bullets not boxes

syllabus.md

@@ -74,23 +74,23 @@ There will not be enough time in the workshop to cover all of `macpan2`, so ther
 
 Participants will learn how to do the following types of tasks required for exploring model simulations.
 
-- [ ]  Find candidate simulation models in the `macpan2` [library](#library-models) of starter models.
-- [ ] Prepare certain [types of data](#types-of-data) so that they can be compared with `macpan2` simulation output, both visually and numerically.
-- [ ] Make [modifications to models](#model-modification-tools) in the library.
-- [ ] Compute [epidemiological summaries](#epidemiological-model-summaries) (e.g., basic reproduction number, $\mathcal{R}_0$).
-- [ ] Cast a model as a particular [dynamical model type](#dynamical-model-types) (e.g. discrete-time recursion, ordinary differential equation).
+* Find candidate simulation models in the `macpan2` [library](#library-models) of starter models.
+* Prepare certain [types of data](#types-of-data) so that they can be compared with `macpan2` simulation output, both visually and numerically.
+* Make [modifications to models](#model-modification-tools) in the library.
+* Compute [epidemiological summaries](#epidemiological-model-summaries) (e.g., basic reproduction number, $\mathcal{R}_0$).
+* Cast a model as a particular [dynamical model type](#dynamical-model-types) (e.g. discrete-time recursion, ordinary differential equation).
 
 
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 #### Session 2: Parameterization
 
 Participants will learn how to do the following types of tasks required for parameterizing models for making inferences about a particular population and public health problem.
 
-- [ ] Modify the default values of parameters in a model.
-- [ ] Express uncertainty in model parameters (e.g., transmission rate) or in [epidemiological summaries](#epidemiological-model-summaries) (e.g., $\mathcal{R}_0$) with prior distributions.
-- [ ] Use [optimization](#optimization) to calibrate parameters (e.g., transmission rate) so that the discrepancy between observed and simulated data is minimized.
-- [ ] Parameterize the [initial values of the state variables](#calibrate-initial-state-variables) (e.g. `S`, `I`) so that they can be optimized.
-- [ ] Calibrate the functional form of time-variation of parameters using machine learning components embedded within epidemiological models. This is a useful technique when the reasons for parameter time-variation are not well-understood.
+* Modify the default values of parameters in a model.
+* Express uncertainty in model parameters (e.g., transmission rate) or in [epidemiological summaries](#epidemiological-model-summaries) (e.g., $\mathcal{R}_0$) with prior distributions.
+* Use [optimization](#optimization) to calibrate parameters (e.g., transmission rate) so that the discrepancy between observed and simulated data is minimized.
+* Parameterize the [initial values of the state variables](#calibrate-initial-state-variables) (e.g. `S`, `I`) so that they can be optimized.
+* Calibrate the functional form of time-variation of parameters using machine learning components embedded within epidemiological models. This is a useful technique when the reasons for parameter time-variation are not well-understood.
 
 
 
@@ -99,24 +99,24 @@ Participants will learn how to do the following types of tasks required for para
 
 Participants will learn how to make the following types of inferences using realistically parameterized models.
 
-- [ ] Visualize goodness-of-fit.
-- [ ] Generate confidence intervals for estimated parameters.
-- [ ] Forecast model variables beyond the last data point.
-- [ ] Calculate prediction intervals measuring uncertainty about these forecasts.
-- [ ] Compare alternative scenarios for counter-factual causal analysis (e.g., how many deaths were saved due to vaccination?).
-- [ ] Produce uncertainty estimates for [epidemiological model summaries](#epidemiological-model-summaries) like $\mathcal{R}_0$.
+* Visualize goodness-of-fit.
+* Generate confidence intervals for estimated parameters.
+* Forecast model variables beyond the last data point.
+* Calculate prediction intervals measuring uncertainty about these forecasts.
+* Compare alternative scenarios for counter-factual causal analysis (e.g., how many deaths were saved due to vaccination?).
+* Produce uncertainty estimates for [epidemiological model summaries](#epidemiological-model-summaries) like $\mathcal{R}_0$.
 
 
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 #### Session 4: Stratification
 
 Participants will learn the following stratification strategies.
 
-- [ ] [Stratify every compartment](#cartesian-product-models) in the same way (e.g. by age, location).
-- [ ] [Stratify infectious compartments](#stratify-infectious-compartments) (e.g. by symptom status).
-- [ ] Expand a single compartment into a sequential chain, to model different distributions of time spent in that compartment.
-- [ ] Combine several single-strain models into a single multi-strain model.
-- [ ] Stratify compartments based on immunity status. This is particularly useful in cases where immunity is partial, can wane, and is caused by a mixture of past infection, vaccination, and cross-immunity.
+* [Stratify every compartment](#cartesian-product-models) in the same way (e.g. by age, location).
+* [Stratify infectious compartments](#stratify-infectious-compartments) (e.g. by symptom status).
+* Expand a single compartment into a sequential chain, to model different distributions of time spent in that compartment.
+* Combine several single-strain models into a single multi-strain model.
+* Stratify compartments based on immunity status. This is particularly useful in cases where immunity is partial, can wane, and is caused by a mixture of past infection, vaccination, and cross-immunity.
 
 
 ## Materials