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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