Abstract
I had proposed a discrete mathematical SEPIR (Susceptible – Exposed - Pre-infectious – Infectious - Recovered stage) model for seasonal influenza. In a subsequent previously study, focusing on infections by a pre-infectious person using pre-existing data, I showed that there super-spreading of seasonal influenza occurred before D-day that the first patients are discovered at Japan Coast Guard Academy. In this study, I found that the infectivity rate from pre-infectious people is 0.041 when the surrounding people don’t take counter-measures against the infection. After D-day in the community, the countermeasures taken reduce the infectivity rate to 0.002 in working spaces and 0.013 in living spaces. And the number of infectious people can be estimated simply by the summing up each group in the community.
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Notes
- 1.
In January 2019 the freshmen had no contacts with other students and were trained by different staff.
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Iwanaga, S. (2023). A Research of Infectivity Rate of Seasonal Influenza from Pre-infectious Person for Data Driven Simulation. In: Kambayashi, Y., Nguyen, N.T., Chen, SH., Dini, P., Takimoto, M. (eds) Artificial Intelligence for Communications and Networks. AICON 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 477. Springer, Cham. https://doi.org/10.1007/978-3-031-29126-5_11
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