 HOME
 Reports
 Simulation of infection spread and control
 Estimated effects of human flow suppression and vaccination certificate Tokyo model
Estimated effects of human flow suppression and vaccination certificate Tokyo model
 Date
 2021.08.31
 Researcher
 Setsuya Kurahashi
 Organization
 Graduate School of Business Sciences, University of Tsukuba
/

Graduate School of Business Sciences, University of Tsukuba
Summary
1.If the human flow in downtown areas at 19:00 can be maintained at the level as of the end of August, the number of positive patients will decrease from September
As a result of statistical estimation of the effective reproduction number by the population staying in downtown areas of Tokyo by time of day and the population coming in from outside of Tokyo, it was shown that the population staying in downtown areas at 19:00 has a strong relationship, and if it can be maintained at the level as of the end of August, the number of positive patients will decrease from September.
2.If the human flow in downtown areas at 19:00 returns to the level as of MayJune, the number of severely ill will increase drastically after December
If the number of people staying in downtown areas at 19:00 increases by 25% to 43%, the number of positive patients could bottom out and the number of severely ill could increase up to 600 next February. This is due to diminishing of vaccine efficacy over time against the Delta variant (infection suppression and immunity effect reduce to 64% after 180 days of the 2nd vaccination).
* Israel Ministry of Health https://www.gov.il/en/departments/news/0507202103
https://www.gov.il/en/departments/news/0607202104
3.Vaccine booster shots are effective in the medium to long term
Vaccine booster shots more than 180 days after inoculation can suppress the number of new positive patients and severely ill, but is limited in the short term against increased human flow.
4.Use of vaccination certificate can be effective in the short term as well
Restrictions on entry to workplaces, restaurants, and event venues based on vaccination certificate are highly effective in the short, medium, and long term, and even a 50% implementation is expected to be effective in suppressing infection.
In the case of human flow is maintained, estimated number of positive patients and severely ill
➡︎If it is possible to maintain the Augustend level of human flow in downtown areas at 19:00, the number of new infections will decrease from September onward.
➡︎The Aug. 29level of people staying in downtown areas after 19:00 is maintained in Sept onward*, population staying the areas after Oct 1 is the same as the prior year level.
➡︎Vaccine efficacy for infection prevention becomes 70% after 180 days, and 50% after 260 days
➡︎The number of severely ill** was estimated by a statistical model from measured numbers based on the Tokyo standard.
* Estimated from the number of people staying in downtown areas of Tokyo (Shinjuku, Ginza, Shibuya, Ueno, Ikebukuro, Roppongi) at 19:00
** Severely ill patients (Tokyo standard): Patients requiring respiratory support and/or using ECMO. (In Tokyo, this standard is effective as of April 27, 2020.)
Red: Number of new positives (0 years old or older)
Green: Number of new positives (039 years old)
Blue: Number of new positives (4059 years old)
Purple: Number of new positives (60 years old and above)
Solid line: Actual number / Wavy line: Estimated number
* Numbers are 7day moving averages
In the case of human flow increases in 25%, estimated number of positive patients and severely ill
➡︎If the level of human flow in downtown areas at 19:00 increases to 1.25x the prior year level, the number of severely ill patients will sharply increase.
➡︎The Aug. 29level of people staying in downtown areas after 19:00 is maintained in Sept onward, population staying the areas after Oct 1 is 1.25x the prior year level.
➡︎Vaccine efficacy for infection prevention becomes 70% after 180 days, and 50% after 260 days
➡︎The number of severely ill** was estimated by a statistical model from measured numbers based on the Tokyo standard.
Graph: Population staying in downtown areas at 19:00 and infection change rate
Red: Number of new positives (0 years old or older)
Green: Number of new positives (039 years old)
Blue: Number of new positives (4059 years old)
Purple: Number of new positives (60 years old and above)
Solid line: Actual number / Wavy line: Estimated number
* Numbers are 7day moving averages
In the case of human flow increases in 43%, estimated number of positive patients and severely ill
➡︎If the level of human flow in downtown areas at 19:00 increases to 1.43x the prior year level, the number of severely ill patients will sharply increase.
➡︎The Aug. 29level of people staying in downtown areas after 19:00 is maintained in Sept onward, population staying the areas after Oct 1 is 1.43x the prior year level. ➡︎Vaccine efficacy for infection prevention becomes 70% after 180 days, and 50% after 260 days ➡︎The number of severely ill** was estimated by a statistical model from measured numbers based on the Tokyo standard.
Red: Number of new positives (0 years old or older)
Green: Number of new positives (039 years old)
Blue: Number of new positives (4059 years old)
Purple: Number of new positives (60 years old and above)
Solid line: Actual number / Wavy line: Estimated number
* Numbers are 7day moving averages
In the case of human flow increases in 25%, estimated effect of vaccine booster shots on suppression
➡︎Booster shots are effective in the medium to long term to prevent infection and severity of symptoms for the elderly.
➡︎The Aug. 29level of people staying in downtown areas after 19:00 is maintained in Sept onward, population staying the areas after Oct 1 is 1.25x the prior year level.
➡︎Vaccine efficacy for infection prevention becomes 85~76 % by vaccine booster shots
➡︎The number of severely ill was estimated by a statistical model from measured numbers based on the Tokyo standard.
Red: Number of new positives (0 years old or older)
Green: Number of new positives (039 years old)
Blue: Number of new positives (4059 years old)
Purple: Number of new positives (60 years old and above)
Solid line: Actual number / Wavy line: Estimated number
* Numbers are 7day moving averages
In the case of human flow increases in 43%, estimated effect of vaccine booster shots on suppression
➡︎Booster shots are effective in the medium to long term to prevent infection and severity of symptoms for the elderly.
➡︎The Aug. 29level of people staying in downtown areas after 19:00 is maintained in Sept onward*, population staying the areas after Oct 1 is 1.43x the prior year level.
➡︎Vaccine efficacy for infection prevention becomes 85~76 % by vaccine booster shots
➡︎The number of severely ill was estimated by a statistical model from measured numbers based on the Tokyo standard.
Red: Number of new positives (0 years old or older)
Green: Number of new positives (039 years old)
Blue: Number of new positives (4059 years old)
Purple: Number of new positives (60 years old and above)
Solid line: Actual number / Wavy line: Estimated number
* Numbers are 7day moving averages
In the case of human flow increases in 43%, estimated effect of vaccination certificate on suppression
➡︎It was suggested that the number of patients who tested positive and the number of patients who were severely ill might decrease by vaccination certification.
➡︎The Aug. 29level of people staying in downtown areas after 19:00 is maintained in Sept onward, population staying the areas after Oct 1 is 1.43x the prior year level.
➡︎50% of workplaces, restaurants, and event venues restrict entry with vaccination certificate
➡︎Vaccine efficacy for infection prevention becomes 70% after 180 days, and 50% after 260 days
➡︎No booster shots
➡︎The number of severely ill was estimated by a statistical model from measured numbers based on the Tokyo standard.
Tokyo Suburban Individualbased Model (Friend Network Estimation)
The effect of vaccination certificate in reducing the number of effective reproductions
is estimated by simulation, and set to SEIR
Red: Number of new positives (0 years old or older)
Green: Number of new positives (039 years old)
Blue: Number of new positives (4059 years old)
Purple: Number of new positives (60 years old and above)
Solid line: Actual number / Wavy line: Estimated number
* Numbers are 7day moving averages
Model Settings
1.Infection model by SEIR mathematical model and AI optimization method
The SEIR model, which takes into account population flow and AI technology (evolutionary optimization + quasiNewton method), were used to optimize infection model estimation within and between three age groups (039 years, 4059 years, and 60 years or older). The positive patient influx from outside the prefecture was estimated from mobile spatial statistics data and LocationMind xPop*1, and incorporated into the model, and the model was trained from the data from March 1 to August 29, 2021.
2.Estimating human flow effect and vaccination effectiveness
By using the estimated number of people staying in downtown areas of Tokyo, the population coming in from outside Tokyo, and the estimated number of infected people coming in from outside Tokyo from 3/1 to 8/29, the change in infection rate is estimated using a nonlinear regression model. Based on the results, the strength of human flow effect was set to SEIR and the effect of increased human flow after 10/1 was estimated.
3.Effects of vaccine and behavior change
・The vaccine effect was 57% for the 1st dose, 94% for the 2nd dose for the Alpha variant to prevent the infection, and 0.9 times for the Delta variant. Measured values are used for changes in the number of effective reproductions and the number of population flows from 3/1 to 8/29. After 8/31 uses the most recent 7day moving average infection change rate. After 10/1 uses the most recent 3day data, last year's infection change rate, and the moving average of human flow.
・Vaccination rate setting
After 3/5 0.05% of the population (1st measured number of medical staff)
After 3/27 0.032%, 0.033% (number of 1st and 2nd medical staff measurements)
After 4/12 0.069%, 0.030% (1st and 2nd actual measurements of medical staff) 0.01% (1st actual measurement of elderly people)
After 5/4 0.064%, 0.078% (1st and 2nd actual measurements of medical staff) 0.065%, 0.006% (1st and 2nd actual measurements of elderly people)
After 6/1 0.064%, 0.078% (1st and 2nd expected medical staff) 0.08%, 0.065% (1st and 2nd expected elderly)
After 6/21 k/2%, k/2% (1st and 2nd expected medical staff) k/2%, k/2% (1st and 2nd expected elderly) k = 1.0%
After 8/15 1.3%
・Diminishing vaccine efficacy
The infection suppression effect was assumed to diminish to 64% (Israel Ministry of Health) after 180 days of the second vaccination. Immunity effect was estimated in each age group by a statistical model (timeseries regression model) from measured numbers of severely ill in Tokyo.
4.Tokyo Suburban Individualbased Model
・Individual base model which recreates households, workplaces, schools, commuter trains, hospitals, shopping malls, restaurants, and event venues in cities near Tokyo.
・The population is 1,350, and households in young, adult, and elderly of oneperson, twoperson, three/fourperson parentchild, and threegeneration are recreated according to the aggregated data of basic resident registration.
・Residents have a network of up to 20 friends and facetoface contact with any of them depending on settings.
*1 "LocationMind xPop" data is data that NTT DOCOMO collectively and statistically processes from mobile phone location information sent with permission from users of applications* provided by NTT DOCOMO. Location information is GPS data (latitude and longitude information) that is measured at a minimum of every five minutes, and does not contain information that identifies an individual. * DOCOMO Map Navi Service (map application, local guide) and some other applications
Model Details
Agespecific Vaccine Effect SEIR Model
Infection Transition Probability by Age
(Propagate from right to left)
Y is 0 to 39 years old, M is 40 to 59 years old, and E is 60 years old or older.
Infectionsa between the ages of 0 and 39 are 82% from the same age group (0 to 39 years old), 18% from 40 to 59 years old, and 0% from 60 years old or older.
The same applies to infections between the ages of 40 and 59 and those aged 60 or older.
Inverse Simulation Model