def METHOD_NAME(show_plot=True):
    """
    Runs the c++ secir model using one age group 
    and plots the results
    """
    # Define Comartment names
    compartments = [
        'Susceptible', 'Exposed', 'InfectedNoSymptoms', 'InfectedSymptoms',
        'InfectedSevere', 'InfectedCritical', 'Recovered', 'Dead']
    # Define population of age groups
    populations = [83000]
    days = 100  # number of days to simulate
    start_day = 1
    start_month = 1
    start_year = 2019
    dt = 0.1
    num_groups = 1
    num_compartments = len(compartments)
    # Initialize Parameters
    model = Model(1)
    A0 = AgeGroup(0)
    # Set parameters
    # Compartment transition duration
    model.parameters.IncubationTime[A0] = 5.2
    model.parameters.TimeInfectedSymptoms[A0] = 6.
    # 4-4.4 // R_2^(-1)+0.5*R_3^(-1)
    model.parameters.SerialInterval[A0] = 4.2
    model.parameters.TimeInfectedSevere[A0] = 12.  # 7-16 (=R5^(-1))
    model.parameters.TimeInfectedCritical[A0] = 8.
    # Initial number of people in each compartment
    model.populations[A0, State.Exposed] = 100
    model.populations[A0, State.InfectedNoSymptoms] = 50
    model.populations[A0, State.InfectedSymptoms] = 50
    model.populations[A0, State.InfectedSevere] = 20
    model.populations[A0, State.InfectedCritical] = 10
    model.populations[A0, State.Recovered] = 10
    model.populations[A0, State.Dead] = 0
    model.populations.set_difference_from_total(
        (A0, State.Susceptible), populations[0])
    # Compartment transition propabilities
    model.parameters.RelativeTransmissionNoSymptoms[A0] = 0.67
    model.parameters.TransmissionProbabilityOnContact[A0] = 1.0
    model.parameters.RecoveredPerInfectedNoSymptoms[A0] = 0.09  # 0.01-0.16
    model.parameters.RiskOfInfectionFromSymptomatic[A0] = 0.25  # 0.05-0.5
    model.parameters.SeverePerInfectedSymptoms[A0] = 0.2  # 0.1-0.35
    model.parameters.CriticalPerSevere[A0] = 0.25  # 0.15-0.4
    model.parameters.DeathsPerCritical[A0] = 0.3  # 0.15-0.77
    # twice the value of RiskOfInfectionFromSymptomatic
    model.parameters.MaxRiskOfInfectionFromSymptomatic[A0] = 0.5
    model.parameters.StartDay = (
        date(start_year, start_month, start_day) - date(start_year, 1, 1)).days
    # model.parameters.ContactPatterns.cont_freq_mat[0] = ContactMatrix(np.r_[0.5])
    model.parameters.ContactPatterns.cont_freq_mat[0].baseline = np.ones(
        (num_groups, num_groups)) * 1
    model.parameters.ContactPatterns.cont_freq_mat[0].minimum = np.ones(
        (num_groups, num_groups)) * 0
    model.parameters.ContactPatterns.cont_freq_mat.add_damping(
        Damping(coeffs=np.r_[0.9], t=30.0, level=0, type=0))
    # Apply mathematical constraints to parameters
    model.apply_constraints()
    # Run Simulation
    result = simulate(0, days, dt, model)
    # interpolate results
    result = interpolate_simulation_result(result)
    print(result.get_last_value())
    num_time_points = result.get_num_time_points()
    result_array = result.as_ndarray()
    t = result_array[0, :]
    group_data = np.transpose(result_array[1:, :])
    # sum over all groups
    data = np.zeros((num_time_points, num_compartments))
    for i in range(num_groups):
        data += group_data[:, i * num_compartments: (i + 1) * num_compartments]
    # Plot Results
    datelist = np.array(
        pd.date_range(
            datetime(start_year, start_month, start_day),
            periods=days, freq='D').strftime('%m-%d').tolist())
    tick_range = (np.arange(int(days / 10) + 1) * 10)
    tick_range[-1] -= 1
    fig, ax = plt.subplots()
    ax.plot(t, data[:, 0], label='#Susceptible')
    ax.plot(t, data[:, 1], label='#Exposed')
    ax.plot(t, data[:, 2], label='#Carrying')
    ax.plot(t, data[:, 3], label='#InfectedSymptoms')
    ax.plot(t, data[:, 4], label='#Hospitalzed')
    ax.plot(t, data[:, 5], label='#InfectedCritical')
    ax.plot(t, data[:, 6], label='#Recovered')
    ax.plot(t, data[:, 7], label='#Died')
    ax.set_title("SECIR model simulation")
    ax.set_xticks(tick_range)
    ax.set_xticklabels(datelist[tick_range], rotation=45)
    ax.legend()
    fig.tight_layout
    fig.savefig('Secir_simple.pdf')
    if show_plot:
        plt.show()
        plt.close()