{

// In Tutorial 1, we created, initialized and simulated MEmilio's ODE-based SECIR-type model without any sociodemographic resolution. All ODE-based models have the possibility to add an arbitrary number of sociodemographic groups which can represent certain certain risk groups, like vaccination or age groups. Adding those groups can have a relevant impact on the simulation outcome. If for example older people have a higher risk of severe and critical infections, that can have an impact on ICU occupancy.

// In the following, we initialize and simulate an ODE-based SECIR-type model with three age groups.

// *** Set up model. ***

// First we create and initialize a SECIR-type model with three age groups. For a detailed description on that, see Tutorial 1.

size_t num_agegroups = 3;

ScalarType total_population = 100000;

ScalarType t0 = 0;

ScalarType tmax = 100;

ScalarType dt = 0.1;

// Create model with three age groups

mio::osecir::Model<ScalarType> model(num_agegroups);

// Now, we have to set the epidemiological model parameters which are dependent on age group. A list of all parameters can be found at https://memilio.readthedocs.io/en/latest/cpp/models/osecir.html.

// We choose an increasing risk of severe and critical infections for age group 2 and 3 compared to age group 1. The other parameters are equal for all age groups.

for (size_t i = 0; i < num_agegroups; i++) {

// Set infection state stay times (in days)

model.parameters.get<mio::osecir::TimeExposed<ScalarType>>()[mio::AgeGroup(i)] = 3.2;

model.parameters.get<mio::osecir::TimeInfectedNoSymptoms<ScalarType>>()[mio::AgeGroup(i)] = 2.;

model.parameters.get<mio::osecir::TimeInfectedSymptoms<ScalarType>>()[mio::AgeGroup(i)] = 6.;

model.parameters.get<mio::osecir::TimeInfectedSevere<ScalarType>>()[mio::AgeGroup(i)] = 12.;

model.parameters.get<mio::osecir::TimeInfectedCritical<ScalarType>>()[mio::AgeGroup(i)] = 9.;

// Set infection state transition probabilities

model.parameters.get<mio::osecir::TransmissionProbabilityOnContact<ScalarType>>()[mio::AgeGroup(i)] = 0.1;

model.parameters.get<mio::osecir::RelativeTransmissionNoSymptoms<ScalarType>>()[mio::AgeGroup(i)] = 0.67;

model.parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<ScalarType>>()[mio::AgeGroup(i)] = 0.2;

model.parameters.get<mio::osecir::RiskOfInfectionFromSymptomatic<ScalarType>>()[mio::AgeGroup(i)] = 0.25;

model.parameters.get<mio::osecir::DeathsPerCritical<ScalarType>>()[mio::AgeGroup(i)] = 0.3;

}

// The groups have an increasing risk of severe and critical infections

model.parameters.get<mio::osecir::SeverePerInfectedSymptoms<ScalarType>>()[mio::AgeGroup(0)] = 0.2;

model.parameters.get<mio::osecir::SeverePerInfectedSymptoms<ScalarType>>()[mio::AgeGroup(1)] = 0.2 * 1.5;

model.parameters.get<mio::osecir::SeverePerInfectedSymptoms<ScalarType>>()[mio::AgeGroup(2)] = 0.2 * 2;

model.parameters.get<mio::osecir::CriticalPerSevere<ScalarType>>()[mio::AgeGroup(0)] = 0.25;

model.parameters.get<mio::osecir::CriticalPerSevere<ScalarType>>()[mio::AgeGroup(1)] = 0.25 * 1.5;

model.parameters.get<mio::osecir::CriticalPerSevere<ScalarType>>()[mio::AgeGroup(2)] = 0.25 * 2;

//EXERCISE: Please increase the percentage of severe cases per symptomatic cases in age group 2 by 10%.

//Set contact frequency

ScalarType contact_frequency = 10;

mio::ContactMatrixGroup<ScalarType>& contact_matrix =

model.parameters.get<mio::osecir::ContactPatterns<ScalarType>>();

contact_matrix[0] = mio::ContactMatrix<ScalarType>(

Eigen::MatrixX<ScalarType>::Constant(num_agegroups, num_agegroups, contact_frequency));

// In this example, 0.5% of the population is initially in `Exposed` and 0.5% is initially in `InfectedNoSymptoms` while the remaining 99% of the population is `Susceptible`. The fractions are equally distributed to all age groups by:

for (size_t i = 0; i < num_agegroups; i++) {

model.populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Exposed}] =

0.005 * total_population / num_agegroups;

model.populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedNoSymptoms}] =

0.005 * total_population / num_agegroups;

model.populations.set_difference_from_group_total<mio::AgeGroup>(

{mio::AgeGroup(i), mio::osecir::InfectionState::Susceptible}, total_population / num_agegroups);

}

// *** Simulate model. ***

// After having initialized the model, dynamics can be simulated. The simulation output is a time series containing the evolution of all compartments per age group over time. In the following we simulate the model from `t0` to `tmax` with initial step size `dt` and subsequently print the time series result:

mio::TimeSeries<ScalarType> result = mio::osecir::simulate<ScalarType>(t0, tmax, dt, model);

// Interpolate time series to full days.

auto interpolated_result = mio::interpolate_simulation_result(result);

// *** Print results. ***

interpolated_result.print_table();

// 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.