Proof From Recent Study that COVID Death Logistics Are Bloviated!

Covid Reports, Clearly Misrepresent Actual Findings. Americans Have Been Deceived. How Long Before We Say ‘Enough Is Enough?’

By: JoLynn Live
July 16, 2020
(C-VINE Vetted)

Americans were made to shut down our lives on the belief we would be safe from harm. Millions lost jobs.  Businesses around the globe have closed indefinitely. We obeyed the lock-down mandates of our governors, mayors and others who convinced us it was for the good of our ‘grandmas.’ Many who refused were publicly shamed and arrested.

We were told to social distance, never to shake hands, and always to wear masks because this would protect others from the spread of the virus.  If mask-wearing really worked, wouldn’t this put the medical industry out of business?

The narrative has been to trust the CDC because they always have our best interest in mind… or do they?  

An individual who wishes to remain anonymous, has provided graphs of information from a study conducted, that clearly proves these COVID numbers are exaggerated on a monumental scale.  These hyper-inflated numbers have created panic and near hysteria in people to which the mainstream media and the CDC should be accredited. 

California is in lock-down once again, and in some cases, cities such as Yakima in WA State, have never even moved into Phase 2. Though the virus is real, based on numbers, the pandemic is not.  This fakery attacks the very heart of all Americans and has threatened our very lives, liberties and the pursuits of our happiness.  Who is to blame?

Opinion: JoLynn Live, C-VINE Contributor 

 

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  1. COVID-19 Death Rate versus Population Density

Background: There has been a lot of discussion on the correct way forward in dealing with COVID-19.  At the heart of the matter is the root cause of more versus less deaths.  What can we affect and what can we not affect?  Does policy matter in terms of controlling the death rate from the virus?  Here we present graphically simple data relating population density versus COVID-19 population death rates as of early May, 2020.  This is not to prescribe any certain policy as correct, but rather to present current data to add to the current body of knowledge in order to make the best decisions going forward.

There have been many pieces of data that have been collected since March 2020, all helping to provide pieces to the puzzle of the novel COVID-19 virus and its behavior.  Ships provided good information on the infection in close, closed quarters.  Early on, the Diamond Princess cruise ship had 3700 passengers, of which 712 (19.2%) tested positive for COVID-19, 331 (46.5%) were asymptomatic positives, 37 (9.7%) required intensive care and 9 (1.3%) died.  The Grand Princess cruise ship had 46.7% positive testing.1 On the USS Theodore Roosevelt as of April 23rd, 840 sailors were COVID-19 positive with one death (0.12%).2

Outside of ships, more data has since been collected.  In Santa Clara, California antibody testing suggested that 1 in 66 people had been infected with COVID-19 changing the estimated county infection estimate from 1,000 to 48,00 to 82,000 and estimating the infection fatality rate (IFR) at 0.1% to 0.2%.  This Santa Clara study sited similarities with German data suggestion that 15% of a village had been infected as of April 9, 2020.3  Los Angeles data from the University of Southern California estimated that 4.1% of Los Angeles County adults had virus antibody.  This equated to 221,000 to 442,000 infections which was 28 to 55 times higher than confirmed case counts4 and which equated at the time to about a 0.2% IFR.  New York data found that 14.9% of those in New York state and 24.7% in New York City were positive for the virus correlating to an IFR of 0.5%.5

    At the heart of the policy debate is what causes more COVID-19 cases.  The presumption is that policies for more restrictions curb the infection rate.  Recent Stanford University epidemiological analysis found that there is no linear regression correlation with lockdown date and severity of the COVID-19 outbreak even controlling for other variables.6

Clearly, in looking at accumulated data the outcomes of concern are the number of people who have been infected (antibody levels) and the number of deaths.  Cases of current positives are wildly misleading in an outbreak where only symptomatic people are tested and where a very large proportion of infections are asymptomatic.  The more people there are who have the antibodies equates to being closer to herd immunity.  The number of deaths relates to the severity of the condition.

Methods: Here we present an attempt to find natural answers as to what most correlated with COVID-19 deaths.  In this simple analysis, deaths per million were assessed first compared to population size.  Among Western European countries, the rough correlation was that the larger the population the more deaths per million.  In this analysis, Sweden is right about where they should be based on poulation size even though they did no formal lockdown.  The data, however, is not so clean a linear correlation.

To add more precision to the data, comparison was made between population density as a natural surrogate to social distancing and deaths per million population as of May 1, 2020.  The whole population was chosen since it has been shown to be difficult to tell just how many people have already had the COVID-19 infection.

In this analysis, there was a more linear correlation though there was a bit of spread around the trend line based on Western Europe data.  The trends were assessed for COVID-19 death rate as the outcome of importance versus population density as the natural measure of intrinsic social distancing.  The whole United States was then assessed as were the most densely populated states using public population data and Centers for Disease Control death counts.

    Patient and Public Involvement: There was no patient contact or public involvement in any portion of this analysis.

Results: United States data was graphed using the above criteria as of May 4, 2020 (Table 1 & Fig. 1) with the axes as noted above.

Table 1

May 4, 2020

Jurisdiction Pop (millions) Pop Density  (sq mi) Deaths per million

(CDC)
New York 19.4 419.3 1260
New Jersey 8.9 1207.8 890
Massachusetts 6.9 866.6 560
Illinois 12.6 231.4 210
California 39.5 251 60
Pennsylvania 12.8 285.7 220
Michigan 5.6 174.7 410
Florida 21.5 375.9 60
Texas 29 104.9 30
Connecticut 3.6 741.2 680
Louisiana 4.6 107.2 420
Georgia 10.6 176.4 110
Maryland 6 614.5 210
Ohio 11.7 283.6 90
Indiana 6.7 184.6 190
Virginia 8.5 211.7 80
Colorado 5.8 52.6 150
Washington 7.6 107.8 110
Tennessee 6.8 160.1 30
North Carolina 10.5 206.2 40
Rhode Island 1.1 1010.8 300
Iowa 3.2 55.9 60
Arizona 7.3 60.1 50
Missouri 6.1 88.3 60
Wisconsin 5.8 106.3 60
Alabama 4.9 95.8 60
Mississippi 3 63.8 100
South Carolina 5.1 162.6 50
Minnesota 5.6 69 70
Nebraska 1.9 24.7 40
Nevada 3 26.3 90
Delaware 1 484.1 180
Utah 3.2 36.5 20
Kentucky 4.5 111.4 60
District of Columbia 0.7 360
Kansas 2.9 35.6 50
Oklahoma 4 57 60
New Mexico 2.1 17.2 70
Arkansas 3 57.2 30
Oregon 4.2 42 30
South Dakota 0.9 11.3 20
New Hampshire 1.4 148.4 60
Idaho 1.8 20 40
West Virginia 1.8 76.6 30
North Dakota 0.8 11 30
Maine 1.3 43.1 40
Vermont 0.6 67.7 80
Hawaii 1.4 222.9 10
Alaska 0.7 1.3 10

Here, the large data set seemed to narrow the states along a very similar trend line as in Western Europe.  There seem to be some better and some worse minor outliers, but the main huge outlier is how much worse New York looks than the rest of the states.

This seems to implicate New York as somehow worse than the norm while South Dakota (red dot), which famously did not lock down, fits nicely on the trend line.  Perhaps New York is just so crowded that this factor overwhelmed even the expected state numbers or perhaps there was some sort of policy issue that played a role.

To look at things even further, New York was broken down by the 20 largest counties and a similar analysis was performed (Table 2 & Fig. 2).

Table 2

May 4, 2020

NY Counties Population in Millions COVID Deaths (CDC) Population Density (sq mi) Death per Million
NY 1.6 2220 72056 1388
Kings 2.6 5678 37253 2184
Bronx 1.4 3642 34194 2601
Queens 2.3 5433 21132 2362
Richmond 0.47 744 8149 1583
Nassau 1.4 1792 4763 1280
Westchester 0.97 1101 2250 1135
Rockland 0.32 413 1866 1290
Suffolk 1.5 1273 1632 849
Monroe 0.74 88 1132 119
Erie 0.92 238 882 259
Schenectady 0.15 22 757 147
Onondaga 0.46 35 596 76
Albany 0.31 39 588 126
Orange 0.38 256 466 674
Putnam 0.1 45 430 450
Niagra 0.21 27 405 129
Dutchess 0.29 81 369 279
Saratoga 0.23 10 281 43
Broome 0.19 10 275 53
Rensselaer 0.16 10 244 62.5
Tompkins 0.1 0 217 0
Chemung 0.09 10 210 111
Oneida 0.23 10 190 43
Ontario 0.11 10 170 91

Several New York counties were much more densely populated than Western Europe or the other states so this might be a closer look comparing apples to apples.  Following suit with the prior graphs, there was a clear correlation between population density and COVID-19 deaths.  This time it appeared in more of a bending curve, but with a similar overall trend.  What was interesting to note was that New York County, which includes Manhattan, was far below the curve in a good direction.  New York County, despite having easily the highest population density, had a lower per million death rate than Kings County, Bronx County, Queens County and Richmond County.   Kings County was also possibly indicating a trend toward lower death rates given its population density.  The graph looked nearly identical doing the same analysis on 5/17/20 comparing to 5/4/20.

This trend continued when looking at the New York counties combined with the US states’ data.  Still, New York County was the major outlier in a good direction.  This is also a lower death per million rate despite data quoted above suggesting that the COVID-19 antibody/infection rate was 24.7% in New York City and 14.9% in the rest of New York state.

To check for consistency of this New York finding, we ran the numbers for states with some of the densest counties to include New Jersey, Massachusetts, Illinois, and Pennsylvania.  In each of these states, there is the same pattern with a linear early increase in death rate per million as population density increases.  Then, just like in New York, the curve bends in each case and usually goes down from the peak as we reach the densest population counties.  See Tables 3 and 4 and Figures 3 and 4 as examples.

Table 3

USA.com CDC 5/6/2020
NJ  Counties Population COVID Deaths Density (sq mi) Death per Million
Hudson 654,878 870 10509 1328
Essex 789616 1319 6091 1670
Union 545236 768 5173 1409
Bergen 920456 1261 3731 1370
Passaic 505403 663 2564 1312
Middlesex 824046 667 2552 809
Camden 512632 180 2255 351
Mercer 369526 280 1614 758
Somerset 328704 306 1078 931
Morris 497103 474 1032 954
Monmouth 629702 398 947 632
Gloucester 289705 61 859 211
Ocean 581413 469 635 807
Burlington 450155 164 549 364
Atlantic 275325 51 410 185
Warren 107624 91 297 846
Hunterdon 126746 38 290 300
Sussex 146888 120 274 817
Cumberland 157439 23 232 146
Salem 65501 10 176 153

Table 4

USA.com CDC 5/6/2020
MA  Counties Population COVID Deaths Density (sq mi) Death per Million
Suffolk 747928 609 6221 814
Middlesex 1539832 1028 1818 668
Norfolk 682860 575 1537 842
Essex 757395 527 914 696
Bristol 551065 210 797 381
Hampden 466447 415 735 890
Worcester 806804 331 511 410
Plymouth 500772 341 458 681
Hampshire 160328 47 294 293
Barnstable 215167 48 165 223
Berkshire 130064 36 137 277
Franklin 71300 37 98 519
Dukes 16915 0 35 0

The same finding was present when adding smaller Western European nations to the assessment.  Monaco which has about 50 times the population density compared to most other Western European nations at over 19,000 people per square km had 102 deaths per million which is among the lowest in Europe.  San Marino has a similar population, but 555 people per square kilometer (35 times less dense than Monaco)  and had the highest death rate in Western Europe with 1208 deaths per million as of May 14, 2020.  Similarly, Washington, D.C. had 343 deaths per million which is close to similarly populated Rhode Island which had 300 deaths per million (5/4/2020), even though Washington, D.C. has 11 times the population density as Rhode Island.  Even more striking was the death rate per million population when assessed in California as the two densest counties really fell off in death rate.  The two densest counties in California, San Francisco and Orange, had 39 and 24 deaths per million which were far lower than six other moderate to larger counties which had rates of 40-152 deaths per million.  Texas looked much like California where the eight most densely populated counties had death rates between 32 and 59 deaths per million as of May 13, 2020, lower death rates than 6 medium sized counties which had between 59 and 120 deaths per million.  Virginia was similar as well where the two densest counties, Alexandria and Arlington, had much lower death rates (205 and 277 per million) than 4 medium density counties (349 to 764 per million) as of May 12, 2020.   Florida’s graph was not clean at all, but then we looked at younger counties versus older counties.  The Florida counties with median age <40 had no significant death rate at any density, but counties with median age of at least 40 had the same pattern as the other states with a clear trend toward much less deaths as population density increases (Table 5 as of 5/17/2020).

Table 5

USA.com CDC 5/17/2020
FL  Counties Population Median Age COVID Deaths Density (sq mi) Death per Million
Pinellas 925030 49 67 1521 72
Broward 1815269 41 279 1372 154
Seminole 432135 39 10 1252 23
Orange 1200241 34 38 1196 32
Miami-Dade 2600861 40 561 1070 216
Hillsborough 1279668 36 53 1011 41
Duval 880750 37 35 959 40
Palm Beach 1359074 45 275 570 202
Pasco 472745 45 10 544 21
Lee 647554 47 78 534 120
Sarasota 386944 56 58 534 150
St Lucie 283988 44 27 413 95
Leon 280882 32 10 400 36
Manatee 335840 48 81 376 241
Bravard 548891 48 10 353 18
Volusia 498981 48 30 348 60
Bravard 548891 48 10 353 18
Volusia 498981 48 30 348 60

Less densely populated states addressed included Michigan, Connecticut and Louisiana and these showed linear increases in deaths with more crowding (generally under 1000 to 2500 people per square mile throughout the state) while less dense states had too few deaths to assess by county.

Conclusions: The data appeared incredibly consistent.  It would seem to suggest a linear increase in infections and death rate with population density until a certain point.  Then perhaps another protective factor starts coming into play.  Maybe some degree of early herd immunity starts to play a role at a tipping point.  Based on our data for the United States, that tipping point at this time appears to be approximately somewhere in to 1000-2500 people per square mile population density range.

    Herd immunity is the situation when enough of the population is immune to a disease such that it protects those without the disease.  An example from Johns Hopkins School of Public Health is: “if 80% of a population is immune to a virus, four out of every five people who encounter someone with the disease won’t get sick (and won’t spread the disease any further)….usually 70% to 90% of a population needs immunity to achieve herd immunity.”7  In the case of more densely populated counties, maybe we are starting to see the effect of herd immunity as we pass ¼ of the population exposed.  The disease will still propagate widely until numbers are closer to 70%, but could it be possible that the deaths per million curve is bending since less than ¾ of those in these counties are susceptible and over ¼ might no longer propagate the disease.?  Perhaps this is why lockdown date appears to have no statistical correlation with COVID-19 deaths, but population density does.  Could very dense populations get COVID-19 worse earlier and start to recover faster due to earlier herd immunity?  In such a case, more people might get antibodies earlier and provide a population where each new infected person has less and less susceptible people to infect.  The surprising aspect of the current data is that it might suggest that even at relatively low population antibody levels we might see a measurable benefit from herd immunity.

For comparison, the Spanish flu of 1918 infected 500 million worldwide with 50 million deaths for a 10% death rate before there was any natural immunity or a vaccine.  Now with herd immunity and vaccines for many decades the flu, including the flu and Spanish flu, have a death rate down to 0.1%.

Centers for Disease Control (CDC) data as of May 7, 2020 shows that of 3,142 counties and county equivalents in the US, only 101 had more than 99 COVID-19 deaths so far. 8 That is, in most cases, less than one COVID-19 death per county per day in 96.8% of US counties.  Everyone has a different threshold, but it seems reasonable for 96.8% of counties not to need extraordinary measures in response to COVID-19 even though there is a linear increase in deaths per million by population density for less densely populated areas.  For the largest 3.2%, significant measures may be the best choice, but death rates in the most densely populated counties suggest that, for public health purposes, at a certain point it may actually be better to expose as many people as possible to lower death rates.  It likely would still be prudent to protect high risk individuals with isolation.  In the case of COVID-19, the question may be whether we are willing to get to herd immunity faster naturally since it appears that policy may not control a virus already present in the entire world from big cities to small towns.

This data provided some expected results in that COVID-19 deaths per million increased steadily with population density.  It also provided a surprising result in that extreme population density in locations where there is already a high COVID-19 antibody prevalence seemed to demonstrate a decrease in COVID-19 deaths per million population even at relatively low antibody percentages in the population.  The cause for this observation is not clear, but this makes for interesting conversation as to the best and fastest way to a solution which has always been herd immunity.

References:

1 Moriarty LF, Plucinski MM,  Marston BJ, Kurbatova EV, Knust B, Murray EL, et al.  Public health responses to COVID-19 outbreaks on cruise ships—worldwide, February—March 2020.  MMWR.  2020 Mar 23; 69(12);347-352.

2 Kheel R. Navy says entire USS Theodore Roosevelt crew has been tested for coronavirus.  The Hill.  2020 Apr 23.

https://thehill.com/policy/defense/494318-navy-says-entire-roosevelt-crew-has-been-tested-for-coronavirus.

3 Mallapaty S. Antibody tests suggest that coronavirus infections vastly exceed official counts.  Nature. 2020 Apr 22.

https://www.nature.com/articles/d41586-020-01095-0.

4 Hopper L.  Early antibody testing suggests COVID-19 infections in L.A. County greatly exceed documented cases.  USC News.  2020 Apr 20.  https://news.usc.edu/168987/antibody-testing-results-covid-19-infections-los-angeles-county/.

5 Saplakoglu Y.  1 in 5 people tested in Ney York City had antibodies for the coronavirus.  Live Science.  2020 Apr 23.  https://www.livescience.com/covid-antibody-test-results-new-york-test.html.

6 Rodgers TJ.  Do lockdowns save many lives?  In most places, the data say no.  Wall Street Journal.  2020 Apr 26. https://www.wsj.com/articles/do-lockdowns-save-many-lives-is-most-places-the-data-say-no-11587930911.

7 D’Souza G and Dowdy D.  What is herd immunity and how can we achieve it with COVID-19?  Johns Hopkins School of Public Health Insights.  2020 Apr 10.   https://www.jhsph.edu/covid-19/articles/achieving-herd-immunity-with-covid19.html.

8 Deaths by County.  Centers for Disease Control. 2020 May 8.  https://cdc.gov.

JoLynn Live

News Posted by: C-VINE Citizen Journalist, JoLynn Live! She is a Singer; a Wife of 36 years; Home-school mom to 10; Grandma to 11; Chicken Farmer; Patriot; and an active C-Vine News contributor.