Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling

Background In the absence of an established gold standard, an understanding of the testing cycle from individual exposure to test outcome report is required to guide the correct interpretation of SARS-CoV-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) results and optimise the testing processes. Bayesian network (BN) models have been used within healthcare to bring clarity to complex problems. We use this modelling approach to construct a comprehensive framework for understanding the real world predictive value of individual RT-PCR results. Methods We elicited knowledge from domain experts to describe the test process from viral exposure to interpretation of the laboratory test, through a facilitated group workshop. A preliminary model was derived based on the elicited knowledge, then subsequently refined, parameterised and validated with a second workshop and one-on-one discussions. Results Causal relationships elicited describe the interactions of multiple variables and their impact on a RT-PCR result. Some interactions are infrequently observable and accounted for across the testing cycle such as pre-testing factors, sample collector experience and RT-PCR platform. By setting the input variables as evidence for a given subject and preliminary parameterisation, three scenarios were simulated to demonstrate potential uses of the model. Conclusions The core value of this model is a deep understanding of the total testing cycle, bridging the gap between a persons true infection status and their test outcome. This model can be adapted to different settings, testing modalities and pathogens, adding much needed nuance to the interpretations of results.