Abstract
The performance of diagnostic tests crucially depends on the disease prevalence, test sensitivity, and test specificity. However, these quantities are often not well known when tests are performed outside defined routine lab procedures which make the rating of the test results somewhat problematic. A current example is the mass testing taking place within the context of the world-wide SARS-CoV-2 crisis. Here, for the first time in history, laboratory test results have a dramatic impact on political decisions. Therefore, transparent, comprehensible, and reliable data is mandatory. It is in the nature of wet lab 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 specificity have to be taken into account when calculating the real test results. As a resolution method, we have developed a Bayesian calculator, the Bayes Lines Tool (BLT), for analyzing disease 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 implemented on any system supporting SQL. We provide an example of influenza test results from California, USA, as well as two examples of SARS-CoV-2 test results from official government reports from The Netherlands and Germany-Bavaria, to illustrate the possible parameter space of prevalence, sensitivity, and specificity consistent with the observed data. Finally, we discuss this 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
After sporadic SARS-CoV-2 positive cases in January5,6 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. These Non-Pharmaceutical Interventions (NPIs) predominantly started worldwide around March 2020 while the virus was characterized as a pandemic on 11 March, 2020.6,7 As a result, for more than one year large parts of the world are in a COVID-19 crisis-mode with daily reporting of SARS-CoV-2 cases in dashboards worldwide
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
Summary
In December 2019, a cluster of patients with pneumonia of unknown origin was associated with the emergence of a novel beta-coronavirus of bat origin, first named 2019-nCoV2 and later specified as severe acute respiratory syndromecoronavirus-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 of bat origin, first named 2019-nCoV2 and later specified as severe acute respiratory syndromecoronavirus-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 of bat origin, first named 2019-nCoV2 and later specified as severe acute respiratory syndromecoronavirus-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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