Abstract
The performance of diagnostic tests crucially depends onthe diseaseprevalence, test sensitivity, and test specificity. However, these quantities are often not well known when tests are performed outside defined routine labprocedureswhich make the rating of the test results somewhat problematic. A current example isthe masstesting taking place within the context of the world-wide SARS-CoV-2 crisis. Here, for the first time in history, laboratory test resultshavea dramatic impact on political decisions. Therefore, transparent, comprehensible, and reliable data is mandatory. It isin the nature of wetlab tests that their quality and outcome are influenced by multiple factors reducing their performance by handling procedures, underlying test protocols,and analytical reagents. These limitations in sensitivity and specificityhave tobetaken into accountwhen calculatingthe realtest results. As a resolution method, we have developed a Bayesian calculator, the Bayes Lines Tool (BLT), foranalyzingdisease prevalence, test sensitivity, test specificity, and, therefore, true positive, false positive, true negative,and false negative numbers from official test outcome reports. The calculator performs a simple SQL (Structured Query Language) query and can easily be implementedonany system supporting SQL. We providean example of influenza test results from California, USA, as well astwoexamples of SARS-CoV-2 test results from official government reports from The Netherlands andGermany-Bavaria, to illustrate the possible parameter space of prevalence, sensitivity, and specificity consistent with the observed data. Finally, wediscussthis tool's multiple applications, including its putative importance for informing policy decisions.
Highlights
This outbreak led to the rapid development of reverse transcriptase - quantitative polymerase chain reaction (RT-qPCR) tests to identify SARS-CoV-2 RNA in specimens obtained from patients
Given the critical role that dashboards and graphs based on SARS-CoV-2 test results play for policymakers, health professionals, and the general public,8 our objective was to develop a Bayesian calculator that could calculate test quantities and prevalence solely based on officially reported numbers of total and positive tests, i.e., without making any a priori assumptions
Number of reported positive results (#positives). The model takes this information as a given fact and uses it to make inferences about the test performance parameters as well as the prevalence - these inferences are essential for estimating the number of true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN)
Summary
In December 2019, a cluster of patients with pneumonia of unknown origin was associated with the emergence of a novel beta-coronavirus, first named 2019-nCoV2 and later specified as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). This outbreak led to the rapid development of reverse transcriptase - quantitative polymerase chain reaction (RT-qPCR) tests to identify SARS-CoV-2 RNA in specimens obtained from patients.2,4After sporadic SARS-CoV-2 positive cases in January to the end of February 2020 worldwide cases of the SARS-CoV2-associated disease ‘COVID-19’ began to accumulate, causing policymakers in many countries to introduce countermeasures. In December 2019, a cluster of patients with pneumonia of unknown origin was associated with the emergence of a novel beta-coronavirus, first named 2019-nCoV2 and later specified as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2).. In December 2019, a cluster of patients with pneumonia of unknown origin was associated with the emergence of a novel beta-coronavirus, first named 2019-nCoV2 and later specified as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2).3 This outbreak led to the rapid development of reverse transcriptase - quantitative polymerase chain reaction (RT-qPCR) tests to identify SARS-CoV-2 RNA in specimens obtained from patients.. After sporadic SARS-CoV-2 positive cases in January to the end of February 2020 worldwide cases of the SARS-CoV2-associated disease ‘COVID-19’ began to accumulate, causing policymakers in many countries to introduce countermeasures. Thereby, a person is considered a case (i.e., infected), once a test turns out positive.
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