A Bayesian Assessment of the Origins of COVID-19 using Spatiotemporal and Zoonotic Data

Andrew Levin ()

No 33428, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper uses Bayesian methods in conjunction with spatiotemporal and zoonotic data to evaluate the odds ratio for two hypotheses regarding the origin of the COVID-19 pandemic, namely, an accidental laboratory leak of a chimera virus or the transmission of a natural virus from an infected wildlife mammal. 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. These four conditional Bayes factors are estimated as 2.3:1, 20:1, 27:1, and 12:1, respectively, and hence the overall odds ratio is 14,900:1, indicating overwhelming evidence in favor of the hypothesis that the pandemic resulted from an accidental lab leak. This conclusion is robust to alternative specifications of the detailed statistical analysis.

JEL-codes: I10 I18 (search for similar items in EconPapers)
Date: 2025-01
Note: EFG EH
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