Posted on 05/12/2020 4:37:15 PM PDT by grundle
When controlling for the differences in population across states, the number of deaths from coronavirus is over three times higher in states with Democratic governors than in states with Republican governors. As of Sunday, April 26, states with Republican governors have experienced 57.53 coronavirus deaths per million of population, states with Democratic governors have 179.74 deaths per million of population. Even excluding the state of New York as an extreme outlier, states with Democratic governors have 138.58 deaths per million from coronavirus, still over twice as many coronavirus deaths per million as deaths in states with Republican governors.
It merits emphasis from the get-go that this relationship is obviously not directly causal. The inauguration of Kentucky’s new Democratic governor on December 10, 2019 did not triple the state’s subsequent mortality from the coronavirus relative to what it would have been had Republican incumbent Matt Bevin been reelected.
The dramatically different death rates between states with Republican and Democratic governors, however, illuminates two issues concerning state-level responses to the coronavirus. First, the dramatically lower death rates in Republican states account for the willingness of Republican governors to consider relaxed shelter-in-place policies relative to governors in Democratic states. As is appropriate in a federal system where significant policy responsibility continues to be exercised at the state level, a shelter-in-place policy appropriate for New York would not necessarily work well in Wyoming. Governors should be encouraged, not condemned, for pursuing policies tailored to the unique characteristics of their states.
Secondly, however, the question, “what did he know and when did he know it,” is not merely a question to ask the President regarding national-level policy responses to the coronavirus threat since February. The near-certainty of a global pandemic of some sort has been well-known in policy circles for decades. The unique demographic characteristics of each state that make them more or less susceptible to pandemic contagion are best known to state politicians, especially state governors. In the U.S. constitutional system in which state governments uniquely hold police powers—defined to be general authority to protect the health, safety, welfare and morality of the people (a power that the US national government does not have today and has never had)—it is a fair question to ask why so many state governors were caught unprepared. Particularly governors in states that had well known characteristics, like large, cosmopolitan cities, likely to exacerbate the risk of pandemic contagion.
Tocqueville observed that the U.S. has a “complex constitution.” Note the small “c.” In discussing the nation’s complex constitution, he was not writing of the complexity of written state and national Constitutions. He was rather discussing how the entire system of governance in the U.S. was constituted – state governments with the national government. Needless to say, the size of the U.S. national government is dramatically different today than it was in the 1830s. At the same time, it remains completely false to suggest that states no longer retain significant authority over vast domains of policy within their states. This is true as a formal Constitutional matter in that the U.S. Supreme Court has consistently denied that the U.S. national government has police power and continues to insist only state governments hold that power—except in limited areas where delegated to the national government. And it is true empirically as well.
For as large as the national government is, state governments nonetheless spend almost as much in total as the national government spends. Even in the exercise of power over everyday life, criminal and civil matters continue to be overwhelmingly defined and litigated under the authority of the states and not under the authority of the national government.
The advantage of a federal system is that it combines the advantages that large nations enjoy with the benefits of small ones. It is a virtue of federal systems that states can craft policies to their unique circumstances. Tocqueville observed that “In centralized great nations, the legislator is obliged to give a uniform character to the laws which does not encompass the diversity of places and mores.”
If the Democrats were so smart and caring, then why this huge divergence of death rates between Republican and Democratic states?
This is no more obviously true, even if much neglected by experts and commentators today, than in state-level policies crafted to respond to the coronavirus. Given the huge differences in the death rates of the virus across the difference states it should be almost immediately obvious that it is appropriate that different states craft different policy responses to virus. Different state policies that reflect different experiences and demographic factors is not a weakness of the U.S. federal system, it is a strength of that system.
The idea that a nation as large and diverse as the U.S. should have a one-size-fits-all national “shelter-in-place” policy is absurd on its face. Yet so much of the mainstream media’s commentary ignores the variation in state-level experience, and criticizes Republican governors for precipitately re-opening their states. This does not mean that Republican governors are necessarily right, but they’re certainly not wrong simply for not aping the policies of Democratic governors.
Secondly, the national government has an obvious and sizeable role in a global pandemic of this sort. It has primary authority over international matters and on matters that cross state borders. But states governments—and state governors—have the primary formal power over the health, safety, and welfare of the people in their state, they also have fine-grained information about their state’s demographic and economic characteristics.
The lack of preparation for a pandemic cannot be laid solely at the feet of the national government in the U.S. The demographic factors and other unique circumstances, say, of New York are well known to New Yorkers. It does not take a genius to recognize the special risks that New York City or San Francisco, or Chicago would face in a pandemic.
I don’t think that Republican governors would have done any better in preparing for the coronavirus pandemic than the Democratic governors have. But that’s not the proper measure: If you listen to Democrats and their cheerleaders in the mainstream media, Democratic politicians are just plain smarter and more caring than Republican politicians. That the death rate from the coronavirus is three times higher in states with Democratic governors than in states with Republican governors challenges this tendentious narrative.
The mainstream media is critical of Trump and his response to the coronavirus threat. But the last four months is not the proper measure for assessing anti-pandemic policy. Andrew Cuomo has been Governor of New York since 2011. He has had that entire time to prepare his state for a pandemic. Yet his state’s death rate is almost ten times the national average. If Cuomo were a Republican governor, this number would not be grounds for proffering his name as a presidential candidate, it would be grounds for impeachment for nonfeasance.
[1] To compute these state averages, I used data from the “Coronavirus Tracker” Table posted on RealClearPolitics, accessed Sunday morning, April 26, 2020. The data reflect the entire “population” of all 50 states. So, computation of statistical significance is not strictly appropriate. Nonetheless, if we think of the actual state data as a sample drawn from a hypothetical set of possible state outcomes, then reporting statistical significance would be appropriate. The difference for state mortality per million between states with Republican governors and Democratic governors was statistically significant at the 0.05 level of significance. The differences were statistically significant both when New York was included in the “sample” and when it was excluded.
Now back to the article. It doesn't surprise me that Democrats are worse leaders than Republicans. But I'm glad someone took the time to examine the issue.
“....the number of deaths from coronavirus is over three times higher in states with Democratic governors than in states with Republican governors...”
There is some justice...and I live in a state with a democrat governor.
Governors of States with large cities and any governors in the Northeast that are small, crowded States aka big cities unto themselves.
Not entirely fair since Blue states tend to have more concentrated populations although they do tend to encourage this in policy with anticar and suburbia regulations.
Just goes to show the numbers are being manipulated.
This virus is about implementing social control, and it is about winning an election.
COVID-19 is an infectious coronavirus disease that particularly afflicts elderly people and people with certain comorbidities. In a paper published by European Respiratory Journal, Wei-Jie Guan et al analyzed the clinical outcomes of 1,590 COVID-19 patients, and found the following comorbidities to be statistically significant with respect to a severe composite endpoint (ICU admission, invasive ventilation, death) after adjusting for age and smoking status:
P-value
COPD 0.002
Diabetes 0.037
Hypertension 0.022
Malignant tumor 0.002
“P-value” is a measure of statistical significance that indicates the probability of a statistical inference being entirely due to chance. For studies such as this, a p-value of 0.050 or less is required to demonstrate statistical significance. (For example, the FDA’s decision-making regarding drug approvals requires the p-value to be less than or equal to 0.050.)
Trump Derangement Syndrome (TDS) is a widely recognized psychological condition first identified in early 2017. (The earliest description of TDS that I’m aware of is linked below.) Despite its having long been recognized in popular literature, TDS is not recognized in the APA’s Diagnostic and Statistical Manual of Mental Disorders, and there are no reliable statistics of how widespread TDS is in the general population. It is known, however, that a vast majority of workers in the news media in the Acela Corridor and virtually the entire entertainment industry on the West Coast of North America suffer from this affliction.
A few weeks back I hypothesized that TDS is significant comorbidity in COVID-19 cases ending in death. I obtained the number of COVID-19 fatalities for each of 2,861 US counties (or county equivalents) and then calculated COVID-19 gross mortality rate using the 2019 population for each county. To test my hypothesis, I used the percentage of Hillary Clinton voters in 2016 as a proxy for the extent of TDS in US counties since there are no direct data on the extent of TDS.
Using Excel’s regression add-in, I determined that TDS is indeed a statistically significant comorbidity in COVID-19 cases ending in death. For each 1% increase in a county’s TDS rate, the gross mortality rate of COVID-19 increases by 2.6 deaths per million population (95% confidence interval is 2.5-2.8). The P-value of this finding is 2.5x10-141. This is effectively p=0.000, which is stronger than the statistical significance of other comorbidities identified in scholarly literature.
One may object that large differences in the populations of US counties distorts this analysis by putting too much weight on sparsely populated counties and insufficient weight on densely populated counties. However, the statistical significance actually increases when each county’s data are weighted by population.
Of course, the standard caveat that “correlation does not imply causation” applies here. However, the correlation plausibly arises from one or more obvious causative mechanisms. It is also arguable that the proxy used for TDS is such that TDS is not necessarily a comorbidity in individual cases but, instead, that the deceased patient’s former residence in a TDS-afflicted county is the true comorbidity. In either case, TDS must be recognized as having fatal consequences.
Much more research into TDS is required. This becomes even more urgent now that it is identified as a comorbidity in COVID-19 cases ending in death.
NB: This is not entirely serious, but it’s not trivial either. The statistical analysis is real. Data sources are listed below.
https://erj.ersjournals.com/content/early/2020/03/17/13993003.00547-2020?utm_source=TrendMD&utm_medium=cpc&utm_campaign=_European_Respiratory_Journal_TrendMD_0
https://www.rasmussenreports.com/public_content/political_commentary/commentary_by_charles_hurt/no_cure_in_sight_for_donald_trump_derangement_syndrome_sweeping_elite
https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv COVID-19 Deaths by US County (or County Equivalent)
https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-total.html 2019 Population by US County (or County Equivalent)
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VOQCHQ 2016 Election Results
All over local news that Red states will die of Covid first once things open up again.
Of course, that is bs.
If you take the Red vs Blue in the 2016 election by county, it shows that Trump won 95% of the real estate in the USA.
The corona virus death map by county looks the same as the 2016 election map.
I mean, it’s fun to do statistics since the democrats seem to love them so much. We know the actual explanation likely has to do with democratic states having populations with higher density, although that explains number of cases more than deaths.
It mostly looks like democratic governors decided to use COVID to take care of their problem with senior citizens voting republican and costing big bucks in the state budgets. As a side effect, once they killed the republican old people, they could use the increased death count to plead for more money.
“I am so tired of people referring to Democrats as Democratic.”
You’re not alone.
In Democrat states everyone moves in with grandma and in Republican states grandma is treated like a queen and is rested and cared for.
It may be unfair, but I don’t care. You really think New York would be the dystopian third world hellhole it currently is if Rudy Giuliani was Mayor?
I take exception to his flat statement: “I don’t think that Republican governors would have done any better in preparing for the coronavirus pandemic than the Democratic governors have.”
And offer Giuliani and Schwarzenegger as counter-point. The CA idiots even sold off the field hospitals and medical supplies that Schwarzenegger had bought for the state. Giuliani’s handling of crime also speaks to being someone who takes the long view and prepares.
There is arounf 83,000 deaths
About 17,500 are from Republican states. The rest are from Democrap states. Only chance to legally kill people.
I wanted to address this article before posting my (overdue) update on the size- and time-adjusted fatalities by country and state.
First, the article's analysis, while legit, is then mis-interpreted later on, i.e., "The dramatically different death rates between states with Republican and Democratic governors." All the writer did was pool the deaths per capital for Blue and Red (as defined by the Gov). Individual states may behave differently, and if one or two states overwhelm the pooled data then it's not a proper analysis.
A better specification would be to examine the size-adjusted fatalities as a function of the Gov being Blue or Red. You could also look at the makeup of the Upper House of the Legislative Branch of the States, and whether or not the State went for Trump in 2016. Finally, comparing the fatalities on any given day fails to take into account the fact that because each State flared up at different times, i.e., Washington State breached the national equivalent of 1,000 cumulative fatalities on March 9 vs March 29 for Pennsylvania. A better approach would be to compare the size- and time-adjusted fatalities as a function of Blue or Red factors.
So, I went and estimated six regressions, using the log of the adjusted fatality value as a function of dummy variables (1=Blue, 0= not) for the Gov, Trump in 2016, and Upper House (there isn't much difference between Upper and Lower blue/red status at the State level). Below I provide the overall p-value for the dummy variable:
| Dependent Variable | Gov=1 if Dem, 0 Otherwise | Trump=1 if Trump won, 0 Otherwise | 1 if Upper Assembly = Dem, 0 Otherwise |
| log(size adj fatalities 5/11/2020) | 0.0252 | 0.0018 | 0.0042 |
| Log(time and size adj fatalities Day 38) | 0.0682 | 0.0746 | 0.0879 |
So, what does this mean? Basically, it's a mixed bag (as some of the posters were saying). If you don't adjust for the timing of when each State heats up, then there is a relatively strong difference in fatalities (adjusted for population differences) along political lines - basically fatalities are higher in States that went for Hillary, have Dem govs, and have Democrat-controlled Houses. However, if you DO adjust for timing differences and use the size-adjusted fatality 38 Days after each state breaches the 1,000 size-adjusted fatality level, then your regression is 'insignificant' at the 5% p-value but not at the 10% p-value. In my experience, once p-value is higher than 5% regressions don't differentiate very much.
I think, at individual state levels, the makeup of governor is pretty important. Wolf in PA is a Dem and Baker in MA is a Republican but both States are disasters, while Hawaii (blue) and Wyoming (Red) are equally doing well. This is not a cut and dry issue.
And now, for the latest installment of the updated tracking of size- and time-adjusted COVID-19 deaths at the state and national level. For those new to the data, I standardize each municipality's cumulative COVID-19 fatality count per GitHub by its population, then multiply the resulting quotient by the US' population of 329MM to put everyone on a common/US scale. I then index each municipality's time-series to Day 1, where their size-adjusted fatality count is right before it breaches 1,000 people. This corrects for what I call the "Golf Problem" where broadcasts of the Masters etc have to show not only each golfer's score, but which hole they're at. I also list the Day 1 date and the population for reference. I've posted the history back to Day 1 on my About page.
Note for the US as nation, that Day 1 is March 25, which means Day 48 is May 11 for that row of data. You can do the math for the other municipalities to find out what Day XX means for them in calendar times; as a general rule, the last actual data point is the freshest date, but on a Golf timeline it gives each municipality's score as of the "48th hole."
The sort order is Day 48 for the international table, with Canada and Israel added based their Day 41 rankings. For the states' data, I sort by Day 44 which generally keeps the relative ranking steady for later days, most notably for the states with higher than national fatality totals.
The international ranking of the US hasn't changed in a long time. Sweden and the Netherlands' "hands off" approach hasn't yielded fewer fatalities than the US, and Sweden's actually gotten worse over the past few days - it's now lost 20k+ more people on an adjusted basis; she deserves her own scrutiny because SE isn't truly a hands-off/'flubro' nirvana. But that doesn't mean we should ignore what they're doing - maybe there is something to this herd immunity thing.
Stateside, for Day 48 the ranking of municipalities adjusted fatalities higher than the US as a whole remains unchanged over the past few weeks: New York (Blue), New Jersey (Blue), Connecticut (Blue), Massachusetts (Blue), Michigan (Red), Louisiana (Red) and District of Columbia (Blue), respectively. Rhode Island, Pennsylvania, Illinois and Delaware are also hotter than the national average on the basis of Day 44.
Pray for everyone impacted: the dead, the infected, and the unemployed. And let's open up the country.
| Country | Date of Day 1=day priot to hitting 1,000+ population-adjusted deaths | Population | Day 41 | Day 42 | Day 43 | Day 44 | Day 45 | Day 46 | Day 47 | Day 48 | Day 49 | Day 50 | Day 51 |
| San Marino | 3/3/20 | 33,574 | 343,554 | 343,554 | 353,370 | 353,370 | 373,001 | 382,817 | 382,817 | 382,817 | 382,817 | 392,633 | 392,633 |
| Belgium | 3/19/20 | 11,524,454 | 209,639 | 214,501 | 217,160 | 220,277 | 222,050 | 224,309 | 226,597 | 229,228 | 238,464 | 240,638 | 243,669 |
| Andorra | 3/21/20 | 77,543 | 178,499 | 182,749 | 186,999 | 191,249 | 191,249 | 195,499 | 195,499 | 199,749 | 199,749 | 203,999 | 203,999 |
| Spain | 3/13/20 | 47,100,396 | 151,951 | 155,030 | 157,598 | 160,243 | 162,258 | 164,574 | 166,680 | 169,850 | 171,725 | 171,725 | 175,622 |
| United Kingdom | 3/20/20 | 66,435,550 | 129,798 | 133,151 | 136,827 | 139,912 | 141,475 | 142,908 | 146,341 | 149,560 | 152,234 | 155,344 | 157,061 |
| Italy | 3/5/20 | 60,243,406 | 115,245 | 118,407 | 121,279 | 124,425 | 127,061 | 129,430 | 131,914 | 134,835 | 137,225 | 139,764 | 142,061 |
| France | 3/18/20 | 67,076,000 | 114,610 | 116,413 | 118,511 | 119,931 | 121,002 | 121,665 | 122,338 | 123,832 | 125,468 | 126,819 | 127,694 |
| Sweden | 3/23/20 | 10,333,456 | 85,120 | 85,439 | 88,309 | 91,020 | 93,795 | 96,952 | 101,258 | 102,693 | 102,852 | 103,841 | - |
| Ireland | 3/25/20 | 4,921,500 | 88,324 | 89,663 | 92,074 | 93,949 | 95,690 | 96,828 | 97,631 | 98,234 | - | - | - |
| Netherlands | 3/17/20 | 17,451,031 | 84,811 | 85,623 | 86,529 | 89,268 | 90,854 | 92,705 | 94,480 | 95,783 | 96,274 | 97,917 | 98,597 |
| United States | 3/25/20 | 329,556,365 | 68,922 | 71,064 | 73,431 | 75,662 | 77,180 | 78,795 | 79,526 | 80,682 | - | - | - |
| Switzerland | 3/16/20 | 8,586,550 | 61,370 | 61,793 | 63,904 | 65,209 | 65,861 | 66,667 | 67,319 | 67,626 | 67,626 | 68,471 | 68,893 |
| Canada | 4/1/20 | 37,979,093 | 44,384 | - | - | - | - | - | - | - | - | - | - |
| Luxembourg | 3/17/20 | 613,894 | 47,241 | 47,241 | 47,778 | 47,778 | 48,315 | 49,388 | 49,388 | 51,536 | 51,536 | 51,536 | 52,609 |
| Macedonia | 3/24/20 | 679,600 | 40,734 | 41,219 | 41,704 | 42,674 | 43,158 | 43,643 | 44,128 | 44,128 | 44,128 | - | - |
| Portugal | 3/23/20 | 10,276,617 | 32,806 | 33,448 | 34,089 | 34,442 | 34,923 | 35,436 | 35,724 | 36,109 | 36,398 | 36,686 | - |
| Germany | 3/25/20 | 83,149,300 | 27,716 | 27,716 | 28,834 | 29,298 | 29,765 | 29,920 | 29,999 | 30,364 | - | - | - |
| Denmark | 3/22/20 | 5,822,763 | 26,035 | 26,884 | 27,393 | 27,903 | 28,469 | 28,639 | 29,091 | 29,544 | 29,771 | 29,940 | 30,167 |
| Austria | 3/23/20 | 8,902,600 | 22,063 | 22,137 | 22,211 | 22,433 | 22,507 | 22,544 | 22,729 | 22,766 | 22,877 | 22,951 | - |
| Iran | 3/9/20 | 83,331,064 | 19,897 | 20,241 | 20,600 | 20,948 | 21,320 | 21,676 | 22,044 | 22,345 | 22,582 | 22,961 | 23,242 |
| Israel | 4/1/20 | 9,180,000 | 9,262 | - | - | - | - | - | - | - | - | - | - |
| Iceland | 3/23/20 | 364,260 | 9,047 | 9,047 | 9,047 | 9,047 | 9,047 | 9,047 | 9,047 | 9,047 | 9,047 | 9,047 | - |
| Municipality | Date of Day 1 = day prior to breaching size-adj 1,000 COVID19 fatalities | Population | Day 41 | Day 42 | Day 43 | Day 44 | Day 45 | Day 46 | Day 47 | Day 48 | Day 49 | Day 50 | Day 51 |
| New York | 3/19/20 | 19,795,791 | 379,187 | 387,677 | 393,154 | 396,900 | 400,130 | 409,136 | 412,666 | 416,661 | 432,110 | 436,272 | 438,803 |
| New Jersey | 3/23/20 | 8,958,013 | 284,820 | 289,566 | 291,001 | 303,289 | 314,509 | 323,780 | 329,335 | 335,369 | 340,482 | 343,609 | - |
| Connecticut | 3/23/20 | 3,590,886 | 223,566 | 223,566 | 234,579 | 241,646 | 249,447 | 256,697 | 263,764 | 269,087 | 272,299 | 276,062 | - |
| Massachusetts | 3/25/20 | 6,794,422 | 198,381 | 204,299 | 214,387 | 220,790 | 228,066 | 234,759 | 241,501 | 247,758 | - | - | - |
| Michigan | 3/24/20 | 9,922,576 | 134,479 | 137,335 | 138,796 | 141,154 | 144,243 | 145,904 | 150,321 | 151,151 | 152,247 | - | - |
| Louisiana | 3/20/20 | 4,670,724 | 127,145 | 131,379 | 135,965 | 137,588 | 138,928 | 140,481 | 144,079 | 147,748 | 150,641 | 151,982 | 154,804 |
| District of Columbia | 3/24/20 | 672,228 | 123,051 | 126,483 | 129,425 | 135,798 | 139,720 | 149,034 | 152,466 | 158,349 | 160,800 | - | - |
| Rhode Island | 3/29/20 | 1,056,298 | 124,485 | 130,413 | 131,661 | 134,156 | - | - | - | - | - | - | - |
| Pennsylvania | 3/28/20 | 12,802,503 | 92,644 | 95,862 | 97,638 | 98,307 | 98,642 | - | - | - | - | - | - |
| Illinois | 3/27/20 | 12,859,995 | 76,290 | 80,442 | 83,594 | 86,156 | 87,796 | 88,642 | - | - | - | - | - |
| Delaware | 3/27/20 | 945,934 | 67,240 | 70,375 | 74,208 | 76,995 | 78,040 | 78,388 | - | - | - | - | - |
| United States | 3/25/20 | 329,556,365 | 68,922 | 71,064 | 73,431 | 75,662 | 77,180 | 78,795 | 79,526 | 80,682 | - | - | - |
| Indiana | 3/26/20 | 6,619,680 | 66,014 | 68,553 | 70,395 | 72,038 | 74,179 | 75,075 | 76,668 | - | - | - | - |
| Colorado | 3/24/20 | 5,456,574 | 50,733 | 51,276 | 54,417 | 55,504 | 56,893 | 57,860 | 58,282 | 58,524 | 59,611 | - | - |
| Mississippi | 3/27/20 | 2,992,333 | 41,190 | 43,613 | 45,045 | 46,366 | 47,357 | 47,908 | - | - | - | - | - |
| Georgia | 3/23/20 | 10,214,860 | 37,457 | 37,683 | 39,618 | 41,199 | 42,232 | 43,006 | 44,458 | 44,522 | 44,619 | 46,587 | - |
| Ohio | 3/29/20 | 11,614,373 | 37,058 | 37,767 | 38,051 | 38,505 | - | - | - | - | - | - | - |
| Virginia | 3/29/20 | 8,382,993 | 31,922 | 32,511 | 32,983 | 33,416 | - | - | - | - | - | - | - |
| Nevada | 3/24/20 | 2,890,845 | 29,868 | 30,324 | 31,464 | 32,604 | 33,402 | 34,314 | 34,884 | 34,884 | 35,568 | - | - |
| Washington | 3/8/20 | 7,170,351 | 28,036 | 28,909 | 29,277 | 30,380 | 31,391 | 31,989 | 32,954 | 33,597 | 34,149 | 34,792 | 35,436 |
| Florida | 3/29/20 | 20,271,272 | 27,117 | 27,865 | 27,963 | 28,206 | - | - | - | - | - | - | - |
| Vermont | 3/18/20 | 626,042 | 24,741 | 24,741 | 24,741 | 25,794 | 26,321 | 26,847 | 27,900 | 27,900 | 27,900 | 27,900 | 28,426 |
| Wisconsin | 3/29/20 | 5,771,337 | 21,927 | 22,727 | 22,841 | 23,355 | - | - | - | - | - | - | - |
| California | 3/27/20 | 39,144,818 | 20,862 | 21,561 | 22,310 | 23,000 | 23,312 | 23,396 | - | - | - | - | - |
| Oklahoma | 3/27/20 | 3,911,338 | 21,317 | 21,907 | 22,412 | 22,749 | 22,918 | 23,086 | - | - | - | - | - |
| South Carolina | 3/27/20 | 4,896,146 | 20,529 | 21,270 | 21,539 | 22,212 | 22,279 | 23,289 | - | - | - | - | - |
| Kansas | 3/29/20 | 2,911,641 | 18,902 | 19,468 | 19,468 | 20,373 | - | - | - | - | - | - | - |
| North Dakota | 3/29/20 | 756,927 | 14,368 | 15,239 | 15,239 | 15,674 | - | - | - | - | - | - | - |
| Idaho | 3/28/20 | 1,654,930 | 13,342 | 13,342 | 13,342 | 13,342 | 13,940 | - | - | - | - | - | - |
| Oregon | 3/27/20 | 4,028,977 | 9,407 | 9,897 | 10,143 | 10,388 | 10,388 | 10,634 | - | - | - | - | - |
| Montana | 3/29/20 | 1,032,949 | 5,105 | 5,105 | 5,105 | 5,105 | - | - | - | - | - | - | - |
| Maryland | 3/31/20 | 6,006,401 | 90,202 | 92,342 | - | - | - | - | - | - | - | - | - |
| Minnesota | 3/31/20 | 5,489,594 | 34,699 | 35,479 | - | - | - | - | - | - | - | - | - |
| New Hampshire | 4/1/20 | 1,330,608 | 32,941 | - | - | - | - | - | - | - | - | - | - |
| New Mexico | 4/1/20 | 2,085,109 | 32,875 | - | - | - | - | - | - | - | - | - | - |
| Iowa | 4/1/20 | 3,123,899 | 28,589 | - | - | - | - | - | - | - | - | - | - |
| Missouri | 4/1/20 | 6,083,672 | 27,844 | - | - | - | - | - | - | - | - | - | - |
| Alabama | 3/31/20 | 4,858,979 | 26,655 | 27,333 | - | - | - | - | - | - | - | - | - |
| Arizona | 3/30/20 | 6,828,065 | 25,677 | 25,870 | 26,160 | - | - | - | - | - | - | - | - |
| Kentucky | 3/30/20 | 4,425,092 | 23,236 | 23,236 | 23,162 | - | - | - | - | - | - | - | - |
| Nebraska | 4/1/20 | 1,896,190 | 17,032 | - | - | - | - | - | - | - | - | - | - |
| Maine | 3/30/20 | 1,329,328 | 15,866 | 15,866 | 16,114 | - | - | - | - | - | - | - | - |
| Tennessee | 3/31/20 | 6,600,299 | 12,083 | 12,533 | - | - | - | - | - | - | - | - | - |
| Puerto Rico | 4/1/20 | 3,680,058 | 10,119 | - | - | - | - | - | - | - | - | - | - |
| Arkansas | 3/31/20 | 2,978,204 | 10,070 | 10,402 | - | - | - | - | - | - | - | - | - |
This whole thing could (should) just as easily apply to States and Counties/Cities. Why should a whole State get taken out, just because one city on the opposite side has a couple deaths?
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