Optimal test allocation

Ely, Jeffrey C.; Galeotti, Andrea; Jann, Ole; Steiner, Jakub

doi:10.1016/j.jet.2021.105236

Cited by 27 publications

(16 citation statements)

References 17 publications

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“…Because the shadow cost of a test is the same for all agents, intuitively, tests should be used on those for whom they provide the most instrumental value. Under individual testing (i.e., without batching), this property is established by both Ely, Galeotti, and Steiner (2020) and Kasy and Teytelboym (2020). As Kasy and Teytelboym (2020) observe, the value of testing is maximized at the quarantine cutoff and is concave in an individually tested agent's type.…”

Section: The Optimal Testing Policymentioning

confidence: 99%

“…A few other recent studies look at optimal non-batched testing for informing quarantine decisions (Deb et al, 2020;Ely, Galeotti, and Steiner, 2020;Kasy and Teytelboym, 2020). Deb et al (2020) study the joint design of testing and financial incentives for a binary self-quarantine decision that induces (through agents' self-selection based on private information) coarse targeted testing.…”

Section: Introductionmentioning

confidence: 99%

“…Kasy and Teytelboym (2020) study how costly perfect tests should be dynamically administered to both inform individuals' quarantine decisions and learn about how disease prevalence covaries with observable characteristics. Ely, Galeotti, and Steiner (2020) study how a finite budget of heterogeneously imperfect tests should be allocated to heterogeneous agents. Because different group sizes and compositions are differentially informative of a given individual's health, the test heterogeneity in Ely, Galeotti, and Steiner (2020) is conceptually related to our model.…”

Section: Introductionmentioning

confidence: 99%

“…Ely, Galeotti, and Steiner (2020) study how a finite budget of heterogeneously imperfect tests should be allocated to heterogeneous agents. Because different group sizes and compositions are differentially informative of a given individual's health, the test heterogeneity in Ely, Galeotti, and Steiner (2020) is conceptually related to our model. Compared to their paper, our focus on group testing requires us to address two novel issues: (i) a lack of separability across jointly tested agents due to inference externalities; and (ii) a quantity-quality trade-off due to the information degradation that arises from testing larger groups.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Pooled Testing for Quarantine Decisions

Lipnowski

Doron

2020

SSRN Journal

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We study optimal testing to inform quarantine decisions for a population exhibiting a heterogeneous probability of carrying a pathogen. Because test supply is limited, the planner may choose to test a pooled sample, which contains the specimens of multiple individuals (Dorfman, 1943). We characterize the unique optimal allocation of tests. This allocation features assortative batching, whereby agents of differing infection risk are never jointly tested. Moreover, the planner tests only individuals whose prior quarantine decision is the most uncertain. Finally, individuals with higher infection risk are tested in smaller batches, because such tests minimize the informational externality of group testing.

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Section: The Optimal Testing Policymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pooled Testing for Quarantine Decisions

Lipnowski

Doron

2020

SSRN Journal

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“…Nevertheless, dilution due to combined samples is always one of the major concerns in the pooled/group testing approach. [10][11][12] Ely et al 14 described allocation of fixed numbers of multiple different test types with different sensitivities and specificities to populations at high/low risk. Under this approach, tests are allocated by a decision-making process for maximizing the value of the tests, mathematically defined as the sum of the test specificity and sensitivity weighted by the loss of the corresponding decision error.…”

Section: Introductionmentioning

confidence: 99%

Optimal diagnostic test allocation strategy during the COVID‐19 pandemic and beyond

Beesley

Lee

et al. 2021

Statistics in Medicine

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Timely diagnostic testing for active SARS-CoV-2 viral infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies with limited resources. In this paper, we provide a mathematical framework for defining an optimal strategy for allocating viral diagnostic tests. The framework accounts for imperfect test results, selective testing in certain high-risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infections, but can also be used for long-time surveillance to detect new outbreaks. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID-19 outbreak in New York City.We extend our proposed method to address the challenge of allocating two different types of diagnostic tests with different costs and accuracy, for example, the RT-PCR and the rapid antigen test (RAT), under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision-making at a community level and adapting testing plans to different stages of an epidemic.The conceptual framework has broader relevance beyond the current COVID-19 pandemic.

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Test sensitivity for infection versus infectiousness of SARS‐CoV‐2

Gans

2021

Manage Decis Econ

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The most commonly used test for the presence of SARS‐CoV‐2 is a PCR test that is able to detect very low viral loads and inform on treatment decisions. Medical research has confirmed that many individuals might be infected with SARS‐CoV‐2 but not infectious. Knowing whether an individual is infectious is the critical piece of information for a decision to isolate an individual or not. This paper examines the value of different tests from an information‐theoretic approach and shows that applying treatment‐based approval standards for tests for infection will lower the value of those tests and likely causes decisions based on them to have too many false positives (i.e., individuals isolated who are not infectious). The conclusion is that test scoring be tailored to the decision being made.

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Optimal test allocation

Cited by 27 publications

References 17 publications

Pooled Testing for Quarantine Decisions

Pooled Testing for Quarantine Decisions

Optimal diagnostic test allocation strategy during the COVID‐19 pandemic and beyond

Test sensitivity for infection versus infectiousness of SARS‐CoV‐2

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