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- Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #19
Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #19
- Date
- 2021.11.16
- Researcher
- Satoshi Kurihara
- Organization
- Faculty of Science and Technology, Keio University
/
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Faculty of Science and Technology, Keio University
Summary
・Both vaccine passports and the antibody cocktail were effective
・Positive administration of antibody cocktails and medication from December was effective
・Positive introduction of policies similar to vaccine passports
・Signs of a brake on increases in flows of people (Japanese morals)
・Toreta data will also start being posted
・Comparison with the same week of 2019 (Monday-Sunday)
・The subject restaurants are 10,000 where Toreta was introduced before January 2019
・Infections spread momentarily with the lifting of the declaration of a state of emergency, but have currently subsided
(The same trend as flows of people)
Simulation settings
・The final vaccination rate for the first and second doses was set to 80%
(Final vaccination rate for each generation: “- 14” 0%, “15 - 39” 80%, “40 - 64” 80%, “65 -” 95%)
・Third vaccination doses started in December at 90% the pace of second doses
・Vaccine passports began in November, restricting the behavior of people who have not had a second dose
With regard to effects, simulations was implemented with three scenarios setting self-restraint to 0%, 20% and 30%
・Antibody cocktails administered from December so that the number of new patients with severe illness is kept to ten people a day
Effect in preventing increased severity of illness set to 80% for patients administered antibody cocktails
・The rates of increased severity of illness are calculated as shown in the table on the right

Tokyo: Number of Newly Infected People

Tokyo: Number of Severely Ill Patients and Number of Antibody Cocktails Required

Tokyo: Severely Ill Patients and Antibody Cocktail Requirements (November 9 Document)

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 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

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