LB21. The Seattle Flu Study: A Community-Based Study of Influenza
Authors: Chu Helen Y , Chu Helen Y , Boeckh Michael , Boeckh Michael , Englund Janet A , Englund Janet A , Famulare Michael , Lutz Barry R , Lutz Barry R , Nickerson Deborah A , Rieder Mark , Rieder Mark , Starita Lea , Starita Lea , Thompson Matthew , Thompson Matthew , Shendure Jay , Bedford Trevor , Adler Amanda , Brandstetter Elisabeth , Brandstetter Elisabeth , Bosua Jeris , Bosua Jeris , Frazar Christian D , Han Peter D , Gulati Reena , Hadfield James , Huang ShiChu , Jackson Michael L , Jackson Michael L , Kiavand Anahita , Kimball Louise E , Kimball Louise E , Lacombe Kirsten , Lacombe Kirsten , Logue Jennifer , Logue Jennifer , Lyon Victoria , Sibley Thomas R , Zigman Suchsland Monica L , Zigman Suchsland Monica L , Wolf Caitlin R , Wolf Caitlin R 
Published: October 2019

Background

Influenza epidemics and pandemics cause significant morbidity and mortality. An effective response to a potential pandemic requires the infrastructure to rapidly detect and contain new and emerging flu strains at a population level. The objective of this study was to use data gathered simultaneously from community and hospital sites to develop a model of how flu enters and spreads in a population.

Methods

In the 2018–2019 season, we enrolled individuals with respiratory illness from community sites throughout the Seattle area, including homeless shelters, childcare facilities, Seattle-Tacoma International Airport, workplaces, college campuses, clinics, and at home (Figure 1). We collected data and nasal swabs from individuals with at least two respiratory symptoms. Additionally, we collected residual nasal swabs and data from individuals who sought care at four regional hospitals. Home-based self-testing for influenza and prediction models for influenza were piloted. Swabs were tested with a multiplex molecular assay, and influenza whole-genome sequencing was performed. Geospatial mapping and computational modeling platforms were developed to characterize regional spread of respiratory pathogens.

Results

A total of 18,847 samples were collected in the 2018–2019 season. Of those tested to date, 291/3,653 (8%) community and 2,393/11,273 (21%) hospital samples have influenza detected. Of the community enrollments, 39% had influenza-like illness. Community enrollees were in age groups not well-represented from hospitals. Influenza A/H3N2 activity peaked on college campuses and homeless shelters 2 weeks before the peak in hospitals. We observed multiple independent introductions of influenza strains into the city and evidence of sustained transmission chains within the city (Figures 2 and 3).

Conclusion

Utilizing the city-wide infrastructure we developed, we observed the introduction of influenza A/H3N2 into the community before the hospital and evidence of transmissions of unique strains into and within the Seattle area. These data provide the blueprint for implementing city-wide, community-based surveillance systems for rapid detection, real-time assessment of transmission patterns, and interruption of spread of seasonal or pandemic strains.

Disclosures

Helen Y. Chu, MD MPH, Merck (Advisor or Review Panel member), Michael Boeckh, MD PhD, Ablynx (Consultant, Grant/Research Support), Ansun Biopharma (Consultant, Grant/Research Support), Bavarian Nordic (Consultant), Gilead (Consultant, Grant/Research Support), GlaxoSmithKline (Consultant), Vir Bio (Consultant, Grant/Research Support), Janet A. Englund, MD, Chimerix (Grant/Research Support), GlaxoSmithKline (Grant/Research Support), MedImmune/Astrazeneca (Grant/Research Support), Meissa Vaccines (Consultant), Merck (Grant/Research Support),Novavax (Grant/Research Support), Sanofi Pastuer (Consultant), Matthew Thompson, MD, Alere Inc. (Research Grant or Support), Roche Molecular Diagnostics (Consultant, Research Grant or Support, Speaker’s Bureau), . Other Authors: No reported disclosures.