Clinical and Economic Impact of Widespread Rapid Testing to Decrease SARS-CoV-2 Transmission

Abstract

Background: The value of frequent, rapid testing to reduce community transmission of SARS-CoV-2 is poorly understood.

Objective: To define performance standards and predict the clinical, epidemiological, and economic outcomes of nationwide, home-based, antigen testing.

Design: A simple compartmental epidemic model estimated viral transmission, clinical history, and resource use, with and without testing.

Data sources: Parameter values and ranges informed by Centers for Disease Control guidance and published literature.

Target population: United States population.

Time horizon: 60 days.

Perspective: Societal. Costs include: testing, inpatient care, and lost workdays.

Intervention: Home-based SARS-CoV-2 antigen testing.

Outcome measures: Cumulative infections and deaths, numbers isolated and/or hospitalized, and total costs.

Results of base-case analysis: Without a testing intervention, the model anticipates 15 million infections, 125,000 deaths, and $10.4 billion in costs ($6.5 billion inpatient; $3.9 billion lost productivity) over a 60-day horizon. Weekly availability of testing may avert 4 million infections and 19,000 deaths, raising costs by $21.5 billion. Lower inpatient outlays ($5.9 billion) would partially offset additional testing expenditures ($12.0 billion) and workdays lost ($13.9 billion), yielding incremental costs per infection (death) averted of $5,400 ($1,100,000).

Results of sensitivity analysis: Outcome estimates vary widely under different behavioral assumptions and testing frequencies. However, key findings persist across all scenarios: large reductions in infections, mortality, and hospitalizations; and costs per death averted roughly an order of magnitude lower than commonly accepted willingness-to-pay values per statistical life saved ($5-17 million).

Limitations: Analysis restricted to at-home testing and limited by uncertainties about test performance.

Conclusion: High-frequency home testing for SARS-CoV-2 using an inexpensive, imperfect test could contribute to pandemic control at justifiable cost and warrants consideration as part of a national containment strategy.

Appendix Figure 1:

Model Structure

Figure 1.. Daily infections as a function of behavioral scenarios.

This figure reports the daily number of infections (vertical axis) under three behavioral scenarios with home-based testing and no home-based testing over a 60-day horizon (horizontal axis). The colored lines denote different testing and behavioral assumptions: no testing (blue); best case (orange); base case (gray); and worst case (yellow).

Figure 2.. Cumulative infections as a function of testing frequency.

In this figure, the number of cumulative infections (vertical axis, in millions) is reported for a range of home-based testing frequencies (horizontal axis, ranging from 1 to 15 days between tests). The colored lines denote different testing and behavioral assumptions: no testing (blue); best case (orange); base case (gray); and worst case (yellow).