Comparative cost-effectiveness of SARS-CoV-2 testing strategies in the USA: a modelling study

Abstract

Background: To mitigate the COVID-19 pandemic, countries worldwide have enacted unprecedented movement restrictions, physical distancing measures, and face mask requirements. Until safe and efficacious vaccines or antiviral drugs become widely available, viral testing remains the primary mitigation measure for rapid identification and isolation of infected individuals. We aimed to assess the economic trade-offs of expanding and accelerating testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the USA in different transmission scenarios.

Methods: We used a multiscale model that incorporates SARS-CoV-2 transmission at the population level and daily viral load dynamics at the individual level to assess eight surveillance testing strategies that varied by testing frequency (from daily to monthly testing) and isolation period (1 or 2 weeks), compared with the status-quo strategy of symptom-based testing and isolation. For each testing strategy, we first estimated the costs (incorporating costs of diagnostic testing and admissions to hospital, and salary lost while in isolation) and years of life lost (YLLs) prevented under rapid and low transmission scenarios. We then assessed the testing strategies across a range of scenarios, each defined by effective reproduction number (R_e), willingness to pay per YLL averted, and cost of a test, to estimate the probability that a particular strategy had the greatest net benefit. Additionally, for a range of transmission scenarios (R_e from 1·1 to 3), we estimated a threshold test price at which the status-quo strategy outperforms all testing strategies considered.

Findings: Our modelling showed that daily testing combined with a 2-week isolation period was the most costly strategy considered, reflecting increased costs with greater test frequency and length of isolation period. Assuming a societal willingness to pay of US$100 000 per YLL averted and a price of $5 per test, the strategy most likely to be cost-effective under a rapid transmission scenario (R_e of 2·2) is weekly testing followed by a 2-week isolation period subsequent to a positive test result. Under low transmission scenarios (R_e of 1·2), monthly testing of the population followed by 1-week isolation rather than 2-week isolation is likely to be most cost-effective. Expanded surveillance testing is more likely to be cost-effective than the status-quo testing strategy if the price per test is less than $75 across all transmission rates considered.

Interpretation: Extensive expansion of SARS-CoV-2 testing programmes with more frequent and rapid tests across communities coupled with isolation of individuals with confirmed infection is essential for mitigating the COVID-19 pandemic. Furthermore, resources recouped from shortened isolation duration could be cost-effectively allocated to more frequent testing.

Funding: US National Institutes of Health, US Centers for Disease Control and Prevention, and Love, Tito's.

Figure 1

Schematic of the individual-based SARS-CoV-2 infection dynamic model Upon infection, susceptible individuals (S) progress to being exposed individuals (E), where they are neither infectious nor symptomatic. A fraction of cases become asymptomatic infectious (A) with lower infectiousness before recovering (R); the remaining cases progress to presymptomatic (P), where they are moderately infectious but not yet symptomatic, followed by symptomatic infectious (Y) and then either recover (R) or are admitted to hospital (H). Individuals admitted to hospital are assumed to be fully isolated and progress either to recovered (R) or deceased (D). Recovered individuals remain protected from future infection for the duration of the 150-day simulation. The various testing strategies assume that individuals are tested at a specified frequency, ranging from daily to monthly, according to an evenly staggered testing schedule, regardless of their disease state. Those testing positive and all members of their household proceed to isolate or quarantine for the specified period (either 7 or 14 days). After isolation, individuals return to non-isolated states corresponding to the current state of their health but no longer participate in surveillance testing. SARS-CoV-2=severe acute respiratory syndrome coronavirus 2.

Figure 2

Estimated costs of testing strategies and YLLs averted, assuming US$5 tests and an R_e of 1·2 (A) or 2·2 (B) Each datapoint corresponds to one of 1000 stochastic simulations for the specified testing strategy, under parameters given in the appendix (pp 3–4). Costs include the price of administering tests, salary lost during isolation after a positive test result, and costs associated with admission to hospital due to COVID-19; YLLs averted considers mortality due to COVID-19. The costs and YLLs averted are all scaled assuming a US population of 328·2 million people, as estimated in 2019.R_e=effective reproduction number. YLLs=years of life lost.

Figure 3

Cost-effectiveness acceptability frontier, assuming an R_e of 1·2 (A, B) or 2·2 (C, D) (A, C) Assuming each test costs US$5, the probability that a candidate strategy has the greatest net benefit under a given willingness to pay per YLL averted (x axis) is based on 1000 rounds of stochastic simulations. In each round, every strategy is simulated and the one resulting in the largest net monetary benefit is deemed optimal. (B, D) Assuming a willingness to pay of $100 000 per YYL averted, the same procedure is applied across a range of test prices (x axis). The graphs depict the best strategy—ie, the one that most often yielded the highest expected net monetary benefit across the 1000 sets of simulation. On appendix p 8, we depict the cost-effectiveness acceptability curves for the top three performing strategies. R_e=effective reproduction number. YLLs=years of life lost.