Merge branch 'main' into issue275

manuscript/index.qmd

@@ -78,19 +78,36 @@ We produce the simulations described in the next section for both of these GIs.
 
 ##### Reproduction number scenarios
 
-We test the following scenario:
-- Piecewise constant Rt
-    - 1.1 for two weeks
-    - 2 for two weeks
-    - 0.5 for two weeks
-    - 1.5 for two weeks
-    - 0.75 for two weeks
-    - 1.1 for six weeks
-    - sine curve centered at 1 with amplitude of 0.3 afterwards
+We test the following scenarios grouped by the **Outbreak**, **Endemic**, and **Miscellaneous** categories:
+
+**Outbreak scenarios**
+
+This is the list of scenarios where the initial number of infections is small but $R_t$ is initially significantly greater the 1 (e.g. $R_t > 1.5$).
+
+- _Susecptible depletion_. A smooth transition over time from $R_t > 1$ to $R_t < 0$. This represents a scenario where decrease in $R_t$ is due to greater population immunity, although it should be noted that we aren't modelling that effect mechanistically.
+- _Susecptible depletion with measures_. A sharp/discontinuous transition over time from $R_t > 1$ to $R_t < 1$, followed by a sharp transition back to $R_t > 1$ and then smooth transition to $R_t < 1$ again. This represents a scenario where initial decrease in $R_t$ is due to implementation of public health measures to reduce transmission. The sharp transition back to $R_t > 1$ is due to later relaxation of measures.
+- _Early outbreak_. Constant $R_t = R_0$ but for a short period.
+- _Early outbreak with random effects_. As _Early outbreak_ scenario but with white noise jitter on $R_t$.
+
+**Endemic scenarios**
+
+- _Regular variation_. A scenario with an endemic disease with sinusoidal variation in $R_t$ around 1 with some period length $P$: e.g. $R_t = 1 + \xi \sin(2 \pi (t - \phi) / P)$.
+- _Regular variation with random effects_. As _Regular variation_ scenario but with white noise jitter on $R_t$.
+
+**Mixed scenario(s)**
+
+- _Piecewise constant with large switches_: This scenario provides both sharp changes at the start of the timeseries and more gradual transitions towards the end. $R_t$ varies according to the following schedule,
+  - 1.1 for two weeks
+  - 2 for two weeks
+  - 0.5 for two weeks
+  - 1.5 for two weeks
+  - 0.75 for two weeks
+  - 1.1 for six weeks
+  - sine curve centered at 1 with amplitude of 0.3 afterwards
 
 We simulate out of this scenario for the GIs described in the previous section.
 
-This scenario provides both sharp changes at the start of the timeseries and more gradual transitions towards the end. The rolling windows allow for exploration of both of these situations in a single case study. The longer fit to the entire timeseries tests the ability to flexibily handle both of these paradigms in a single fit.
+The rolling windows allow for exploration of both of these situations in a single case study. The longer fit to the entire timeseries tests the ability to flexibily handle both of these paradigms in a single fit.
 
 ##### Inference scenarios
 We explore the following misspecification scenarios for the generation interval: