The debate on covid-19 origins has been politically fraught. Yet setting aside conspiracy theories and the most implausible of the lab-leak hypotheses, there is significant disagreement among qualified experts.  Some are adamant that the case should be considered closed in favor of zoonosis, while others view the evidence as weak, even if they concede that prior knowledge about previous epidemics favors zoonosis, and a few maintain that some sort of laboratory leak is a firm possibility.   

This project applies the methodology developed in Social Inquiry and Bayesian Inference (CUP 2022) to reassess the debate. We apply Bayesian reasoning to evaluate the inferential weight of available evidence in favor of zoonosis vs. lab-leak hypotheses, drawing on published scientific research, journalistic sources, and interviews with scientists and China experts. The analysis highlights the flexibility of Bayesian reasoning—this approach can be used to evaluate any kind of evidence, quantitative or qualitative, including genetic data, epidemiological data, and information from interviews and observational fieldwork.  

In addition to clarifying the debate by separating prior odds, 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 effectively, and illuminate points of agreement or disagreement among experts on questions with significant public policy implications. 

 

The table of contents and first chapter of our book are available at: https://tashafairfield.wixsite.com/home/bayes-book

This is joint work with Andrew Charman (Dept. of Physics, UC Berkeley).