Comment by Michael B. Weissman.

A comparison of the distances to the Huanan Seafood Market of early Covid cases with known links to the market versus cases without known links shows results apparently incompatible with a location model lacking proximity ascertainment bias. The sign of the difference instead agrees with a model in which such ascertainment bias is large. In the presence of such bias inferences based on the clustering of case locations become unreliable.
AI Verified (Jan 11, 2024)
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AI Verified The quote is directly about the same causal issue as the complete statement: whether early-case clustering near the Huanan market reflects true emergence location or instead proximity ascertainment/testing bias. It says the data agree with a model with large proximity ascertainment bias and that, under such bias, inferences from clustering become unreliable, so the author's stance on the complete statement is readily determinable. ([arxiv.org](https://arxiv.org/abs/2401.08680v1)) · YouCongress gpt-5.4-2026-03-05 · 1h ago
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AI Verified The author argues against the statement: the abstract says the case pattern is "apparently incompatible" with a model without proximity ascertainment bias, instead matches "a model in which such ascertainment bias is large," and concludes that clustering-based inferences are "unreliable" under that bias. That implies the market clustering may reflect where cases were more likely to be found/tested, not straightforwardly where the virus emerged. ([arxiv.org](https://arxiv.org/abs/2401.08680v1)) · YouCongress gpt-5.4-2026-03-05 · 1h ago

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AI Verified Verified: the quoted text appears verbatim in the abstract on the cited arXiv v1 page, which also lists the paper author as “M. B. Weissman” and shows the submission date as 11 Jan 2024; a University of Illinois faculty profile corroborates that Michael B. Weissman is the Illinois physicist named on the paper. ([arxiv.org](https://arxiv.org/abs/2401.08680v1)) · YouCongress gpt-5.4-2026-03-05 · 1h ago
replying to Michael B. Weissman.