Bayesian SIR model with change points with application to the Omicron wave in Singapore

The Omicron variant has led to a new wave of the COVID-19 pandemic worldwide, with unprecedented numbers of daily confirmed new cases in many countries and areas. To analyze the impact of society or policy changes on the development of the Omicron wave, the stochastic susceptible-infected-removed (S...

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Autores principales: Gu, Jiaqi, Yin, Guosheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718478/
https://www.ncbi.nlm.nih.gov/pubmed/36460721
http://dx.doi.org/10.1038/s41598-022-25473-y
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author Gu, Jiaqi
Yin, Guosheng
author_facet Gu, Jiaqi
Yin, Guosheng
author_sort Gu, Jiaqi
collection PubMed
description The Omicron variant has led to a new wave of the COVID-19 pandemic worldwide, with unprecedented numbers of daily confirmed new cases in many countries and areas. To analyze the impact of society or policy changes on the development of the Omicron wave, the stochastic susceptible-infected-removed (SIR) model with change points is proposed to accommodate the situations where the transmission rate and the removal rate may vary significantly at change points. Bayesian inference based on a Markov chain Monte Carlo algorithm is developed to estimate both the locations of change points as well as the transmission rate and removal rate within each stage. Experiments on simulated data reveal the effectiveness of the proposed method, and several stages are detected in analyzing the Omicron wave data in Singapore.
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spelling pubmed-97184782022-12-04 Bayesian SIR model with change points with application to the Omicron wave in Singapore Gu, Jiaqi Yin, Guosheng Sci Rep Article The Omicron variant has led to a new wave of the COVID-19 pandemic worldwide, with unprecedented numbers of daily confirmed new cases in many countries and areas. To analyze the impact of society or policy changes on the development of the Omicron wave, the stochastic susceptible-infected-removed (SIR) model with change points is proposed to accommodate the situations where the transmission rate and the removal rate may vary significantly at change points. Bayesian inference based on a Markov chain Monte Carlo algorithm is developed to estimate both the locations of change points as well as the transmission rate and removal rate within each stage. Experiments on simulated data reveal the effectiveness of the proposed method, and several stages are detected in analyzing the Omicron wave data in Singapore. Nature Publishing Group UK 2022-12-02 /pmc/articles/PMC9718478/ /pubmed/36460721 http://dx.doi.org/10.1038/s41598-022-25473-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Gu, Jiaqi
Yin, Guosheng
Bayesian SIR model with change points with application to the Omicron wave in Singapore
title Bayesian SIR model with change points with application to the Omicron wave in Singapore
title_full Bayesian SIR model with change points with application to the Omicron wave in Singapore
title_fullStr Bayesian SIR model with change points with application to the Omicron wave in Singapore
title_full_unstemmed Bayesian SIR model with change points with application to the Omicron wave in Singapore
title_short Bayesian SIR model with change points with application to the Omicron wave in Singapore
title_sort bayesian sir model with change points with application to the omicron wave in singapore
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718478/
https://www.ncbi.nlm.nih.gov/pubmed/36460721
http://dx.doi.org/10.1038/s41598-022-25473-y
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