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

Yue Wu, David Foley, Jessica Ramsay, Owen Woodberry, Steven Mascaro, Ann E. Nicholson, Tom Snelling

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)


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 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 thus the test result and its interpretation. By setting the input variables as âževidenceâ for a given subject and preliminary parameterisation, four scenarios were simulated to demonstrate potential uses of the model.

Original languageEnglish
Number of pages6
JournalEpidemiology and Infection
Publication statusPublished - 23 Jun 2021


  • Bayesian belief model
  • Causal diagram
  • Diagnostic decision support
  • False negative
  • RT-PCR test
  • SARS-CoV-2

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