add goal steps questions subtitles

Author: avallecam

instructors/01-practical-tutors.qmd

@@ -53,20 +53,15 @@ This practical has two activities.
 
 ## Activity 1: Clean and standardize raw data
 
+**Goal:**
+
 Get a clean and standardized data frame using the following available inputs:
 
 - Raw messy data frame in CSV format
 
-Within your room, Write your answers to these questions:
-
-- Diagnose the raw data. What data cleaning operations need to be performed on the dataset? Write all of them before writing the code.
-- What time unit best describes the time span to calculate?
-- Print the report: What features do you find useful to communicate with a decision-maker?
-- Compare: What differences do you identify from other room outputs? (if available)
-
 **Steps:**
 
-- Open file `01-practical-activity-1.R` and fill in your `room_number` in the script.
+- Open file `01-practical-activity-1.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
 - First, complete the argument to read the data. Paste the link as a “string” in `read_csv()`.
 - Second, complete the cleaning process. Add functions based on the data needs. Connect them using the pipe `%>%`:
     - Standardize column names 
@@ -80,6 +75,14 @@ Within your room, Write your answers to these questions:
 - Third, complete the cleanepi::timespan() arguments. Access the help manual running `?cleanepi::timespan()` in the console.
 - Paste the outputs. Reply to questions.
 
+**Questions:**
+
+Within your room, Write your answers to these questions:
+
+- Diagnose the raw data. What data cleaning operations need to be performed on the dataset? Write all of them before writing the code.
+- What time unit best describes the time span to calculate?
+- Print the report: What features do you find useful to communicate with a decision-maker?
+- Compare: What differences do you identify from other room outputs? (if available)
 
 ### Inputs
 
@@ -166,10 +169,23 @@ Write your answers to the questions above:
 
 ## Activity 2: Validate linelist and plot epicurve
 
+**Goal:**
+
 Get a validated linelist and incidence plot using the following available inputs:
 
 - Clean data frame object
 
+**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.
+
+**Questions:**
+
 Within your room, Write your answers to these questions:
 
 - In the validation step, Do you need to allow for extra variable names and types for the `Date` and `Categorical` variable? 
@@ -180,15 +196,6 @@ Within your room, Write your answers to these questions:
 - Interpret: How would you communicate these results to a decision-maker?
 - Compare: What differences do you identify from other room outputs? (if available)
 
-**Steps:**
-
-- Open the file `01-practical-activity-2.R` and fill in your `room_number` in the script.
-- 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.
-
 ### Inputs
 
 Use outputs from activity 1. 

instructors/02-practical-tutors.qmd

@@ -52,21 +52,16 @@ This practical has two activities.
 
 ## Activity 1: Transmission
 
+**Goal:**
+
 Estimate $R_{t}$, _new infections_, _new reports_, _growth rate_, and _doubling/halving time_ using the following available inputs:
 
 - Incidence of reported cases per day
 - Reporting delay
 
-Within your room, Write your answers to these questions:
-
-- What phase of the epidemic are you observing? (Exponential growth phase, near peak, or decay end phase)
-- Is the expected change in daily reports consistent with the estimated effective reproductive number, growth rate, and doubling time?
-- Interpret: How would you communicate these results to a decision-maker?
-- Compare: What differences do you identify from other room outputs? (if available)
-
 **Steps:**
 
-- Open the file `02-practical-activity-1.R` and fill in your `room_number` in the script.
+- Open the file `02-practical-activity-1.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
 - Paste the URL link as a string to read input data.
 - Keep the reading function that corresponds to your input data disease.
 - Define a generation time:
@@ -80,6 +75,15 @@ Within your room, Write your answers to these questions:
 - Run `EpiNow2::epinow()` and print the summary and plot outputs.
 - Paste the outputs. Reply to questions.
 
+**Questions:**
+
+Within your room, Write your answers to these questions:
+
+- What phase of the epidemic are you observing? (Exponential growth phase, near peak, or decay end phase)
+- Is the expected change in daily reports consistent with the estimated effective reproductive number, growth rate, and doubling time?
+- Interpret: How would you communicate these results to a decision-maker?
+- Compare: What differences do you identify from other room outputs? (if available)
+
 ### Inputs
 
 | Room | Incidence | Link |
@@ -347,30 +351,34 @@ there is no statistical evidence of extinction if the 90% credible intervals of:
 
 ## Activity 2: Severity
 
+**Goal:**
+
 Estimate the _naive CFR (nCFR)_ and _delay-adjusted CFR (aCFR)_ using the following inputs:
 
 - Reported cases (aggregate incidence by date of onset)
 - Onset to death delay
 
-Within your room, Write your answers to these questions:
-
-- What phase of the epidemic are you observing? (Exponential growth phase, near peak, or decay end phase)
-- Does the time series include all the possible deaths to observe from known cases?
-- How much difference is there between the nCFR and aCFR estimates?
-- Interpret: How would you communicate these results to a decision-maker?
-- Compare: What differences do you identify from other room outputs? (if available)
-
 **Steps:**
 
-- Open the file `02-practical-activity-2.R` and fill in your `room_number` in the script.
+- Open the file `02-practical-activity-2.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
 - Paste the URL link as a string to read input data.
 - Fill in the argument to plot an incidence curve.
 - Evaluate if the input data format needs adaptation to {cfr}.
 - Access to the probability distribution for the delay from case onset to death.
-- Evaluate if you need to keep dates or omit using `dplyr::filter()`.
+- Evaluate if you are requested to keep dates or omit using `dplyr::filter()`.
 - Estimate the naive and delay-adjusted CFR.
 - Paste the outputs. Reply to questions.
 
+**Questions:**
+
+Within your room, Write your answers to these questions:
+
+- What phase of the epidemic are you observing? (Exponential growth phase, near peak, or decay end phase)
+- Does the time series include all the possible deaths to observe from known cases?
+- How much difference is there between the nCFR and aCFR estimates?
+- Interpret: How would you communicate these results to a decision-maker?
+- Compare: What differences do you identify from other room outputs? (if available)
+
 ### Inputs
 
 | Room | Data | Action on data input | Link |

instructors/03-practical-tutors.qmd

@@ -52,14 +52,24 @@ This practical has two activities.
 
 ## Activity 1: Account for superspreading
 
+**Goal:**
+
 Estimate the extent of individual-level variation (i.e. the dispersion parameter) of the offspring distribution, which refers to the variability in the number of secondary cases per individual, and assess the implications for variation in transmission for decision-making using the following available inputs:
 
 - Line list of cases
 - Contact tracing data
 
 **Steps:**
 
-Open the file `03-practical-activity-1.R` and fill in all the `#<COMPLETE>` lines following the `steps:` detailed in the R file.
+- Open the file `03-practical-activity-1.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
+- Paste the URL links as a string to read input data.
+- Create a *directed* contact network using the linelist and contacts data inputs. Paste a screenshot of the network in the report.
+- Calculate the *out-degree* for each node (infector case) in the contact network, using *all* the cases observed in the linelist. Paste the output histogram in the report.
+- Use the vector with the number of secondary cases per infector case to fit a Negative Binomial distribution using {fitdistrplus}. Paste the output parameters in the report.
+- Use {superspreading} to calculate the probability (proportion) of new cases originating from a cluster of a given size (cluster size), using as input the offspring distribution parameters: the reproduction number and dispersion. Paste the output result in the report.
+
+
+**Questions:**
 
 Within your room, Write your answers to these questions:
 
@@ -308,14 +318,32 @@ Interpretation Helpers:
 
 ## Activity 2: Simulate transmission chains
 
+**Goal:**
+
 Estimate the potential for large outbreaks that could occur based on 1000 simulated outbreaks with one initial case, using the following available inputs:
 
 - Basic reproduction number
 - Dispersion parameter
 
 **Steps:**
 
-Open the file `03-practical-activity-2.R` and fill in all the `#<COMPLETE>` lines following the `steps:` detailed in the R file.
+- Open the file `03-practical-activity-2.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
+- Use the input parameter for this room.
+- Create generation time as an <epiparameter> object using epiparameter::epiparameter() with prob_distribution "gamma" and summary statistics: mean = 3, sd = 1
+- Create 1000 simulation runs with 1 initial case. Add the input offspring distribution parameters to the corresponding arguments. Add the input generation time of class <epiparameter> as a function. Run set.seed() and epichains::simulate_chains() together, in the same run.
+- Read the output of the selected chain to observe. Paste the screenshot in the report. Write in the report a paragraph describing:
+    - the number of unknown and known infectors, their IDs.
+    - the number of generations.
+    - who infected whom in each generation, and when? i.e., the time range in days of these infections per generation.
+- Run the code to create a summary data frame of the whole simulation. Paste the plot output in the report. 
+- Use the plot or summary data frame (or any other calculation) to write in the report a description of:
+    - How many chains reached a 100 case threshold?
+    - What is the maximum size of chain? (The cumulative number of case)
+    - What is the maximum length of chain? (The number of days until the chain stops)
+- Write in the report: interpretation and comparison between rooms.
+
+
+**Questions:**
 
 Within your room, Write your answers to these questions:
 

instructors/04-practical-tutors.qmd

@@ -53,6 +53,8 @@ This practical has three activities.
 
 ## Activity 1: Generate Disease Trajectories Across Age Groups
 
+**Goal:**
+
 Generate disease trajectories of **infectious individuals** and **new infections** across age groups using the following available inputs:
 
 - Social contact matrix
@@ -61,7 +63,16 @@ Generate disease trajectories of **infectious individuals** and **new infections
 
 **Steps:**  
 
-Open the file `04-practical-activity-1.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
+- Open the file `04-practical-activity-1.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
+- Paste the survey link for your room
+- Generate contact matrix by defining survey class object, country name, age limits from table of parameters, and whether to make a symmetric matrix.
+- Combine the initial conditions add `initial_conditions_inf` or `initial_conditions_free` to the each age group as given in table of parameter.
+- Prepare the population to model as affected by the epidemic add the name of the country, the symmetric and transposed contact matrix, the vector with the population size of each age group, the binded matrix with initial conditions for each age group.
+- Rates. add the values as given in table of parameter
+- In each function argument add the population object, each of the previously defined rates, the total simulation time as given in table of parameter.
+- Paste plot of total number of individual per compartment and table output with peak size and time in report
+- Paste plot output of new infections in report
+
 
 **Questions:**
 
@@ -249,6 +260,8 @@ Figures using `socialmixr::matrix_plot(contact_data$matrix * contact_data$demogr
 
 ## Activity 2: Compare the Baseline Scenario with a Single Intervention
 
+**Goal:**
+
 Compare the disease trajectories of **new infections** in the whole population under two conditions: 
 
 1. The baseline scenario (no intervention)
@@ -263,7 +276,17 @@ Use the following inputs to define and explore the intervention scenario:
 
 **Steps:**  
 
-Open the file `04-practical-activity-2.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
+- Open the file `04-practical-activity-2.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
+- Create the intervention object: identify if you need to keep: epidemics::intervention() or epidemics::vaccination(). then add:
+    - name of the intervention
+    - type of intervention ("rate" or "contacts"), if needed
+    - time when the intervention begins and ends (as values or matrix*) as given in table of inputs
+    - reduction or vaccination rate (as values or matrix*)
+    *if matrix, values follow same order as in the social contact matrix
+- Add the intervention argument as a list (for interventions against contacts or transmission rate) or as an object (for vaccination)
+- Paste plot and table output in report
+- Plot new infections. Add intervention name if your intervention is vaccination, then activate the argument exclude_compartments, run and paste plot output in report
+
 
 **Questions:**
 
@@ -427,11 +450,17 @@ Try modifying the intervention start time from day 200 to day 100, or changing t
 
 ## Activity 3: Combine Multiple Interventions
 
+**Goal:**
+
 Compare the baseline scenario with a simulation that includes two overlapping or sequential interventions. Use the intervention parameters described in the previous activity.
 
 **Steps:**  
 
-Open the file `04-practical-activity-3.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
+- Open the file `04-practical-activity-3.R` and complete all the lines marked with `#<COMPLETE>`, following the detailed steps provided within the R file.
+- Complete the intervention or vaccination arguments
+- Paste table output in report
+- Plot new infections. add intervention name if your intervention is vaccination, then activate the argument exclude_compartments, run and paste plot output in report
+
 
 **Questions:**
 

instructors/files/01-practical-tutors.md

@@ -23,25 +23,18 @@ This practical has two activities.
 
 ## Activity 1: Clean and standardize raw data
 
+**Goal:**
+
 Get a clean and standardized data frame using the following available
 inputs:
 
 - Raw messy data frame in CSV format
 
-Within your room, Write your answers to these questions:
-
-- Diagnose the raw data. What data cleaning operations need to be
-  performed on the dataset? Write all of them before writing the code.
-- What time unit best describes the time span to calculate?
-- Print the report: What features do you find useful to communicate with
-  a decision-maker?
-- Compare: What differences do you identify from other room outputs? (if
-  available)
-
 **Steps:**
 
-- Open file `01-practical-activity-1.R` and fill in your `room_number`
-  in the script.
+- Open file `01-practical-activity-1.R` and complete all the lines
+  marked with `#<COMPLETE>`, following the detailed steps provided
+  within the R file.
 - First, complete the argument to read the data. Paste the link as a
   “string” in `read_csv()`.
 - Second, complete the cleaning process. Add functions based on the data
@@ -58,6 +51,18 @@ Within your room, Write your answers to these questions:
   manual running `?cleanepi::timespan()` in the console.
 - Paste the outputs. Reply to questions.
 
+**Questions:**
+
+Within your room, Write your answers to these questions:
+
+- Diagnose the raw data. What data cleaning operations need to be
+  performed on the dataset? Write all of them before writing the code.
+- What time unit best describes the time span to calculate?
+- Print the report: What features do you find useful to communicate with
+  a decision-maker?
+- Compare: What differences do you identify from other room outputs? (if
+  available)
+
 ### Inputs
 
 | Room | Data | Link | Calculate time span | Categorize time span |
@@ -68,11 +73,26 @@ Within your room, Write your answers to these questions:
 
 ## Activity 2: Validate linelist and plot epicurve
 
+**Goal:**
+
 Get a validated linelist and incidence plot using the following
 available inputs:
 
 - Clean data frame object
 
+**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.
+
+**Questions:**
+
 Within your room, Write your answers to these questions:
 
 - In the validation step, Do you need to allow for extra variable names
@@ -92,16 +112,6 @@ Within your room, Write your answers to these questions:
 - Compare: What differences do you identify from other room outputs? (if
   available)
 
-**Steps:**
-
-- Open the file `01-practical-activity-2.R` and fill in your
-  `room_number` in the script.
-- 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.
-
 ### Inputs
 
 Use outputs from activity 1.