Comparison of antigen- and RT-PCR-based testing strategies for detection of SARS-CoV-2 in two high-exposure settings

Abstract

Surveillance testing for infectious disease is an important tool to combat disease transmission at the population level. During the SARS-CoV-2 pandemic, RT-PCR tests have been considered the gold standard due to their high sensitivity and specificity. However, RT-PCR tests for SARS-CoV-2 have been shown to return positive results when performed to individuals who are past the infectious stage of the disease. Meanwhile, antigen-based tests are often treated as a less accurate substitute for RT-PCR, however, new evidence suggests they may better reflect infectiousness. Consequently, the two test types may each be most optimally deployed in different settings. Here, we present an epidemiological model with surveillance testing and coordinated isolation in two congregate living settings (a nursing home and a university dormitory system) that considers test metrics with respect to viral culture, a proxy for infectiousness. Simulations show that antigen-based surveillance testing coupled with isolation greatly reduces disease burden and carries a lower economic cost than RT-PCR-based strategies. Antigen and RT-PCR tests perform different functions toward the goal of reducing infectious disease burden and should be used accordingly.

Fig 1. Schematic of the COVID-19 epidemiologic model.

Individuals in the population start as susceptible (S) and become exposed (E) at a rate β. Exposed individuals become infectious (I) at a rate σ, and recover (R) at a rate γ. Simultaneously, surveillance testing occurs at a rate proportional to the population make up, and individuals awaiting test results are in a “leaky” quarantine. Tested susceptibles (T_S) can become exposed (T_E), then infectious (T_I), and can infect susceptibles (both S and T_S). Since tested infectious individuals (T_I) are quarantining, their infectiousness is reduced by a factor q. Likewise, since tested susceptibles (T_S), are also quarantining, their susceptibility is reduced by a factor q. Individuals who test positive are isolated (Q). We assume isolation is perfect, and thus individuals can only progress through their disease process (Q_E -> Q_I -> Q_R); susceptibles who are isolated cannot become infected and infected individuals who are isolated cannot cause infections. After the isolation period is over (14 days in the standard condition), individuals are returned to the general population, retaining their current disease state. Antigen and RT-PCR tests have a specified positive (ϕ_e) and negative percent agreement (ϕ_p) with viral culture (see text). ϕ_a is 0.0001, representing imperfect test specificity. Note that we use positive percent agreement for tests of infectious individuals (I) and negative percent agreement for both exposed (E) and recovered (R) individuals, as it has been shown that RT-PCR may detect infection among individuals who are no longer infectious [19]. Here, the color and line-type of the arrows indicate whether or not the test was “correct” or “incorrect” with respect to infectiousness, with dotted lines indicating incorrect test results (i.e., false negative or false positive), red lines indicating a positive test, blue lines indicating a negative test, black lines indicating that no test result was returned.

Fig 2. Epidemic curves showing infections over time in the nursing home (left) and residence hall (right) group living settings and at 2% and 10% daily surveillance testing.

Higher levels of testing reduce infections. Different test strategies perform similarly. Line color indicates testing strategy. Curves for ‘antigen’ and ‘retest negative symptomatic PCR’ strategies are largely overlapping in every panel.

Fig 3. Percent infections averted for different testing strategies at different daily surveillance testing percentages.

Red = 10%, green = 5%, blue = 2%, black = 1% daily surveillance testing. Filled circles show means, whiskers show 95% CI. Text labels indicate values of upper and lower 95% confidence interval bounds.