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diff --git a/inst/app/templates/content/estimation/about-estimators/seq_bayes.html b/inst/app/templates/content/estimation/about-estimators/seq_bayes.html index f6df3ee..8f66ab4 100644 --- a/inst/app/templates/content/estimation/about-estimators/seq_bayes.html +++ b/inst/app/templates/content/estimation/about-estimators/seq_bayes.html @@ -1 +1,9 @@ -This is a short description of the seqB method. +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. |