The influenza virus has been responsible for large economic losses and is a great public health challenge. Pandemic strains that emerge from genetic reassortment and recombination may cause a major disaster in the future. During the 20th century, three influenza pandemics killed millions of people during short outbreak periods. Recent human cases of H5N1 avian influenza in Asia have alerted public health workers, policy makers, and the population as a whole to the possibility of the emergence of a new influenza pandemic in the near future. This research project will use computational genetics and geographic approaches to build a public, spatially referenced avian influenza virus (AIV) genotype database and will investigate relationships between human-environment factors and AIV evolution. The investigators' specific objectives are (1) to classify influenza viral genotypes using their genomic sequence data, (2) to construct a public Influenza Genotype-Geographic Database (IGGD), and (3) to analyze the impacts of human-environment ecosystem factors on influenza viral evolution. This project will generate a systematic description of the spatial and temporal patterns of influenza viral genotypes and enhance basic understanding of ecosystem drivers of influenza viral evolution. The investigators will use influenza genome sequencing data from the National Center for Biotechnology Information and the Los Alamos National Laboratory. The analysis in this project will focus on H5N1 AIV. The first phase of the project will involve development of a new influenza genotyping algorithm that will further categorize H5N1 AIVs within a public genotype database. This proposed genotyping algorithm will categorize the genotypes of AIVs by avoiding multiple sequence alignment and phylogeny tree construction. The new genotypes will be determined by the cluster index for each segment of each virus. The deliverables for this phase include an influenza genotyping algorithm and genotype associated with each AIV isolate. The second phase will involve developing the public IGGD to store and manage the influenza viral genotype information and associated human-environment ecosystem factors. The IGGD will be the first spatial-temporal influenza genotype database of its kind. It will include genotype information as well as the ecosystems factors that have been hypothesized to be related to AIV evolution. This database web server will provide an important resource for not only pandemic strain prediction but also future medical geography and molecular epidemiological analysis. The last phase will analyze the impacts of human-environment ecosystem factors on AIV evolution, including climate, bird migration patterns, human population distributions, farming systems, agricultural output, and land-use and land-cover parameters. Relationships between the AIV genotype and the ecosystems variables will be analyzed using both a conventional statistics and by building ecological niche models.
The project will develop tools to study AIV evolution and the ecosystems factors in which it is associated. The ultimate goal of this effort is to enhance basic understanding of human-environment ecosystem drivers of influenza viral evolution. Most medical geographic studies are conducted at the population level, but this study will include scales from regional-level environmental data to molecular-level genetic information. The factors that influence the evolution of influenza are not well understood because previous studies have not jointly looked at both human and environmental ecosystem factors. This study will investigate them simultaneously.
