Filtering and improved Uncertainty Quantification in the dynamic estimation of effective reproduction numbers

The effective reproduction number [Formula: see text] measures an infectious disease’s transmissibility as the number of secondary infections in one reproduction time in a population having both susceptible and non-susceptible hosts. Current approaches do not quantify the uncertainty correctly in estimating [Formula: see text] , as expected by the observed variability in contagion patterns. We elaborate on the Bayesian estimation of [Formula: see text] by improving on the Poisson sampling model of Cori et al. (2013). By adding an autoregressive latent process, we build a Dynamic Linear Model on the log of observed [Formula: see text] s, resulting in a filtering type Bayesian inference. We use a conjugate analysis, and all calculations are explicit. Results show an improved uncertainty quantification on the estimation of [Formula: see text] ’s, with a reliable method that could safely be used by non-experts and within other forecasting systems. We illustrate our approach with recent data from the current COVID19 epidemic in Mexico.