Analysis of the Spread of COVID-19: Multiagent-based Simulation by Using Human Mobility Data
Research and Development by
Masaki Onishi, AIST
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
Analysis of a large-scale Human Mobility in Japan: Trends in mobility patterns based on internal and external prefectural population movements
The GPS-based data is categorized into three types of mobility patterns: (1) prefectural, (2) regional , and (3) inter-regional movements, shown as a percent (%). 11 regions are referred to the Japan Meteorological regions.
(Mobility data: January 2020 to the end of October, shared by Blogwatcher Inc.)
- Human mobility gradually increased in September, especially during weekends and holidays.
- Mobility increase in September shows a similar trend in three consecutive holidays in March 2020.
Analysis of a large-scale Human Mobility in Japan: Trends in Mobility Patterns in Nightlife Spots
Visitors in nightlife spots, Susukino in Hokkaido and Kabukicho in Tokyo 17-25 September, 2020 before, during and after 4-day holidays.
As a general trend, the September data shows that Human Mobility decreased in both nightlife spots during 4-day holidays. However, the number of the visitors from the outside of Hokkaido was doubled in Susukino, compared with the regular weekdays.
Multiagent-based Simulation of the virus spreads: By using the nation–wide GPS mobility data in Japan
Simulation of the virus spreads
Sim-1 (blue line) failed to estimate the infected cases after June 2020 due to iterative use of its short-term data. Sim-2 (green line) estimated the explosive outbreak around April 25, 2020, by using the daily mobility data.
- Speed up the simulation by increasing the time resolution.
- Assimilate the mobility data as a key parameter of the simulation along with changeable infection rates (data assimilation).
- Forecast the virus spreads with the updated data.
Visualization of the human-to-human infection networks
The visualization shows the forecasts the number of people that an infected case has transmitted the infection to (effective reproduction number).
- Visualize the simulation outcomes with high accuracy.
- Perform comparative analysis of the confirmed cases and the forecasted cases.
In this project, we have simulated the spreads with long-term mobility data, and visualized the human-to-human infection networks. We will conduct data assimilation along with changeable trends and comparative analysis to evaluate the simulation results for the better.