Welfare-Maximizing Pooled Testing

Simon Finster; Michelle González Amador; Edwin Lock; Francisco Marmolejo Cossio; Evi Micha; Ariel Procaccia

doi:10.1145/3580507.3597709

Welfare-Maximizing Pooled Testing

Simon Finster, Michelle González Amador, Edwin Lock, Francisco Marmolejo Cossio, Evi Micha, Ariel Procaccia

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

In an epidemic, how should an organization with limited testing resources safely return to in-person activities after a lockdown? We study this question in a setting where the population is heterogeneous in both utility for in-person activities and probability of infection. During a period of re-integration, tests can be used as a certificate of non-infection, whereby those in negative tests are permitted to return to in-person activities for a designated amount of time. Under the assumption that samples can be pooled, the question of how to allocate a limited testing budget in the population to maximize the aggregate utility (i.e. welfare) of negatively-tested individuals who return to in-person activities is non-trivial, with a large space of potential testing allocations.

Original language	English
Title of host publication	EC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation
Publisher	Association for Computing Machinery (ACM)
Pages	699
ISBN (Electronic)	9798400701047
DOIs	https://doi.org/10.1145/3580507.3597709
Publication status	Published - 9 Jul 2023
Event	24th ACM Conference on Economics and Computation - London, United Kingdom Duration: 9 Jul 2023 → 12 Jul 2023 Conference number: 24

Conference

Conference	24th ACM Conference on Economics and Computation
Abbreviated title	EC 2023
Country/Territory	United Kingdom
City	London
Period	9/07/23 → 12/07/23

Keywords

approximation algorithms
COVID-19
non-overlapping and overlapping testing
pooled testing
welfare maximization

Access to Document

10.1145/3580507.3597709

Cite this

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title = "Welfare-Maximizing Pooled Testing",

abstract = "In an epidemic, how should an organization with limited testing resources safely return to in-person activities after a lockdown? We study this question in a setting where the population is heterogeneous in both utility for in-person activities and probability of infection. During a period of re-integration, tests can be used as a certificate of non-infection, whereby those in negative tests are permitted to return to in-person activities for a designated amount of time. Under the assumption that samples can be pooled, the question of how to allocate a limited testing budget in the population to maximize the aggregate utility (i.e. welfare) of negatively-tested individuals who return to in-person activities is non-trivial, with a large space of potential testing allocations.",

keywords = "approximation algorithms, COVID-19, non-overlapping and overlapping testing, pooled testing, welfare maximization",

author = "Simon Finster and Amador, {Michelle Gonz{\'a}lez} and Edwin Lock and Cossio, {Francisco Marmolejo} and Evi Micha and Ariel Procaccia",

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AU - Lock, Edwin

AU - Cossio, Francisco Marmolejo

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AB - In an epidemic, how should an organization with limited testing resources safely return to in-person activities after a lockdown? We study this question in a setting where the population is heterogeneous in both utility for in-person activities and probability of infection. During a period of re-integration, tests can be used as a certificate of non-infection, whereby those in negative tests are permitted to return to in-person activities for a designated amount of time. Under the assumption that samples can be pooled, the question of how to allocate a limited testing budget in the population to maximize the aggregate utility (i.e. welfare) of negatively-tested individuals who return to in-person activities is non-trivial, with a large space of potential testing allocations.

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BT - EC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation

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