1132 Rename public members of IDE model

cpp/examples/ide_initialization.cpp

@@ -84,7 +84,7 @@ int main(int argc, char** argv)
             init.add_time_point(init.get_last_time() + dt,
                                 Vec::Constant((int)mio::isecir::InfectionTransition::Count, 1. * dt));
         }
-        model.m_transitions = init;
+        model.transitions = init;
     }
     else {
         // Use the real data for initialization.

cpp/examples/ide_secir.cpp

@@ -75,9 +75,9 @@ int main()
 
     // Uncomment one of the two lines to use a different method to initialize the model using the TimeSeries init.
     // Initialization method with Susceptibles.
-    // model.m_populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Susceptible] = 1000;
+    // model.populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Susceptible] = 1000;
     // Initialization method with Recovered.
-    // model.m_populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Recovered] = 0;
+    // model.populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Recovered] = 0;
 
     // Set working parameters.
     mio::SmootherCosine smoothcos(2.0);

cpp/examples/ide_secir_ageres.cpp

@@ -61,12 +61,12 @@ int main()
 
     // Uncomment these lines to use a different method to initialize the model using the TimeSeries init.
     // Initialization method with Susceptibles.
-    // model.m_populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Susceptible] = 1000;
-    // model.m_populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Count +
+    // model.populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Susceptible] = 1000;
+    // model.populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Count +
     //                                      (Eigen::Index)mio::isecir::InfectionState::Susceptible] = 1000;
     // Initialization method with Recovered.
-    // model.m_populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Recovered] = 0;
-    // model.m_populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Count +
+    // model.populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Recovered] = 0;
+    // model.populations.get_last_value()[(Eigen::Index)mio::isecir::InfectionState::Count +
     //                                      (Eigen::Index)mio::isecir::InfectionState::Recovered] = 0;
 
     // Set working parameters.

cpp/models/ide_secir/README.md

@@ -28,7 +28,7 @@ The model parameters used are the following:
 | $\xi_{I_{NS}}$               |  `RelativeTransmissionNoSymptoms`               | Proportion infected people with no symptoms who are not isolated. |
 | $\xi_{I_{Sy}}$               | `RiskOfInfectionFromSymptomatic`                | Proportion of infected persons with symptoms who are not isolated. |
 | $N$                         | `m_N`   | Total population. |
-| $D$                         |  Entry of `m_populations`  | Number of dead people. |
+| $D$                         |  Entry of `populations`  | Number of dead people. |
 | $\mu_{z_1}^{z_2}$              |   `TransitionProbabilities`              | Probability of transitioning from compartment $z_1$ to compartment $z_2$. |  
 | $\gamma_{z_1}^{z_2}(\tau)$              |   `TransitionDistributions`              | Expected proportion of people who are still in compartment $z_1$ $\tau$ days after entering this compartment and who will move to compartment $z_2$ later in the course of the disease. |  
 
@@ -47,6 +47,6 @@ An example can be found at:
 - There are various options for initializing a fictional scenario. Regardless of the approach, you must provide a history of values for the transitions and additional information to compute the initial distribution of the population in the compartments. This information must be of the following type:  
 
     - You can state the number of total confirmed cases `total_confirmed_cases` at time $t_0$. The number of recovered people is set accordingly and the remaining values are derived in the model before starting the simulation.
-    - You can set the number of people in the `Susceptible` compartment at time $t_0$ via `m_populations`. Initial values of the other compartments are derived in the model before starting the simulation.
-    - You can set the number of people in the `Recovered` compartment at time $t_0$ via `m_populations`. Initial values of the other compartments are derived in the model before starting the simulation.
+    - You can set the number of people in the `Susceptible` compartment at time $t_0$ via `populations`. Initial values of the other compartments are derived in the model before starting the simulation.
+    - You can set the number of people in the `Recovered` compartment at time $t_0$ via `populations`. Initial values of the other compartments are derived in the model before starting the simulation.
     - If none of the above is used, the force of infection formula and the values for the initial transitions are used consistently with the numerical scheme proposed in [Messina et al (2022)](https://doi.org/10.3934/jcd.2021029) to set the `Susceptible`s.