COVID-19

[Slim]

I asked ChatGPT if there was any truth in this. Very handy for fact-checking.



Short answer: No — the argument has no validity.

1. "Only 10–14% of PCR-positives had IgG." → Not valid.

Why IgG cannot be used to confirm every infection:

• IgG appears 2–3 weeks after infection — PCR detects earlier.
• IgG wanes in mild infections.
• Early IgG tests missed many true infections.
• Some infections never produce long-lasting blood IgG.

→ Low IgG positivity among PCR-positives does NOT mean PCR positives weren't real infections.

2. "A quarter of the population had IgG by end of 2020."

• Plausible that infections were undercounted early.
• But this does NOT imply PCR testing was meaningless.
• And it contradicts RKI's published national serosurveys, which showed lower infection prevalence before the vaccine rollout.

3. "PCR incidence was meaningless because it depended on test volume."

Yes: case numbers depend on who is being tested.
But: PCR incidence tracked hospitalisations, ICU load, and excess deaths extremely closely.
Germany also used positivity rate, hospital admissions, and sentinel surveillance.

→ Imperfect, but not meaningless.

4. "Authorities secretly ran a national IgG study and hid it."

• RKI did run serosurveys — and published them.
• ALM lab clients are not a representative population sample.
• No evidence of a hidden study; the claim arises from misinterpreting routine lab data.

5. Why this argument sounds convincing to some

• Misunderstands IgG biology
• Cherry-picks one dataset
• Uses rhetorical framing suggesting scandal
• Ignores the wider published evidence

[Slim]

I asked for some sources:

Academic & Official Sources Showing Why IgG Cannot Be Used to "Disprove" PCR-Confirmed Infections

1. German population serosurvey (RKI): Low IgG prevalence despite widespread PCR positivity

Conclusion: IgG alone underestimates true infections; many infections do not leave long-lasting detectable IgG.
Link: https://pmc.ncbi.nlm.nih.gov/articles/PMC11719803/

Summary: A large population-based study found only ~2% IgG seropositivity in late 2020, far lower than expected if IgG always remained detectable. Demonstrates that using IgG prevalence as the "true infection count" is invalid.

2. ORCHESTRA Europe-wide longitudinal serology study (incl. Germany, France, Luxembourg)

Conclusion: A large fraction of previously infected individuals lose detectable IgG ("seroreversion") within months.
Link: https://pmc.ncbi.nlm.nih.gov/articles/PMC12063904/

Summary: In a 12-month follow-up, many individuals who initially had IgG became IgG-negative later. Proves that "IgG-negative ≠ never infected."

3. Science Immunology: Antibody levels in mild infections drop rapidly

Conclusion: Mild/asymptomatic infections often generate weak or short-lived IgG responses.
Link: https://www.science.org/doi/10.1126/sciimmunol.abe0240

Summary: IgG titres decline significantly over months, especially in mild cases — exactly the group most likely to be tested by PCR. Supports why IgG surveys consistently underestimate true infection counts.

4. Neutralising-antibody kinetics: rapid decline even after antigen exposure

Conclusion: SARS-CoV-2 antibody responses are dynamic and often short-lived.
Link: https://pmc.ncbi.nlm.nih.gov/articles/PMC12247477/

Summary: Neutralising antibodies — stronger than general IgG — show fast waning. This further explains why IgG cannot be a reliable retrospective infection indicator.

5. Infectious Diseases Society of America (IDSA) – Serology Guidelines

Conclusion: Serology (IgG) must NOT be used to diagnose acute or past SARS-CoV-2 infection because the timing, sensitivity, and waning make it unreliable for that purpose.

Link: https://www.idsociety.org/practice-guideline/covid-19-guideline-serology/

Summary: Explicit clinical recommendation that IgG is not a definitive marker of infection — directly contradicts claims that "only IgG proves infection."

6. JAMA / NEJM early serology analyses: Timing issues make IgG unreliable for confirming infection
Conclusion: Seroconversion typically occurs >2 weeks after infection; early PCR positives will often test IgG-negative.
Example link (NEJM serology paper): https://www.nejm.org/doi/full/10.1056/NEJMc2025179

Summary: PCR detects infection early; IgG appears late. If you compare PCR results and IgG results at the same time, many true infections appear "IgG negative." This directly refutes the German ALM interpretation.

7. Review: Serology underestimates past infection due to waning & individual variability
Conclusion: Seroprevalence studies should not be used to estimate cumulative infections without adjusting for antibody decay.
Link: https://pmc.ncbi.nlm.nih.gov/articles/PMC7995091/

Summary: Antibody decay is well documented — failure to account for it leads to major underestimation of infection rates. Shows why claiming "only 10–14% of PCR positives had IgG → most weren't infected" is flawed.

8. Blood donor serosurveys (SeBluCo, Germany): IgG varies widely, especially by age, severity, and time since infection
Conclusion: IgG detection is not consistent across the population and cannot be used to invalidate PCR diagnoses.
Link: https://pmc.ncbi.nlm.nih.gov/articles/PMC8647674/

Summary: Many previously infected individuals had no detectable IgG. Demonstrates real-world seroreversion and variability.

Overall Evidence Summary

• IgG appears late → many PCR positives will be IgG-negative at the time of testing.
• IgG wanes → many genuinely infected people become IgG-negative later.
• Serology undercounts infections → population IgG is always lower than true cumulative infections.
• Clinical guidelines explicitly warn that IgG cannot confirm or rule out infection.

Therefore:

Claims that "only 10–14% of PCR positives had IgG, so most weren't infected" are contradicted by every authoritative source on SARS-CoV-2 immunology and serology.

[The Picnic Wasp]

Had my 'flu jab five days ago. I'm trying to stay indoors until I get to at least ten in the hope of decent immunity as 'flu figures are going through the roof in the west of Scotland at the moment. Thank goodness for Amazon.

[David L]

I thought some of those calculations in Section. 2 looked incorrect.

[Slim]

Another nail in the coffin of the lab leak theory.