Investigation Reveals Dubious Calculations: The Truth About 60,000 Coronavirus Cases

Read the full article: The more you risk, the more money you get

Reading time: 6 minutes

Great article

Alex Washburne haswrite a very good article partly based on the information that has appeared in the relationship until Peter Daszak was questioned on May 1. It is a very long article, but the content is impressive. And then you wonder what scientists can do without correction.

The most important conclusion that can be drawn about the way Peter Daszak and apparently many colleagues work is that they greatly exaggerate the danger to people/humanity to get a lot of money for their work that claims to protect the same humanity. And when something goes wrong on their job, they do everything they can to deceive the outside world. Helped by colleagues like Marion Koopmans, with similar interests.

In his article this is best demonstrated by the way Alex wraps up Daszak et al’s argument that around 60,000 people are infected every year because of Coronaviruses jumping from bats. A number that is then used to relate the probability of SARS-CoV2 starting to the market and away from a laboratory leak.

I have translated that part of his article and simplified it a bit, and when you read it, the scales will fall from your eyes.

60,000 a year???

In September 2021, Peter Daszak and several others wrote article in which they showed a method to estimate how many times a year a person in Southeast Asia was caught with Coronavirus that jumped from a bat. The output was 60,000 times a year.

Alex Washburne shows how much Daszak has thrown away any scientific idea to reach this very high number and thus also gives the impression that there was a revolution on the market in Wuhan far more likely than a lab leak. As well as other scientists then refer to that article.

If you really want to know the details of Alex Washburne’s description, I recommend it go to his article. Below is my attempt at a summary

The calculation method

This was the approach of Daszak et al.:

1. Estimate the prevalence of bat + SARSr-CoV from bat field samples
2. Estimate where bats lived
3. Estimating where people and bats crossed
4. Estimate human diseases resulting from bat-human interactions

The first three points are easy and trivial. It only applies to the fourth point: Understand that the most important barrier to human disease and spillage is No the overlap with bats, but rather the virological barriers: receptor binding and entry of SARSr-CoV bat cells into human cells, leading to human infection.

When we swim in the ocean, we come across billions of viruses, but people rarely get infected with viruses in the ocean because viruses in the ocean cannot enter human cells. We kiss our dogs when they have kennel cough and we don’t get sick because that pathogen can’t enter our cells either. Some virus variants may be able to make the jump, and in fact this is why DARPA’s PREEMPT call called for “jumpable quasispecies” and restricted this narrow range of possible variants jump from entering people.

What about SARS-CoVs? Why haven’t we seen many SARS-CoV outbreaks before?

How did the authors get around this lack of evidence for spill effects by still estimating over 60,000 spills from SARS-CoV each year?

The perfect approach

Before entering a scientific article, it is worth asking yourself: how do you estimate the number of people who are infected each year with SARS-related CoVs directly from bats?

Ideally, we could do a random sample of people, either a PCR test on patients with a specific primary complaint or perhaps serosurveys providing immunological evidence of past exposure in a representative set of people in the population. Ideally, the serosurveys would be very specific and done in a way that reduces the chance of false positives from exposure to other coronaviruses, because serosurveys can respond to things that are not the target we are looking for, and so we have to change for that fake. positive things.

It really has to be a coronavirus, because viruses are very different in their ability to infect people when they come into contact and in the ways in which people come into contact with the viruses. Choosing the right species for comparison is always an art of the biological sciences, but we can make a good choice by focusing on the basic ecology (including molecular virology) of the species or between -interesting ecological operation.

Sinning against every rule

But this is not the case. The authors are sinning against every rule. Make it with figures from other viruses. Use possible false positives to increase their own numbers. And last but not least: count human-to-human diseases like bat contamination.

A few examples:

• They use the 2 positive tests out of 796 tests done to make the calculation. But ignore the fact that there is a good chance that these are wrong things.
• They start measuring with the Ebola outbreak from 2015, overestimating the figures by a factor of 6 to 7 and also ignoring that some diseases are caused by human-to-human transmission instead of an animal.

But here’s the worst part: the highest seroprevalence they estimate – and which they use in their model to estimate the rate of SARS-CoV shedding from bats – comes from serological analysis of SARS-CoV-2 after SARS-CoV-2 caused a pandemic. As with the Ebola virus serosurvey in the Congo, we cannot say what proportion of these SARS-CoV-2 seropositive samples were due to shedding from bats and what proportion were SARS-CoV-2 cases that was the result of human-to-human transmission. Alex Washburne then says that he is willing to bet almost his money that these 3 seropositive SARS-CoV-2 cases from 12 samples are more likely to be people exposed to the virus that distribution in a global pandemic of 3 independent bat outbreaks.

The authors’ estimates of SARSr-CoV transmission by bats come from serosurveys of many other bat viruses transmitted through very different ecological processes (e.g. fruits dropped by fruit bats, consumption of bush meat for Ebola virus, consumption of date palm juice for Nipah virus). The results of the serological studies are a mixture of either unrecognizable from a large percentage of false serological tests, overestimated compared to the literature cited without justification, or most likely due to human-to-human transmission such as their serological analysis of SARS-CoV-2 and not due to independent bat shedding events.
A total of 31 seropositive tests out of approximately 1500 serological tests performed, or 2% of seropositive individuals with tests that have a specificity of less than 98% for bat viruses whose spread is caused by interactions completely different ecology than SARS-CoVs.

From these 31 seropositive tests of questionable relevance for SARSr-CoV outbreaks, the authors estimate 60,000 SARSr-CoV outbreaks per year.

If we were to correct for false positives from nonspecific experiments and remove viruses that appear as a result of interactions that never occur with insectivorous microbats, the resulting estimate would be less than 1 SARS-CoV outbreak per year, as we have no empirical outbreaks. documentation of such spills, with the exception of one outbreak of SARS-CoV-1 and Mojiang miners infected with a virus associated with RaTG13.

A careful examination of the data indicates that any numbers extracted from the above serosurveys will overestimate the number of SARS-CoV outbreaks – true infections – in the human population. per year and the fact is that we have no evidence of 60,000 spills per year. That number was raised by a collection of methods, which can be traced back to the inappropriate problem of serosurveys that were not corrected for low specificity and various ecological reasons for disease.

Closing

Alex Washburne concludes: “As you can see, I try to do my due diligence by carefully researching the methods AND additional information of the papers I cite. Daszak and his co-authors say they estimate 60,000 SARSr-CoV outbreaks each year, but among the vast number of methods, the results come entirely from serosurveys in which There is no information on SARSr-CoV shedding rates!

When I see people like Baric repeating these numbers without reading the articles properly or the limitations of the statistical methods (methods I helped develop!), it turns my stomach. They repeat these claims as if they were sound and unbiased, unable to deceive people who have the most to lose in the event of a laboratory accident. Who will use these projections to blow away the evidence of a lab accident.

I can’t help but express concern that Baric, a member of the National Academy of Sciences, an organization created to provide unbiased scientific assessments to policy makers, is not providing unbiased scientific assessments to policy makers. Forgive me, but I feel it is a civic duty to report the numbers honestly and not play the scientific phone by citing the numbers of persons under investigation that are likely to cause a pandemic.”