: 121,980 (+573 | Δ Change: ↓20.31% | Σ Increase: ↑0.47%)

6/21: <Sunday>

• Cases: 2,356,657 (+26,079 | Δ Change: ↓21.89% | Σ Increase: ↑1.12%)
• Deaths: 122,247 (+267 | Δ Change: ↓53.40% | Σ Increase: ↑0.22%)

6/22:

• Cases: 2,388,153 (+31,496 | Δ Change: ↑20.77% | Σ Increase: ↑1.34%)
• Deaths: 122,610 (+363 | Δ Change: ↑35.96% | Σ Increase: ↑0.30%)

6/23:

• Cases: 2,424,168 (+36,015 | Δ Change: ↑14.35% | Σ Increase: ↑1.51%)
• Deaths: 123,473 (+863 | Δ Change: ↑137.74% | Σ Increase: ↑0.70%)
• Death toll inflated by older counts recently dumped by DE

6/24:

• Cases: 2,462,554 (+38,386 | Δ Change: ↑6.18% | Σ Increase: ↑1.58%)
• Deaths: 124,281 (+808 | Δ Change: ↓6.37% | Σ Increase: ↑0.65%)

6/25:

• Cases: 2,504,588 (+42,034 | Δ Change: ↑9.50% | Σ Increase: ↑1.71%)
• Deaths: 126,780 (+2,499 | Δ Change: ↑209.28% | Σ Increase: ↑2.01%)
• Death toll inflated by older counts recently dumped by NJ

6/26:

• Cases: 2,552,940 (+48,352 | Δ Change: ↑15.03% | Σ Increase: ↑1.93%)
• Deaths: 127,640 (+860 | Δ Change: ↓65.59% | Σ Increase: ↑0.68%)
• Death Δ Change affected by older counts recently dumped by NJ on 6/25

6/27:

• Cases: 2,596,537 (+43,597 | Δ Change: ↓9.83% | Σ Increase: ↑1.71%)
• Deaths: 128,152 (+512 | Δ Change: ↓40.47% | Σ Increase: ↑0.40%)

6/28: <Sunday>

• Cases: 2,637,077 (+40,540 | Δ Change: ↓7.01% | Σ Increase: ↑1.56%)
• Deaths: 128,437 (+285 | Δ Change: ↓44.34% | Σ Increase: ↑0.22%)

6/29:

• Cases: 2,681,802 (+44,725 | Δ Change: ↑10.32% | Σ Increase: ↑1.70%)
• Deaths: 128,779 (+342 | Δ Change: ↑20.00% | Σ Increase: ↑0.27%)

6/30 (Yesterday):

• Cases: 2,727,853 (+46,051 | Δ Change: ↑2.96% | Σ Increase: ↑1.72%)
• Deaths: 130,122 (+1,343 | Δ Change: ↑292.69% | Σ Increase: ↑1.04%)

7/1 (Today):

• Cases: 2,779,953 (+52,100 | Δ Change: ↑13.14% | Σ Increase: ↑1.91%)
• Deaths: 130,798 (+676 | Δ Change: ↓49.66% | Σ Increase: ↑0.52%)

[Dr. Arch]

So, we have finally breached the 50k new cases a day mark... Things are precipitating quickly.

[emailking]

Broke our record by 4K but Trump thinks it's all going to disappear soon.

[Koharu]

My stepdad, who has COPD, high blood pressure, diabetes, and just turned 68 in June, is likely one of the numbers in Georgia's big increase. I don't know for sure, but the symptoms he mentioned on Facebook before going incommunicado were COVID symptoms. He was hospitalized one week after his birthday, which involved visitors with no masks. I'm very angry about these circumstances and many other personal things. He believed it was a hoax and overhyped. There is no pleasure in being proven right when knowing he is suffering significantly. At this point, it sounds unlikely he will make it.

I'm angry at Georgia for allowing restaurants to be open. I'm angry at Fox and other disinformation sources feeding people these lies. I'm angry at him. I'm angry at the people he moved in with who were supposed to be protecting him. I'm angry at Trump for pretending this is no big deal. I'm angry I can't go be with him. This didn't have to happen.

This didn't have to happen and it infuriates me.

[100% pro-life no matter what]

Some interesting developments in what people are calling this virus, according to Google trends.

In March and April, there were 5 times as many searches for "coronavirus" as there were for "covid".  Now, there are are almost 30% more searches for "covid" as for "coronavirus".

Even more stark is the initial popularity of the term "corona" that has largely faded away.  Through about the fourth week of March, "corona" was a more popular search term than "covid".  Searches for "corona" even peaked higher than searches for "covid" did.  But, now "covid" is more than 8 times as popular on Google as "corona" is.  You could write an almost identical story about the simple word "virus", which was also more searched than "covid" in the early stages of the lockdown.

Searches for "covid" have doubled in the last month (after dropping during May), while searches for "coronavirus" have only increased about 50% and searches for "corona" have basically remained steady.

[emailking]

Koharu, that's awful. Hope for the best for your stepdad.

[Dr. Arch]

Koharu, that's awful. Hope for the best for your stepdad.

Koharu, I echo emailking's sentiments. I'm so sorry to hear about this, and I hope that he makes a full recovery.

[Penn_Quaker_Girl]

Governor Wolf (PA) expanding statewide mask order:

https://mobile.twitter.com/GovernorTomWolf/status/1278405412748959745?ref_src=tw...

According to the order, this includes outdoor public spaces IF the ability to maintain six feet of physical separation is not possible (so primarily aimed at outdoor promenades, restaurants, etc.)

[Person Man]

My stepdad, who has COPD, high blood pressure, diabetes, and just turned 68 in June, is likely one of the numbers in Georgia's big increase. I don't know for sure, but the symptoms he mentioned on Facebook before going incommunicado were COVID symptoms. He was hospitalized one week after his birthday, which involved visitors with no masks. I'm very angry about these circumstances and many other personal things. He believed it was a hoax and overhyped. There is no pleasure in being proven right when knowing he is suffering significantly. At this point, it sounds unlikely he will make it.

I'm angry at Georgia for allowing restaurants to be open. I'm angry at Fox and other disinformation sources feeding people these lies. I'm angry at him. I'm angry at the people he moved in with who were supposed to be protecting him. I'm angry at Trump for pretending this is no big deal. I'm angry I can't go be with him. This didn't have to happen.

This didn't have to happen and it infuriates me.

People who still think COVID is a conspiracy should be castrated.

[Landslide Lyndon]

https://twitter.com/LizAnnSonders/status/1278657772679290881

[Penn_Quaker_Girl]

https://mobile.twitter.com/YahooFinance/status/1278667433679695875

https://mobile.twitter.com/USDOL/status/1278667295410262017

[BudgieForce]

Probably kills momentum on a second stimulus.

[jimrtex]

I updated my Stata model to include test positivity rate and its interaction with case.  These are the results, estimating daily deaths from April 1 to June 30.

Here, "avgcases" is the average number of cases over the previous 7 days, while "averagecase7" is the average for the 7 days before that (etc. for 14, 21, and 28 days before that). "{osperc" is test positivity rate on the given day, while "posminus7" is the lagged positivity rate 7 days earlier (etc for 14, 21, and 28 days earlier).  "Casespos" (and its lagged variables) are the interaction between total cases and positivity rate.  "Deathminus7" is the number of deaths 7 days earlier.  I also include fixed effects for each day of the week.

The overall impact of lagged cases is still very similar.  Average cases lagged one week are the only variable with any substantive or statistical significance on their own.  

For the most part, adding positivity rate has no significant effect, with one exception.  Positivity is significant in interaction with 14-day lagged cases.  So while cases lagged 7-days always have an effect on deaths, cases lagged 14-days only have a major effect when test positivity (also lagged 14-days) was high.  For example, an additional 1000 cases 14 days earlier would result in 8 additional deaths when test positivity was 3%, but 21 additional deaths when positivity was 10%.  Current cases, as well as cases 3 weeks ago or more, never have a significant effect on deaths.

. reg deaths deathminus7 days avgcases avgcases7 avgcases14 avgcases21 avgcases28 posperc posminus7 p
> osminus14 posminus21 posminus28 casespos casespos7 casespos14 casespos21 casespos28 i.weekday if da
> ys>31&days<123

      Source |       SS           df       MS      Number of obs   =        91
-------------+----------------------------------   F(24, 66)       =     60.11
       Model |  35728644.6        24  1488693.53   Prob > F        =    0.0000
    Residual |  1634438.67        66  24764.2223   R-squared       =    0.9563
-------------+----------------------------------   Adj R-squared   =    0.9403
       Total |  37363083.3        90   415145.37   Root MSE        =    157.37

------------------------------------------------------------------------------
      deaths |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 deathminus7 |   .4674046    .104832     4.46   0.000     .2581007    .6767085
        days |  -11.89638   5.265929    -2.26   0.027    -22.41015   -1.382614
    avgcases |  -.0095963   .0120319    -0.80   0.428    -.0336188    .0144262
   avgcases7 |   .0395809   .0173289     2.28   0.026     .0049827    .0741792
  avgcases14 |  -.0491985   .0171413    -2.87   0.006    -.0834222   -.0149747
  avgcases21 |   .0159292   .0149175     1.07   0.289    -.0138546    .0457129
  avgcases28 |  -.0051158   .0109589    -0.47   0.642     -.026996    .0167645
     posperc |  -1690.344   2372.583    -0.71   0.479    -6427.358     3046.67
   posminus7 |   715.4029   2176.551     0.33   0.743    -3630.221    5061.027
  posminus14 |  -5678.463   2039.865    -2.78   0.007    -9751.185   -1605.741
  posminus21 |   676.9361   1301.756     0.52   0.605    -1922.104    3275.976
  posminus28 |  -99.96956   868.0994    -0.12   0.909    -1833.186    1633.247
    casespos |   .0746608   .0778795     0.96   0.341    -.0808307    .2301522
   casespos7 |  -.0269732   .0704437    -0.38   0.703    -.1676185    .1136721
  casespos14 |   .2120465   .0679098     3.12   0.003     .0764602    .3476329
  casespos21 |  -.0446484   .0502626    -0.89   0.378    -.1450009    .0557042
  casespos28 |   .0143544   .0445213     0.32   0.748    -.0745353     .103244
             |
     weekday |
          2  |   82.38582   73.89428     1.11   0.269    -65.14887    229.9205
          3  |   420.2182   111.0866     3.78   0.000     198.4268    642.0097
          4  |   343.6183   116.7902     2.94   0.004     110.4391    576.7975
          5  |   227.4863   113.5001     2.00   0.049     .8759877    454.0966
          6  |    136.587   126.1011     1.08   0.283     -115.182     388.356
          7  |   101.4743   93.32886     1.09   0.281    -84.86273    287.8114
          8  |  -281.1558   156.3947    -1.80   0.077    -593.4079    31.09634
             |
       _cons |    1633.96   693.1888     2.36   0.021     249.9637    3017.956
------------------------------------------------------------------------------

If you click on "Source Mode" in the lower right, and then paste from a spreadsheet, it will be formatted so the columns line up better. Note clicking on "Source Mode" actually toggles you into "Display Mode". You can click on "Display Mode" to return you to Source Mode and edit any text.

I'd probably also trim some of your digits.

Trying to make sense of the numbers. Is it the fact that coef has the largest absolute value that indicates that deaths are lagging detection by about 14 days?? That the coef for 7 days is the next largest suggests that if might be less than 14, but to to refine the number may be impossible due to the weekly periodicity of reporting??

[Person Man]

https://mobile.twitter.com/YahooFinance/status/1278667433679695875

https://mobile.twitter.com/USDOL/status/1278667295410262017

I could see this. A lot of people probably decided to hire a second person when they put out for jobs. That's how I got on to my current project.

However, even if the virus is now only effecting people who "can handle it" for the most part, and fewer per capita will die, will this mean we are about to face a lot more sick days? At my girlfriend's work, two have called out sick out of her unit of 20.

[NHI]

Probably kills momentum on a second stimulus.

Unfortunately I'd say so, not that it would take much to stop a second one. Cue the tweets.

[Absentee Voting Ghost of Ruin]

Mr. Trump appears to have had an extra helping of incoherence this morning:
https://twitter.com/therecount/status/1278686839210233859

[Calthrina950]

Mandatory mask orders are definitely picking up momentum now, with the recent upsurge in cases. In Pennsylvania, Governor Wolf has expanded his prior mask order, now mandating the use of facial coverings in all public spaces, indoor or outdoor, where social distancing is not possible. And Tennessee's Governor Bill Lee is now considering making masks mandatory for the entire state: https://krdo.com/news/national-world/2020/07/02/gov-bill-lee-considering-mask-mandate-for-tennessee/.

If he does so, Tennessee would become the first Safe Republican state to issue a mask order, and the first state with a Republican governor that is not Baker or Hogan. It looks like Republicans are finally recognizing that public health overrides dubious concerns about "liberty" and "personal rights". At this point, I wouldn't be surprised if mask orders are issued in the vast majority of states.

[Fmr. Gov. NickG]

I updated my Stata model to include test positivity rate and its interaction with case.  These are the results, estimating daily deaths from April 1 to June 30.

Here, "avgcases" is the average number of cases over the previous 7 days, while "averagecase7" is the average for the 7 days before that (etc. for 14, 21, and 28 days before that). "{osperc" is test positivity rate on the given day, while "posminus7" is the lagged positivity rate 7 days earlier (etc for 14, 21, and 28 days earlier).  "Casespos" (and its lagged variables) are the interaction between total cases and positivity rate.  "Deathminus7" is the number of deaths 7 days earlier.  I also include fixed effects for each day of the week.

The overall impact of lagged cases is still very similar.  Average cases lagged one week are the only variable with any substantive or statistical significance on their own.  

For the most part, adding positivity rate has no significant effect, with one exception.  Positivity is significant in interaction with 14-day lagged cases.  So while cases lagged 7-days always have an effect on deaths, cases lagged 14-days only have a major effect when test positivity (also lagged 14-days) was high.  For example, an additional 1000 cases 14 days earlier would result in 8 additional deaths when test positivity was 3%, but 21 additional deaths when positivity was 10%.  Current cases, as well as cases 3 weeks ago or more, never have a significant effect on deaths.

. reg deaths deathminus7 days avgcases avgcases7 avgcases14 avgcases21 avgcases28 posperc posminus7 p
> osminus14 posminus21 posminus28 casespos casespos7 casespos14 casespos21 casespos28 i.weekday if da
> ys>31&days<123

      Source |       SS           df       MS      Number of obs   =        91
-------------+----------------------------------   F(24, 66)       =     60.11
       Model |  35728644.6        24  1488693.53   Prob > F        =    0.0000
    Residual |  1634438.67        66  24764.2223   R-squared       =    0.9563
-------------+----------------------------------   Adj R-squared   =    0.9403
       Total |  37363083.3        90   415145.37   Root MSE        =    157.37

------------------------------------------------------------------------------
      deaths |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 deathminus7 |   .4674046    .104832     4.46   0.000     .2581007    .6767085
        days |  -11.89638   5.265929    -2.26   0.027    -22.41015   -1.382614
    avgcases |  -.0095963   .0120319    -0.80   0.428    -.0336188    .0144262
   avgcases7 |   .0395809   .0173289     2.28   0.026     .0049827    .0741792
  avgcases14 |  -.0491985   .0171413    -2.87   0.006    -.0834222   -.0149747
  avgcases21 |   .0159292   .0149175     1.07   0.289    -.0138546    .0457129
  avgcases28 |  -.0051158   .0109589    -0.47   0.642     -.026996    .0167645
     posperc |  -1690.344   2372.583    -0.71   0.479    -6427.358     3046.67
   posminus7 |   715.4029   2176.551     0.33   0.743    -3630.221    5061.027
  posminus14 |  -5678.463   2039.865    -2.78   0.007    -9751.185   -1605.741
  posminus21 |   676.9361   1301.756     0.52   0.605    -1922.104    3275.976
  posminus28 |  -99.96956   868.0994    -0.12   0.909    -1833.186    1633.247
    casespos |   .0746608   .0778795     0.96   0.341    -.0808307    .2301522
   casespos7 |  -.0269732   .0704437    -0.38   0.703    -.1676185    .1136721
  casespos14 |   .2120465   .0679098     3.12   0.003     .0764602    .3476329
  casespos21 |  -.0446484   .0502626    -0.89   0.378    -.1450009    .0557042
  casespos28 |   .0143544   .0445213     0.32   0.748    -.0745353     .103244
             |
     weekday |
          2  |   82.38582   73.89428     1.11   0.269    -65.14887    229.9205
          3  |   420.2182   111.0866     3.78   0.000     198.4268    642.0097
          4  |   343.6183   116.7902     2.94   0.004     110.4391    576.7975
          5  |   227.4863   113.5001     2.00   0.049     .8759877    454.0966
          6  |    136.587   126.1011     1.08   0.283     -115.182     388.356
          7  |   101.4743   93.32886     1.09   0.281    -84.86273    287.8114
          8  |  -281.1558   156.3947    -1.80   0.077    -593.4079    31.09634
             |
       _cons |    1633.96   693.1888     2.36   0.021     249.9637    3017.956
------------------------------------------------------------------------------

If you click on "Source Mode" in the lower right, and then paste from a spreadsheet, it will be formatted so the columns line up better. Note clicking on "Source Mode" actually toggles you into "Display Mode". You can click on "Display Mode" to return you to Source Mode and edit any text.

I'd probably also trim some of your digits.

Trying to make sense of the numbers. Is it the fact that coef has the largest absolute value that indicates that deaths are lagging detection by about 14 days?? That the coef for 7 days is the next largest suggests that if might be less than 14, but to to refine the number may be impossible due to the weekly periodicity of reporting??

I repasted only the significant effects here:

------------------------------------------------------------
      deaths |      Coef.   Std. Err.      t    P>|t|     
-------------+---------------------------------------------

 deathminus7 |   .4674046    .104832     4.46   0.000 
 
        days |  -11.89638   5.265929    -2.26   0.027   

  avgcases7 |   .0395809   .0173289     2.28   0.026   

  avgcases14 |  -.0491985   .0171413    -2.87   0.006
  posminus14 |  -5678.463   2039.865    -2.78   0.007   
  casespos14 |   .2120465   .0679098     3.12   0.003     

   _cons |    1633.96   693.1888     2.36   0.021   

The positive coefficient for 7-day lagged cases (avgcases7), with no significant effects on 7-day test positivity, is straightforward.  For every additional case in the 7-day lagged average, you get an additional .04 deaths (25 cases=1 death).

The effect of the 14-day lagged period is more complicated and can only be understood by looking at the three variables in conjunction with each other.  "Casepos14" is the product of 14-day lagged cases and 14-day lagged positivity rate (an interaction term).  When you have a positive coefficient for the interaction term, but a negative coefficient for both uninteracted terms, that indicates that you should only see a positive effect when both terms are high.   So 14-day lagged cases increase deaths much more when the 14-day lagged test positivity rate was also high.   

It's also worth noting that when we talk about positivity rate being "high", we're not so much referring to the variation over the last few weeks, when national positivity rate has been in the 4-7% range, but rather in March and April, when it was 15-20%.

Finally, I should also mention that cases and positivity rate end up explaining only a very small portion of variation in deaths.  You'll see the R^2 value of the model above is .956.  That's really high, and means the model explains about 96% of the variation in the data.  However, you can get to an R^2 value of .916 in a model that includes only data on lagged deaths and day of the week, with no information about cases or tests whatsoever.  I've posted the predictions of this model, which I called "crudepredict" below (green line), contrasted with the actual data (blue line) and the full model (red line).  You'll see the green line is a slightly less accurate predictor of the blue line data than the red line, but they both do pretty well. 

Just knowing the previous death rate and the day of the week gets us 92% of the way there.  Adding in those 15 extra variables on cases and tests only gets us an additional 4%.

[Landslide Lyndon]

https://twitter.com/JesseRodriguez/status/1278702322831495169

[Storebought]

Reports of 40,000+ new infections per day don't matter if they lead to 4+ million new jobs in this month's job report, and I'm not being sarcastic.

[GeorgiaModerate]

Reports of 40,000+ new infections per day doesn't matter if it leads to 4+ million new jobs in this months job report, and I'm not being sarcastic.

That's short-sighted.  If this rate of case creation continues, the recovery will be slowed down and there won't be such a jobs report next month.

[Storebought]

Reports of 40,000+ new infections per day doesn't matter if it leads to 4+ million new jobs in this months job report, and I'm not being sarcastic.

That's short-sighted.  If this rate of case creation continues, the recovery will be slowed down and there won't be such a jobs report next month.

It's not about what I think -- it's about what the White House and GOP governors think and what they are willing to accept as an acceptable risk: a booming economy in the midst of a pandemic is a booming economy. That's why I said I wasn't being sarcastic.

[Landslide Lyndon]

Reports of 40,000+ new infections per day doesn't matter if it leads to 4+ million new jobs in this months job report, and I'm not being sarcastic.

That's short-sighted.  If this rate of case creation continues, the recovery will be slowed down and there won't be such a jobs report next month.

It's not about what I think -- it's about what the White House and GOP governors think and what they are willing to accept as an acceptable risk: a booming economy in the midst of a pandemic is a booming economy. That's why I said I wasn't being sarcastic.

When states start to shut down business again, like Arizona did, then the economy won't be booming. It's not that hard to understand.