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Mathematical and statistical models provide powerful means to understand disease dynamics in natural populations (e.g., parasite transmission patterns and the spatial spread of disease). The Summer
Institute in Statistics and Modeling in Infectious Diseases at the
University of Washington, Seattle offered a conceptual understanding of and hands-on experience in using modern analytical tools associated with epidemiological research. I attended the training modules on Markov chain Monte Carlo methods, spatial statistics and molecular phylogenetics. For my talk, I will primarily focus on the phylogenetics module and discuss the application of Bayesian approaches in phylogenetics and evolutionary analyses. The construction of phylogenetic trees is a standard approach in deciphering ancestral relationships from sequence data and Bayesian statistics allows us to explore complex models of sequence evolution.A phylogeny can give us insights about evolutionary relatedness between organisms, speciation events and divergence times. One of my research goals is to understand the phylogeography and evolutionary dynamics of avian malaria parasites in tropical sky island bird communities of the Western Ghats, southern India. I will present examples from our preliminary analysis of host/parasite phylogenies to shed light on the eco-evolutionary dynamics of host-parasite interactions.
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