학술논문
Sentinel Surveillance System Implementation and Evaluation for SARS-CoV-2 Genomic Data, Washington, USA, 2020–2021
Document Type
article
Author
Hanna N. Oltean; Krisandra J. Allen; Lauren Frisbie; Stephanie M. Lunn; Laura Marcela Torres; Lillian Manahan; Ian Painter; Denny Russell; Avi Singh; JohnAric MoonDance Peterson; Kristin Grant; Cara Peter; Rebecca Cao; Katelynn Garcia; Drew Mackellar; Lisa Jones; Holly Halstead; Hannah Gray; Geoff Melly; Deborah Nickerson; Lea Starita; Chris Frazar; Alexander L. Greninger; Pavitra Roychoudhury; Patrick C. Mathias; Michael H. Kalnoski; Chao-Nan Ting; Marisa Lykken; Tana Rice; Daniel Gonzalez-Robles; David Bina; Kelly Johnson; Carmen L. Wiley; Shaun C. Magnuson; Christopher M. Parsons; Eugene D. Chapman; C. Alexander Valencia; Ryan R. Fortna; Gregory Wolgamot; James P. Hughes; Janet G. Baseman; Trevor Bedford; Scott Lindquist
Source
Emerging Infectious Diseases, Vol 29, Iss 2, Pp 242-251 (2023)
Subject
Language
English
ISSN
1080-6040
1080-6059
1080-6059
Abstract
Genomic data provides useful information for public health practice, particularly when combined with epidemiologic data. However, sampling bias is a concern because inferences from nonrandom data can be misleading. In March 2021, the Washington State Department of Health, USA, partnered with submitting and sequencing laboratories to establish sentinel surveillance for SARS-CoV-2 genomic data. We analyzed available genomic and epidemiologic data during presentinel and sentinel periods to assess representativeness and timeliness of availability. Genomic data during the presentinel period was largely unrepresentative of all COVID-19 cases. Data available during the sentinel period improved representativeness for age, death from COVID-19, outbreak association, long-term care facility–affiliated status, and geographic coverage; timeliness of data availability and captured viral diversity also improved. Hospitalized cases were underrepresented, indicating a need to increase inpatient sampling. Our analysis emphasizes the need to understand and quantify sampling bias in phylogenetic studies and continue evaluation and improvement of public health surveillance systems.