eprintid: 10144627 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/14/46/27 datestamp: 2022-03-04 11:57:48 lastmod: 2022-03-04 11:57:48 status_changed: 2022-03-04 11:57:48 type: article metadata_visibility: show sword_depositor: 699 creators_name: Yang, Xian creators_name: Wang, Shuo creators_name: Xing, Yuting creators_name: Li, Ling creators_name: Xu, Richard Yi Da creators_name: Friston, Karl J creators_name: Guo, Yike title: Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19 ispublished: inpress divisions: C07 divisions: F83 divisions: B02 divisions: UCL divisions: D07 note: © 2022 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). 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. date: 2022 date_type: published publisher: Public Library of Science (PLoS) official_url: https://doi.org/10.1371/journal.pcbi.1009807 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1942507 doi: 10.1371/journal.pcbi.1009807 medium: Print-Electronic pii: PCOMPBIOL-D-21-00782 lyricists_name: Friston, Karl lyricists_id: KJFRI52 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public publication: PLoS Computational Biology volume: 18 number: 2 article_number: e1009807 event_location: United States citation: Yang, Xian; Wang, Shuo; Xing, Yuting; Li, Ling; Xu, Richard Yi Da; Friston, Karl J; Guo, Yike; (2022) Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19. PLoS Computational Biology , 18 (2) , Article e1009807. 10.1371/journal.pcbi.1009807 <https://doi.org/10.1371/journal.pcbi.1009807>. (In press). Green open access document_url: https://discovery-pp.ucl.ac.uk/id/eprint/10144627/1/journal.pcbi.1009807.pdf