diff --git a/episodes/create-forecast.Rmd b/episodes/create-forecast.Rmd
index 7e85c7e6..f85bd125 100644
--- a/episodes/create-forecast.Rmd
+++ b/episodes/create-forecast.Rmd
@@ -399,7 +399,7 @@ In all `{EpiNow2}` output figures, shaded regions reflect 90%, 50%, and 20% cred
 
 ::::::::::::::::::::::::::::::::::::: challenge
 
-Download the file [ebola_cases.csv](data/ebola_cases.csv) and read it into R. The simulated data consists of the date of symptom onset and number of confirmed cases of the early stages of the Ebola outbreak in Sierra Leone in 2014.
+Download the file [`ebola_cases.csv`](data/ebola_cases.csv) and read it into R. The simulated data consists of the date of symptom onset and number of confirmed cases of the early stages of the Ebola outbreak in Sierra Leone in 2014.
 
 Using the first 3 months (120 days) of data:
 
diff --git a/episodes/severity-static.Rmd b/episodes/severity-static.Rmd
index b0d3b94a..491a164e 100644
--- a/episodes/severity-static.Rmd
+++ b/episodes/severity-static.Rmd
@@ -777,21 +777,21 @@ The `{cfr}` package has a function called `cfr_time_varying()` with functionalit
 
 ### When to use cfr_rolling()?
 
-**cfr_rolling()** shows the estimated CFR on each outbreak day, given that future data on cases and deaths is unavailable at the time. The final value of _cfr_rolling()_ estimates is identical to _cfr_static()_ on the same data.
+`cfr_rolling()` shows the estimated CFR on each outbreak day, given that future data on cases and deaths is unavailable at the time. The final value of `cfr_rolling()` estimates is identical to `cfr_static()` on the same data.
 
-Remember, as shown above, _cfr_rolling()_ is helpful to get early-stage CFR estimates and check whether an outbreak's CFR estimate has stabilised. Thus, _cfr_rolling()_ is not sensitive to the length or size of the epidemic.
+Remember, as shown above, `cfr_rolling()` is helpful to get early-stage CFR estimates and check whether an outbreak's CFR estimate has stabilised. Thus, `cfr_rolling()` is not sensitive to the length or size of the epidemic.
 
 :::::::::::::::::
 
 ::::::::::::::::: callout
 
-### When to use cfr_time_varying()?
+### When to use `cfr_time_varying()`?
 
-On the other hand, **cfr_time_varying()** calculates the CFR over a moving window and helps to understand changes in CFR due to changes in the epidemic, e.g. due to a new variant or increased immunity from vaccination.
+On the other hand, `cfr_time_varying()` calculates the CFR over a moving window and helps to understand changes in CFR due to changes in the epidemic, e.g. due to a new variant or increased immunity from vaccination.
 
-However, _cfr_time_varying()_ is sensitive to sampling uncertainty. Thus, it is sensitive to the size of the outbreak. The higher the number of cases with expected outcomes on a given day, the more reasonable estimates of the time-varying CFR we will get. 
+However, `cfr_time_varying()` is sensitive to sampling uncertainty. Thus, it is sensitive to the size of the outbreak. The higher the number of cases with expected outcomes on a given day, the more reasonable estimates of the time-varying CFR we will get. 
 
-For example, with 100 cases, the fatality risk estimate will, roughly speaking, have a 95% confidence interval ±10% of the mean estimate (binomial CI). So if we have >100 cases with expected outcomes *on a given day*, we can get reasonable estimates of the time varying CFR. But if we only have >100 cases *over the course of the whole epidemic*, we probably need to rely on **cfr_rolling()** that uses the cumulative data.
+For example, with 100 cases, the fatality risk estimate will, roughly speaking, have a 95% confidence interval ±10% of the mean estimate (binomial CI). So if we have >100 cases with expected outcomes *on a given day*, we can get reasonable estimates of the time varying CFR. But if we only have >100 cases *over the course of the whole epidemic*, we probably need to rely on `cfr_rolling()` that uses the cumulative data.
 
 We invite you to read this [vignette about the `cfr_time_varying()` function](https://epiverse-trace.github.io/cfr/articles/estimate_time_varying_severity.html).