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Title: Analysis of the risk and pre-emptive control of viral outbreaks accounting for within-host dynamics: SARS-CoV-2 as a case study
Authors: Hart, William S.
Park, Hyeongki
Jeong, Yong Dam
Kim, Kwang Su
Yoshimura, Raiki
Thompson, Robin N.
Iwami, Shingo
Author's alias: 吉村, 雷輝
岩見, 真吾
Keywords: INFECTIOUS DISEASE MODELLING
OUTBREAK RISK
SARS-COV-2
COVID-19
ANTIGEN TESTING
Issue Date: 10-Oct-2023
Publisher: Proceedings of the National Academy of Sciences
Journal title: Proceedings of the National Academy of Sciences of the United States of America
Volume: 120
Issue: 41
Thesis number: e2305451120
Abstract: In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multiscale modelling framework for estimating the local outbreak risk for a viral disease (the probability that a major outbreak results from a single case introduced into the population), accounting for within-host viral dynamics. Compared to population-level models previously used to estimate outbreak risks, our approach enables more detailed analysis of how the risk can be mitigated through pre-emptive interventions such as antigen testing. Considering SARS-CoV-2 as a case study, we quantify the within-host dynamics using data from individuals with omicron variant infections. We demonstrate that regular antigen testing reduces, but may not eliminate, the outbreak risk, depending on characteristics of local transmission. In our baseline analysis, daily antigen testing reduces the outbreak risk by 45% compared to a scenario without antigen testing. Additionally, we show that accounting for heterogeneity in within-host dynamics between individuals affects outbreak risk estimates and assessments of the impact of antigen testing. Our results therefore highlight important factors to consider when using multiscale models to design pre-emptive interventions against SARS-CoV-2 and other viruses.
Description: 世界初・新型コロナウイルス感染によるクラスター発生確率の計算に成功 --数理モデルに基づく効果的な感染症対策の確立へ重要な一歩--. 京都大学プレスリリース. 2023-10-05.
Rights: Copyright © 2023 the Author(s). Published by PNAS.
This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
URI: http://hdl.handle.net/2433/285496
DOI(Published Version): 10.1073/pnas.2305451120
PubMed ID: 37788317
Related Link: https://ashbi.kyoto-u.ac.jp/ja/news/20231005_research-result_shingo-iwami/
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