**Finished by March 31, 2013**

The University of Tokyo, Graduate School of Information Science and Technology, Associate Professor

Data assimilation modeling of the spread of influenza for analysis and prediction

In order to develop prevention and mitigation strategies against an influenza pandemic, analysis and prediction with mathematical modeling are expected to be an effective methodology. However, numerical simulations of mathematical models are not necessarily consistent with reality. In this study, I develop a mathematical foundation, which allows realistic simulation based on data assimilation techniques, for analysis and prediction of the spread of influenza.