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
Number of pages	1
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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doi = "10.1145/3580507.3597709",

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AU - Amador, Michelle González

AU - Lock, Edwin

AU - Cossio, Francisco Marmolejo

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AU - Procaccia, Ariel

N1 - Conference code: 24

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N2 - 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.

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.

KW - approximation algorithms

KW - COVID-19

KW - non-overlapping and overlapping testing

KW - pooled testing

KW - welfare maximization

U2 - 10.1145/3580507.3597709

DO - 10.1145/3580507.3597709

M3 - Conference article in proceeding

SP - 699

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

PB - Association for Computing Machinery (ACM)

T2 - 24th ACM Conference on Economics and Computation

Y2 - 9 July 2023 through 12 July 2023

ER -

Welfare-Maximizing Pooled Testing

Abstract

Conference

Keywords

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