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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability. In this study, we analyze a data-driven COVID-19 testing program implemented at a mid-sized university, which utilized two simple, diverse, and easily interpretable machine learning models to predict which students were at elevated risk and should be tested. The program produced a positivity rate of 0.53% (95% CI 0.34\u20130.77%) from 20,862 tests, with 1.49% (95% CI 1.15\u20131.89%) of students testing positive within five days of the initial test\u2014a significant increase from the general surveillance baseline, which produced a positivity rate of 0.37% (95% CI 0.28\u20130.47%) with 0.67% (95% CI 0.55\u20130.81%) testing positive within five days. Close contacts who were predicted by the data-driven models were tested much more quickly on average (0.94 days from reported exposure; 95% CI 0.78\u20131.11) than those who were manually contact traced (1.92 days; 95% CI 1.81\u20132.02). We further discuss how other universities, business, and organizations could adopt similar strategies to help quickly identify positive cases and reduce community transmission.<\/jats:p>","DOI":"10.1038\/s41746-022-00562-4","type":"journal-article","created":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T06:05:04Z","timestamp":1644559504000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Data-driven testing program improves detection of COVID-19 cases and reduces community transmission"],"prefix":"10.1038","volume":"5","author":[{"given":"Steven J.","family":"Krieg","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3947-9907","authenticated-orcid":false,"given":"Carolina","family":"Avendano","sequence":"additional","affiliation":[]},{"given":"Evan","family":"Grantham-Brown","sequence":"additional","affiliation":[]},{"given":"Aaron","family":"Lilienfeld Asbun","sequence":"additional","affiliation":[]},{"given":"Jennifer J.","family":"Schnur","sequence":"additional","affiliation":[]},{"given":"Marie Lynn","family":"Miranda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3932-5956","authenticated-orcid":false,"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,11]]},"reference":[{"key":"562_CR1","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1126\/science.372.6549.1375","volume":"372","author":"K Kupferschmidt","year":"2021","unstructured":"Kupferschmidt, K. & Wadman, M. 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