diff options
Diffstat (limited to 'inst/web/templates/content/estimation/about-estimators')
5 files changed, 36 insertions, 0 deletions
diff --git a/inst/web/templates/content/estimation/about-estimators/id.html b/inst/web/templates/content/estimation/about-estimators/id.html new file mode 100644 index 0000000..fc70b1c --- /dev/null +++ b/inst/web/templates/content/estimation/about-estimators/id.html @@ -0,0 +1,3 @@ +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/web/templates/content/estimation/about-estimators/idea.html b/inst/web/templates/content/estimation/about-estimators/idea.html new file mode 100644 index 0000000..67548f8 --- /dev/null +++ b/inst/web/templates/content/estimation/about-estimators/idea.html @@ -0,0 +1,4 @@ +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/web/templates/content/estimation/about-estimators/panel.html b/inst/web/templates/content/estimation/about-estimators/panel.html new file mode 100644 index 0000000..98fe155 --- /dev/null +++ b/inst/web/templates/content/estimation/about-estimators/panel.html @@ -0,0 +1,14 @@ +<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/web/templates/content/estimation/about-estimators/seq_bayes.html b/inst/web/templates/content/estimation/about-estimators/seq_bayes.html new file mode 100644 index 0000000..8f66ab4 --- /dev/null +++ b/inst/web/templates/content/estimation/about-estimators/seq_bayes.html @@ -0,0 +1,9 @@ +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/web/templates/content/estimation/about-estimators/wp.html b/inst/web/templates/content/estimation/about-estimators/wp.html new file mode 100644 index 0000000..c6f4580 --- /dev/null +++ b/inst/web/templates/content/estimation/about-estimators/wp.html @@ -0,0 +1,6 @@ +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. |