Booster vaccination is mandatory if Omicron variant is at or above risk of severe disease – liberalization of doctors’ judgment is the deciding factor

Lattice of SEIR circuits model
Ohsawa, Y., Hayashi, Y., Kondo, S., Effects of Interregional Travels and Vaccination in Infection Spreads Simulated by Lattice of SEIRS Circuits (arxiv.org)
https://arxiv.org/abs/2104.09719
• Figure on p.3 of the following page
• Reflecting travel between prefectures

State transition due to inoculation
The effects of emergency declarations, booster vaccinations and vaccine passports | COVID-19 AI & Simulation Project (covid19-ai.jp)
https://www.covid19-ai.jp/ja-jp/presentation/2021_rq3_countermeasures_simulation/articles/article143/
・p.4

(Infection rate: doubled for δ variant; vaccine effect: reduced to 30%)

(Infection rate: doubled for δ variant; vaccine effect: reduced to 30%)

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.

*1. v1_det = min(v1, v1 – weeks elapsed since first vaccination* v_det_rate / v_det_term)
*2. v2_det = min(v2, v2 - weeks elapsed since second vaccination * v_det_rate / v_det_term)
*3. v3_det = min(v3, v3- weeks elapsed since third vaccination * v_det_rate / v_det_term)

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

※Betweeness centralityベース

Even if infectivity is 1.5 times higher than the Delta variant, if the number of new infections is the KPI for each booster/no booster, V/T implementation scenario, the result is that the number of new infections increases on a large scale if the start of booster vaccinations is in February. However, because of the range m0https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242766).

If the January booster scenario is expanded, it is expected that it will be possible to suppress new infections considerably by V/T implementation.

Assuming that the Omicron variant has a high risk of severe illness (similar risk to the Delta variant), there is a danger that up to about 10,000 hospital beds will need to be secured if there are no booster vaccinations. If the risk of severe illness is low, it will be possible to suppress infections by V/T without boosters.

If only the boosters start in January scenario is expanded, it will be possible to almost suppress the expansion of the number of severely ill patients (= pressure on medical system) if V/T is also implemented.

・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