Abstract

Seasonal influenza is responsible for a high disease burden worldwide, which has received great attention from researchers around the global. Progress has been made in the statistical analysis based on evolution of sequence and simulation of dynamic models, providing help for the effective daily prevention and control of seasonal influenza. However, it is still difficult to make quantitative or long-term predictions for influenza infection intensity, due to the limitations of the existing methods. Quantitative long-term prediction of the intensity of influenza infection is of crucial importance. Combining the evolutionary information for long-term predictions, and dynamic models for quantitative analysis of the intensity of infection, we can quantitatively predict the long-term influenza infection intensity. This model can correctly predict the seasonal influenza epidemic in the United States in recent years. Its theoretical framework also provides new ideas for forecasting and early warning of infectious diseases based on data and models.

Presenter Profile

Du Xiangjun, Professor (doctoral supervisor) of the School of Public Health, Sun Yat-Sen University (Shenzhen).He got his master degree at Huazhong University of Science and Technology, and Ph.D. degree at Institute of Biophysics, Chinese Academy of Sciences. Hereafter, he did his research work at National Institutes of Health · National Center for Biotechnology Information (NCBI), University of Michigan and University of Chicago in the Unite States on Computational Systems Biology. He has extensive experience in applying cross-disciplinary methods to study the public health-related infectious diseases. Representative achievements include rapid and accurate antigen monitoring of seasonal in？uenza based on sequence information, vaccine strain recommendations, dynamic simulation, and prediction of intensity. His laboratory focuses on the method of computational systems biology, comprehensive data analysis and theoretical modeling to quantitatively study how a variety of related factors are related to the occurrence, evolution, transmission, and pathogenesis of the infectious diseases, to reveal the biology mechanism behind, and to apply them for disease control.