It is difficult to "predict" the spread of Covid infections because unknown causes, such as changes in people's lifestyles (how and where they eat, contact with other people, etc.) and virus mutations, keep coming into play. For this reason, assumptions such as a similar temporal change in the effective reproduction number as in previous years were used as necessary to predict the spread of infection, but people's lives may change due to the appearance of mutated variants or major events such as the Olympics. On the other hand, Ohsawa et al. have used simulations to suggest that SwC is intrinsic to the strategy of vaccine tilting and the strategy of phasing out self-restraint. The youth-first, activity-first, and urban-first principles can be placed in that system.
Purpose of Research
Rather than the accuracy of predictions about the spread of infection, we will develop qualitative understanding of the causes and explanatory knowledge about the effects of infection, and methods to acquire this knowledge. For example, last year, we presented action guidelines such as "Stay with Your Community (SwC)" and "Avoid traveling to remote areas (where you don't usually go), but only interact with people you mutually recognize as necessary". But this year, we will develop action guidelines that involve diverse contact among people (by occupation, attribute, region, etc.).
・A survey on contact with people and objects was conducted with guidelines based on SwC principles and an accountable self-restraint plan was developed, developing a wider range of locations and situations than last year
・The social network model developed in the previous year was extended to a hierarchical model in which (a) work relationships, (b) friendships, and (c) other layers are interconnected. (1) Expansion of synthetic population composition data by Kansai University (Murata), (2) Use of questionnaire survey data that closely examines human interaction in each level
・Last year, due to machine constraints, calculations were performed on a network of up to 10,000 agents, but in order to run simulations with a more realistic number of agents, the network was connected to the SEIR lattice, as shown in the conceptual diagram on the right, and we will consider the feasibility of a hybrid approach of micro and macro where interactions between individuals with faces and hands can be analyzed in multi-people and multi-region.
・Hierarchical infection network model. Multi-people and multi-region effect verification by integrating micro and macro
・Qualitative understanding of the causes of the spread of infection and the explanatory knowledge of its impact
・By the above development of policy messages (Example: rather than "Don't go out drinking", show the real reason "SwC deviation is high in bars" and consider bar design to reduce risk)