2020.12.21

Multiagent model simulation : Prevention of infection in the downtown area

Research and Development by

Setsuya KURAHASHI, University of Tsukuba

Corresponding Research Area

Simulation and designing countermeasures against possible COVID-19 resurgence: predicting spreading of infection, estimating and verifying the effectiveness of countermeasures, and predicting deman


Prevention of infection in a downtown area

Assessing the impact of downtown pubs and nightspot services on the spread of infection in an individual-based simulation (Modelling a city of 10,000 people based on cluster cases to assess the risk of spread of infection)

Impact of accompanying persons in restaurants and pubs in the downtown area

Result of impact by accompanying persons

  • Between companies (A) 1.00x
  • Within companies (B) 0.83x
  • Within the department (C) 0.76x
  • 4 persons within the department(D) 0.74x
  • 4 persons within the department and reduced opening hours(E) 0.70x

Compared to eating and drinking with inter-company people, the number of infected people is reduced by 17% when limited to people within the company and 23% when limited to people within the department.

The number of infected people is reduced by up to 30% when eating and drinking in pubs with shortened hours is restricted to people in the department.

Limiting the scope of accompanying persons has some effect.

Courtesy of University of Tsukuba

→The number of infected people is reduced by up to 30% when eating and drinking in restaurants and pubs with shortened opening hours is restricted to people in the department.

Inflow Risk of Naha City in Okinawa

Evaluating the impact of the inflow risk on the spread of infection by using individual-based simulation  (A model of 4,000 people for Naha City.)

Comparison of the inflow risk of infected persons and the impact of infection prevention measures

Inflow risk data (LocationMind xPop © LocationMind Inc.)
LocationMind xPop uses aggregated people flow data originally collected by NTT Docomo, Inc through their application service “Docomo Map Navi” using only the cell phone‘s location data collected upon user consent to the service’s auto GPS function, and then processed by NTT Docomo in entirety and statistically before being provided to LocationMind Inc. The original location data is GPS data (latitude, longitude) sent at a frequency of every 5 minutes at the shortest interval and does not include information that specifies individuals.

Results

The inflow risk of the movement of 2 persons/day (December) is 1.3x higher than that of 1 person/day (October) and 2.1x higher than that of 0.5 persons/day (July) (A).

The risk is 3.1 x for severe cases (B).

Reducing contact with tourists, reduced opening hours in eating and drinking areas is highly effective (70-85% reduction)(C).

To intensify the tracing of close contacts (chain tracing*) increases the number of confirmed positive cases, but it is highly effective in reducing the number of severe cases (≒ closing downtown areas and regular PCR testing of the employees)(D).

* chain tracing: tracing of close contacts of close contacts, up to three levels in the experiment

Courtesy of University of Tsukuba

→The number of beds increases two to three times due to the increased inflow risk. Reduced contact with tourists and intensified tracing is highly effective (70-85% reduction).