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Diffstat (limited to 'inst/app/templates/content/estimation/about-estimators')
5 files changed, 0 insertions, 36 deletions
diff --git a/inst/app/templates/content/estimation/about-estimators/id.html b/inst/app/templates/content/estimation/about-estimators/id.html deleted file mode 100644 index fc70b1c..0000000 --- a/inst/app/templates/content/estimation/about-estimators/id.html +++ /dev/null @@ -1,3 +0,0 @@ -The Incidence Decay (ID) estimator uses the method of least squares to estimate <em>R</em><sub>0</sub>. -This method assumes the serial interval is known, and is built under the SIR assumption. -We note that the use of this method might result in the underestimation of <em>R</em><sub>0</sub>. diff --git a/inst/app/templates/content/estimation/about-estimators/idea.html b/inst/app/templates/content/estimation/about-estimators/idea.html deleted file mode 100644 index 67548f8..0000000 --- a/inst/app/templates/content/estimation/about-estimators/idea.html +++ /dev/null @@ -1,4 +0,0 @@ -The Incidence Decay and Exponential Adjustment (ID) estimator is an alternative formulation of the Incidence Decay (ID) model which includes a decay factor to reflect the often observed outbreak decline. -This addresses the potential underestimation of the <em>R</em><sub>0</sub> estimate when using the ID method. -The method of least squares is used to estimate <em>R</em><sub>0</sub>, and similar to the ID model, the serial interval is assumed to be known and this method is developed assuming the SIR model. -We note that, since we need to obtain a minimizer of the decay factor to solve the optimization problem, we require that the number of cases in the dataset be at least 2. diff --git a/inst/app/templates/content/estimation/about-estimators/panel.html b/inst/app/templates/content/estimation/about-estimators/panel.html deleted file mode 100644 index 98fe155..0000000 --- a/inst/app/templates/content/estimation/about-estimators/panel.html +++ /dev/null @@ -1,14 +0,0 @@ -<div class="accordion-item"> - <h2 class="accordion-header"> - <button class="accordion-button collapsed" type="button" - data-bs-toggle="collapse" data-bs-target="#{{ id }}"> - <h4>{{ header }}</h4> - </button> - </h2> - <div id="{{ id }}" class="accordion-collapse collapse" data-bs-parent="#estimators-accordion"> - <div class="accordion-body"> - <p>Reference: <a href="{{ reference_url }}" target="_blank"><em>{{ reference_label }}</em></a></p> - <p>{{ htmlTemplate(paste0("templates/content/estimation/about-estimators/", id, ".html")) }}</p> - </div> - </div> -</div> diff --git a/inst/app/templates/content/estimation/about-estimators/seq_bayes.html b/inst/app/templates/content/estimation/about-estimators/seq_bayes.html deleted file mode 100644 index 8f66ab4..0000000 --- a/inst/app/templates/content/estimation/about-estimators/seq_bayes.html +++ /dev/null @@ -1,9 +0,0 @@ -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. diff --git a/inst/app/templates/content/estimation/about-estimators/wp.html b/inst/app/templates/content/estimation/about-estimators/wp.html deleted file mode 100644 index c6f4580..0000000 --- a/inst/app/templates/content/estimation/about-estimators/wp.html +++ /dev/null @@ -1,6 +0,0 @@ -The White and Pagano (WP) estimator uses maximum likelihood estimation to estimate <em>R</em><sub>0</sub>. -In this method, the serial interval (SI) is either known, or can be estimated along with <em>R</em><sub>0</sub>. -It is assumed that the number of infectious individuals are observable at discrete time points (ie. daily or weekly). -Further, this method also assumes an underlying 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, WP 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. |