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The sequential Bayes (seqB) estimator uses a Bayesian approach to estimate <em>R</em><sub>0</sub> which updates the reproductive number estimate as data accumulates over time.
This approach is based on the SIR model, and assumes that the mean of the serial distribution (ie. the serial interval (SI)) is known.
It is assumed that infectious counts are observed at periodic times (ie. daily, weekly).
This method cannot handle datasets where there are no new infections observed in a time interval, thus, to remedy this, 
some manipulation may be necessary to make the times at which infectious counts are observed sufficiently course (ie. weeks instead of days).
Further, this method is also inappropriate in situations where long intervals between cases are observed in the initial stages of the epidemic.
Finally, the <em>R</em><sub>0</sub> approximation behaves similarly to a branching process, which means that throughout, the population size “available” to be infected remains constant.
We note that this assumption does not hold for the SIR/SEIR/SEAIR compartmental models.
As such, seqB estimates should only really be considered early on in an epidemic, ie. before the inflection point of an epidemic, if the dataset being used follows these models.