Deem, Michael W.2009-06-032009-06-032008Zhou, Hao. "Stochastic simulation for viral diseases: Dengue and avian influenza." (2008) Diss., Rice University. <a href="https://hdl.handle.net/1911/22146">https://hdl.handle.net/1911/22146</a>.https://hdl.handle.net/1911/22146Our immune system protects us against viral invasion. Nevertheless, variability helps viruses to escape from immune suppression. Viruses may vary either by existing in multiple subtype forms or by mutating at a non-negligible rate. Effective design of vaccines for viral diseases requires some estimation of the escape mechanisms as well as the variability of the virus. However, prediction of viral escape is non-trivial due to the complexity of the virus system. I propose to use stochastic simulation to model viral diseases at the sequence-level. I focus on dengue virus and avian influenza. Dengue virus, having four closely related serotypes, represents a prime example of where vaccine development has been delayed because of incomplete understanding of the immune response to multiple components. Here I extend the generalized NK model theory to study dengue virus dynamics. I elucidate the mechanism for two puzzling phenomena: original antigenic sin and immunodominance. I also suggest new polytopic vaccination strategies to protect against the four serotypes of dengue virus. Previously generalized NK model was applied to B cell immunity. I extend the model to apply to T cell immunity, which is important in the control of dengue virus. Influenza is a highly mutating virus. Influenza pandemic occurs periodically. The H5N1 bird virus is regarded as one candidate pandemic-causing strain. Various countries around the world have started to create stockpiles of H5N1 avian influenza vaccines. However, since the avian influenza is mutating and multiple virus introduction is a possibility, how many and which strains should be stockpiled? I analyze the influenza database and simulate the avian influenza virus evolution to obtain optimized strains combinations. I also suggest population at risk (PaR) as a metric for infectious disease risk management. For both dengue and influenza, simulation results can not only help us to assess new vaccines and provide strategies for specific diseases, but also provide us with insights to other diseases, such as HIV and cancer.136 p.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.BiostatisticsBiomedical engineeringVirologyBiophysicsThesisTHESIS PHYS. 2008 ZHOU