{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T04:20:26Z","timestamp":1747196426299,"version":"3.40.5"},"reference-count":13,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The onset of the COVID-19 pandemic commenced an era of widespread disruptions in the academic world, including shut downs, periodic shifts to online learning, and disengagement from students. In an effort to transition back to in-person learning, many universities and schools tried to implement policy that balanced student learning with community health. While academic administrators have little control over some aspects of COVID-19 spread, they often choose to use temporary shutdowns of in-person teaching based on perceived hotspots of COVID-19. Specifically, if administrators have substantial evidence of within-group transmission for a class or other academic unit (a \u201chotspot\u201d), the activities of that class or division of the university might be temporarily moved online. In this article, we describe an approach used to make these types of decisions. Using demographic information and weekly COVID-19 testing outcomes for university students, we use an XGBoost model that produces an estimated probability of testing positive for each student. We discuss variables engineered from the demographic information that increased model fit. As part of our approach, we simulate semesters under the null hypothesis of no in-class transmission, and compare the distribution of simulated outcomes to the observed group positivity rates to find an initial <jats:italic>p<\/jats:italic>-value for each group (e.g., section, housing area, or major). Using a simulation-based modification of a standard false discovery rate procedure, we identify possible hot spots\u2014classes or groups whose COVID-19 rates exceed the levels expected for the demographic mix of students in each group of interest. We use simulation experiments and an anonymized example from Fall 2020 to illustrate the performance of our approach. While our example is based on hotspot detection in a university setting, the approach can be used for monitoring the spread of infectious disease within any interconnected organization or population.<\/jats:p>","DOI":"10.1371\/journal.pone.0289254","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T17:23:15Z","timestamp":1715880195000},"page":"e0289254","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["COVID-19 hotspot detection in a university setting"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6978-3556","authenticated-orcid":true,"given":"Garrett","family":"Duncan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William F.","family":"Christensen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Camilla","family":"Handley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"issue":"17","key":"pone.0289254.ref001","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1001\/jama.2020.20027","article-title":"Preventing and Responding to COVID-19 on College Campuses","volume":"324","author":"HT Walke","year":"2020","journal-title":"JAMA"},{"issue":"1","key":"pone.0289254.ref002","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/1476-072X-12-6","article-title":"Tracking and visualization of space-time activities for a micro-scale flu transmission study","volume":"12","author":"F Qi","year":"2013","journal-title":"International journal of health geographics"},{"issue":"9","key":"pone.0289254.ref003","doi-asserted-by":"crossref","first-page":"e12948","DOI":"10.1371\/journal.pone.0012948","article-title":"Social Network Sensors for Early Detection of Contagious Outbreaks","volume":"5","author":"NA Christakis","year":"2010","journal-title":"PloS one"},{"issue":"1","key":"pone.0289254.ref004","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Y Benjamini","year":"1995","journal-title":"Journal of the Royal statistical society: series B (Methodological)"},{"key":"pone.0289254.ref005","first-page":"1165","article-title":"The control of the false discovery rate in multiple testing under dependency","author":"Y Benjamini","year":"2001","journal-title":"Annals of statistics"},{"issue":"4","key":"pone.0289254.ref006","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1080\/01944363.2020.1777891","article-title":"Does Density Aggravate the COVID-19 Pandemic?","volume":"86","author":"S Hamidi","year":"2020","journal-title":"Journal of the American Planning Association"},{"key":"pone.0289254.ref007","unstructured":"CDC. Coronavirus Disease 2019 (covid-19); 2021. https:\/\/www.cdc.gov\/coronavirus\/2019-ncov\/index.html."},{"key":"pone.0289254.ref008","unstructured":"Microsoft. Microsoft Bing Maps; 2020. https:\/\/www.microsoft.com\/en-us\/maps\/."},{"key":"pone.0289254.ref009","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, et al. Xgboost: extreme gradient boosting. R package version 04-2. 2015;1(4):1\u20134."},{"key":"pone.0289254.ref010","unstructured":"R Core Team. R: A Language and Environment for Statistical Computing; 2022. Available from: https:\/\/www.R-project.org\/."},{"issue":"1-2","key":"pone.0289254.ref011","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S0378-3758(99)00041-5","article-title":"Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics","volume":"82","author":"D Yekutieli","year":"1999","journal-title":"Journal of Statistical Planning and Inference"},{"issue":"3","key":"pone.0289254.ref012","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1093\/biomet\/93.3.509","article-title":"False discovery control with p-value weighting","volume":"93","author":"CR Genovese","year":"2006","journal-title":"Biometrika"},{"issue":"3","key":"pone.0289254.ref013","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/10485252.2010.482154","article-title":"Randomised P-values and nonparametric procedures in multiple testing","volume":"23","author":"JD Habiger","year":"2011","journal-title":"Journal of nonparametric statistics"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0289254","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T17:23:25Z","timestamp":1715880205000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0289254"}},"subtitle":[],"editor":[{"given":"Claire Seungeun","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,5,16]]},"references-count":13,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5,16]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0289254","relation":{},"ISSN":["1932-6203"],"issn-type":[{"type":"electronic","value":"1932-6203"}],"subject":[],"published":{"date-parts":[[2024,5,16]]}}}