Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #21

■If virus infectivity strengthens, the number of new infections will increase dramatically.
■There are also concerns of an increase in the number of people infected due to a decline in vaccine effects.
■This can be countered by third vaccination doses, vaccine passports and medication.
■However, it all depends on the data on whether or not illness will increase in severity
■People should not overreact.

■At worst, a circuit breaker will have to be activated.

■Prerequisites
　・Booster vaccinations delayed for seven months (speed of vaccinations the same as for second doses)
　・The immunological effect of the third dose is 95%, gradually decreasing
　・Therapeutic agents have not been considered
　・No settings such as restrictions on going out
　・No settings of vaccine passports

■Omicron
　・Infectivity (reproduction number) 1, 1.25 and 1.5 times that of Delta
　・Vaccine effect 1, 0.75, and 0.5 times that of Delta

Kabukicho, Tokyo (Number of people in the area by time axis)

Shibuya Center Gai, Tokyo (Number of people in the area by time axis)

Ikebukuro, Tokyo (Number of people in the area by time axis)

Harajuku, Tokyo (Number of people in the area by time axis)

Ueno Ameyoko, Tokyo (Number of people in the area by time axis)

South side of Shinagawa Station, Tokyo (Number of people in the area by time axis)

Tokyo Station, Tokyo (Number of people in the area by time axis)

Odaiba/Tokyo Teleport, Tokyo (Number of people in the area by time axis)

Yokohama Station, Kanagawa (Number of people in the area by time axis)

Kawasaki Station, Kanagawa (Number of people in the area by time axis)

Hiyoshi Station, Kanagawa (Number of people in the area by time axis)

Kitashinchi, Osaka (Number of people in the area by time axis)

Kawaramachi, Kyoto (Number of people in the area by time axis)

Nakasu Kawabata, Fukuoka (Number of people in the area by time axis)

Susukino Station, Hokkaido (Number of people in the area by time axis)

Aggregation Definition

•Comparison with the same week (Monday-Sunday) of 2019
•The subject restaurants are 10,000 where Toreta was introduced before January 2019
•Because there was a typhoon on the weekend of Week 41 (second week of October) in 2019 in the Kanto region, the results may be a little high.

National average

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By number of customers

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By time

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By timing of reservation

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By restaurant size

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By frequency of visits

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

Hokkaido

*Periods of the declaration of a state of emergency or pre-emergency measures in Hokkaido

Tohoku

*Periods of the declaration of a state of emergency or pre-emergency measures in Miyagi Prefecture

Kanto

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

Hokuriku

*Periods of the declaration of a state of emergency or pre-emergency measures in Ishikawa Prefecture

Tokai

*Periods of the declaration of a state of emergency or pre-emergency measures in Aichi Prefecture

Kinki

*Periods of the declaration of a state of emergency or pre-emergency measures in Osaka Prefecture

Chugoku

*Periods of the declaration of a state of emergency or pre-emergency measures in Hiroshima Prefecture

Shikoku

*Periods of the declaration of a state of emergency or pre-emergency measures in Ehime Prefecture

Kyushu/Okinawa

*Periods of the declaration of a state of emergency or pre-emergency measures in Fukuoka Prefecture