Brittany A Petros1, Jillian S Paull2, Christopher H Tomkins-Tinch3, Bryn C Loftness4, Katherine C DeRuff5, Parvathy Nair6, Gabrielle L Gionet5, Aaron Benz7, Taylor Brock-Fisher5, Michael Hughes8, Leonid Yurkovetskiy9, Shandukani Mulaudzi10, Emma Leenerman8, Thomas Nyalile9, Gage K Moreno5, Ivan Specht5, Kian Sani5, Gordon Adams5, Simone V Babet11, Emily Baron12, Jesse T Blank8, Chloe Boehm13, Yolanda Botti-Lodovico5, Jeremy Brown8, Adam R Buisker8, Timothy Burcham14, Lily Chylek5, Paul Cronan14, Ann Dauphin9, Valentine Desreumaux11, Megan Doss15, Belinda Flynn8, Adrianne Gladden-Young5, Olivia Glennon14, Hunter D Harmon8, Thomas V Hook11, Anton Kary16, Clay King17, Christine Loreth5, Libby Marrs14, Kyle J McQuade16, Thorsen T Milton11, Jada M Mulford16, Kyle Oba14, Leah Pearlman5, Mark Schifferli14, Madelyn J Schmidt8, Grace M Tandus11, Andy Tyler8, Megan E Vodzak5, Kelly Krohn Bevill18, Andres Colubri19, Bronwyn L MacInnis5, A Zeynep Ozsoy16, Eric Parrie12, Kari Sholtes20, Katherine J Siddle21, Ben Fry14, Jeremy Luban22, Daniel J Park5, John Marshall8, Amy Bronson23, Stephen F Schaffner5, Pardis C Sabeti24. 1. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA; Harvard/MIT MD-PhD Program, Boston, MA 02115, USA; Systems, Synthetic, and Quantitative Biology PhD Program, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. 2. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Systems, Synthetic, and Quantitative Biology PhD Program, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. Electronic address: jpaull@broadinstitute.org. 3. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. Electronic address: tomkinsc@broadinstitute.org. 4. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Computer Science and Engineering, Colorado Mesa University, Grand Junction, CO 81501, USA; Complex Systems and Data Science PhD Program, University of Vermont, Burlington, VT 05405, USA; Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA. Electronic address: bloftnes@broadinstitute.org. 5. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. 6. Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA. 7. Degree Analytics, Inc., Austin, TX 78758, USA. 8. Colorado Mesa University, Grand Junction, CO 81501, USA. 9. Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA. 10. Harvard Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA 02115, USA. 11. Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, CO 80309, USA. 12. COVIDCheck Colorado, LLC, Denver, CO 80202, USA. 13. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Princeton University Molecular Biology Department, Princeton, NJ 08544, USA. 14. Fathom Information Design, Boston, MA 02114, USA. 15. Warrior Diagnostics, Inc., Loveland, CO 80538, USA. 16. Department of Biological Sciences, Colorado Mesa University, Grand Junction, CO 81501, USA. 17. Department of Mathematics and Statistics, Colorado Mesa University, Grand Junction, CO 81501, USA. 18. Department of Computer Science and Engineering, Colorado Mesa University, Grand Junction, CO 81501, USA. 19. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; University of Massachusetts Medical School, Worcester, MA 01655, USA. 20. Department of Computer Science and Engineering, Colorado Mesa University, Grand Junction, CO 81501, USA; Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, CO 80309, USA. 21. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. 22. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA; Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01655, USA; Massachusetts Consortium on Pathogen Readiness, Boston, MA 02115, USA. 23. Physician Assistant Program, Department of Kinesiology, Colorado Mesa University, Grand Junction, CO 81501, USA. 24. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Massachusetts Consortium on Pathogen Readiness, Boston, MA 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Abstract
BACKGROUND: Universities are vulnerable to infectious disease outbreaks, making them ideal environments to study transmission dynamics and evaluate mitigation and surveillance measures. Here, we analyze multimodal COVID-19-associated data collected during the 2020-2021 academic year at Colorado Mesa University and introduce a SARS-CoV-2 surveillance and response framework. METHODS: We analyzed epidemiological and sociobehavioral data (demographics, contact tracing, and WiFi-based co-location data) alongside pathogen surveillance data (wastewater and diagnostic testing, and viral genomic sequencing of wastewater and clinical specimens) to characterize outbreak dynamics and inform policy. We applied relative risk, multiple linear regression, and social network assortativity to identify attributes or behaviors associated with contracting SARS-CoV-2. To characterize SARS-CoV-2 transmission, we used viral sequencing, phylogenomic tools, and functional assays. FINDINGS: Athletes, particularly those on high-contact teams, had the highest risk of testing positive. On average, individuals who tested positive had more contacts and longer interaction durations than individuals who never tested positive. The distribution of contacts per individual was overdispersed, although not as overdispersed as the distribution of phylogenomic descendants. Corroboration via technical replicates was essential for identification of wastewater mutations. CONCLUSIONS: Based on our findings, we formulate a framework that combines tools into an integrated disease surveillance program that can be implemented in other congregate settings with limited resources. FUNDING: This work was supported by the National Science Foundation, the Hertz Foundation, the National Institutes of Health, the Centers for Disease Control and Prevention, the Massachusetts Consortium on Pathogen Readiness, the Howard Hughes Medical Institute, the Flu Lab, and the Audacious Project.
BACKGROUND: Universities are vulnerable to infectious disease outbreaks, making them ideal environments to study transmission dynamics and evaluate mitigation and surveillance measures. Here, we analyze multimodal COVID-19-associated data collected during the 2020-2021 academic year at Colorado Mesa University and introduce a SARS-CoV-2 surveillance and response framework. METHODS: We analyzed epidemiological and sociobehavioral data (demographics, contact tracing, and WiFi-based co-location data) alongside pathogen surveillance data (wastewater and diagnostic testing, and viral genomic sequencing of wastewater and clinical specimens) to characterize outbreak dynamics and inform policy. We applied relative risk, multiple linear regression, and social network assortativity to identify attributes or behaviors associated with contracting SARS-CoV-2. To characterize SARS-CoV-2 transmission, we used viral sequencing, phylogenomic tools, and functional assays. FINDINGS: Athletes, particularly those on high-contact teams, had the highest risk of testing positive. On average, individuals who tested positive had more contacts and longer interaction durations than individuals who never tested positive. The distribution of contacts per individual was overdispersed, although not as overdispersed as the distribution of phylogenomic descendants. Corroboration via technical replicates was essential for identification of wastewater mutations. CONCLUSIONS: Based on our findings, we formulate a framework that combines tools into an integrated disease surveillance program that can be implemented in other congregate settings with limited resources. FUNDING: This work was supported by the National Science Foundation, the Hertz Foundation, the National Institutes of Health, the Centers for Disease Control and Prevention, the Massachusetts Consortium on Pathogen Readiness, the Howard Hughes Medical Institute, the Flu Lab, and the Audacious Project.
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