differences for PR #49

config.yaml

@@ -0,0 +1,82 @@
+#------------------------------------------------------------
+# Values for this lesson.
+#------------------------------------------------------------
+
+# Which carpentry is this (swc, dc, lc, or cp)?
+# swc: Software Carpentry
+# dc: Data Carpentry
+# lc: Library Carpentry
+# cp: Carpentries (to use for instructor training for instance)
+# incubator: The Carpentries Incubator
+carpentry: 'incubator'
+
+# Overall title for pages.
+title: 'Epiverse-TRACE'
+
+# Date the lesson was created (YYYY-MM-DD, this is empty by default)
+created: ~
+
+# Comma-separated list of keywords for the lesson
+keywords: 'forecasts, epidemic models, interventions' #FIXME
+
+# Life cycle stage of the lesson
+# possible values: pre-alpha, alpha, beta, stable
+life_cycle: 'pre-alpha'
+
+# License of the lesson
+license: 'CC-BY 4.0' #FIXME
+
+# Link to the source repository for this lesson
+source: 'https://github.com/epiverse-trace/tutorials'
+
+# Default branch of your lesson
+branch: 'main'
+
+# Who to contact if there are any issues
+contact: '[email protected]' #FIXME
+
+# Navigation ------------------------------------------------
+#
+# Use the following menu items to specify the order of
+# individual pages in each dropdown section. Leave blank to
+# include all pages in the folder.
+#
+# Example -------------
+#
+# episodes:
+# - introduction.md
+# - first-steps.md
+#
+# learners:
+# - setup.md
+#
+# instructors:
+# - instructor-notes.md
+#
+# profiles:
+# - one-learner.md
+# - another-learner.md
+
+# Order of episodes in your lesson
+episodes:
+- simulating-transmission.Rmd
+- model-choices.Rmd
+- modelling-interventions.Rmd
+- compare-interventions.Rmd
+
+# Information for Learners
+learners:
+
+# Information for Instructors
+instructors:
+
+# Learner Profiles
+profiles:
+
+# Customisation ---------------------------------------------
+#
+# This space below is where custom yaml items (e.g. pinning
+# sandpaper and varnish versions) should live
+
+sandpaper: 'carpentries/sandpaper#533'
+

md5sum.txt

@@ -4,12 +4,12 @@
 "config.yaml" "ba8182fefb4b3a156058872157fb2d7d" "site/built/config.yaml" "2023-10-31"
 "index.md" "d45b8843b661f7f6d628b6b57447b6c5" "site/built/index.md" "2023-10-31"
 "links.md" "8184cf4149eafbf03ce8da8ff0778c14" "site/built/links.md" "2023-10-31"
-"episodes/simulating-transmission.Rmd" "9c915400856003c134ca46edd1c52d87" "site/built/simulating-transmission.md" "2023-10-31"
-"episodes/model-choices.Rmd" "113c11e0dccc812744f685df84733fdf" "site/built/model-choices.md" "2023-10-31"
+"episodes/simulating-transmission.Rmd" "a09170bca196d6a7ba714ddf159e2e2a" "site/built/simulating-transmission.md" "2023-11-02"
+"episodes/model-choices.Rmd" "38e8e793daeee48caf58e50e88518f0a" "site/built/model-choices.md" "2023-11-02"
 "episodes/modelling-interventions.Rmd" "01eff78117bf3f53015154ca9b961651" "site/built/modelling-interventions.md" "2023-10-31"
 "episodes/compare-interventions.Rmd" "d594dc0466f735d14ccbcc7b2a9d56ef" "site/built/compare-interventions.md" "2023-10-31"
 "instructors/instructor-notes.md" "ca3834a1b0f9e70c4702aa7a367a6bb5" "site/built/instructor-notes.md" "2023-10-31"
-"learners/reference.md" "451e0fd931938d38cf92a313192c1cdb" "site/built/reference.md" "2023-10-31"
+"learners/reference.md" "334277298b83c92dd98c28de895d43cb" "site/built/reference.md" "2023-11-02"
 "learners/setup.md" "b3c6bfa13fd687f926bb1a3e772a2516" "site/built/setup.md" "2023-10-31"
 "profiles/learner-profiles.md" "60b93493cf1da06dfd63255d73854461" "site/built/learner-profiles.md" "2023-10-31"
 "renv/profiles/lesson-requirements/renv.lock" "da153090a3bb8fe62bcd300c69550054" "site/built/renv.lock" "2023-10-31"

model-choices.md

@@ -47,7 +47,7 @@ When deciding whether an existing model can be used, we must consider :
 
 A model may already exist for your study disease, or there may be a model for an infection that has the same transmission pathways and epidemiological features that can be used. 
 
-+ Do we need a deterministic or stochastic model? 
++ Do we need a [deterministic](../learners/reference.md#deterministic) or [stochastic](../learners/reference.md#stochastic) model? 
 
 Model structures differ for whether the disease has pandemic potential or not. When predicted numbers of infection are small, stochastic variation in output can have an effect on whether an outbreak takes off or not. Outbreaks are usually smaller in magnitude than epidemics, so its often appropriate to use a stochastic model to characterise the uncertainty in the early stages of the outbreak. Epidemics are larger in magnitude than outbreaks and so a deterministic model is suitable as we have less interest in the stochastic variation in output. 
 

reference.md

@@ -4,22 +4,20 @@ title: 'Glossary of Terms: Epiverse-TRACE'
 
 ## A
 
-#### Airborne transmission
-
-Individuals become infected via contact with infectious particles in the air. Examples include influenza, COVID-19. [More information on airborne transmission](https://www.ncbi.nlm.nih.gov/books/NBK531468/).
+[Airborne transmission]{#airborne}
+: Individuals become infected via contact with infectious particles in the air. Examples include influenza, COVID-19. [More information on airborne transmission](https://www.ncbi.nlm.nih.gov/books/NBK531468/).
 
 <!--  ## B -->
 
 <!-- ## C -->
 
 ## D
 
-#### Deterministic models
-Models that will always have the same trajectory for given initial conditions and parameters. Examples include ordinary differential equations and difference equations. 
-
-#### Direct transmission
+[Deterministic model]{#deterministic}
+: Models that will always have the same trajectory for given initial conditions and parameters. Examples include ordinary differential equations and difference equations. 
 
-Individuals become infected via direct contact with other infected humans. Airborne transmitted infections are often modelled as directly transmitted infections as they require close contact with infected individuals for successful transmission. 
+[Direct transmission]{#direct}
+: Individuals become infected via direct contact with other infected humans. Airborne transmitted infections are often modelled as directly transmitted infections as they require close contact with infected individuals for successful transmission. 
 
 <!-- ## E -->
 
@@ -31,23 +29,20 @@ Individuals become infected via direct contact with other infected humans. Airbo
 
 ## I 
 
-#### Incubation period
-
-The time between becoming infected and the onset of infectiousness, same as 'Latent period'.
+[Incubation period]{#incubation}
+: The time between becoming infected and the onset of infectiousness, same as [latent period](#latent).
 
-#### Indirect transmission
-
-Indirectly transmitted infections are passed on to humans via contact with vectors, animals or contaminated environment. Vector-borne infections, zoonoses and water-borne infections are modelled as indirectly transmitted. 
+[Indirect transmission]{#indirect}
+: Indirectly transmitted infections are passed on to humans via contact with vectors, animals or contaminated environment. Vector-borne infections, zoonoses and water-borne infections are modelled as indirectly transmitted. 
 
 <!-- ## J -->
 
 <!-- ## K -->
 
 ## L
 
-#### Latent period
-
-The time between becoming infected and the onset of infectiousness, same as 'Incubation period'.
+[Latent period]{#latent}
+: The time between becoming infected and the onset of infectiousness, same as [incubation period](#incubation).
 
 
 <!-- ## M -->
@@ -64,8 +59,8 @@ The time between becoming infected and the onset of infectiousness, same as 'Inc
 
 ## S
 
-#### Stochastic model
-A model that includes some stochastic process resulting in variation in model simulations for the same initial conditions and parameters. Examples include stochastic differential equations, branching process models. For more detail see [Allen (2017)](https://doi.org/10.1016/j.idm.2017.03.001).
+[Stochastic model]{#stochastic}
+: A model that includes some stochastic process resulting in variation in model simulations for the same initial conditions and parameters. Examples include stochastic differential equations, branching process models. For more detail see [Allen (2017)](https://doi.org/10.1016/j.idm.2017.03.001).
 
 
 <!-- ## T -->
@@ -74,8 +69,8 @@ A model that includes some stochastic process resulting in variation in model si
 
 ## V
 
-#### Vector-borne transmission 
-Vector-borne transmission means an infection can be passed from a vector (e.g. mosquitoes) to humans. Examples of vector-borne diseases include malaria and dengue. [More information on vector-borne transmission](https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases).  
+[Vector-borne transmission]{#vectorborne}
+: Vector-borne transmission means an infection can be passed from a vector (e.g. mosquitoes) to humans. Examples of vector-borne diseases include malaria and dengue. [More information on vector-borne transmission](https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases).  
 
 
 <!-- ## W -->