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