def simulation_contact_tracing(n, p, times):
interventions = []
mapping = {
'S': 'QS',
'E': 'QE',
'I1': 'Q1',
'I2': 'Q2',
'I3': 'Q3',
'R': 'R',
'D': 'D',
'Q1': 'Q1',
'Q2': 'Q2',
'Q3': 'Q3',
'QS': 'QS',
'QE': 'QE'
}
for time in times:
interventions.append(
ContactTracingIntervention(["I1", "I2", "I3"], mapping, p, time,)
)
LG = setup_LG(n)
M = QuorontracingModel(0, 0, 0)
c_observer = CountObserver(quolor_palette.keys())
observers = [c_observer]
M.init_states(LG, 5)
simulate(LG, M, interventions, 50, observers) # scale x -axis
return c_observer.data
def simulation_age_model(n, b_kids, g_kids, p_kids, b_normal, g_normal, p_normal, b_risk, g_risk, p_risk):
LG = setup_LG(n)
AM = AgeModel(b_kids, g_kids, p_kids, b_normal, g_normal, p_normal,
b_risk, g_risk, p_risk)
c_observer = CountObserver(kolor_palette.keys())
AM.init_states(LG, 5)
simulate(LG, AM, set(), 50, [c_observer])
return c_observer.data
def simulation_max_class(n, class_size, degree, p):
if degree is None and p is None:
LG, CH = setup_CH(n, classsize=class_size)
else:
LG, CH = setup_CH(n, classsize=class_size, degree=degree, p=p)
CH.init_states(LG, 5)
c_observer = CountObserver(color_palette.keys())
simulate(LG, CH, set(), 50, [c_observer])
return c_observer.data
def simulation_max_quarantine_rates(n, q1, q2, q3):
LG = setup_LG(n)
CH = CoronaQuarantineModel(q1, q2, q3)
# Observer
c_observer = CountObserver(qolor_palette.keys())
observers = [c_observer]
# Simulation
CH.init_states(LG, 5)
simulate(LG, CH, set(), 50, observers)
return c_observer.data
def simulation_age_max_class(n, class_size, degree, p):
if degree is None and p is None:
LG = setup_LG(n, classsize=class_size)
else:
LG = setup_LG(n, classsize=class_size, degree=degree, p=p)
AM = AgeModel(B_KIDS, G_KIDS, P_KIDS, B_NORMAL, G_NORMAL, P_NORMAL,
B_NORMAL, G_NORMAL, P_NORMAL)
AM.init_states(LG, 5)
c_observer = CountObserver(kolor_palette.keys())
simulate(LG, AM, set(), 50, [c_observer])
return c_observer.data
def simulation_max_time(n, t):
LG, CH = setup_CH(n)
# Interventions
layernames_to_p = {
"Households": 1, "Schools": 0, "Workplaces": 0.01,
"R_Workplaces": 0, "Social": 0, "parties": 0.00, "basic": 0.1}
um_intervention1 = UpdateMultipleLayerIntervention(layernames_to_p, t)
# Observer
c_observer = CountObserver(color_palette.keys())
observers = [c_observer]
# Simulation
CH.init_states(LG, 5)
simulate(LG, CH, {um_intervention1}, 50, observers)
return c_observer.data
def simulation_second_wave():
LG, CH = setup_CH(N)
# Interventions
layernames_to_p1 = {
"Households": 0.5, "Schools": 0, "Workplaces": 0.01,
"R_Workplaces": 0, "Social": 0.01, "parties": 0.00,
"basic": 0.01
}
layernames_to_p2 = {
"Households": 1, "Schools": 1, "Workplaces": 1,
"R_Workplaces": 1, "Social": 1, "parties": 1,
"basic": 1
}
um_intervention1 = UpdateMultipleLayerIntervention(layernames_to_p1, 6)
# 7.8
um_intervention2 = UpdateMultipleLayerIntervention(layernames_to_p2, 28.7)
# 29.7
# Observer
c_observer = CountObserver(color_palette.keys())
# Simulation
CH.init_states(LG, 5)
interventions = {um_intervention1, um_intervention2}
simulate(LG, CH, interventions, 50, [c_observer])
return c_observer.data
def simulation_no_intervention(n):
LG, CH = setup_CH(n)
c_observer = CountObserver(color_palette.keys())
CH.init_states(LG, 5)
simulate(LG, CH, set(), 50, [c_observer])
return c_observer.data
def simulation_half_edges():
LG, CH = setup_CH(N, p=0.5)
c_observer = CountObserver(color_palette.keys())
CH.init_states(LG, 5)
simulate(LG, CH, set(), 50, [c_observer])
return c_observer.data