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

Xian Yang, Shuo Wang, Yuting Xing, Ling Li, Richard Yi Da Xu, Karl J. Friston, Yike Guo*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)


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-ofthe- 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.

Original languageEnglish
Article numbere1009807
JournalPLoS Computational Biology
Issue number2
Publication statusPublished - 23 Feb 2022

Scopus Subject Areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cellular and Molecular Neuroscience
  • Molecular Biology
  • Ecology
  • Computational Theory and Mathematics
  • Modelling and Simulation

User-Defined Keywords

  • Algorithms
  • Basic Reproduction Number
  • Bayes Theorem
  • COVID-19/epidemiology
  • Humans
  • SARS-CoV-2/isolation & purification


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