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Recasting the Debate on SARS-CoV-2 Origins in Bayesian Terms [Virtual Only]

Pictured: Tasha Fairfield

Tasha Fairfield

Bayesian reasoning serves as a bridge between quantitative and qualitative research that allows one to analyze any and all kinds of data within a single, unified inferential framework (Social Inquiry and Bayesian Inference, CUP 2022).  This paper demonstrates the strength and flexibility of the Bayesian approach by reconsidering the debate on the origins of SARS-CoV-2.

The question of COVID origins is politically fraught, and instances of motivating reasoning abound.  Yet setting aside the most implausible of the lab-leak hypotheses, there is significant disagreement among qualified experts.  Some are adamant that the case is closed in favor of zoonosis, while others maintain that a lab accident remains a firm possibility.    

We evaluate prior odds and then assess the inferential weight of available evidence in favor of zoonosis vs. lab-leak hypotheses, drawing on published scientific research, journalistic sources, and our own interviews with scientists and area experts.  We consider a broad range of evidence, including genomic data, epidemiological data, and qualitative information from testimonial accounts and observational fieldwork.  

In addition to clarifying the debate by separating priors, informed by what we know from previous epidemics, from the weight of evidence pertaining directly to SARS-CoV-2, the goals include evaluating to what extent a Bayesian framework can help improve reasoning when evidence is limited, communicate degrees of uncertainty more precisely and more effectively, and illuminate points of agreement or disagreement among experts on questions with significant public policy implications.