17th May 2020
Notes on the German (Heinsberg) serological study – IFR 0.37%
- “the study sampled entire households. That can lead to overestimating infections, because people living together often infect each other” [Source]. To me that’s a serious shortcoming.
- “Streeck and his colleagues claimed the commercial antibody test they used has “more than 99% specificity,” but a Danish group found the test produced three false positives in a sample of 82 controls, for a specificity of only 96%. That means that in the Heinsberg sample of 500, the test could have produced more than a dozen false positives out of roughly 70 the team found.” [Source] – that is another very serious shortcoming.
A German antibody survey was the first out of the gate several weeks ago. At a press conference on 9 April, virologist Hendrik Streeck from the University of Bonn announced preliminary results from a town of about 12,500 in Heinsberg, a region in Germany that had been hit hard by COVID-19. He told reporters his team had found antibodies to the virus in 14% of the 500 people tested. By comparing that number with the recorded deaths in the town, the study suggested the virus kills only 0.37% of the people infected. (The rate for seasonal influenza is about 0.1%.) The team concluded in a two-page summary that “15% of the population can no longer be infected with SARS-CoV-2, and the process of reaching herd immunity is already underway.” They recommended that politicians start to lift some of the regions’ restrictions.
Streeck had argued even before the study that the virus is less serious than feared and that the effects of long shutdowns may be just as bad if not worse than the damage the virus could do. However, Christian Drosten, a virologist at Charité University Hospital in Berlin, told reporters later that day that no meaningful conclusions could be drawn from the antibody study based on the limited information Streeck presented. Drosten cited uncertainty about what level of antibodies provides protection and noted that the study sampled entire households. That can lead to overestimating infections, because people living together often infect each other. [Source]