Simulating evolutionary dynamics of influenza virus
In the gene arrangement of a pathogen, the history offending and defending against the host is engraved. Data assimilation was considered accumulation, and by using pathogen gene arrangement, we developed a mechanistic based mathematical to allow to estimate when the epidemic will happen. The fitness landscape of a pathogen changes by the immune response and so, the herd immunity of the host changes by its pathogen. Thus, by developing an innovative theoretical approach of the conventional population dynamics framework, we can capture the evolutionary dynamics of influenza virus. We would like to unfold a new research field using big data, focusing on the control of infectious diseases, from the basic research field and contribute to the society.
The following clip is an example showing the evolutionary dynamics of influenza virus in the past 100 years, calculated on the strain space. Each lattice point of the stain point are each virus strain, and the distance of each lattice point is the genetic distance and reflecting the immunology. The candidate strain is showing all strain that will appear in each year, and it can be interpreted that evolution is happening running away from strains that have already emerged. This is the effect of host population and its construction of herd immunity. The clip, “epidemic strain_1” and “epidemic strain_2” is an example of the calculation of evolution of influenza virus in different 10 strain spaces. The evolutionary dynamics of epidemic strain seems to follow a loop-erased random walk; in terms of mathematics we think it can be inferred as an approximate of Hamiltonian walk. By looking at the changes in detail of the statistics we defined on the strain space (such as amount of mean square displacement) we aim to find the hiding law of influenza virus mutation, and apply this in estimating future pandemics.