Research Article
A Research of Infectivity Rate of Seasonal Influenza from Pre-infectious Person for Data Driven Simulation
@INPROCEEDINGS{10.1007/978-3-031-29126-5_11, author={Saori Iwanaga}, title={A Research of Infectivity Rate of Seasonal Influenza from Pre-infectious Person for Data Driven Simulation}, proceedings={Artificial Intelligence for Communications and Networks. 4th EAI International Conference, AICON 2022, Hiroshima, Japan, November 30 - December 1, 2022, Proceedings}, proceedings_a={AICON}, year={2023}, month={3}, keywords={Epidemic model Seasonal influenza Super-spread Infectivity rate}, doi={10.1007/978-3-031-29126-5_11} }- Saori Iwanaga
Year: 2023
A Research of Infectivity Rate of Seasonal Influenza from Pre-infectious Person for Data Driven Simulation
AICON
Springer
DOI: 10.1007/978-3-031-29126-5_11
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.
