Virginia Tech has been a world leader in electronic theses and dissertation initiatives for more than 20 years. On January 1, 1997, Virginia Tech was the first university to require electronic submission of theses and dissertations (ETDs). Ever since then, Virginia Tech graduate students have been able to prepare, submit, review, and publish their theses and dissertations online and to append digital media such as images, data, audio, and video.
University Libraries staff are currently digitizing thousands of pre-1997 theses and dissertations and loading them into VTechWorks. Most of these theses and dissertations are fully available to the public, but we will, in general, honor requests by the item's author to restrict access to Virginia Tech only. See our process for Requesting that Material be Amended or Removed.
Materials that are restricted to Virginia Tech only may be requested via your own university or public library's Interlibrary Loan program or through the VTechWorks request form that appears when you try to access the item. You might also be able to obtain a copy of the work through ProQuest's database of theses and dissertations. If you are on a Virginia Tech campus but are unable to find the pre-1997 thesis or dissertation you are seeking in VTechWorks, you may also be able to order a physical copy from library storage. Please check the library catalog at http://www.lib.vt.edu/ for physical copies.
Influenza is a seasonal viral disease affecting over 1 billion people annually around the globe, as reported by the World Health Organization (WHO). The influenza virus has been around for decades causing multiple pandemics and encouraging researchers to perform extensive analysis of its evolutionary patterns. Current research uses phylogenetic trees as the basis to guide population genetics and other phenotypic characteristics when describing the evolution of the influenza genome. Phylogenetic trees are one form of representing the evolutionary trends of sequenced genomes, but that do not capture the multidimensional complexity of mutational pathways. We suggest representing antigenic drifts within influenza A/H1N1 hemagglutinin (HA) protein as a graph, $G = (V, E)$, where $V$ is the set of vertices representing each possible sequence and $E$ is the set of edges representing single amino acid substitutions. Each transition is characterized by a Malthusian fitness model incorporating the genetic adaptation, vaccine similarity, and historical epidemiological response using mortality as the metric where available. Applying reinforcement learning with the vertices as states, edges as actions, and fitness as the reward, we learn the high likelihood mutational pathways and optimal policy, without exploring the entire space of the graph, $G$. Our average predicted versus actual sequence distance of $3.6 \pm 1.2$ amino acids indicates that our novel approach of using naive Q-learning can assist with influenza strain predictions, thus improving vaccine selection for future disease seasons.