Multi-agent system collision model to predict the transmission of seasonal influenza in Tokyo from 2014–2015 to 2018–2019 seasons

• Main Authors: Nobuo Tomizawa, Kanako K. Kumamaru, Koh Okamoto, Shigeki Aoki
• Format: Article
• Language: English
• Published: Elsevier 2021-08-01
• Series: Heliyon
• Subjects: Disease transmission; Influenza; Multi-agent system; SIR model
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405844021019629

The objective of this study was to apply the multi-agent system (MAS) collision model to predict seasonal influenza epidemic in Tokyo for 5 seasons (2014–2015 to 2018–2019 seasons). The MAS collision model assumes each individual as a particle inside a square domain. The particles move within the domain and disease transmission occurs in a certain probability when an infected particle collides a susceptible particle. The probability was determined based on the basic reproduction number calculated using the actual data. The simulation started with 1 infected particle and 999 susceptible particles to correspond to the onset of an influenza epidemic. We performed the simulation for 150 days and the calculation was repeated 500 times for each season. To improve the accuracy of the prediction, we selected simulations which have similar incidence number to the actual data in specific weeks. Analysis including all simulations corresponded good to the actual data in 2014–2015 and 2015–2016 seasons. However, the model failed to predict the sharp peak incidence after the New Year Holidays in 2016–2017, 2017–2018, and 2018–2019 seasons. A model which included simulations selected by the week of peak incidence predicted the week and number of peak incidence better than a model including all simulations in all seasons. The reproduction number was also similar to the actual data in this model. In conclusion, the MAS collision model predicted the epidemic curve with good accuracy by selecting the simulations using the actual data without changing the initial parameters such as the basic reproduction number and infection time.