Summary
The rate of infection is too high
➡ Infections will spread considerably until current suppression policies produce effects
➡ Conversely, excessive suppression policies have a negative impact on the economy
The rate of severe illness needs to be focused on
➡ At any rate, accelerate booster vaccinations
Simulation settings
■Prerequisites
・Booster vaccinations delayed by 6 months
・Booster vaccination is possible 4 months after the second vaccination
・The infection prevention effect of the third vaccination is 95% that for previous variants and gradually decreases
・Therapeutic agents have not been considered
■Omicron variant
・1.7 times more infectious (effective reproduction number under similar conditions)
・Vaccine effect is 0.5 times that of Delta
■Implementation scenario
・Quasi state of emergency Not implemented / implemented January 24
・Speed of 3rd vaccination Same as 2nd vaccination / twice second dose
Booster Vaccination Speed _ Tokyo
• If the speed of booster vaccinations is increased, the vaccination rate can be expected to increase early, centered on young people.
• Attention needs to be paid to the point that there is a time lag from the time of vaccination to the expression of its effect.
Number of Newly Infected People _ Tokyo
*Speed of booster vaccinations is the same as for second vaccinations
Mobile Spatial Statistics (Population Change)
Kabukicho, Tokyo (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Shibuya Center Street, Tokyo (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Ikebukuro, Tokyo (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Harajuku, Tokyo (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Uneno Ameyoko, Tokyo (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
South side of Shinagawa Station, Tokyo (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Tokyo Station, Tokyo (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Odaiba/Tokyo Teleport, Tokyo (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Yokohama Station, Kanagawa (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Kawasaki Station, Kanagawa (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Hiyoshi Station, Kanagawa (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Kitashinchi, Osaka (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Kawaramachi, Kyoto (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Nakasu Kawabata, Fukuoka (Number of people in the area by time axis)
Mobile Spatial Statistics (Population Change)
Susukino Station, Hokkaido (Number of people in the area by time axis)
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
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.
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
National Average
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
By number of customers
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
By time
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
By timing of reservation
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
By restaurant size
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
By frequency of visits
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
Hokkaido
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
Tohoku
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
Kanto
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
Hokuriku
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
Tokai
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
Kinki
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
Chugoku
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
Shikoku
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
Data on the Number of Customers at Restaurants Using the Toreta, Inc. App
Kyushu/Okinawa
*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo
