Piloting an integrated SARS-CoV-2 testing and data system for outbreak containment among college students: A prospective cohort study.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2021
Historique:
received: 10 11 2020
accepted: 07 01 2021
entrez: 26 1 2021
pubmed: 27 1 2021
medline: 30 1 2021
Statut: epublish

Résumé

Colleges and universities across the country are struggling to develop strategies for effective control of COVID-19 transmission as students return to campus. We conducted a prospective cohort study with students living on or near the UC Berkeley campus from June 1st through August 18th, 2020 with the goal of providing guidance for campus reopening in the safest possible manner. In this cohort, we piloted an alternative testing model to provide access to low-barrier, high-touch testing and augment student-driven testing with data-driven adaptive surveillance that targets higher-risk students and triggers testing notifications based on reported symptoms, exposures, or other relevant information. A total of 2,180 students enrolled in the study, 51% of them undergraduates. Overall, 6,247 PCR tests were administered to 2,178 students over the two-month period. Overall test positivity rate was 0.9%; 2.6% of students tested positive. Uptake and acceptability of regular symptom and exposure surveys was high; 98% of students completed at least one survey, and average completion rate was 67% (Median: 74%, IQR: 39%) for daily reporting of symptoms and 68% (Median: 75%, IQR: 40%) for weekly reporting of exposures. Of symptom-triggered tests, 5% were PCR-positive; of exposure-triggered tests, 10% were PCR-positive. The integrated study database augmented traditional contact tracing during an outbreak; 17 potentially exposed students were contacted the following day and sent testing notifications. At study end, 81% of students selected their desire "to contribute to UC Berkeley's response to COVID-19" as a reason for their participation in the Safe Campus study. Our results illustrate the synergy created by bringing together a student-friendly, harm reduction approach to COVID-19 testing with an integrated data system and analytics. We recommend the use of a confidential, consequence-free, incentive-based daily symptom and exposure reporting system, coupled with low-barrier, easy access, no stigma testing. Testing should be implemented alongside a system that integrates multiple data sources to effectively trigger testing notifications to those at higher risk of infection and encourages students to come in for low-barrier testing when needed.

Sections du résumé

BACKGROUND
Colleges and universities across the country are struggling to develop strategies for effective control of COVID-19 transmission as students return to campus.
METHODS AND FINDINGS
We conducted a prospective cohort study with students living on or near the UC Berkeley campus from June 1st through August 18th, 2020 with the goal of providing guidance for campus reopening in the safest possible manner. In this cohort, we piloted an alternative testing model to provide access to low-barrier, high-touch testing and augment student-driven testing with data-driven adaptive surveillance that targets higher-risk students and triggers testing notifications based on reported symptoms, exposures, or other relevant information. A total of 2,180 students enrolled in the study, 51% of them undergraduates. Overall, 6,247 PCR tests were administered to 2,178 students over the two-month period. Overall test positivity rate was 0.9%; 2.6% of students tested positive. Uptake and acceptability of regular symptom and exposure surveys was high; 98% of students completed at least one survey, and average completion rate was 67% (Median: 74%, IQR: 39%) for daily reporting of symptoms and 68% (Median: 75%, IQR: 40%) for weekly reporting of exposures. Of symptom-triggered tests, 5% were PCR-positive; of exposure-triggered tests, 10% were PCR-positive. The integrated study database augmented traditional contact tracing during an outbreak; 17 potentially exposed students were contacted the following day and sent testing notifications. At study end, 81% of students selected their desire "to contribute to UC Berkeley's response to COVID-19" as a reason for their participation in the Safe Campus study.
CONCLUSIONS
Our results illustrate the synergy created by bringing together a student-friendly, harm reduction approach to COVID-19 testing with an integrated data system and analytics. We recommend the use of a confidential, consequence-free, incentive-based daily symptom and exposure reporting system, coupled with low-barrier, easy access, no stigma testing. Testing should be implemented alongside a system that integrates multiple data sources to effectively trigger testing notifications to those at higher risk of infection and encourages students to come in for low-barrier testing when needed.

Identifiants

pubmed: 33497404
doi: 10.1371/journal.pone.0245765
pii: PONE-D-20-35310
pmc: PMC7837458
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0245765

Déclaration de conflit d'intérêts

We declare that no competing interests exist, however, one of our authors is employed by a commercial company: Facente Consulting. Shelley N. Facente is affiliated with Facente Consulting. Facente Consulting provided support in the form of salary for author SF, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. We confirm that this affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials.

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Auteurs

Laura Packel (L)

Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, United States of America.

Arthur Reingold (A)

Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, United States of America.

Lauren Hunter (L)

Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, United States of America.

Shelley Facente (S)

Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, United States of America.
Facente Consulting, Richmond, California, United States of America.

Yi Li (Y)

Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, United States of America.

Anna Harte (A)

University Health Services, University of California Berkeley, Berkeley, California, United States of America.

Guy Nicolette (G)

University Health Services, University of California Berkeley, Berkeley, California, United States of America.

Fyodor D Urnov (FD)

Innovative Genomics Institute, University of California Berkeley, Berkeley, California, United States of America.

Michael Lu (M)

Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, United States of America.

Maya Petersen (M)

Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, United States of America.

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