The 1918-20 influenza pandemic was the largest, most lethal epidemic in modern: history. It spread over the entire world in about six months and killed between 20 and 100 million people. Yet, despite such a devastating event, there is little detailed information on how and when the influenza epidemic spread throughout cities and countries in the world, especially the U.S. Because the last influenza pandemic was in 1968, the threat of a future pandemic looms large. It is, therefore, important for public health in the U.S. to not only understand the timing and spatial course of the 1918 pandemic, but to also understand how this great event might have affected the timing and spread of Influenza in non-pandemic seasons. This knowledge could inform targeted prevention strategies for a future pandemic. The proposed research seeks to use weekly mortality data from historical sources and lab surveillance data from the National Respiratory and Enteric Viruses Surveillance System (NREVSS) to study the timing and spread of influenza through the U.S. during pandemic and non pandemic seasons. The first aim of this research is to develop models of the temporal and spatial aspects of influenza In the U.S. for four pandemic and non-pandemic periods: the 1918- 20 pandemic, 1914-1917 Influenza seasons, 1920-1923 influenza seasons, and 1997-2007 influenza seasons. For each influenza season, the week of the first report of influenza mortality or lab confirmation will be plotted for each city and spread vectors will be constructed from this sequence of dates. Models will also be constructed by determining the average time to death or lab confirmation for each city for each season and then constructing linear trend surfaces, which will be plotted on a separate map. The direction of the wave progression will be marked by a vector on each map. The second aim of this study is to compare the time and spatial progression of the 1918 pandemic in the U.S. with those of non-pandemic years. The U.S. will be divided into nine regions to allow for easier analysis and bi-proportionate analysis will be used to compare the spatial and temporal aspects of influenza in pandemic years with those in non-pandemic years. This method will allow the analysis of the combined space-time variability in influenza in the U.S. by standardizing the relative intensity of influenza throughout the years. The completion of these aims and these methods has broad application for disaster preparedness, such as pandemic planning and bioterrorism. Knowledge of the timing and spatial course of influenza in the U.S. could severely limit the impact of a future pandemic and allow public health professionals to more effectively utilize interventions. PUBLIC HEALTH RELEVANCE: I am currently pursuing the Kirschstein-NRSA Diversity Fellowship in order to be better prepared for a long and fruitful career in public health. I wish to become federal epidemiologist so that I can best help the nation's public health and scientific community refine the methods of infectious disease surveillance. I believe the Kirschstein-NRSA Diversity Fellowship will build upon my graduate education and provide me with the training and support I need to accomplish my above-stated goals and, consequently, become a significant future epidemiologist and scientific researcher. I expect that training from the Kirschstein-NRSA Fellowship will increase my practical application of epidemiologic methods and analysis and will allow me to gain new techniques, such as bi-proportionate analysis and spatial modeling. This fellowship will provide me with an opportunity to use surveillance data to develop methods that will not only better my understanding of the transmission process of Influenza In the U.S., but may also better the influenza surveillance system in the U.S. I believe the training from this fellowship will make me a more talented epidemiologist and I am confident that I would be a strong contributing member to the Kirschstein-NRSA graduate fellow and alumnus community as well as the overall public health community.