The effectiveness of COVID-19 testing and contact tracing in a US city

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dc.creatorWang, Xutong
dc.creatorDu, Zhanwei
dc.creatorJames, Emily
dc.creatorFox, Spencer J.
dc.creatorLachmann, Michael
dc.creatorMeyers, Lauren Ancel
dc.creatorBhavnani, Darlene
dc.date.accessioned2024-07-29T20:41:50Z
dc.date.available2024-07-29T20:41:50Z
dc.date.issued2022-08
dc.description.abstractAlthough testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries world-wide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.
dc.description.departmentDell Medical School
dc.description.departmentIntegrative Biology
dc.description.sponsorshipThis research was supported by NIH grant R01 AI151176 (to X.W., Z.D., S.J.F., and L.A.M.), CDC grant U01 IP001136 (to X.W.,Z.D., S.J.F., and L.A.M.), and a donation from Love, Tito’s (the philanthropic arm of Tito’s Homemade Vodka, Austin, TX) to the University of Texas to support the modeling of COVID-19 mitigation strategies (to X.W., Z.D., M.L., L.A.M., and D.B.). D.B.’s effort on this project was also supported by core funds of the Dell Medical School at UT.
dc.identifier.doi10.1073/pnas.2200652119
dc.identifier.urihttps://hdl.handle.net/2152/126289
dc.identifier.urihttps://doi.org/10.26153/tsw/52826
dc.publisherPNAS
dc.rightsAttribution NonCommercial NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.source.urihttps://www.pnas.org/doi/abs/10.1073/pnas.2200652119
dc.subjectCOVID-19
dc.subjectpandemic
dc.subjectmathematical model
dc.subjectcontact tracing
dc.subjectCOVID-19 testing
dc.titleThe effectiveness of COVID-19 testing and contact tracing in a US city
dc.typeJournalArticle

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