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

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.

Citation:
Wu Y, Foley D, Ramsay J, Woodberry O, Mascaro S, Nicholson AE, Snelling T. Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling. Epidemiol Infect. 2021.

Keywords:
Bayesian belief model; Causal diagram; Diagnostic decision support; SARS-CoV-2; severe acute respiratory syndrome coronavirus 2

Abstract:
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.