Influenza A virus (IAV) is a critical respiratory pathogen in commercial swine in the United States. The broad range of IAV genetic and antigenic diversity in swine has created a challenge to developing effective vaccines, complicating efforts to control the virus. The objective of the studies within this dissertation was to use informatics approaches to monitor and predict biologically important genetic changes in influenza A virus in swine.

This objective was pursued by collating available IAV data collected in a veterinary diagnostic lab setting to facilitate analysis of the genetic and abiotic patterns of the virus. This effort led to the creation of the ISU FLUture database, a web platform that shares summarized case data in an accessible graphical format. This database facilitated the centralization of genetic sequences and case related metadata. Access to this data allowed for the analysis of neuraminidase sequence data to identify patterns of genetic divergence, evaluate its co-evolution with the hemagglutinin gene, associate it with the case related metadata, and emphasize its importance in the control of IAV in swine. Finally, a computational tool was designed using hemagglutination inhibition data and IAV genetic data to make predictions about IAV hemagglutinin antigenic phenotype. The model’s validity was tested on four previously uncharacterized IAV, and was able to identify amino acids of interest within the H3 subtype based on their contribution to the model.

Each aspect of this dissertation focuses on applying computational methods to quantify or predict the genetic or antigenic diversity in swine IAV. Each chapter builds on the next by collating, analyzing, or predicting information about IAV in swine that is derived from routine surveillance. This research shows that leveraging informatics methods can improve the identification of biologically important genetic changes in IAV circulating in the swine population.