add space between text and bullets to render

Author: avallecam

episodes/compare-interventions.Rmd

@@ -110,10 +110,12 @@ Learners should familiarise themselves with following concept dependencies befor
 ## Introduction
 
 In this tutorial, we will compare intervention scenarios against each other. To quantify the effect of an intervention, we need to compare our intervention scenario to a counterfactual (baseline) scenario. The *counterfactual* here is the scenario in which nothing changes, often referred to as the "do-nothing" scenario. The counterfactual scenario may feature:
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 - No interventions at all, or
 - Existing interventions in place (if we are investigating the potential impact of an additional intervention)
 
 We must also define our *outcome of interest* to make comparisons between intervention and counterfactual scenarios. The outcome of interest can be:
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 - Direct model outputs (e.g., number of infections, hospitalizations)
 - Epidemiological metrics (e.g., epidemic peak time, final outbreak size)
 - Health impact measures (e.g., Quality-Adjusted Life Years [QALYs] or Disability-Adjusted Life Years [DALYs])

episodes/contact-matrices.Rmd

@@ -51,6 +51,7 @@ library(socialmixr)
 ## The contact matrix
 
 The basic contact matrix represents the amount of contact or mixing within and between different subgroups of a population. The subgroups are often age categories but can also be:
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 - Geographic areas (e.g., different regions or countries)
 - Risk groups (e.g., high/low risk occupations)
 - Social settings (e.g., household, workplace, school)
@@ -69,6 +70,7 @@ In this example, we would use this to represent that children meet, on average,
 
 ### A Note on Notation
 For a contact matrix with rows $i$ and columns $j$:
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 - $C[i,j]$ represents the average number of contacts that individuals in group $i$ have with individuals in group $j$
 - This average is calculated as the total number of contacts between groups $i$ and $j$, divided by the number of individuals in group $i$
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@@ -109,6 +111,7 @@ For the mathematical explanation see [the corresponding section in the socialmix
 One of the arguments we gave the function `contact_matrix()` is `symmetric=TRUE`. This ensures that the total number of contacts from one group to another is equal to the total from the second group back to the first (see the `socialmixr` [vignette](https://cran.r-project.org/web/packages/socialmixr/vignettes/socialmixr.html) for more detail). 
 
 However, when contact matrices are estimated from surveys or other sources, the *reported* number of contacts may differ by age group for several reasons:
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 - Recall bias: Different age groups may have different abilities to remember and report contacts accurately
 - Reporting bias: Some groups may systematically over- or under-report their contacts
 - Sampling uncertainty: Limited sample sizes can lead to statistical variations

episodes/disease-burden.Rmd

@@ -27,15 +27,18 @@ exercises: 10 # exercise time in minutes
 ## Introduction
 
 In previous tutorials we have used mathematical models to generate trajectories of infections, but we may also be interested in measures of disease burden. These measures could include:
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 - Health outcomes in the population (e.g., mild vs. severe infections)
 - Healthcare system impacts (e.g., hospitalizations, ICU admissions)
 - Economic impacts (e.g., productivity loss, healthcare costs)
 
 In mathematical models, we can track disease burden in different ways:
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 1. **Integrated approach**: Include burden compartments directly in the transmission model (e.g., hospital compartments in ODE models)
 2. **Separated approach**: First simulate transmission, then use the results to estimate burden
 
 The choice between these approaches depends on whether burden affects transmission. For example:
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 - For Ebola, hospitalizations are important for transmission due to high-risk healthcare settings
 - For many respiratory infections, severe illness typically occurs after the infectious period, so burden can be modeled separately
 
@@ -66,11 +69,13 @@ We will extend the influenza example from the [Simulating transmission](../episo
 2. **Burden model**: Converts new infections to hospitalizations by accounting for delays between infection and hospitalization
 
 To convert infections to hospitalizations, we need to consider:
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 - The probability that an infection leads to hospitalization (infection-hospitalization ratio, IHR)
 - The time delay from infection to hospital admission
 - The time spent in hospital before discharge
 
 We'll use the `{epiparameter}` package to define these delay distributions. The Gamma distribution is commonly used for these delays because:
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 - It's flexible and can model various shapes
 - It's bounded at zero (negative delays don't make sense)
 - It's supported by empirical data for many infectious diseases
@@ -322,12 +327,14 @@ ggplot(data = hosp_df_long) +
 ## Summary
 
 In this tutorial, we learned how to estimate hospitalizations based on daily new infections from a transmission model. This approach can be extended to other measures of disease burden, such as:
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 - ICU admissions
 - Deaths
 - Disability-Adjusted Life-Years (DALYs)
 - Healthcare costs
 
 These burden estimates are valuable for:
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 - Healthcare system planning
 - Health economic analyses
 - Policy decision-making

episodes/model-choices.Rmd

@@ -72,6 +72,7 @@ Model structures differ depending on the scale and nature of the outbreak. When
 ## What is the outcome of interest?
 
 The outcome of interest is typically a measurable quantity derived from the mathematical model. This could include:
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 - The number of infections over time
 - The peak number of hospitalizations
 - The total number of severe disease cases
@@ -100,6 +101,7 @@ There can be subtle differences in model structures for the same infection or ou
 ## Will any interventions be modelled? 
 
 Finally, interventions such as vaccination, social distancing, or treatment programs may be of interest. Different models have varying capabilities to incorporate interventions:
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 - Some models can simulate continuous interventions (e.g., ongoing vaccination programs)
 - Others handle discrete interventions (e.g., one-time school closures)
 - Some models may not include intervention capabilities at all