Data assimilation modeling of the spread of influenza for analysis and prediction
Finished by March 31, 2013
The University of Tokyo, Graduate School of Information Science and Technology, Associate Professor
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.