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Abstract: Spatial patterns of influenza A virus spread across compartments in commercial swine farms in the United States

Multiple genetic variants of H1 and H3 influenza A viruses (IAVs) circulate concurrently in US swine farms. Understanding the spatial transmission patterns of IAVs among these farms is crucial for developing effective control strategies and mitigating the emergence of novel IAVs. In this study, we analyzed 1,909 IAV genomic sequences from 785 US swine farms, representing 33 farming systems across 12 states, primarily in the Midwest from 2004 to 2023. Bayesian phylogeographic analyses were performed to identify the dispersal patterns of both H1 and H3 virus genetic lineages and to elucidate their spatial migration patterns within and between different systems. Our results showed that both intra-system and inter-system migrations occurred between the swine farms, with intra-system migrations being more frequent. However, migration rates for H1 and H3 IAVs were similar between intra-system and inter-system migration events. Spatial migration patterns aligned with expected pig movement across different compartments of swine farming systems. Sow-Farms were identified as key sources of viruses, with bi-directional migration observed between these farms and other parts of the system, including Wean-to-Finish and Gilt-Development-Units. High intra-system migration was detected across farms in the same region, while spread to geographically distant intra- and inter- system farms was less frequently. These findings suggest that prioritizing resources towards systems frequently confronting influenza problems and targeting pivotal source farms, such as sow farms, could be an effective strategy for controlling influenza in US commercial swine operations.

Hatuwal B, Goel V, Deliberto TJ, Lowe J, Emch M, Webby RJ, Wan XF. Spatial patterns of influenza A virus spread across compartments in commercial swine farms in the United States. Emerg Microbes Infect. 2024 Sep 2:2400530. doi: 10.1080/22221751.2024.2400530. Epub ahead of print. PMID: 39221652.