## Model (A) Lattice of SEIR Circuits Model Taking Into Account State Transition Due to Vaccinations

## Estimate of the Number of New Infections with Omicron Variant by Lattice of SEIRS Circuits

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

## Estimate of the Number of Patients with Severe Illness with Omicron Variant by Lattice of SEIRS Circuits

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

## Model (B) Multilayer Concept

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.

## Model (B) of MultiLayer-MultiAgent for SEIR Flow

*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)

## Model (B) of MultiLayer-MultiAgent for SEIR: Settings

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

## Model B Results: Number of Newly Infected People(Medium risk of severe illness scenario)

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).

## Model B Results: Number of Newly Infected People(Medium risk of severe illness scenario. Boosters start in January only)

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

## Model B Results: Number of Seriously Ill Patients

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.

## Model B Results: Number of Seriously Ill Patients(Boosters start in January scenario only)

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.

## Appendix: MLN Layer Generation Logic

・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.

## Household

・Overview

・Layers that form cliques by household

・Parameter

・None

・Generation logic

・Combine all identical household_id

## Occupation

・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)

## School

・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)

## Distance

・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)

## Random

・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