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Comment by Andrew T. Levin

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Economist, Professor at Dartmouth College and NBER Research Associate
The overall Bayes factor is decomposed into 4 components: (1) the odds that the outbreak would occur in the People’s Republic of China (PRC); (2) the odds that the outbreak would occur in Wuhan, conditional on its location in PRC; (3) the odds of observing the spatiotemporal pattern of confirmed COVID-19 cases with no known link to the specific wholesale market where wildlife mammals were being sold, conditional on the outbreak taking place in Wuhan; and (4) the odds of observing the spatiotemporal pattern of confirmed vendor cases at that market, conditional on the outbreak taking place in Wuhan.
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Bayesian analysis is the right framework for settling the COVID-19 origins question
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AI Verified Relevant: in the source, this quote is not a stray statistical aside; it is the author’s description of the Bayesian framework used to compare the competing COVID-19 origins hypotheses. The paper explicitly says it 'uses Bayesian methods' to evaluate the odds ratio for those hypotheses and presents this four-part Bayes-factor decomposition as the assessment method, which makes support for Bayesian analysis as the framework substantially more likely than the alternatives. ([nber.org](https://www.nber.org/papers/w33428?utm_source=openai)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
AI Verified Relevant: the quote directly describes the paper’s Bayes-factor decomposition for comparing COVID-19 origin hypotheses, and the source context says the study’s objective is to formulate a Bayesian framework to assess the relative weight of evidence between those hypotheses. That makes a supportive stance on using Bayesian analysis for this question clearly determinable. ([nber.org](https://www.nber.org/system/files/working_papers/w33428/w33428.pdf)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
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AI Verified The quote is methodological support: it decomposes the 'overall Bayes factor' into the key evidentiary components for comparing the two COVID-origins hypotheses, and the paper frames Bayesian methods as the way to 'evaluate the odds ratio' between them and to make the analysis transparent. That strongly implies the author sees Bayesian analysis as the appropriate framework for settling the origins question, even though he does not use that exact phrase. ([nber.org](https://www.nber.org/system/files/working_papers/w33428/w33428.pdf)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
AI Verified The source page presents the paper as a Bayesian assessment of COVID-19’s origins: it says the study uses Bayesian methods to compare the two origin hypotheses, breaks the evidence into an overall Bayes factor, and then draws a decisive odds-ratio conclusion from that framework. That strongly implies the author sees Bayesian analysis as the proper way to resolve the origins question, even though the quote does not literally say “the right framework.” ([nber.org](https://www.nber.org/papers/w33428)) · YouCongress gpt-5.4-2026-03-05 · 2h ago

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AI Verified Authentic. The exact text appears verbatim in the abstract on the NBER landing page for Working Paper 33428 (lines 138–140) and in the linked PDF abstract (page 1, lines 23–33). Both sources attribute the paper to Andrew T. Levin and date it January 2025, so the stored author, date, and source are consistent and the provided URL does contain the quote. ([nber.org](https://www.nber.org/papers/w33428)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
AI Verified The quote is authentic and verbatim. It appears in the abstract on the NBER page for Working Paper 33428, which lists Andrew T. Levin as the sole author and gives the Issue Date as January 2025; the PDF at the same source repeats the same text on page 1. The submitted author, month/year date, and source URL are therefore consistent with the canonical source. ([nber.org](https://www.nber.org/papers/w33428)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
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