Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework for R_t Estimation
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In epidemiological modelling, the instantaneous reproduction number, R_t, is important to understand the transmission dynamics of infectious diseases. Current R_t estimates often suffer from problems such as lagging, averaging and uncertainties demoting the usefulness of R_t. To address these problems, we propose a new method in the framework of sequential Bayesian inference where a Data Assimilation approach is taken for R_t estimation, resulting in the state-of-the-art 'DAR_t' system for R_t estimation. With DAR_t, the problem of time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and R_t; the drawback of averaging is improved by instantaneous updating upon new observations and a model selection mechanism capturing abrupt changes caused by interventions; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DAR_t through simulations and demonstrate its power in revealing the transmission dynamics of COVID-19.
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