No need to be afraid of infection numbers – Towards post-corona era (low severity scenario)

Assumed layer characteristics
・Households: A network that is fully integrated within a household and completely isolated from other households.
・Occupations: A network of people in the same occupation who adhere to a W-m0 relationship and are connected.
・Schools: Children in the same area and grade level, while adhering to the W-m0 relationship. Network linking them
・Neighbors: A network of agents who are close to each other and adhere to the W-m0 relationship.
・Random: A network that is connected to n people randomly without any specific rules.

Reasons for separating layers
・The loosely and tightly coupled relationships of the network and the concentration and frequency of contact will differ depending on the context of how people are connected.
・It can be installed and removed in different contexts, and can be thinned and amplified, making it easy to use as a material for implementing emergency declarations and examining economic stimulus policies.
・Multi-layered formats are very useful for modeling such events.

・Data: Artificial synthesis data from Professor Murata of Kansai University
・Infection transmission model:
　✔️Adopted the SEIRS model after considering multi-agent based vaccine effectiveness.
　✔️Considered the transition of status I from mild to serious illness, and then from serious illness to death
　✔️Calculated when each layer is thinned in half
・Vaccination: Vaccination reduces the transition probability of S/V1/V2/V3 nodes to E by -X%. Vaccine effectiveness declines gradually

If contact is equivalent to 2019, the assumed scenario is that the number of infections will reach 1 million to nearly 1.5 million per week by the middle of February, and then peak out. Low level stability is also expected if contact is restored to the level at the time of the October 2020 survey to prevent the spread of infections.

If the rate of increased severity of illness with the Omicron variant is similar to that of the Delta strain, there is a danger that close to 20,000 people will be severely ill by the end of February if they have contact at the 2019 level. If contact is kept to the level of October 2020 by measures to prevent the spread of infections, it is expected infections will be kept to a low level.

If we assume that the rate of increased severity of the Omicron variant is about 1/10 that of the Delta variant, even if contact returns to the level of 2019, serious severely ill cases are not expected if measures such as V/T are taken.

・Household
・Occupation
・School
・Distance
・Random

* Data sampling of data is done by randomly sampling a specified number of households, so the number of households can be specified, but the number of nodes cannot be specified.

・Overview
　・Layers that form cliques by household
・Parameter
　・None
・Generation logic
　・Combine all identical household_id

・Overview
　・Layer that forms a constrained network based on industry_W and industry_m_0
・Parameter
　・industry_W: The maximum number of connections to others (industry_W - industry_m_0 is the maximum number of connections that can be accepted from others)
　・industry_m_0: The number of hands extended by the individual
・Generation logic (pseudo code)

・Overview
　・Layer where school children form a constrained network based on school_W and school_m_0
・Parameter
　・school_W: Maximum number of connections to other people (school_W - only school_m_0 accepts other people's hands)
　・school_m_0: The number of hands extended by the individual
・Generation logic (pseudo code)

・Overview
　・Layer that forms a constrained network from distances based on distance_W and distance_m_0
・Parameter
　・distance_W: Maximum number of connections to others (distance_W - distance_m_0 to accept others' hands)
　・distance_m_0: The number of hands extended by the individual
・Generation logic (pseudo code)

・Overview
　・Layers created by random graphs
・Parameter
　・p: Probability of edge selection
　・Generation logic
・Decide which edge to adopt with probability p from n(n - 1) / 2 edges and generate a graph