Mathematical model-based quantitative data analysis for COVID-19

The recent spread of corona threatens the health of people around the world. We urgently need strategies to reduce COVID-19 spread and to enhance antiviral drug development for individual patients. Mathematics could contribute to control of COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, and implementation of social distancing measures and other interventions. My group is conducting interdisciplinary research to elucidate "Quantitative Population Dynamics" with original mathematical theory and computational simulation, which are both our CORE approach. Our mathematical model-based approach has quantitatively improved a current gold-standard approach essentially relying on the statistical analysis of "snapshot data" during dynamic interaction processes in virus infection. In my talk, I would like to discuss how our approach improves our current understanding of COVID-19 research, and help an establishment of a "standard antiviral treatment" for COVID-19 as well.