From 8cb750dee1f49d32ee364f023d3a23196a5e66f0 Mon Sep 17 00:00:00 2001
From: Tatiana Krikella <106450627+TKrikella@users.noreply.github.com>
Date: Thu, 14 Nov 2024 12:59:37 -0500
Subject: Update seq_bayes.html
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-This is a short description of the seqB method.
+The sequential Bayes (seqB) estimator uses a Bayesian approach to estimate R0 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 R0 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.
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