Published September 26, 2020 | Version 1.0.1
Journal article Open

Outlines of a probabilistic evaluation of possible SARS-CoV-2 origins

Description

We explore the relative probabilities of a lab-related accident against a non-lab-related zoonotic event being at the root of the current COVID-19 pandemic. We show that, based on present knowledge, the relative probability of a lab-related accident against a non-lab related zoonotic event is not negligible across a wide range of defensible input probabilities.

For instance, under a reference set of input probabilities, the relative probabilities are at least 55% for a lab-related event against 45% at most for a non-lab-related zoonotic event. Even under a particularly conservative set of assumptions the relative probability of the lab-related accident is still 6% (to 94% for the non-lab related zoonotic event).

Through a review of the Chinese specialised literature, we further show that our underlying estimate for the probability of lab-acquired infection is consistent with risk assessments from Chinese authorities and specialists. As part of this study, we list 112 individual BSL-3 labs in China, across 62 lab complexes.

We then review a list of common probabilistic misunderstandings that are often associated with discussions about COVID-19 origins and conclude by discussing how such a probabilistic treatment can also offer a way to properly guide an investigation into the causes of the pandemic while being able to embrace different estimates of the underlying probabilities.


Note: This paper is produced by the authors strictly in their personal capacity, and should not be constructed as representing in any way the opinion of any of the institutions the authors are employed or associated to. 

Notes

This paper comes with a spreadsheet listing 112 individual BSL-3 labs in China across 62 lab-complexes.

Files

Probabilistic_Treatment_zenodo.pdf

Files (1.5 MB)

Name Size Download all
md5:af43ce6dba50c712424af24bbc94b81a
91.4 kB Download
md5:549382b4ed74dbfd3c200ab67cd31305
1.4 MB Preview Download