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211 Using team science to support outbreak management in a large urban region during the COVID-19 pandemic

Published online by Cambridge University Press:  19 April 2022

Tony Kuo
Affiliation:
Population Health Program, UCLA Clinical and Translational Science Institute; Department of Family Medicine, UCLA David Geffen School of Medicine; Department of Epidemiology, UCLA Fielding School of Public Health
Moira Inkelas
Affiliation:
Population Health Program, UCLA Clinical and Translational Science Institute; Department of Health Policy and Management, UCLA Fielding School of Public Health
Vladimir Manuel
Affiliation:
Population Health Program, UCLA Clinical and Translational Science Institute; Department of Family Medicine, UCLA David Geffen School of Medicine
Roch A. Nianogo
Affiliation:
Population Health Program, UCLA Clinical and Translational Science Institute; Department of Epidemiology, UCLA Fielding School of Public Health; California Center for Population Health Research
Douglas E. Morrison
Affiliation:
Department of Epidemiology, UCLA Fielding School of Public Health
Onyebuchi A. Arah
Affiliation:
Population Health Program, UCLA Clinical and Translational Science Institute; Department of Epidemiology, UCLA Fielding School of Public Health; Department of Statistics, UCLA College of Letters and Science
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Abstract

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OBJECTIVES/GOALS: To describe how the UCLA Clinical and Translational Science Institute (CTSI) assembled and deployed a science team in support of a local jurisdictions effort to manage and control COVID-19 outbreaks in one of the nations largest metropolitan regions, Los Angeles County (LAC). METHODS/STUDY POPULATION: During the COVID-19 pandemic (2020-21), building an efficient data infrastructure to support outbreak management became a priority for the local health department. In response, the UCLA CTSI assembled a science team with expertise across the translational continuum: epidemiology, laboratory and microbiology, machine learning, health policy, medicine and clinical care, and community engagement. The team partnered with a new LAC Data Science Team to foster a collaborative learning environment for scientists and public health personnel, employing improvement and implementation science to help mitigate COVID-19 outbreaks in sectors including healthcare, skilled nursing facilities, and K-12 education. The goal was a public health workforce that is prepared to problem-solve complex, evolving outbreaks. RESULTS/ANTICIPATED RESULTS: The science team created a learning environment with data modeling and visualization, problem-based learning, and active knowledge and skills acquisition. First, control charts and time series methods were used to visualize COVID-19 data and find signals for action. Second, a series of 16 Grand Rounds offered interactive sessions on problem-solving of outbreak challenges in different sectors. Third, a biweekly Public Health Digest provided fieldworkers with the latest scientific studies on COVID-19. All three elements guided and empowered the workforce to implement timelier, efficient outbreak mitigation strategies in the field. The partnered team also identified barriers to adoption of selected new data and management techniques, revealing areas for further skill-building and data-driven leadership. DISCUSSION/SIGNIFICANCE: The UCLA CTSI science team offered a backbone science infrastructure for helping public health and other sector agencies manage COVID-19 outbreaks and mitigation. It showed promise in bringing and translating science into public health practice. It revealed future priorities for CTSI innovation and scientific support of public agencies.

Type
Education
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2022. The Association for Clinical and Translational Science