"try yourself" Exercises 1,3,5

Author: jubicker

exercises/exercise01.py

@@ -315,6 +315,20 @@ def _(osecir, plt, result):
     return
 
 
+@app.cell
+def _(mo):
+    mo.md(r"""
+    ## Try yourself
+
+    You have seen how to set up and run MEmilio's ODE-SECIR model. You can now explore the model yourself. Here are some suggestions what you can do:
+
+    - **Controlling the transmission process**: What happens if you modify the transmission probability or the contact frequency in the same way?
+    - **Diseases with severe courses**: Increase the proportion of severe or critical cases. What happens to the number of deaths?
+    - **Asymptomatic courses**: How does disease dynamics behave if we only have pre-symptomatic and no asymptomatic cases? What happens if we increase the average time in the non-symptomatic state?
+    """)
+    return
+
+
 @app.cell
 def _(mo):
     mo.md(r"""

exercises/exercise3.py exercises/exercise03.pyexercises/exercise3.py renamed to exercises/exercise03.py

@@ -105,7 +105,7 @@ def _():
 @app.cell
 def _(mo):
     mo.md(r"""
-    After the model initialization, we add a contact reduction (`Damping`) that represents an NPI like e.g. mask wearing or social distancing. Dampings are a factor applied to the contact frequency and can be added to the model at fixed simulation time points before simulating. They have a *Level* and a *Type*. A damping with a given level and type replaces the previously active one with the same level and type, while all currently active dampings of one level and different types are summed up. If two dampings have different levels (independent of the type) they are combined multiplicatively. In the following we apply a `Damping` of 0.9 after 10 days and another damping of 0.6 after 20 days which means that the contacts are reduced by 10% and 40%, respectively. In general, it is also possible to increase the contact rate by using negative Damping values. To always retain a minimum level of contacts, a minimum contact frequency can be set that is never deceeded. In our example we set this minimum contact rate to 0.
+    After the model initialization, we add a contact reduction (`Damping`) that represents an NPI like e.g. mask wearing or social distancing. Dampings are a factor applied to the contact frequency and can be added to the model at fixed simulation time points before simulating. They have a *Level* and a *Type*. A damping with a given level and type replaces the previously active one with the same level and type, while all currently active dampings of one level and different types are summed up. If two dampings have different levels (independent of the type) they are combined multiplicatively. In the following we apply a `Damping` of 0.9 after 10 days and another damping of 0.6 after 20 days which means that the contacts are reduced by 10% and 40%, respectively. In general, it is also possible to increase the contact rate by using Damping values greater than 1. To always retain a minimum level of contacts, a minimum contact frequency can be set that is never deceeded. In our example we set this minimum contact rate to 0.
     """)
     return
 
@@ -220,5 +220,18 @@ def _(osecir, plt, result):
     return
 
 
+@app.cell
+def _(mo):
+    mo.md(r"""
+    ## Try yourself
+
+    After having learned how to use dampings, you can now try out yourself. Some suggestions what you can try are:
+
+    - **Lifting non-pharmaceutical interventions (NPIs)**: Can you temporarily implement an NPI and lift it again after 2 weeks?
+    - **Increasing contact rates**: Increase the contact rate after 20 days. What does this do to the simulated disease spread?
+    """)
+    return
+
+
 if __name__ == "__main__":
     app.run()

exercises/exercise5.py exercises/exercise05.pyexercises/exercise5.py renamed to exercises/exercise05.py

@@ -235,6 +235,19 @@ def _(osecir, plt, result):
     return
 
 
+@app.cell
+def _(mo):
+    mo.md(r"""
+    ## Try yourself
+
+    Now you can explore an age-resolved SECIR-model yourself. Examples what you can do are:
+
+    - **Even more age groups**: Initialize a model with 5 (age) groups with each group having a 2 times higher risk of dying from critical infection than the group before.
+    - **Age-dependent contact rates**: How can you include varying contact rates between age groups? Increase the contact rate within age group 1 and between age group 1 and 2.
+    """)
+    return
+
+
 @app.cell
 def _(mo):
     mo.md(r"""