Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening

Abstract

The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with presymptomatic, symptomatic, and asymptomatic infections, the reopening of societies and the control of virus spread will be facilitated by robust population screening, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are too low to detect, followed by exponential viral growth, leading to peak viral load and infectiousness and ending with declining titers and clearance. Given the pattern of viral load kinetics, we model the effectiveness of repeated population screening considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective screening depends largely on frequency of testing and speed of reporting and is only marginally improved by high test sensitivity. We therefore conclude that screening should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.

Fig. 1. Population screening regimen effectiveness depends on frequency.

(A) An example viral load trajectory is shown with LOD thresholds of two tests and a hypothetical positive test on day 6, 2 days after peak viral load. Twenty other stochastically generated viral loads are shown to highlight trajectory diversity (light gray; see Materials and Methods). dx, diagnosis. (B) Relative infectiousness for the viral load shown in (A) before test, totaling 35% (blue), and post-isolation, totaling 65% (black). (C) Screening programs using tests at LODs of 10³ and 10⁵ at frequencies indicated were applied to 10,000 individuals’ trajectories of whom 35% would undergo symptomatic isolation near their peak viral load if they had not been tested and isolated first. Total infectiousness removed during screening (colors) and self-isolation (hatch) are shown for repeated population screening as indicated, relative to total infectiousness with no screening or self-isolation. (D) The impact of repeated population screening on the infectiousness of 100 individuals is shown for each screening regimen and no testing, as indicated, with each individual colored by test if their infection was detected during infectiousness (medians, black lines) or colored blue if their infection was missed by screening or detected positive after their infectious period (medians, blue lines). Units are arbitrary and scaled to the maximum infectiousness of sampled individuals.

Fig. 2. Repeated population screening affects disease dynamics.

Both the fully mixed compartmental model (top row) and agent-based model (bottom row) are affected by repeated population screening. (A and B) More frequent testing reduces the effective reproductive number R, shown as the percentage by which R₀ is reduced, 100 × (R₀ − R)/R₀. Values of R were estimated from 50 independent simulations of dynamics with 100% of the population participating (see Materials and Methods). (C and D) Relative to no testing (gray bars), screening suppresses the total number of infections in both models when testing every day or every 3 days but only partially mitigates total cases for weekly or biweekly testing. Error bars indicate inner 95% quantiles of 50 independent simulations each.

Fig. 3. Example simulation trajectories from fully mixed model with repeated population screening.

Simulation trajectories show the number of infected individuals in a population of N = 20,000 with a constant rate of external infection set to 1/N per person per day, i.e., around one imported case per day, and full participation in the testing regimen. Infections are classified as freely mixing in the population (blue), isolated because of a positive test (black), or isolated because of symptoms (red) in four simulated example scenarios with R₀ = 2.5. (A) No screening. (B) Weekly testing at LOD 10³. (C) Weekly testing at LOD 10⁵. (D) Testing every 3 days with LOD 10⁵. Note the variation in the vertical axis scales. The models are fully described in Materials and Methods.

Fig. 4. Effectiveness of screening is compromised by delays in reporting.

(A) An example viral load trajectory is shown with LOD thresholds of two tests and a hypothetical positive test on day 6 but with results reported on day 8. Twenty other stochastically generated viral loads are shown to highlight trajectory diversity (light gray; see Materials and Methods). (B) Relative infectiousness for the viral load shown in (A) pretest (totaling 35%; blue) and posttest but before diagnosis (totaling 34%; green) and after isolation (totaling 31%; black). (C) Population screening programs using tests at LODs of 10³ and 10⁵ at frequencies indicated and with results returned after 0, 1, or 2 days (indicated by small text beneath bars) were applied to 10,000 individuals’ trajectories of whom 35% were symptomatic and self-isolated after peak viral load if they had not been tested and isolated first. Total infectiousness removed during screening (colors) and self-isolation (hatch) is shown, relative to total infectiousness with no screening or self-isolation. Delays substantially affect the fraction of infectiousness removed. (D) The impact of screening with delays in returning diagnosis of 0, 1, or 2 days (small text beneath the axis) on the infectiousness of 100 individuals is shown for each population screening regimen and no testing, as indicated, with each individual colored by test if their infection was detected during infectiousness (medians, black lines) or colored blue if their infection was missed by screening or diagnosed positive after their infectious period (medians, blue lines). Units are arbitrary and scaled to the maximum infectiousness of sampled individuals. a.u., arbitrary units.

Fig. 5. Delays in reporting decrease the epidemiological impact of testing-driven isolation.

The effectiveness of population screening programs is markedly diminished by delays in reporting in both the fully mixed compartmental model (top row) and agent-based model (bottom row). (A and B) The impact of testing every day, 3 days, weekly, or biweekly on the reproductive number R, calculated as 100 × (R₀ − R)/R₀, is shown for LODs 10³ and 10⁵ and delays of 0, 1, or 2 days (small text below the axis). Values of R were estimated from 50 independent simulations of dynamics (see Materials and Methods). (C and D) Relative to no testing (gray bars), repeated population screening suppresses the total number of infections in both models when testing every day or every 3 days, but delayed results lead to only partial mitigation of total cases, even for testing every day or 3 days. Error bars indicate inner 95% quantiles of 50 independent simulations each.

Fig. 6. Repeated population screening suppresses an ongoing epidemic.

Widespread testing and isolation of infected individuals drive prevalence downward for both (A) the fully mixed compartmental model and (B) the agent-based model. Time series of prevalence, measured as the total number of infectious individuals, are shown for no intervention (solid) and population screening scenarios (various dashed lines; see legend) for individual stochastic simulations. Screening began only when prevalence reached 4% (box), and time series are shifted such that testing begins at t = 0. Scenarios show the impact of a test with LOD 10⁵, no delay in results, and with 10% of samples assumed to be incorrectly collected (and therefore negative) to reflect decreased sensitivity incurred at sample collection in a mass testing scenario. Annotations show total number of post-intervention infections, as a percentage of the no-intervention scenario, labeled as 100% (see fig. S8 for identical simulations using a test with LOD 10⁶).

Fig. 7. Example asymptomatic and symptomatic viral loads with model control points.

Examples of model viral loads (lines) and corresponding stochastically drawn control points (squares and circles) are shown for (A) an asymptomatic viral load trajectory and (B) a symptomatic viral load trajectory. Because simulations took place in discrete time, black dots show points at which this example viral load would have been sampled. Light gray lines show 20 alternative trajectories in each panel to illustrate the diversity of viral loads drawn from the simple model. Red circles indicate the control points that are modified in symptomatic trajectories to account for symptom onset and prolonged time until clearance.