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episodes/create-forecast.Rmd

@@ -399,7 +399,7 @@ In all `{EpiNow2}` output figures, shaded regions reflect 90%, 50%, and 20% cred
 
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-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:
 

episodes/severity-static.Rmd

@@ -96,7 +96,7 @@
 What are data sources can we use to estimate the clinical severity of a disease outbreak? [Verity et al., 2020](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext) summarises the spectrum of COVID-19 cases:
 
 ![Spectrum of COVID-19 cases. The CFR aims to estimate the proportion of Deaths among confirmed cases in an epidemic. 
 ([Verity et al., 2020](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext#gr1))](fig/cfr-spectrum-cases-covid19.jpg)
 
 - At the top of the pyramid, those who met the WHO case criteria for **severe** or critical cases would likely have been identified in the hospital setting, presenting with atypical viral pneumonia. These cases would have been identified in mainland China and among those categorised internationally as local transmission. 
 - Many more cases are likely to be **symptomatic** (i.e., with fever, cough, or myalgia) but might not require hospitalisation. These cases would have been identified through links to international travel to high-risk areas and through contact-tracing of contacts of confirmed cases. They might be identifiable through population surveillance of, for example, influenza-like illness. 
@@ -777,21 +777,21 @@
 
 ### 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.
 
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-### 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).