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ThinkMind // eKNOW 2021, The Thirteenth International Conference on Information, Process, and Knowledge Management // View article eknow_2021_1_20_68003


An Agent-based Model in Activity-Driven Network of COVID-19 Epidemic Using Mobility and Infection Data in Tokyo 2020

Authors:
Kazumoto Takayanagi
Setsuya Kurahashi

Keywords: agent-based modeling; activity-driven network; approximate Bayesian computation; vaccination strategies; vaccine passport.

Abstract:
In situations where the pandemic of Coronavirus Disease 2019 (COVID-19) has been destroying the daily lives of global human community, a model that reliably predicts the spread of infection within society would be extremely helpful for a variety of purposes. This paper presents an agent-based model over temporal networks that are fitted to real mobility data reported in Tokyo. The parameters of the model are inferred via approximate Bayesian computation to ensure that the model represents well the observed infection data. Through the simulations using this model, we demonstrate a comparison of the effectiveness of different vaccination strategies.

Pages: 8 to 13

Copyright: Copyright (c) IARIA, 2021

Publication date: July 18, 2021

Published in: conference

ISSN: 2308-4375

ISBN: 978-1-61208-874-7

Location: Nice, France

Dates: from July 18, 2021 to July 22, 2021

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