Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks.

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.

[1]  A. Soong,et al.  Serial Intervals and Incubation Periods of SARS-CoV-2 Omicron and Delta Variants, Singapore , 2023, Emerging infectious diseases.

[2]  A. Fine,et al.  COVID-19 Case Investigation and Contact Tracing in New York City, June 1, 2020, to October 31, 2021 , 2022, JAMA network open.

[3]  Elizabeth C. Lee,et al.  Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation , 2022, The Lancet Digital Health.

[4]  N. Hens,et al.  Serial Intervals for SARS-CoV-2 Omicron and Delta Variants, Belgium, November 19–December 31, 2021 , 2022, Emerging infectious diseases.

[5]  P. Davidson,et al.  Centralized COVID-19 Contact Tracing in a Home-Rule State , 2022, Public health reports (1974).

[6]  Michael W. Dusenberry,et al.  Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States , 2022, Proceedings of the National Academy of Sciences of the United States of America.

[7]  M. Halloran,et al.  Household Secondary Attack Rates of SARS-CoV-2 by Variant and Vaccination Status , 2022, JAMA network open.

[8]  Ali R. Roghanizad,et al.  A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19 , 2022, npj Digital Medicine.

[9]  A. Mann,et al.  Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge in young adults , 2022, Nature Medicine.

[10]  K. Telle,et al.  Secondary Attack Rates for Omicron and Delta Variants of SARS-CoV-2 in Norwegian Households. , 2022, JAMA.

[11]  M. Plescia,et al.  COVID-19 Case Investigation and Contact Tracing Programs and Practice: Snapshots From the Field , 2022, Journal of public health management and practice : JPHMP.

[12]  Benjamin D. Trump,et al.  The United States COVID-19 Forecast Hub dataset , 2021, Scientific Data.

[13]  D. Spiegelman,et al.  Lessons Learned From COVID-19 Contact Tracing During a Public Health Emergency: A Prospective Implementation Study , 2021, Frontiers in Public Health.

[14]  A. Fleischauer,et al.  COVID-19 Case Investigation and Contact Tracing in the US, 2020 , 2021, JAMA network open.

[15]  J. Salomon,et al.  The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic , 2021, Medical decision making : an international journal of the Society for Medical Decision Making.

[16]  P. Du,et al.  Efficacy of a student-led community contact tracing program partnered with an academic medical center during the coronavirus disease 2019 pandemic , 2020, Annals of Epidemiology.

[17]  Erika Samoff,et al.  COVID-19 Contact Tracing in Two Counties — North Carolina, June–July 2020 , 2020, MMWR. Morbidity and mortality weekly report.

[18]  Tara C Smith,et al.  SARS-CoV-2 Transmission Dynamics Should Inform Policy , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[19]  Ganna Rozhnova,et al.  Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study , 2020, The Lancet Public Health.

[20]  Joseph G. Allen,et al.  Mechanistic transmission modeling of COVID-19 on the Diamond Princess cruise ship demonstrates the importance of aerosol transmission , 2020, Proceedings of the National Academy of Sciences.

[21]  Eric H. Y. Lau,et al.  Temporal dynamics in viral shedding and transmissibility of COVID-19 , 2020, Nature Medicine.

[22]  M. Kretzschmar,et al.  Applications and Recruitment Performance of Web-Based Respondent-Driven Sampling: Scoping Review , 2019, Journal of medical Internet research.

[23]  Douglas D. Heckathorn,et al.  Network Sampling: From Snowball and Multiplicity to Respondent-Driven Sampling , 2017 .

[24]  Lisa G. Johnston,et al.  A Systematic Review of Published Respondent-Driven Sampling Surveys Collecting Behavioral and Biologic Data , 2016, AIDS and Behavior.

[25]  Isaac S Kohane,et al.  Time for a Patient-Driven Health Information Economy? , 2016, The New England journal of medicine.

[26]  L. Johnston,et al.  A simulative comparison of respondent driven sampling with incentivized snowball sampling--the "strudel effect". , 2014, Drug and alcohol dependence.

[27]  Douglas D. Heckathorn,et al.  Comment: Snowball versus Respondent-Driven Sampling , 2011 .

[28]  Matthew J. Salganik,et al.  5. Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling , 2004 .