Try yourself exercise 7 and cpp exercises

Author: jubicker

cpp-tutorials/exercises/exercise01.cpp

@@ -63,4 +63,10 @@ int main()
 
     // We export the results as csv which is saved in the current folder. Then we can plot the results using plot_secir_results.py.
     auto export_status = result.export_csv("../../cpp-tutorials/exercises/results_ode.csv");
+
+    // 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?
 }

cpp-tutorials/exercises/exercise03.cpp

@@ -61,4 +61,9 @@ int main()
 
     // We export the results as csv which is saved in the current folder. Then we can plot the results using plot_secir_results.py.
     auto export_status = result.export_csv("../../cpp-tutorials/exercises/results_ode_npis.csv");
+
+    //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?
 }

cpp-tutorials/exercises/exercise05.cpp

@@ -74,4 +74,9 @@ int main()
 
     // We export the results as csv which is saved in the current folder. Then we can plot the results using plot_secir_results.py.
     auto export_status = result.export_csv("../../cpp-tutorials/exercises/results_ode_ageres.csv");
+
+    // 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.
 }

cpp-tutorials/exercises/exercise07.cpp

@@ -114,4 +114,9 @@ int main()
 
     // We export the results for region 0 as csv which is saved in the current folder. Then we can plot the results using plot_secir_results.py.
     auto export_status = result_region0.export_csv("../../cpp-tutorials/exercises/results_ode_region0.csv");
+
+    // Try yourself
+    // You can now explore the Graph-ODE model yourself. Here are some suggestions what you can do:
+    // - **Exchanging individuals between regions with different frequencies**: Set up two identical models with two regions (nodes) and symmetric exchange between regions. The first model exchanges individuals twice a day and the second every hour. How does that influence infection dynamics?
+    // - **Spatial heterogeneity**: Add a third region to the model. Commuters of the third region only commute to the second region with a rate twice as high as the mobility  rate between the first and second region.
 }

exercises/exercise07.py

@@ -239,11 +239,31 @@ def _(mo):
 
 
 @app.cell
-def _(graph, mio, model, np, osecir):
+def _(model, np):
     # One coefficient per (age group x compartment)
     mobility_coefficients = 0.1 * np.ones(model.populations.numel())
-    # Dead individuals do not commute
-    mobility_coefficients[osecir.InfectionState.Dead] = 0
+    return (mobility_coefficients,)
+
+
+@app.cell
+def _(mo):
+    mo.md(r"""
+    ### Exercise
+
+    Set the mobility coefficients for `Dead` individuals to zero:
+    Hint: For the first age group this can be done via `mobility_coefficients[0 * int(osecir.InfectionState.Dead) + osecir.InfectionState.Dead] = 0`.
+    """)
+    return
+
+
+@app.cell
+def _():
+    # Insert code here
+    return
+
+
+@app.cell
+def _(graph, mio, mobility_coefficients):
     mobility_params = mio.MobilityParameters(mobility_coefficients)
     # Add two edges to graph
     graph.add_edge(0, 1, mobility_params)
@@ -359,5 +379,18 @@ def _(osecir, plt, result_region0_interpolated, result_region1_interpolated):
     return
 
 
+@app.cell
+def _(mo):
+    mo.md(r"""
+    ## Try yourself
+
+    You can now explore the Graph-ODE model yourself. Here are some suggestions what you can do:
+
+    - **Exchanging individuals between regions with different frequencies**: Set up two identical models with two regions (nodes) and symmetric exchange between regions. The first model exchanges individuals twice a day and the second every hour. How does that influence infection dynamics?
+    - **Spatial heterogeneity**: Add a third region to the model. Commuters of the third region only commute to the second region with a rate twice as high as the mobility  rate between the first and second region.
+    """)
+    return
+
+
 if __name__ == "__main__":
     app.run()