"In order to test the detection sensitivity and specificity of the COVID-19 IgG-IgM combined antibody test, blood samples were collected from COVID-19 patients from multiple hospitals and Chinese CDC laboratories. The tests were done separately at each site. A total of 525 cases were tested: 397 (positive) clinically confirmed (including PCR test) SARS-CoV-2-infected patients and 128 non- SARS-CoV-2-infected patients (128 negative). The testing results of vein blood without viral inactivation were summarized in the Table 1. Of the 397 blood samples from SARS-CoV-2-infected patients, 352 tested positive, resulting in a sensitivity of 88.66%. Twelve of the blood samples from the 128 non-SARS-CoV-2 infection patients tested positive, generating a specificity of 90.63%."
That gives us 62% false positive ratio according to (where a study finds the prevalence to be 6% of subjects using the test):
In some cases we have research being carried out with such low positive results that they can entirely be accounted for by the low specificity. So for example if you took samples from 100 people, based on 90% specificity, even if everyone had never had corona, 10 could be found positive.
I wonder what's the process through which false positives happen in this case. Previous infection by milder Coronaviruses?
Edit: I'm looking at the reddit post but I have a lot of reservations with the "prevalence 0.06", unless we'll use the test to test absolutely everybody and not only people who are suspect. Has that calculator been validated as well?
If the test was 12 false positives in 128 negatives, how come they can claim the false positive rate is 60%?
"Our data from this week and last tell a very similar story. In both weeks, 6% of participants tested positive for COVID-19 antibodies, which equates to 165,000 Miami-Dade County residents"
That is what the commentator is referring to in the linked post.
So if you plug their own figures into the calculator:
Sensitivity .8866
Specificity .9063
and a Prevalence of .06 based on the study, you get the 62% false positive rate.
As the prevalence increases, as with the NYC study which found the positive rate to be 21% (prevalence), the false positive rate decreases, down to 28% of the NYC study.
The password you need to Google for why it happens is "antibody cross reactivity." Not necessarily other coronaviruses but I imagine they're disproportionately more likely to cause it.
This is from ARCPoint Labs, where I took my antibody test:
The Antibody test is a serology test which measures the amount of antibodies or proteins present in the blood when the body is responding to a specific infection. This test hasn’t been reviewed by the FDA. Negative results don’t rule out SARS-CoV-2 infection, particularly in those who have been in contact with the virus. Follow-up testing with a molecular diagnostic lab should be considered to rule out infection in these individuals. Results from antibody testing shouldn’t be used as the sole basis to diagnose or exclude SARS-CoV-2 infection. Positive results may be due to past or present infection with non-SARS-CoV-2 coronavirus strains, such as coronavirus HKU1, NL63, OC43, or 229E.
Yes. That’s one possible explanation. Interestingly quite a lot of people might be somewhat immune to the new Corona virus due to anti bodies from previous Corona cold infections. More than 30% showed such antibodies in a recent study.
https://www.finanzen.net/nachricht/aktien/drosten-hinweis-au...
(Sorry that the only source I have ready right now)
There are many different tests, from different manufacturers. Some of the tests have higher false-positive rates than others. Some have higher false-negative rates. Even a survey with an imperfect test can be designed to yield reliable data.
