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- Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #29
Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #29
- Date
- 2022.02.15
- Researcher
- Satoshi Kurihara
- Organization
- Faculty of Science and Technology, Keio University
/
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Faculty of Science and Technology, Keio University
Overview
Is the slowing trend of the sixth wave due to reaching the peak of infections through herd immunity in the sense that almost everyone is infected?
⇒ Have we reached that state of infections?
Or, have we reached the peak of infections due to changes in people’s movement, the same as up to the fifth wave?
⇒This is more likely.
⇒Therefore, consideration of the possibility of a seventh wave with infections continuing at a high level is required
Simulation settings
Prerequisites
Booster vaccinations delayed by 6 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) Assumption that the vaccine effect is 0.5 times that of Delta → Set so that the infection prevention effect falls to about 0.3 with the third vaccination.
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
This is because a reduction in movement by people can be seen from the Toreta mobile spatial statistics.
Newly Infected People in 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)
Harajuku, 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
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 Prefecture

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

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