A recently released study out of Stanford University tested residents of California's Santa Clara County, looking at how widely people had been infected by the novel coronavirus using seroprevalence data which measures the level of antibodies in the blood of participants. The paper entitled "COVID-19 Antibody Seroprevalence in Santa Clara County, California" has not yet been peer-reviewed but provides some much needed balance to the COVID-19 pandemic narrative being touted by governments and their bought-and-paid-for "experts".
To accurately estimate or project fatality rates of epidemics or pandemics, it is important to properly estimate the total number of infections. To date, there have been three biasing processes that have prevented the health care community from accurately estimating the total number of infections:
1.) PCR testing which measures only the presence of the live virus and cannot determine which individuals have been infected and cleared the virus from their system, in the process, creating antibodies.
2.) the majority of cases tested early in the epidemic have been acutely ill and highly symptomatic while individuals that are either asymptomatic or mildly symptomatic have not been tested.
3.) PCR testing rates have been highly variable across contexts and over time leading to a "noisy" database.
The first two cases of COVID-19 appeared in Santa Clara County on January 31, 2020 and February 1, 2020 in returning travellers and the third case was identified on February 27, 2020. In the following month, nearly 1000 new cases were identified, suggest community transmission as well as the scaling up of testing for the virus. At the time of the study, Santa Clara County had a total of 1,094 confirmed cases.
The authors tested the prevalence of antibodies to the SARS-CoV-2 virus using the Premier Biotech serology test, drawing from a sampling of 3300 volunteers (children and adults) who were recruited using Facebook advertising. Advertising was aimed to ensure that there was balanced representation across the county using zip code data. The prevalence of antibodies to the novel coronavirus are as follows:
Unadjusted prevalence – 1.5 percent
Population adjusted prevalence – 2.81 percent (balanced gender and race)
After adjusting for population and test performance characteristics, the authors found that between 2.49 percent and 4.16 percent (range between 1.8 percent and 5.7 percent) tested positive for antibodies to SARS-CoV-2 in Santa Clara County. This is not out of line of studies showing that at least 10 percent of the population of Robbio, Italy was seropositive and 14 percent of the population of Gangelt Germany was seropositive. The most important implication of these findings is quoted here:
"The most important implication of these findings is that the number of infections is much greater than the reported number of cases. Our data imply that, by April 1 (three days prior to the end of our survey) between 48,000 and 81,000 people had been infected in Santa Clara County. The reported number of confirmed positive cases in the county on April 1 was 956, 50-85-fold lower than the number of infections predicted by this study. The infection to case ratio, also referred to as an under-ascertainment rate, of at least 50, is meaningfully higher than current estimates…This is likely a function of reliance on PCR for case identification which misses convalescent cases, early spread in the absence of systematic testing and asymptomatic or lightly symptomatic infections that go undetected." (my bold)
Now, let's see how the authors used this data to estimate the infection fatality rate. As of April 10, 2020, a total of 50 people had died of COVID-19 in Santa Clara County. If one uses the study's number of infections ranging from 48,000 to 81,000 and projects deaths to April 22 (3 weeks from time of infection to death), there should be roughly 100 deaths by that date. This results in an infection fatality rate ranging from 0.12 percent to 0.2 percent, a rate that is not substantially different than normal seasonal influenza. If antibodies take more than 3 days to appear, if the time taken from case identification to death is less than 3 weeks or if the epidemic wave has peaked and there are fewer deaths per day as time passes, then the infection fatality rate is lower than noted above. It is the number of fatalities that concerned politicians the most since it is likely that most of these cases would have required the use of intensive care units and ventilators, both of which are in short supply given the cuts to health care spending by the same politicians who feign concern.
If you are interested in further information on this study, please listen to this very informative interview with Dr. Jay Battacharya, one of the authors of the study who is both a physician and an economist:
Here is another fascinating interview with Dr. John Ioannidis, a co-author of the paper who also discusses the study:
This study by Stanford University strongly suggests that the current COVID-19 infection rate is far higher than the headline number of infections would suggest. With a much higher level of infections and the fact that the fatality rate is calculated as shown here:
Fatality Rate = Number of Deaths
Number of Infections
…as the number of infections rises, the fatality rate of an infection drops. In the case of the Stanford study, their initial calculations suggest that the fatality rate of COVID-19 may be no worse than seasonal influenza.
A recent news item from the University of Southern California notes a similar infection trend:
Here's a quote:
"Based on the results of the first round of testing, the research team estimates that approximately 4.1% of the county’s adult population has an antibody to the virus. Adjusting this estimate for the statistical margin of error implies about 2.8% to 5.6% of the county’s adult population has an antibody to the virus — which translates to approximately 221,000 to 442,000 adults in the county who have been infected. That estimate is 28 to 55 times higher than the 7,994 confirmed cases of COVID-19 reported to the county at the time of the study in early April. The number of COVID-related deaths in the county has now surpassed 600." (my bold)
Click HERE to read more from this author.