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instructors/01-practical-tutors.qmd

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 This practical is based in the following tutorial episodes:
 
 - <https://epiverse-trace.github.io/tutorials-early/clean-data.html>
-- <https://epiverse-trace.github.io/tutorials-early/validate.html>
 - <https://epiverse-trace.github.io/tutorials-early/describe-cases.html>
 
 
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 [^1]: Context of Serological data: Participants of a study are exposed to COVID-19 vaccines, then their serum samples are collected and challenged to emerging SARS-CoV-2 variants. They measure the titer of this immunological response. The higher the titre, the higher the antigenic response. Let's focus on describing the change in the frequency of vaccine categories (last vaccine exposure) through time (last date of exposure). Ref: https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(24)00484-5/fulltext
 
 
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 ### Your Answers
 
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-## Activity 2: Validate linelist and plot epicurve
+## Activity 2: Plot epicurve and delays
 
 **Goal:**
 
-Get a validated linelist and incidence plot using the following available inputs:
+Using a clean linelist data frame, produce:
 
-- Clean data frame object
+- An incidence plot (epicurve) showing case counts over time.
+- A delay distribution plot showing time between two epidemiological events.
 
 **Steps:**
 
-- Open the file `01-practical-activity-2.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
-- First, complete linelist::make_linelist() arguments.
-- Second, complete the {linelist} function that can validate a linelist.
-- Third, complete the arguments of the incidence2::incidence()
-- Fourth, keep, drop, or change argument values in function plot()
-- Paste the outputs. Reply to questions.
+- Open `01-practical-activity-2.R` and complete all lines marked with `#<COMPLETE>`.
+- Complete the arguments of `incidence2::incidence()` to generate the incidence object.
+- Adjust the arguments of `plot()` to get the most informative epicurve. Read the [`plot()` reference manual](https://www.reconverse.org/incidence2/manual.html#sec:man-plot.incidence2) to find available arguments.
+- Use `{ggplot2}` to plot the delay distribution(s) from the output of `cleanepi::timespan()`.
+- Paste your plots and reply to the discussion questions.
 
 **Questions:**
 
-Within your room, Write your answers to these questions:
+- Which combination of time unit and case categories best captures the outbreak pattern and why?
+- What does the shape of your epicurve suggest about how this outbreak spread?
+- Which is larger in your delay distribution: the mean or the median, and what does that tell you about its shape?
+- How might delays in the data collection process affect your interpretation of the most recent cases?
 
-- In the validation step, Do you need to allow for extra variable names and types for the `Date` and `Categorical` variable? 
-    - _[Read this GitHub issue as a hint](https://github.com/epiverse-trace/linelist/issues/176) to allow for extra variables._
-- What is the most apprioriate time unit to aggregate the incidence plot, based on visual inspection? 
-- Does keeping or dropping arguments like `fill`, `show_cases`, `angle`, `n_breaks` improve the incidence plot? 
-    - _[Read `plot()` reference manual](https://www.reconverse.org/incidence2/manual.html#sec:man-plot.incidence2) to find its arguments._
-- Interpret: How would you communicate these results to a decision-maker?
-- Compare: What differences do you identify from other room outputs? (if available)
+Discuss your answers with your group before sharing with the wider room.
 
 ### Inputs
 
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 | 2 | Date onset | Outcome |
 | 3 | Last exposure date | Last vaccine type |
 
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 ### Your Answers
 
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 ### Solution