{"doi":"10.1002/jmv.27054","title":"Optimal uses of pooled testing for COVID‐19 incorporating imperfect test performance and pool dilution effect: An application to congregate settings in Los Angeles County","abstract":"INTRODUCTION: Pooled testing is a potentially efficient alternative strategy for COVID-19 testing in congregate settings. We evaluated the utility and cost-savings of pooled testing based on imperfect test performance and potential dilution effect due to pooling and created a practical calculator for online use. METHODS: We developed a 2-stage pooled testing model accounting for dilution. The model was applied to hypothetical scenarios of 100 specimens collected during a one-week time-horizon cycle for varying levels of COVID-19 prevalence and test sensitivity and specificity, and to 338 skilled nursing facilities (SNFs) in Los Angeles County (Los Angeles) (data collected and analyzed in 2020). RESULTS: Optimal pool sizes ranged from 1 to 12 in instances where there is a least one case in the batch of specimens. 40% of Los Angeles SNFs had more than one case triggering a response-testing strategy. The median number (minimum; maximum) of tests performed per facility were 56 (14; 356) for a pool size of 4, 64 (13; 429) for a pool size of 10, and 52 (11; 352) for an optimal pool size strategy among response-testing facilities. The median costs of tests in response-testing facilities were $8250 ($1100; $46,100), $6000 ($1340; $37,700), $6820 ($1260; $43,540), and $5960 ($1100; $37,380) when adopting individual testing, a pooled testing strategy using pool sizes of 4, 10, and optimal pool size, respectively. CONCLUSIONS: Pooled testing is an efficient strategy for congregate settings with a low prevalence of COVID-19. Dilution as a result of pooling can lead to erroneous false-negative results.","journal":"Journal of Medical Virology","year":2021,"id":206652,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9366,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":285638,"name":"I.O. Emeruwa","orcid":"0000-0002-1424-6437","position":1,"is_corresponding":false},{"id":521128,"name":"Prabhu Gounder","orcid":"0000-0002-8600-0348","position":2,"is_corresponding":false},{"id":308072,"name":"Vladimir Manuel","orcid":"0000-0001-8101-5885","position":3,"is_corresponding":false},{"id":699339,"name":"Nathaniel Anderson","orcid":"0000-0002-4305-2447","position":4,"is_corresponding":false},{"id":708702,"name":"Tony Kuo","orcid":"0000-0002-4120-8559","position":5,"is_corresponding":false},{"id":341927,"name":"Moira Inkelas","orcid":"0000-0003-1963-2430","position":6,"is_corresponding":false},{"id":291004,"name":"Onyebuchi A. Arah","orcid":"0000-0002-9067-1697","position":7,"is_corresponding":false},{"id":772586,"name":"Roch A. Nianogo","orcid":"0000-0001-5932-6169","position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-18T23:51:41.571653Z","pmid":"33930195","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}