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- Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #30
Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #30
- Date
- 2022.02.22
- Researcher
- Satoshi Kurihara
- Organization
- Faculty of Science and Technology, Keio University
/
-
慶應義塾大学理工学部
Simulation settings
■Vaccination settings
Booster vaccinations delayed by 6 months
Booster vaccination is possible 6 months after the second vaccination
Therapeutic agents have not been considered
■Settings for Omicron
1.7 times more infectious (effective reproduction number under
similar conditions)
Assumption that the vaccine effect is 0.5 times that of the first and
second doses for Delta
■With the third vaccination, the infection prevention effect is set to
drop to 0.9 for Delta and about 0.5 for Omicron
■ Implementation scenario
From January 25 to February 28 ⇒ Suppression of flows of people at the level
of a declaration of a state of emergency
From March 1 on ⇒ Assumption of the declaration of a state of emergency, quasi state of emergency level and somewhere in between

Relationship between the Degree of Self-Restraint and the Number of Newly Infected People (Tokyo)

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

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 Miyagi Prefecture

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 Ishikawa Prefecture

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 Aichi Prefectur

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 Osaka Prefecture

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 Hiroshima Prefecture

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 Ehime Prefecture

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 Fukuoka Prefecture

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