Error rates in SARS-CoV-2 testing examined with Bayes' theorem.

The SARS-CoV-2 pandemic has created a demand for large scale testing, as part of the effort to understand and control transmission. It is important to quantify the error rates of test equipment under field conditions, which might differ significantly from those obtained in the laboratory. A literature review on SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR) is used to construct a test confusion matrix. A simple correction method for bulk test results is then demonstrated with examples. The required sensitivity and specificity of a test are explored for societal needs and use cases, before a sequential analysis of common example scenarios is explored. The analysis suggests that many of the people with mild symptoms and positive test results are unlikely to be infected with SARS-CoV-2 in some regions. It is concluded that current and foreseen alternative tests can not be used to "clear" people as being non-infected. Recommendations are given that regional authorities must establish a programme to monitor operational test characteristics before launching large scale testing; and that large scale testing for tracing infection networks in some regions is not viable, but may be possible in a focused way that does not exceed the working capacity of the laboratories staffed by competent experts. RT-PCR tests can not be solely relied upon as the gold standard for SARS-CoV-2 diagnosis at scale, instead clinical assessment supported by a range of expert diagnostic tests should be used.