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- Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #26
Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #26
- Date
- 2022.01.25
- Researcher
- Satoshi Kurihara
- Organization
- Faculty of Science and Technology, Keio University
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Faculty of Science and Technology, Keio University
Simulation Conditions
■Prerequisites
・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
・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
・Level of severe illness is 1/10 of that of Delta.
■Implementation scenario
・Quasi state of emergency Implemented on January 21
・Speed of 3rd vaccination Same as 2nd vaccination
Number of Newly Infected People _ Tokyo

*The error bar is not shown due to the focus on ease of viewing
Number of Severely Ill Patients _ Tokyo

*The error bar is not shown due to the focus on ease of viewing
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.
■If infections are to be brought under control with the herd immunity effect, doing so in Japan will take time (a time lag before booster vaccinations).
*It is also possible in Japan that the spread of infections is quick because boosters have been delayed.
■Is it possible to give booster vaccinations sooner?
■It is understood from the mobile statistics that the effects of the quasi state of emergency have begun to appear.
Mobile Spatial Statistics (Population Change)
Kabukicho, Tokyo (Number of people in the area by time axis)

Mobile Spatial Statistics (Population Change)
Shibuya Center Gai, 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)
Ueno 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

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