Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.

Yang, X; Wang, S; Xing, Y; Li, L; Xu, RYD; Friston, KJ; Guo, Y

Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.

Yang, X Wang, S Xing, Y Li, L Xu, RYD Friston, KJ Guo, Y

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Publisher:: PUBLIC LIBRARY SCIENCE
Publication Type:: Journal Article
Citation:: PLoS Comput Biol, 2022, 18, (2), pp. e1009807
Issue Date:: 2022-02

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Field	Value	Language
dc.contributor.author	Yang, X
dc.contributor.author	Wang, S
dc.contributor.author	Xing, Y
dc.contributor.author	Li, L
dc.contributor.author	Xu, RYD
dc.contributor.author	Friston, KJ
dc.contributor.author	Guo, Y
dc.contributor.editor	Hill, AL
dc.date.accessioned	2023-03-20T04:32:10Z
dc.date.available	2022-01-05
dc.date.available	2023-03-20T04:32:10Z
dc.date.issued	2022-02
dc.identifier.citation	PLoS Comput Biol, 2022, 18, (2), pp. e1009807
dc.identifier.issn	1553-734X
dc.identifier.issn	1553-7358
dc.identifier.uri	http://hdl.handle.net/10453/167758
dc.description.abstract	Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	PUBLIC LIBRARY SCIENCE
dc.relation.ispartof	PLoS Comput Biol
dc.relation.isbasedon	10.1371/journal.pcbi.1009807
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.subject.mesh	Algorithms
dc.subject.mesh	Basic Reproduction Number
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	COVID-19
dc.subject.mesh	Humans
dc.subject.mesh	SARS-CoV-2
dc.subject.mesh	Humans
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Algorithms
dc.subject.mesh	Basic Reproduction Number
dc.subject.mesh	COVID-19
dc.subject.mesh	SARS-CoV-2
dc.subject.mesh	Algorithms
dc.subject.mesh	Basic Reproduction Number
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	COVID-19
dc.subject.mesh	Humans
dc.subject.mesh	SARS-CoV-2
dc.title	Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.
dc.type	Journal Article
utslib.citation.volume	18
utslib.location.activity	United States
utslib.for	01 Mathematical Sciences
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-20T04:32:05Z
pubs.issue	2
pubs.publication-status	Published online
pubs.volume	18
utslib.citation.issue	2

Abstract:

Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.

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http://hdl.handle.net/10453/167758