Testing at scale during the COVID-19 pandemic

Abstract

Assembly and publication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome in January 2020 enabled the immediate development of tests to detect the new virus. This began the largest global testing programme in history, in which hundreds of millions of individuals have been tested to date. The unprecedented scale of testing has driven innovation in the strategies, technologies and concepts that govern testing in public health. This Review describes the changing role of testing during the COVID-19 pandemic, including the use of genomic surveillance to track SARS-CoV-2 transmission around the world, the use of contact tracing to contain disease outbreaks and testing for the presence of the virus circulating in the environment. Despite these efforts, widespread community transmission has become entrenched in many countries and has required the testing of populations to identify and isolate infected individuals, many of whom are asymptomatic. The diagnostic and epidemiological principles that underpin such population-scale testing are also considered, as are the high-throughput and point-of-care technologies that make testing feasible on a massive scale.

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1. Changes in modes of testing across a generalized COVID-19 infectious course.

a | Shedding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA genome (yellow shading) typically increases rapidly following infection and peaks at the presentation of symptoms (although many patients can be asymptomatic) before gradually declining. Levels of viral proteins (red shading) also rapidly increase, albeit within a narrower window. In response to infection, the body produces IgM (green shading) and IgG (blue shading) antibodies, which persist for weeks to months. b | The SARS-CoV-2 RNA genome can be detected by molecular assays, such as reverse transcription–quantitative polymerase chain reaction (RT–qPCR), before the development of symptoms. Serological tests can detect reactive IgG and IgM, which indicate past infection with SARS-CoV-2. c | Assay sensitivity is dependent on both technical performance aspects and viral load. A test might not detect viral RNA even when an individual is infectious (false negative, red shaded area) or, alternatively, might detect persistent viral RNA after an individual is no longer infectious (false positive), which demonstrates that test positivity correlates poorly with infectivity. Owing to the rapid increase in viral shedding, only a narrow window exists wherein a more sensitive assay (test B) will outperform a less sensitive assay (test A). Note that this figure illustrates a generalized COVID-19 infectious course, and in practice the relative duration of detectability and analyte abundance differ considerably between individuals. NAAT, nucleic acid antigen testing; NGS, next-generation sequencing.

Fig. 2. How test sensitivity, specificity and disease prevalence influence the interpretation of test results.

a,b | In a population with a low prevalence (5% here) of COVID-19 cases, even a highly sensitive and specific test (part a) returns many false-positive results. c,d | By contrast, a high infection prevalence (25% here) increases the likelihood that a positive result is true, despite the test performance remaining unchanged. The positive predictive value (PPV) of a test describes the probability that an individual who tests positive is actually infected, and thus depends on both the specificity of the test and the prevalence of infection. At low prevalence values, the proportion of false-positive results is increased and the PPV is reduced. Even a highly specific test returns mostly false-positive results (and therefore has a low PPV) when the prevalence of infection is low. Adapted from ref., Springer Nature Limited.