Fix time_varying severity vignette

vignettes/estimate_time_varying_severity.Rmd

@@ -27,7 +27,6 @@ knitr::opts_chunk$set(
 
 There are many biological, epidemiological and behavioural reasons why the severity of a disease might change during the course of an outbreak, including the introduction of vaccines or therapeutics, reducing the relative risk of death, or the emergence of pathogen variants, which may alter the risk of hospitalisation or death associated with infection.
 
-Estimating the severity of an outbreak that has undergone substantial changes is of critical importance for surveillance and planning purposes.
 Running the `estimate_static()` function on datasets corresponding to different stages of the outbreak would lead to two static estimates, which may or may not differ.
 This could be useful, but a more detailed and easier to interpret approach is to use the function demonstrated in this vignette, `estimate_time_varying()`.
 
@@ -49,6 +48,10 @@ We wish to estimate how common severity quantities _change over time_ --- such a
 * A time-series of cases, hospitalisations or some other proxy for infections over time;
 * A time-series of deaths;
 * A delay distribution, describing the probability an individual will die $t$ days after they were initially infected.
+
+The first two elements are expected be included in a dataframe with the columns "dates", "cases", and "deaths"; see below for examples.
+
+The delay distribution is expected to be specified as an object of the class `<epidist>` from the package [_epiparameter_](https://epiverse-trace.github.io/epiparameter/).
 :::
 
 First load _cfr_ and packages to access and plot data.