Infection Simulation based on Proposed Human Behavior Models derived from SNS and Press Data #22

Physical simulations
(Tsunami simulation, fire simulation, etc.)
Understanding, predicting and taking measures against phenomena in the real environment in accordance with the laws of physics.

➡ Long-term forecasts are difficult.

Social simulations (AI simulation/multi-agent simulation)
(Evacuation simulation, infection simulation, marketing simulation, etc.)
Understanding, predicting and taking measures against social phenomena by modeling people’s behavior.

AI that models people is known as an “agent.”
Agents move around in a virtual environment to reproduce the movements of the real world.

➡ People are the unit, which is much more difficult than a physical simulation.

If you have an enormous amount of historical data, it is possible to make predictions based on AI learning.
➡ Long-term forecasts are difficult.

*The quantity of flows of people very likely demonstrated a restraining effect (many vaccinated people moving suppresses movement by unvaccinated people)

The actual situation of the vaccine effect
may be the vaccine effect + leukocyte type?

*The quantity of flows of people very likely demonstrated a restraining effect.
(many vaccinated people moving suppresses movement by unvaccinated people)

In a social structure in which living bases are connected by railways and plane routes, there are two sides to the effects,
one that causes infections to spread all at once and, conversely, the other that suppresses the spread of infections.

As the fifth wave was brought under control, the effect that suppressed the spread of infections was demonstrated due to the vaccination rate reaching about 70% and the modes of action caused by the moral consciousness of Japanese people.

The effect that causes infections to spread has been demonstrated in Europe and America.
➡ Behavior that does not observe the “three Cs”

Which effect will be demonstrated depends on how people move.
The effect that suppresses the spread of infections is demonstrated easily in Japan, where people observe the basic “three Cs” and engage in moderate and moral behavior.

・The main factor in bringing the fifth wave under control was that a temporary herd immunity effect due to vaccination (natural infection) was demonstrated even with a vaccination rate of about 70% due to the characteristics of the movement network.
　➡A time when the vaccine effect was high nationwide due to intensive vaccinations
　➡A movement network with densely populated areas, cities, linked by railways and plane routes
・No other specific factors were observed [no special settings were made either].
・There was no great decrease in flows of people during the fifth wave.
　➡The movement of vaccinated people suppresses the movement of unvaccinated people, leading to an effect that suppresses the spread of infections.
・There is an underlying reason why there is no major spread of infections in Japan in the first place.
　➡Japanese people’s trivial differences in awareness of daily prevention such as the use of masks and gargling are connected to big changes.[The butterfly effect]

* When the vaccination rate is 30% or 50%, the proportion of unvaccinated people who become infected is lower in a small-world network than in a random network.

*In a scale-free network, when there are many unvaccinated people in a hub, conversely, infections spread (middle figure).
If all of the top hub is vaccinated, infections virtually do not spread (right figure).
➡ Movement by moderate people (Dr. Osawa) prevents the spread of infections.

• It was a time when vaccine effects were high due to vaccinations in a short period
• Japanese people’s trivial differences in awareness of prevention such as the use of masks and gargling are connected to big changes.
➡ The butterfly effect

・The butterfly effect (Does a butterfly flapping its wings in Brazil cause a tornado in Texas?)
　In complex systems, small differences can lead to large changes (that cannot be felt at the individual level)

　　The social morals and awareness of prevention of the majority of Japanese people create a big difference [an X factor]

　　　*For each individual, although behavior did not change sufficiently for the fifth wave to be brought under control rapidly, they would have been able to feel it peculiar because it was a big change.

It stems from the question posed by the meteorologist Edward Lorenz “Do very small disturbances like a butterfly flapping its wings affect the weather in distant places?” and, if that is true, his suggestion that accurate long-term predictions are fundamentally difficult unless observational errors can be eliminated, which emerged from research on numerical weather prediction [4] [5]. - wikipedia

Kabukicho, Tokyo (Number of people in the area by time axis)

Shibuya Center Gai, Tokyo (Number of people in the area by time axis)

Ikebukuro, Tokyo (Number of people in the area by time axis)

Harajuku, Tokyo (Number of people in the area by time axis)

Ueno Ameyoko, Tokyo (Number of people in the area by time axis)

South side of Shinagawa Station, Tokyo (Number of people in the area by time axis)

Tokyo Station, Tokyo (Number of people in the area by time axis)

Odaiba/Tokyo Teleport, Tokyo (Number of people in the area by time axis)

Yokohama Station, Kanagawa (Number of people in the area by time axis)

Kawasaki Station, Kanagawa (Number of people in the area by time axis)

Hiyoshi Station, Kanagawa (Number of people in the area by time axis)

Kitashinchi, Osaka (Number of people in the area by time axis)

Kawaramachi, Kyoto (Number of people in the area by time axis)

Nakasu Kawabata, Fukuoka (Number of people in the area by time axis)

Susukino Station, Hokkaido (Number of people in the area by time axis)

National average

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By number of customers

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By time

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By timing of reservation

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By restaurant size

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

By frequency of visits

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

Hokkaido

*Periods of the declaration of a state of emergency or pre-emergency measures in Hokkaido

Tohoku

*Periods of the declaration of a state of emergency or pre-emergency measures in Miyagi Prefecture

Kanto

*Periods of the declaration of a state of emergency or pre-emergency measures in Tokyo

Hokuriku

*Periods of the declaration of a state of emergency or pre-emergency measures in Ishikawa Prefecture

Tokai

*Periods of the declaration of a state of emergency or pre-emergency measures in Aichi Prefecture

Kinki

*Periods of the declaration of a state of emergency or pre-emergency measures in Osaka Prefecture

Chugoku

*Periods of the declaration of a state of emergency or pre-emergency measures in Hiroshima Prefecture

Shikoku

*Periods of the declaration of a state of emergency or pre-emergency measures in Ehime Prefecture

Kyushu/Okinawa

*Periods of the declaration of a state of emergency or pre-emergency measures in Fukuoka Prefecture