Message from the new model – 1) Flexible and Strong SWTC; 2) Additional vaccination availability; 3) Choose connections

■① Inspection report on Stay with Your Community measures (hereinafter, SWYC) in Shimoda City
　■Shimoda City is located at the southernmost tip of the Izu Peninsula and is a tourist destination with a lot of young people coming and going. 　■SWYC is considered to be an initiative to give out cards on which people can record how many people they have met who they do not know and call attention to not exceeding the number of people who they know, and it is a model that can be shared as a best practice. 　■This initiative, which began involving the entire city on July 22, has been effective in reducing clusters and the number of infected people per day.

■② Improvement of the model and verification of the effect of 3rd vaccination
　■Those who have been immunized by vaccination and those who have been immunized by natural infection have different aspects of subsequent state transitions.
　■In addition, the serious illness rate at the time of subsequent infection differs between those who have been vaccinated and those who have not been vaccinated.
　■Taking this into consideration, we improved the model based on the difference in the state transition between the infectious state and the immunized state depending on whether being vaccinated or not.
　■By improving this model, it will be possible to make a detailed analysis of the effect of 3rd vaccination, which may be an issue in the future.
　■This document reports the analysis results (flash version), and it is thought that it is necessary to enable a 3rd booster vaccination when immunity is reduced in order to resolve the infection, because the infection does not converge with only two sets of vaccination (especially it does not lead to a suppression of the number of seriously ill patients). Therefore, it is considered that there is an urgent need to build this system.

■③ Interim report of Multilayer-MultiAgent model
　■This model, which is currently under construction, is an analysis that is in line with the contexts of people's lives (family, workplace, school, etc.) and is positioned as a model that contributes to the examination of various measures. 　■In this document, as a flash version, we report on the results of confirming the effect of reducing the contact frequency for each context. 　■Although reducing contact frequency at school is not very effective, it is understood that reducing the frequency of contact in cases such as going to work, traveling, and socializing with neighbors is highly effective in reducing the number of infected people. (※The effect of reducing the frequency of contact in relation to school will be examined in the future)

※Arrows are only transitions between the initial state (N) and elliptical state and serious illness (including death) due to infection

※Corrected the previous time by referring to trial calculation of vaccination pace (made by Office for Novel Coronavirus Disease Control, Cabinet Secretariat) (not used as it is)
※The issue is the qualitative scenario of if… then… rather than the real value
※Since the previous simulation did not distinguish between S and Su, it was assumed that one person could be vaccinated as many times as there was a quantity. Therefore, the outline is close to the next page
What if the vaccine can be given a third time?
···(next page)

・Progress
・Report of the current calculation parameters and calculation results

・Data
　✔️Using artificial synthetic data of Dr. Murata of Kansai University (information such as household, age, gender, occupation, latitude, and longitude can be used)

・Infection transmission model
　✔️A general multi-agent based SEIRS model was adopted

・The model allows for adjustment (thinning out) of the coupling relationship of the edges of each layer
　✔️Can be thinned out, not all edges connected
　✔️Thinning rate can be adjusted on a layer-by-layer basis
　✔️This is done taking into consideration state of emergency, school closures, guidance to companies on telework rate targets, etc.

・Vaccination
　✔️Probabilistically determine to vaccinate or not at each node
　✔️It is possible to set a rule to reduce the transition probability to E of a node in the state of S by ▲X% by inoculation
　✔️Assuming to have expansion such as vaccination with a certain priority based on information on age and latitude / longitude from Dr. Murata

[[premise of calculation]]
・ 5,000 households (about 10,000 agents), Random seed 1
・ [W-m0 relationship] family: fully combine, workplace: W=3, m0=1, school: W=4, m0=2, neighborhood: W=4, m0=2, travel events: randomly with two people
・ [Vaccination effect] 1st dose: ▲30%, 2nd dose: ▲95%, 2nd dose probability: 20%, vaccination pace 1% daily

[[premise of calculation]]
・ 5,000 households (about 10,000 agents), Random seed 1
・ [W-m0 relationship] family: fully combine, workplace: W=3, m0=1, school: W=4, m0=2, neighborhood: W=4, m0=2, travel events: randomly with two people
・ [Vaccination effect] 1st dose: ▲30%, 2nd dose: ▲95%, 2nd dose probability: 20%, vaccination pace 1% daily

[[premise of calculation]]
・ 5,000 households (about 10,000 agents), Random seed 1
・ [W-m0 relationship] family: fully combine, workplace: W=3, m0=1, school: W=4, m0=2, neighborhood: W=4, m0=2, travel events: randomly with two people
・ [Vaccination effect] 1st dose: ▲30%, 2nd dose: ▲95%, 2nd dose probability: 20%, vaccination pace 1% daily

[[premise of calculation]]
・ 5,000 households (about 10,000 agents), Random seed 1
・ [W-m0 relationship] family: fully combine, workplace: W=3, m0=1, school: W=4, m0=2, neighborhood: W=4, m0=2, travel events: randomly with two people
・ [Vaccination effect] 1st dose: ▲30%, 2nd dose: ▲95%, 2nd dose probability: 20%, vaccination pace 1% daily