- HOME
- Reports
- Early detection of spread of infection
- Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #27
Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #27
- Date
- 2022.02.01
- Researcher
- Satoshi Kurihara
- Organization
- Faculty of Science and Technology, Keio University
/
-
慶應義塾大学理工学部
Simulation settings
Vaccinations
Booster vaccinations delayed by 7 months
Booster vaccination is possible 6 months after the second vaccination
The infection prevention effect of the third vaccination is 95% that for previous variants and gradually decreases
Speed of 3rd vaccination Same as 2nd vaccination
No consideration of therapeutic agents
Omicron variant
Set to 1.7 times more infectious (effective reproduction number under similar conditions)
Vaccine effect set to 0.5 times that of Delta
Simulation implementation scenario
Quasi state of emergency
Number of newly infected people in Tokyo Start of implementation on January 21, 2022
Number of newly infected people nationwide Adapted to each prefecture based on data as of January 28, 2022
Results of Simulation of the Cumulative Number of Infected People in Tokyo
*It is assumed that there are five to ten times more infected people than those detected
*If a herd immunity effect is reached at 5 to 6 million people, it is possible that the peak will be reached in mid to late February

Results of Simulation of the Number of Newly Infected People Nationwide

Considerations
■If the peak is reached in places such as South Africa and Europe due to the acquisition of herd immunity, the actual number of infected people in Japan will be far more than the number reported.
■Bringing infections under control through booster vaccination effects is also possible.
■It is assumed that there are five to ten times more infected people than those detected.
■If a herd immunity effect is reached at 5 to 6 million people, it is possible that the peak will be reached in mid to late February.
----
■It is understood from the mobile statistics that the effects of the quasi state of emergency are being seen prominently.
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

Related reports
Report by Satoshi Kurihara
-
Understanding and prediction of infection status based on the basic model of social atmosphere, people, and movement #16
-
Understanding and prediction of infection status based on the basic model of social atmosphere, people, and movement #15
-
Understanding and prediction of infection status based on the basic model of social atmosphere, people, and movement #14