- HOME
- Reports
- Early detection of spread of infection
- Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #23
Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #23
- Date
- 2021.12.21
- Researcher
- Satoshi Kurihara
- Organization
- Faculty of Science and Technology, Keio University
/
-
Faculty of Science and Technology, Keio University
Summary
・Even if infectivity is about 1.25 times that of Delta, infections will spread explosively without thorough implementation of V/T. If infectivity is set at 2 times that of Delta, there is a very high possibility of a considerable number of severely ill people, even if the degree of increased severity is low.
・If infectivity is about 1.25 times that of Delta, the combined use of antibody cocktails and medication will be essential, although it may be possible to suppress infections if restraints are at the level of the second to fourth declarations of a state of emergency.
・If infectivity is about 2 times that of Delta, a policy close to lockdown will become necessary. (This will depend on the degree of severity of illness)
・Based on Toreta data, movement by people accompanying eating and drinking is being maintained at its present level (tending to decrease slightly).
・The mobile phone statistics are also showing movement has not returned to pre-COVID-19 levels.
(Realistically, the brakes are being applied moderately)
Simulation settings
■Prerequisites
・Booster vaccinations delayed for seven months (speed of vaccinations the same as for second doses)
・The immunological effect of the third dose is 95%, gradually decreasing
・No settings such as restrictions on going out
・Vaccine passport
(1) Case of non-implementation (2) Weak effect (3) Strong effect
*Strong effect: Level of the second to fourth declarations of a state of emergency
■Settings for Omicron
・Infectivity (reproduction number) 1.25 and 1.5 times that of Delta
・Vaccine effect 0.75, and 0.5 times that of Delta
No V/T, Omicron Infectivity 1.25 Times that of Delta: Number of Infected People in Tokyo

V/T, Omicron Infectivity 1.25 Times that of Delta: Number of Infected People in Tokyo

V/T Strong Effect, Omicron Infectivity 1.25 Times that of Delta: Number of Infected People in Tokyo

No V/T, Omicron Infectivity 1.5 Times that of Delta: Number of Infected People in Tokyo

V/T, Omicron Infectivity 1.5 Times that of Delta: Number of Infected People in Tokyo

V/T strong effect, Omicron Infectivity 1.5 Times that of Delta: Number of Infected People in Tokyo

No V/T, Omicron Infectivity 1.25 Times that of Delta: Number of Infected People in Tokyo

V/T, Omicron Infectivity 1.25 Times that of Delta: Number of Infected People in Tokyo

V/T strong effect, Omicron Infectivity 1.25 Times that of Delta: Number of Infected People in Tokyo

No V/T, Omicron Infectivity 1.5 Times that of Delta: Severely Ill People in Tokyo

V/T, Omicron Infectivity 1.5 Times that of Delta: Severely Ill People in Tokyo

V/T strong effect, Omicron Infectivity 1.5 Times that of Delta: Severely Ill 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 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

Twitter Data

Twitter Data

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