Meisner, JulianneWyckoff, Elizabeth2026-02-052026-02-052025Wyckoff_washington_0250O_28955.pdfhttps://hdl.handle.net/1773/55105Thesis (Master's)--University of Washington, 2025BackgroundHighly pathogenic avian influenza (HPAI) H5N1 clade 2.3.4.4b is driving the most severe global panzootic on record, affecting wild birds, poultry, and—since 2024—dairy cattle. The detection of influenza A in cattle marked the first such event in ruminants and raised urgent questions about new interspecies transmission pathways and spillover risks to humans. This thesis applies a One Health genomic epidemiology framework to investigate whether viral genetic diversity can serve as a proxy for surveillance sensitivity during the U.S. 2024–2025 outbreak. Methods A cross-sectional analysis was conducted using 3,139 hemagglutinin (HA) and 3,118 neuraminidase (NA) sequences from GISAID, spanning poultry, dairy cattle, and human infections across 42 states. Viral sequences were aligned (MAFFT), quality-assessed (AliStat), and analyzed for pairwise nucleotide differences as a measure of genetic diversity. Case count data from USDA and CDC were merged with genomic diversity metrics at the state-month level, and a one-sided Welch’s t-test for non-inferiority (δ = 0.001) compared diversity across high- (≥3 cases) versus low-reporting (0–2 cases) bins. Results Results showed that low-reporting bins consistently exhibited lower diversity than high-reporting bins for both HA and NA segments, and non-inferiority was not demonstrated. Findings suggest that observed diversity largely tracked with reported case counts and did not reveal systematic underreporting across species. However, variation in host sampling composition—such as cattle-dominant sequencing in California compared to mixed-host representation in smaller states—highlighted how surveillance strategy influences observed diversity. Discussion This study demonstrates both the promise and limitations of using genomic diversity to evaluate surveillance quality for zoonotic influenza. By integrating genomic data across human, animal, and environmental health, the analysis underscores the importance of a One Health approach and offers a framework for applying genomic epidemiology to strengthen outbreak detection and response.application/pdfen-USnoneEpidemiologyTo Be AssignedThesis