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The 2017-2018 Influenza season was marked by the death of 80,000 Americans: the highest flu-related death toll in a decade. Further, the yearly economic toll to the US healthcare system and society is on the order of tens of billions of dollars. It is vital that we gain a better

The 2017-2018 Influenza season was marked by the death of 80,000 Americans: the highest flu-related death toll in a decade. Further, the yearly economic toll to the US healthcare system and society is on the order of tens of billions of dollars. It is vital that we gain a better understanding of the dynamics of influenza transmission in order to prevent its spread. Viral DNA sequences examined using bioinformatics methods offer a rich framework with which to monitor the evolution and spread of influenza for public health surveillance. To better understand the influenza epidemic during the severe 2017-2018 season, we established a passive surveillance system at Arizona State University’s Tempe Campus Health Services beginning in January 2018. From this system, nasopharyngeal samples screening positive for influenza were collected. Using these samples, molecular DNA sequences will be generated using a combined multiplex RT-PCR and NGS approach. Phylogenetic analysis will be used to infer the severity and temporal course of the 2017-2018 influenza outbreak on campus as well as the 2018-2019 flu season. Through this surveillance system, we will gain knowledge of the dynamics of influenza spread in a university setting and will use this information to inform public health strategies.
ContributorsMendoza, Lydia Marie (Author) / Scotch, Matthew (Thesis director) / Hogue, Brenda (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Phylogenetic analyses that were conducted in the past didn't have the ability or functionality to inform and implement useful public health decisions while using clustering. Models can be constructed to conduct any further analyses for the result of meaningful data to be used in the future of public health informatics.

Phylogenetic analyses that were conducted in the past didn't have the ability or functionality to inform and implement useful public health decisions while using clustering. Models can be constructed to conduct any further analyses for the result of meaningful data to be used in the future of public health informatics. A phylogenetic tree is considered one of the best ways for researchers to visualize and analyze the evolutionary history of a certain virus. The focus of this study was to research HIV phylodynamic and phylogenetic methods. This involved identifying the fast growing HIV transmission clusters and rates for certain risk groups in the US. In order to achieve these results an HIV database was required to retrieve real-time data for implementation, alignment software for multiple sequence alignment, Bayesian analysis software for the development and manipulation of models, and graphical tools for visualizing the output from the models created. This study began by conducting a literature review on HIV phylogeographies and phylodynamics. Sequence data was then obtained from a sequence database to be run in a multiple alignment software. The sequence that was obtained was unaligned which is why the alignment was required. Once the alignment was performed, the same file was loaded into a Bayesian analysis software for model creation of a phylogenetic tree. When the model was created, the tree was edited in a tree visualization software for the user to easily interpret. From this study the output of the tree resulted the way it did, due to a distant homology or the mixing of certain parameters. For a further continuation of this study, it would be interesting to use the same aligned sequence and use different model parameter selections for the initial creation of the model to see how the output changes. This is because one small change for the model parameter could greatly affect the output of the phylogenetic tree.
ContributorsNandan, Meghana (Author) / Scotch, Matthew (Thesis director) / Liu, Li (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05