Then you ought to know that seeing more circumstantial evidence for A than B does not imply that A is more likely. What would imply that A is more likely is if you find more circumstantial evidence for A than whatever amount you would expect to find if A didn't happen.
That's why good Bayesians place so little weight on circumstantial evidence: because it's difficult or impossible to predict the expected amount of circumstantial evidence for something that didn't happen. It would involve answering questions like, "When a novel coronavirus moves from the animal population to humans without a lab accident, what are the odds that it will happen within X miles of a lab studying such viruses?" That's pretty difficult to answer, given that we don't know a lot about how or why that happens yet.
And it shouldn't even need to be said that this all goes double when the thing being argued over is political (because, even if you personally are unbiased, the people gathering and publishing the evidence you rely on may not be) and treble when the evidence is technical and outside your area of expertise.