differences for PR #132

Author: actions-user

.DS_Store

@@ -314,7 +314,7 @@ output %>%
   )
 ```
 
-<img src="fig/BF_measles-rendered-unnamed-chunk-2-1.png" style="display: block; margin: auto;" />
+<img src="fig/BF_measles-rendered-unnamed-chunk-2-1.png" alt="" style="display: block; margin: auto;" />
 
 
 ## Expected Epidemic Size & Herd Immunity
@@ -511,7 +511,7 @@ output1 %>%
   )
 ```
 
-<img src="fig/BF_measles-rendered-unnamed-chunk-6-1.png" style="display: block; margin: auto;" />
+<img src="fig/BF_measles-rendered-unnamed-chunk-6-1.png" alt="" style="display: block; margin: auto;" />
 
 
 ## Effective Reproduction Number ($\mathcal{R}_{eff}$)
@@ -711,7 +711,7 @@ ggplot(data_combined) +
   )
 ```
 
-<img src="fig/BF_measles-rendered-unnamed-chunk-9-1.png" style="display: block; margin: auto;" />
+<img src="fig/BF_measles-rendered-unnamed-chunk-9-1.png" alt="" style="display: block; margin: auto;" />
 
 ## Vaccination Model
 
@@ -785,7 +785,7 @@ output3 %>%
   )
 ```
 
-<img src="fig/BF_measles-rendered-unnamed-chunk-10-1.png" style="display: block; margin: auto;" />
+<img src="fig/BF_measles-rendered-unnamed-chunk-10-1.png" alt="" style="display: block; margin: auto;" />
 
 We can visualize the outcomes of both the baseline model and the
 vaccine-intervention model to highlight the differences in infection
@@ -867,7 +867,7 @@ ggplot(data_combined2) +
   )
 ```
 
-<img src="fig/BF_measles-rendered-unnamed-chunk-11-1.png" style="display: block; margin: auto;" />
+<img src="fig/BF_measles-rendered-unnamed-chunk-11-1.png" alt="" style="display: block; margin: auto;" />
 
 
 ## Combining interventions
@@ -927,4 +927,4 @@ output4 %>%
   )
 ```
 
-<img src="fig/BF_measles-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" />
+<img src="fig/BF_measles-rendered-unnamed-chunk-12-1.png" alt="" style="display: block; margin: auto;" />

BF_measles.md

@@ -124,7 +124,7 @@ output %>%
   )
 ```
 
-<img src="fig/compare-interventions-rendered-unnamed-chunk-2-1.png" style="display: block; margin: auto;" />
+<img src="fig/compare-interventions-rendered-unnamed-chunk-2-1.png" alt="" style="display: block; margin: auto;" />
 
 If we wanted to quantify the impact of the intervention over the model output through time, we could consider the cumulative number of infectious people in the baseline scenario compared to the intervention scenario: 
 
@@ -168,7 +168,7 @@ output %>%
 `.groups` argument.
 ```
 
-<img src="fig/compare-interventions-rendered-unnamed-chunk-3-1.png" style="display: block; margin: auto;" />
+<img src="fig/compare-interventions-rendered-unnamed-chunk-3-1.png" alt="" style="display: block; margin: auto;" />
 
 
 ### Vacamole model
@@ -186,7 +186,7 @@ The Vacamole model is a deterministic model based on a system of Ordinary Differ
 
 The diagram below describes the flow of individuals through the different compartments.
 
-<img src="fig/compare-interventions-rendered-unnamed-chunk-4-1.png" width="80%" style="display: block; margin: auto;" />
+<img src="fig/compare-interventions-rendered-unnamed-chunk-4-1.png" alt="" width="80%" style="display: block; margin: auto;" />
 
 
 ::::::::::::::::::::::::::::::::::::: challenge
@@ -375,7 +375,7 @@ output_vacamole %>%
 `.groups` argument.
 ```
 
-<img src="fig/compare-interventions-rendered-unnamed-chunk-7-1.png" style="display: block; margin: auto;" />
+<img src="fig/compare-interventions-rendered-unnamed-chunk-7-1.png" alt="" style="display: block; margin: auto;" />
 
 
 

compare-interventions.md

@@ -303,7 +303,7 @@ Whereas a contact matrix gives the average number of contacts that one groups ma
 
 Consider the SIR model where individuals are categorized as either susceptible $S$, infected but not yet infectious $E$, infectious $I$ or recovered $R$. The schematic below shows the processes which describe the flow of individuals between the disease states $S$, $I$ and $R$ and the key parameters for each process.
 
-<img src="fig/contact-matrices-rendered-diagram-1.png" style="display: block; margin: auto;" />
+<img src="fig/contact-matrices-rendered-diagram-1.png" alt="" style="display: block; margin: auto;" />
 
 The [differential equations](../learners/reference.md#ordinary) below describe how individuals move from one state to another [(Bjørnstad et al. 2020)](https://doi.org/10.1038/s41592-020-0822-z).
 

config.yaml

@@ -211,7 +211,7 @@ ggplot(data = density_df, aes(x = days, y = density_admission))  +
   )
 ```
 
-<img src="fig/disease-burden-rendered-unnamed-chunk-5-1.png" style="display: block; margin: auto;" />
+<img src="fig/disease-burden-rendered-unnamed-chunk-5-1.png" alt="" style="display: block; margin: auto;" />
 
 
 ::::::::::::::::::::::::::::::::::::: challenge 
@@ -249,7 +249,7 @@ ggplot(data = density_df, aes(x = days, y = density_discharge)) +
   )
 ```
 
-<img src="fig/disease-burden-rendered-unnamed-chunk-6-1.png" style="display: block; margin: auto;" />
+<img src="fig/disease-burden-rendered-unnamed-chunk-6-1.png" alt="" style="display: block; margin: auto;" />
 
 :::::::::::::::::::::::::::::::::
 ::::::::::::::::::::::::::::::::::::::::::::::::
@@ -352,7 +352,7 @@ ggplot(data = hosp_df_long) +
   )
 ```
 
-<img src="fig/disease-burden-rendered-unnamed-chunk-11-1.png" style="display: block; margin: auto;" />
+<img src="fig/disease-burden-rendered-unnamed-chunk-11-1.png" alt="" style="display: block; margin: auto;" />
 
 ## Summary
 

contact-matrices.md

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disease-burden.md

@@ -154,7 +154,7 @@ Consider the following questions:
 
 A deterministic SEIR model with age specific direct transmission. 
 
-<img src="fig/model-choices-rendered-diagram-1.png" style="display: block; margin: auto;" />
+<img src="fig/model-choices-rendered-diagram-1.png" alt="" style="display: block; margin: auto;" />
 
 
 The model is capable of simulating an Ebola type outbreak, but as the model is deterministic, we are not able to explore stochastic variation in the early stages of the outbreak.
@@ -173,7 +173,7 @@ The key parameters affecting the transition between states are:
 
 **Note: the functional relationship between the preinfectious  period ($\rho^E$) and the transition rate between exposed and infectious ($\gamma^E$) is $\rho^E = k^E/\gamma^E$ where $k^E$ is the shape of the Erlang distribution. Similarly for the infectious period $\rho^I = k^I/\gamma^I$. For more detail on the stochastic model formulation refer to the section on [Discrete-time Ebola virus disease model](https://epiverse-trace.github.io/epidemics/articles/model_ebola.html#details-discrete-time-ebola-virus-disease-model) in the "Modelling responses to a stochastic Ebola virus epidemic" vignette. ** 
 
-<img src="fig/model-choices-rendered-unnamed-chunk-1-1.png" style="display: block; margin: auto;" />
+<img src="fig/model-choices-rendered-unnamed-chunk-1-1.png" alt="" style="display: block; margin: auto;" />
 
 The model has additional parameters describing the transmission risk in hospital and funeral settings: 
 
@@ -303,7 +303,7 @@ output %>%
   )
 ```
 
-<img src="fig/model-choices-rendered-unnamed-chunk-3-1.png" style="display: block; margin: auto;" />
+<img src="fig/model-choices-rendered-unnamed-chunk-3-1.png" alt="" style="display: block; margin: auto;" />
 
 2. Run model 100 times and plot the mean, upper and lower 95% quantiles of the number of infectious individuals through time
 
@@ -348,7 +348,7 @@ output_replicates %>%
   )
 ```
 
-<img src="fig/model-choices-rendered-unnamed-chunk-4-1.png" style="display: block; margin: auto;" />
+<img src="fig/model-choices-rendered-unnamed-chunk-4-1.png" alt="" style="display: block; margin: auto;" />
 
 :::::::::::::::::::::::::::
 

md5sum.txt

@@ -281,7 +281,7 @@ output %>%
   )
 ```
 
-<img src="fig/modelling-interventions-rendered-baseline-1.png" style="display: block; margin: auto;" />
+<img src="fig/modelling-interventions-rendered-baseline-1.png" alt="" style="display: block; margin: auto;" />
 
 We can see that with the intervention in place, the infection still spreads through the population and hence accumulation of immunity contributes to the eventual peak-and-decline. However, the peak number of infectious individuals is smaller (green dashed line) than the baseline with no intervention in place (red solid line), showing a reduction in the absolute number of cases.
 
@@ -363,7 +363,7 @@ output %>%
   )
 ```
 
-<img src="fig/modelling-interventions-rendered-plot_masks-1.png" style="display: block; margin: auto;" />
+<img src="fig/modelling-interventions-rendered-plot_masks-1.png" alt="" style="display: block; margin: auto;" />
 
 ::::::::::::::::::::::::::::::::::::: callout
 ### Intervention types
@@ -392,7 +392,7 @@ Pharmaceutical interventions (PIs) are measures such as vaccination and mass tre
 
 The diagram below shows the SEIRV model implemented using `model_default()` where susceptible individuals are vaccinated and then move to the $V$ class.
 
-<img src="fig/modelling-interventions-rendered-diagram_SEIRV-1.png" style="display: block; margin: auto;" />
+<img src="fig/modelling-interventions-rendered-diagram_SEIRV-1.png" alt="" style="display: block; margin: auto;" />
 
 
 
@@ -488,7 +488,7 @@ output %>%
   )
 ```
 
-<img src="fig/modelling-interventions-rendered-plot_vaccinate-1.png" style="display: block; margin: auto;" />
+<img src="fig/modelling-interventions-rendered-plot_vaccinate-1.png" alt="" style="display: block; margin: auto;" />
 
 From the plot, we see that the peak number of total number of infectious individuals when vaccination is in place is much lower compared to school closures and mask-wearing interventions. 
 
@@ -538,7 +538,7 @@ infections_baseline_interv %>%
   theme_bw()
 ```
 
-<img src="fig/modelling-interventions-rendered-unnamed-chunk-6-1.png" style="display: block; margin: auto;" />
+<img src="fig/modelling-interventions-rendered-unnamed-chunk-6-1.png" alt="" style="display: block; margin: auto;" />
 
 To get an age-stratified plot, keep the default `by_group = TRUE` and then add `linetype = demography_group` when declaring variables in `ggplot(aes(...))`.