D. Flattening of the Curve ?

The CDC and others have referred to flattening of the curve and the importance of mitigation. The CDC published a computer generated graph entitled “Flatten the Curve,” which is shown in Figure 8.

Figure 8 is a graph published by the CDC. Duration of Time is the horizontal axis. Number of Cases is the vertical axis. The red curve is with no intervention. The blue curve is with intervention.

A search was made for data substantiating the two types of curves, as applicable to COVID-19, i.e. a serious epidemic where the virus has become widespread.  But no such data was located. 

The mortality data from Italy, Spain and the UK are displayed in Figure 9.  That data does not support the flattening of the curve.  Instead, it does the opposite, illustrating the classic peak diagram shown in red in Figure 8.  All three graphs show a steep climb to the peak followed by a less steep decline. All three countries took unprecedented isolation action.  They should have displayed a signal of a smooth, rounded, flat curve.

Figure 9.  These are plots of mortality curves for Italy, Spain and UK applicable to COVID-19.   The horizontal axis is the week number from January 2020.  The vertical axis is the weekly deaths.  The data for the graphs was obtained from http://www.worldometers.info. 

The curves in Figure 9 are very similar to the COVID-19 mortality curve in the United States. Additionally, all four are similar to the influenza curve in the United States in 2018.

Intervention, as referenced in Figure 8, encompasses everything from quarantine to drugs and vaccines. Quarantine is an effective intervention. If caught early, it eliminates the curve entirely, as illustrated by the South Korean and Japanese COVID-19 mortality curves. China, by locking everyone in Wuhan, was practicing an emergency quarantine. Even though it included healthy people within the quarantine borders, this may have been the reason that China’s mortality outside of Wuhan was low.

No studies have been located showing that defensive isolation works under any virus spread level. Italy, Spain, the UK and the United States all have mortality curves that suggest defensive isolation does not work.

In the CDC Figure 8, it states that the curves are based on “number of cases.” If it is referring to number of infections, then it raises issues regarding data credibility. The number of actual infections is rarely known. And, with respect to COVID-19, they could be off by 700 to 8500 percent. Only a valid statistical analysis using random selection could provide accurate information. This has not been done. On the other hand, the COVID-19 pandemic does have death and hospitalization numbers. These are more accurate and can be used in lieu of infections.

However, there may be a problem with Medicare approving higher payments to hospitals for COVID-19 deaths.    Senator Scott Jensen reported that the American Medical Association encouraged the doctors to over count the COVID-19 deaths.    State of New York has recently added about 1000 new COVID-19 deaths as -just discovered.   It suggests that the deaths are being overstated.  These statements are opinions without citation to actual data.  But, manipulation of death records appeared to have occurred in Puerto Rico with Hurricane Maria in 2018. 

Quorvita looked at the upslope/downslope ratio to determine if there is a factual basis to suspect a manipulation of the death rate to blame it on COVID19.  Below is Table 4, which compares the ratio with the 2018 influenza curve, the Italy COVID19, Spain COVID19, and UK COVID 19 with the current USA COVID19.

Table 4.  This is a table of the Upslope, Downslope, Slope Ratio and Percent above or below average for the 2018 US Influenza, and the COVID19 numbers from Italy, Spain, UK and for the USA.  The Influenza data came from the CDC and the COVID19 death rates came from http://www.worldometer.info.  

Table 4 provides a data based analysis to determine if there were some anomalies in the reporting.  There were some significant anomalies.  It shows that the 2018 Influenza, Italy, Spain, and UK has a slope ratio near 2.  If those 4 data points are used, then the average ratio is 2.1.  The US COVID19 ratio of 3.31 appears far outside the norm.  It suggests that manipulation may be occurring and an investigation should be done to verify this point.  Medicare by giving more money for COVID19 related deaths and treatment may have inadvertently provided an incentive to over-claim and by doing so may have corrupted the true COVID19 deaths by as much as 41% after April 21st, 2020.  As of now, Quorvita considers the number of COVID19 deaths reported after April 21st as suspect. 

Using the US mortality data for COVID-19, there was no reduction signal or flattening of the curve that could be attributed to defensive isolation practices. The curves show the typical virus peak and drop.

Weekly deaths should be used because the daily deaths fluctuate wildly. There is also a delay and bunching of numbers associated with daily reporting, particularly on the weekends.

The COVID-19 mortality curve began to drop six weeks after the influenza virus dropped in the United States. The slopes of the diminishing influenza virus and COVID-19 deaths were remarkably in sync. This is shown in Figure 7 below. It provides strong evidence of the existence of a concurrent cause situation and not just COVID-19. 

Figure 7.  Influenza Spread Rating superimposed on COVID-19 Deaths for 2020.  The horizontal axis is the number of Weeks from March to December 2020.  The left hand side vertical axis is the Weekly Deaths in the US.  The right hand side vertical axis is the weekly Flu Map Rating total for all 50 states plus Puerto Rico and the District of Columbia for 2020.  Weekly Deaths data compiled from http://www.worldometers.info. Flu Map data from the CDC.

Figure 7 above is based on death rates.  There may be some problems with the death rate data.  Published data suggesting that CDC decreased flu deaths after week 10 and below residual flu deaths reported in previous years and reclassified them as COVID-19 deaths.   This is consistent with the data shown in Table 4 that the COVID-19 deaths could be overstated by as much as 41%.