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 with 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.
【저자키워드】 COVID-19, SARS-CoV-2, Chest computed tomography, Serological tests, reverse transcription–polymerase chain reaction, multiple tests integration, 【초록키워드】 pandemic, Bayesian, SARS-COV-2 infection, diagnostic test, outcome, novel coronavirus disease, Spread, Probability, infections, Accuracy, Sensitivity and specificity, clinical evaluation, false-negative, Diagnostic method, online tool, Precision, subject, posterior probability, physician, COVID-19 tests, patient treatment, multiple testing, consequence, diagnostic outcome, detect, calculated, demonstrated, assist, assertion, Bayesian posterior, build, 【제목키워드】 Bayesian, COVID-19 test, diagnostic outcome,