From 02ac6e95133a4382dab910a8ff86a7282fc25e6a Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 28 Mar 2024 20:00:49 +0000 Subject: [PATCH 1/4] add callout about bayesian terminology --- episodes/quantify-transmissibility.Rmd | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/episodes/quantify-transmissibility.Rmd b/episodes/quantify-transmissibility.Rmd index 1ecf1b9a..c4434566 100644 --- a/episodes/quantify-transmissibility.Rmd +++ b/episodes/quantify-transmissibility.Rmd @@ -68,6 +68,14 @@ In Bayesian inference, we use prior knowledge (prior distributions) with data (i :::::::::::::::::::::::::::::::::::::::::::::::: +:::::::::::::::::::::::::::::::::::::::::::::::: instructor + +We refer to the Prior probability distribution and the [Posterior probability](https://en.wikipedia.org/wiki/Posterior_probability) distribution. + +Lines below, in the "`Expected change in daily cases`" callout, by "the posterior probability that $R_t < 1$", we refer specifically to the [area under the posterior probability distribution curve](https://www.nature.com/articles/nmeth.3368/figures/1). + +:::::::::::::::::::::::::::::::::::::::::::::::: + The first step is to load the `{EpiNow2}` package : From 604bc7f829d3c14ee9bdd20ad329405ba4868066 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 28 Mar 2024 20:01:29 +0000 Subject: [PATCH 2/4] add specific key metric names --- episodes/quantify-transmissibility.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/episodes/quantify-transmissibility.Rmd b/episodes/quantify-transmissibility.Rmd index c4434566..13c3d662 100644 --- a/episodes/quantify-transmissibility.Rmd +++ b/episodes/quantify-transmissibility.Rmd @@ -12,8 +12,8 @@ withr::local_options(list(mc.cores = 4)) :::::::::::::::::::::::::::::::::::::: questions -- How can I estimate key transmission metrics from a time series of case data? -- How can I quantify geographical heterogeneity in these metrics? +- How can I estimate the time-varying reproduction number ($Rt$) and growth rate from a time series of case data? +- How can I quantify geographical heterogeneity in these transmission metrics? :::::::::::::::::::::::::::::::::::::::::::::::: From c536d3d81f5b5881c4bfdb616c0b2afe4b34c80c Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 28 Mar 2024 20:01:45 +0000 Subject: [PATCH 3/4] fix repo linking --- config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config.yaml b/config.yaml index c9b07d54..e5722d19 100644 --- a/config.yaml +++ b/config.yaml @@ -27,7 +27,7 @@ life_cycle: 'pre-alpha' license: 'CC-BY 4.0' # Link to the source repository for this lesson -source: 'https://github.com/epiverse-trace/tutorials' +source: 'https://github.com/epiverse-trace/tutorials-middle' # Default branch of your lesson branch: 'main' From d97e35530487c31449c52e3934686ac9bbb2f132 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 28 Mar 2024 20:02:56 +0000 Subject: [PATCH 4/4] fix sigma notation for sd --- episodes/quantify-transmissibility.Rmd | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/episodes/quantify-transmissibility.Rmd b/episodes/quantify-transmissibility.Rmd index 13c3d662..e931c460 100644 --- a/episodes/quantify-transmissibility.Rmd +++ b/episodes/quantify-transmissibility.Rmd @@ -157,7 +157,7 @@ The number of delays and type of delay is a flexible input that depends on the d The distribution of incubation period can usually be obtained from the literature. The package `{epiparameter}` contains a library of epidemiological parameters for different diseases obtained from the literature. -We specify a (fixed) gamma distribution with mean $\mu = 4$ and standard deviation $\sigma^2= 2$ (shape = $4$, scale = $1$) using the function `dist_spec()` as follows: +We specify a (fixed) gamma distribution with mean $\mu = 4$ and standard deviation $\sigma= 2$ (shape = $4$, scale = $1$) using the function `dist_spec()` as follows: ```{r} incubation_period_fixed <- dist_spec( @@ -184,15 +184,15 @@ For all types of delay, we will need to use distributions for positive values on #### Including distribution uncertainty -To specify a **variable** distribution, we include uncertainty around the mean $\mu$ and standard deviation $\sigma^2$ of our gamma distribution. If our incubation period distribution has a mean $\mu$ and standard deviation $\sigma^2$, then we assume the mean ($\mu$) follows a Normal distribution with standard deviation $\sigma_{\mu}^2$: +To specify a **variable** distribution, we include uncertainty around the mean $\mu$ and standard deviation $\sigma$ of our gamma distribution. If our incubation period distribution has a mean $\mu$ and standard deviation $\sigma$, then we assume the mean ($\mu$) follows a Normal distribution with standard deviation $\sigma_{\mu}$: $$\mbox{Normal}(\mu,\sigma_{\mu}^2)$$ -and a standard deviation ($\sigma^2$) follows a Normal distribution with standard deviation $\sigma_{\sigma^2}^2$: +and a standard deviation ($\sigma$) follows a Normal distribution with standard deviation $\sigma_{\sigma}$: -$$\mbox{Normal}(\sigma^2,\sigma_{\sigma^2}^2).$$ +$$\mbox{Normal}(\sigma,\sigma_{\sigma}^2).$$ -We specify this using `dist_spec` with the additional arguments `mean_sd` ($\sigma_{\mu}^2$) and `sd_sd` ($\sigma_{\sigma^2}^2$). +We specify this using `dist_spec` with the additional arguments `mean_sd` ($\sigma_{\mu}$) and `sd_sd` ($\sigma_{\sigma}$). ```{r} incubation_period_variable <- dist_spec(