Supplementary material from "A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests"
Posted on 2021-09-06 - 14:44
The novel coronavirus disease 2019 (COVID-19) has spread worldwide and threatened human life. Diagnosis is crucial to contain the spread of SARS-CoV-2 infections and save lives. Diagnostic tests for COVID-19 have varying sensitivity and specificity, and the false-negative results would have substantial consequences to patient treatment and pandemic control. To detect all suspected infections, multiple testing is widely used. However, it may be challenging to build an assertion when the testing results are inconsistent. Considering the situation where there is more than one diagnostic outcome for each subject, we proposed a Bayesian probabilistic framework based on the sensitivity and specificity of each diagnostic method to synthesize a posterior probability of being infected by SARS-CoV-2. We demonstrated that the synthesized posterior outcome outperformed each individual testing outcome. A user-friendly web application was developed to implement our analytic framework with free access via http://www2.ccrb.cuhk.edu.hk/statgene/COVID_19/. The web application enables the real-time display of the integrated outcome incorporating two or more tests and calculated based on Bayesian posterior probability. A simulation-based assessment demonstrated higher accuracy and precision of the Bayesian probabilistic model compared to a single-test outcome. The online tool developed in this study can assist physicians in making clinical evaluations by effectively integrating multiple COVID-19 tests.
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Cao, Lirong; Zhao, Shi; Li, Qi; Ling, Lowell; Wu, William K. K.; Zhang, Lin; et al. (2021). Supplementary material from "A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests". The Royal Society. Collection. https://doi.org/10.6084/m9.figshare.c.5605007.v1
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AUTHORS (14)
LC
Lirong Cao
SZ
Shi Zhao
QL
Qi Li
LL
Lowell Ling
WW
William K. K. Wu
LZ
Lin Zhang
JL
Jingzhi Lou
MC
Marc K. C. Chong
ZC
Zigui Chen
EW
Eliza L. Y. Wong
BZ
Benny C. Y. Zee
MC
Matthew T. V. Chan
PC
Paul K. S. Chan
MW
Maggie H. Wang