Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks.
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2023-11
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Abstract
Objective
Scalable strategies to reduce the time burden and increase contact tracing efficiency are crucial during early waves and peaks of infectious transmission.Design
We enrolled a cohort of SARS-CoV-2-positive seed cases into a peer recruitment study testing social network methodology and a novel electronic platform to increase contact tracing efficiency.Setting
Index cases were recruited from an academic medical center and requested to recruit their local social contacts for enrollment and SARS-CoV-2 testing.Participants
A total of 509 adult participants enrolled over 19 months (384 seed cases and 125 social peers).Intervention
Participants completed a survey and were then eligible to recruit their social contacts with unique "coupons" for enrollment. Peer participants were eligible for SARS-CoV-2 and respiratory pathogen screening.Main outcome measures
The main outcome measures were the percentage of tests administered through the study that identified new SARS-CoV-2 cases, the feasibility of deploying the platform and the peer recruitment strategy, the perceived acceptability of the platform and the peer recruitment strategy, and the scalability of both during pandemic peaks.Results
After development and deployment, few human resources were needed to maintain the platform and enroll participants, regardless of peaks. Platform acceptability was high. Percent positivity tracked with other testing programs in the area.Conclusions
An electronic platform may be a suitable tool to augment public health contact tracing activities by allowing participants to select an online platform for contact tracing rather than sitting for an interview.Type
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Publication Info
Pasquale, Dana K, Whitney Welsh, Andrew Olson, Mark Yacoub, James Moody, Brisa A Barajas Gomez, Keisha L Bentley-Edwards, Jonathan McCall, et al. (2023). Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks. Journal of public health management and practice : JPHMP, 29(6). pp. 863–873. 10.1097/phh.0000000000001780 Retrieved from https://hdl.handle.net/10161/33631.
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Scholars@Duke
Dana Kristine Pasquale
Dr. Dana K Pasquale earned a PhD in infectious disease epidemiology from UNC-Chapel Hill, earned an MPH in health behavior from East Carolina University, and completed three years as a postdoc in the Duke University Department of Sociology. She combines social network and pathogen genetic data to study infectious disease transmission networks. The majority of Dana’s work is domestic, examining HIV and syphilis transmission in North Carolina. She also uses clonal bacterial data, pathogen genetic data, and location information to study hospital-acquired multi-drug resistant infections. Dana is the PI of Duke RDS^2: Respondent-Driven Sampling for Respiratory Disease Surveillance, a CDC-funded snowball sampling study to locate active, undiagnosed SARS-CoV-2 cases in Durham County. She is also externally funded by NIH and NSF as a co-Investigator.
James Moody
James Moody is the Robert O. Keohane professor of sociology at Duke University. He has published extensively in the field of social networks, methods, and social theory. His work has focused theoretically on the network foundations of social cohesion and diffusion, with a particular emphasis on building tools and methods for understanding dynamic social networks. He has used network models to help understand school racial segregation, adolescent health, disease spread, economic development, and the development of scientific disciplines. Moody's work is funded by the National Science Foundation, the National Institutes of Health and the Robert Wood Johnson Foundation and has appeared in top social science, health and medical journals. He is winner of INSNA's (International Network for Social Network Analysis) Freeman Award for scholarly contributions to network analysis, founding director of the Duke Network Analysis Center and editor of the on-line Journal of Social Structure.
Keisha L. Bentley-Edwards
Dr. Keisha Bentley-Edwards is the Associate Director of Research for the Samuel DuBois Cook Center on Social Equity and an Associate Professor at Duke University’s School of Medicine, Division of General Internal Medicine. She is the Co-Director of Duke’s CTSI Center for Equity in Research. Dr. Bentley-Edwards’ research focuses on how racism, gender, and culture influence health and education outcomes throughout the lifespan, especially for African Americans. Her research emphasizes cultural strengths and eliminating structural barriers to support healthy development in communities, families, and students. Dr. Bentley-Edwards nurtures complex conversations around race and racism in ways that not only identify disparities but prompt meaningful strategies for remedying these disparities around infant and maternal health, cardiovascular disease, and kidney disease, as well as educational disparities.
Jessilyn Dunn
Developing new AI and sensing tools and infrastructure for multi-modal biomedical data integration to drive precision/personalized methods for early detection, intervention, and prevention of disease.
Erich Senin Huang
Former Chief Data Officer for Quality, Duke Health
Former Director of Duke Forge
Former Director of Duke Crucible
Former Assistant Dean for Biomedical Informatics
Dr. Huang is currently Associate Chief Clinical Officer for Informatics & Technology at Verily (Google's life sciences subsidiary), and is now adjunct faculty at Duke. Dr. Huang’s research interests span applied machine learning, research provenance and data infrastructure. Projects include building data provenance tools funded by the NIH’s Big Data to Knowledge program, regulatory science funded by the Burroughs Wellcome Foundation. Applied machine learning applications include “Deep Care Management” a highly interdisciplinary project with Duke Connected Care, Duke’s Accountable Care Organization, that integrates claims and EHR data for predicting unplanned admissions and risk stratifying patients for case management; CALYPSO, a collaboration with the Department of Surgery for utilizing machine learning to predict surgical complications. My team is also building the data platform for the Department of Surgery's "1000 Patients Project" an intensive biospecimen and biomarker study based around patients undergoing the controlled injury of surgery.
As Director of Duke Forge, Dr. Huang built a data science culture and infrastructure across Duke University that focused on actionable health data science. The Forge emphasized scientific rigor, awareness that technology does not supersede clinicians’ responsibilities and human relationship with their patients, and the role of data science in society.
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