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.
Sentinel Surveillance System Implementation and Evaluation for SARS-CoV-2 Genomic Data, Washington, USA, 2020-2021.
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作者:Oltean Hanna N, Allen Krisandra J, Frisbie Lauren, Lunn Stephanie M, Torres Laura Marcela, Manahan Lillian, Painter Ian, Russell Denny, Singh Avi, Peterson JohnAric MoonDance, Grant Kristin, Peter Cara, Cao Rebecca, Garcia Katelynn, Mackellar Drew, Jones Lisa, Halstead Holly, Gray Hannah, Melly Geoff, Nickerson Deborah, Starita Lea, Frazar Chris, Greninger Alexander L, Roychoudhury Pavitra, Mathias Patrick C, Kalnoski Michael H, Ting Chao-Nan, Lykken Marisa, Rice Tana, Gonzalez-Robles Daniel, Bina David, Johnson Kelly, Wiley Carmen L, Magnuson Shaun C, Parsons Christopher M, Chapman Eugene D, Valencia C Alexander, Fortna Ryan R, Wolgamot Gregory, Hughes James P, Baseman Janet G, Bedford Trevor, Lindquist Scott
| 期刊: | Emerging Infectious Diseases | 影响因子: | 6.600 |
| 时间: | 2023 | 起止号: | 2023 Feb;29(2):242-251 |
| doi: | 10.3201/eid2902.221482 | ||
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