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

• Published in: Epidemiology and infection Vol. 149; pp. 1 - 6
• Main Authors: Wu, Yue, Foley, David, Ramsay, Jessica, Woodberry, Owen, Mascaro, Steven, Nicholson, Ann E., Snelling, Tom
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 23.06.2021
• Subjects: Bayesian analysis; Coronaviruses; COVID-19; Dictionaries; Disease transmission; Epidemiology; Infections; Knowledge; Laboratories; Mathematical models; Medical diagnosis; Model testing; Modelling; Parameterization; Pathogens; Polymerase chain reaction; Public health; RNA-directed DNA polymerase; Severe acute respiratory syndrome coronavirus 2; Short Paper; Subject specialists; Variables; Viral diseases; Workshops; Bayesian belief model; causal diagram; false negative; SARS-CoV-2; RT-PCR test; diagnostic decision support
• ISSN: 0950-2688; 1469-4409; 1469-4409
• DOI: 10.1017/S0950268821001357

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 severe acute respiratory syndrome-coronavirus-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) results and optimise the testing processes. Bayesian network 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. We elicited knowledge from domain experts to describe the test process 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. Causal relationships elicited describe the interactions of pre-testing, specimen collection and laboratory procedures and RT-PCR platform factors, and their impact on the presence and quantity of virus and thus the test result and its interpretation. By setting the input variables as ‘evidence’ for a given subject and preliminary parameterisation, four scenarios were simulated to demonstrate potential uses of the model. The core value of this model is a deep understanding of the total testing cycle, bridging the gap between a person's 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.