② Evolving the module unit of the SEIRS circuit lattice (circuit corresponding to one area)
※Arrows are only transitions between the initial state (N) and elliptical state and serious illness (including death) due to infection
Very concerned about the future with only 2 vaccinations (current situation) (difficult to capture in a simple circuit)
※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)
③ Interim report of MultiLayer-MultiAgent model
・Progress
・Report of the current calculation parameters and calculation results
Model Overview
・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
The number of newly infected people when the vaccination effect is fixed
[[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
The number of infected people when the vaccination effect is fixed
[[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
The number of newly infected people without thinning out by layers
[[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
The number of newly infected people without thinning out
[[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
