P[R] is the prior, which captures given knowledge of the distribution of R. The distribution P[DT(t+t)rDT(t)] is independent of R, and corresponds to a trivial normalization. From successive applications of Bayes' theorem, a sequential estimation scheme, that uses streaming epidemiological observations performed in real time, can be constructed using the posterior distribution for R, at time t as the prior in the next estimation step at time t+t, leading to an update scheme via iteration of Eq. [13] . The resulting probability distribution for R includes information on all observations up to time t, and contrasts with the ''instantaneous'' R t , used above, which only considers the data at times t and t+t. Thus, R is a robust estimator of the effective reproduction number assumed to be constant for the whole epidemic up to time t. Any changes in R over time result from the assimilation of each new data point, leading to an updated estimate of R. This in turn allows the use of our estimation procedure as an anomaly detection tool (see below).