{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T07:30:54Z","timestamp":1768548654853,"version":"3.49.0"},"posted":{"date-parts":[[2020,6,11]]},"group-title":"Infectious Diseases (except HIV\/AIDS)","reference-count":33,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2021,1,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Many risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Among aged adults (69.3 \u00b1 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4,510 participants with 7,539 test cases. We downloaded baseline data from 10-14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The \u201cbest-fit\u201d model for predicting COVID-19 risk achieved excellent discrimination (AUC=0.969, 95% CI=0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization \u201cbest-fit\u201d model was more modest (AUC=0.803, 95% CI=0.663-0.943) and included only serology titers.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Accurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2020.06.09.20127092","type":"posted-content","created":{"date-parts":[[2020,6,11]],"date-time":"2020-06-11T11:05:35Z","timestamp":1591873535000},"source":"Crossref","is-referenced-by-count":6,"title":["Using machine learning to predict COVID-19 infection and severity risk among 4,510 aged adults: a UK Biobank cohort study"],"prefix":"10.64898","author":[{"given":"Auriel A.","family":"Willette","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sara A.","family":"Willette","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Colleen","family":"Pappas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brandon S.","family":"Klinedinst","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brittany","family":"Larsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amy","family":"Pollpeter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicole","family":"Brenner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Waterboer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2021010708351452000_2020.06.09.20127092v3.1","doi-asserted-by":"publisher","DOI":"10.1038\/s41564-020-0695-z"},{"key":"2021010708351452000_2020.06.09.20127092v3.2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.120.047659"},{"key":"2021010708351452000_2020.06.09.20127092v3.3","doi-asserted-by":"publisher","DOI":"10.1002\/oby.22831"},{"key":"2021010708351452000_2020.06.09.20127092v3.4","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30566-3"},{"key":"2021010708351452000_2020.06.09.20127092v3.5","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.27.20082107"},{"key":"2021010708351452000_2020.06.09.20127092v3.6","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbi.2020.05.059"},{"key":"2021010708351452000_2020.06.09.20127092v3.7","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30232-2"},{"key":"2021010708351452000_2020.06.09.20127092v3.8","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciaa248"},{"key":"2021010708351452000_2020.06.09.20127092v3.9","doi-asserted-by":"publisher","DOI":"10.1086\/381535"},{"key":"2021010708351452000_2020.06.09.20127092v3.10","doi-asserted-by":"publisher","DOI":"10.1186\/s12979-020-00185-x"},{"key":"2021010708351452000_2020.06.09.20127092v3.11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2249.2008.03785.x"},{"key":"2021010708351452000_2020.06.09.20127092v3.12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001779"},{"key":"2021010708351452000_2020.06.09.20127092v3.13","doi-asserted-by":"crossref","unstructured":"Armstrong, J. et al. 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