def __init__(self,
R0,
recovery_rate,
symptomatic_rate,
population_size=1.0):
infection_rate = R0 * recovery_rate
MatrixEpiModel.__init__(self, list("SEIR"), population_size)
self.set_quadratic_rates([
("S", "I", "S", -infection_rate),
("S", "I", "E", +infection_rate),
])
self.add_transition_processes([
("E", symptomatic_rate, "I"),
("I", recovery_rate, "R"),
])
def __init__(self,
R0,
recovery_rate,
quarantine_rate,
containment_rate,
population_size=1.0):
infection_rate = R0 * recovery_rate
MatrixEpiModel.__init__(self, list("SIRXH"), population_size)
self.set_quadratic_rates([
("S", "I", "S", -infection_rate),
("S", "I", "I", +infection_rate),
])
self.add_transition_processes([
("S", containment_rate, "H"),
("I", recovery_rate, "R"),
("I", containment_rate + quarantine_rate, "X"),
])
def mean_field_SIRX_tracing(kw):
R0 = kw['R0']
Q0 = kw['Q0']
waning_quarantine_rate = omega = kw['waning_quarantine_rate']
recovery_rate = rho = kw['recovery_rate']
quarantine_rate = kappa = rho * Q0 / (1-Q0)
infection_rate = eta = R0 * (rho)
app_participation_ratio = theta = kw['app_participation_ratio']
k0 = kw['k0']
model = MatrixEpiModel(list("SIXRQ"))
model.add_transition_processes([
( "Q", waning_quarantine_rate,"S"),
( "I", recovery_rate,"R"),
( "I", kappa,"X"),
])
model.add_transmission_processes([
( "S", "I", eta, "I", "I"),
( "I", "I", theta**2*kappa*k0, "I", "X"),
( "S", "I", theta**2*kappa*k0, "I", "Q"),
])
#for C, M in zip(model.compartments, model.quadratic_rates):
# print(C, M)
I0 = kw["I0"]
model.set_initial_conditions({"S":1-I0,"I":I0})
t = kw["t"]
result = model.integrate(t)
return t, result
MatrixEpiModel.__init__(self, list("SEIR"), population_size)
self.set_quadratic_rates([
("S", "I", "S", -infection_rate),
("S", "I", "E", +infection_rate),
])
self.add_transition_processes([
("E", symptomatic_rate, "I"),
("I", recovery_rate, "R"),
("R", waning_immunity_rate, "S"),
])
if __name__ == "__main__": # pragma: no cover
epi = MatrixEpiModel(list("SEIR"))
print(epi.compartments)
print()
epi.add_transition_processes([
("E", 1.0, "I"),
("I", 1.0, "R"),
])
print(epi.linear_rates)
epi.set_quadratic_rates([
("S", "I", "S", -1.0),
("S", "I", "E", +1.0),
])
print()
for iM, M in enumerate(epi.quadratic_rates):
print(epi.get_compartment(iM), M)
from epipack import MatrixEpiModel, get_2D_lattice_links
import numpy as np
from time import time
start = time()
N = 1000
links = [(i, i + 1, 1.0) for i in range(N - 1)]
base_compartments = "SIR"
compartments = [(node, C) for node in range(N) for C in "SIR"]
model = MatrixEpiModel(compartments)
infection_rate = 2
recovery_rate = 1
mobility_rate = 0.1
quadratic_processes = []
linear_processes = []
print("defining processes")
for node in range(N):
quadratic_processes.append(
((node, "S"), (node, "I"), infection_rate, (node, "I"), (node, "I")), )
linear_processes.append(((node, "I"), recovery_rate, (node, "R")))
for u, v, w in links:
from time import time
start = time()
network, config, _ = nw.load('MHRN.json')
# get the network properties
N = len(network['nodes'])
links = [ ( link['source'], link['target'], 1.0 ) for link in network['links'] ]
base_compartments = "SIR"
compartments = [ (node, C) for node in range(N) for C in base_compartments ]
model = MatrixEpiModel(compartments)
infection_rate = 3
recovery_rate = 1
mobility_rate = 0.05
quadratic_processes = []
linear_processes = []
print("defining processes")
for node in range(N):
quadratic_processes.append(
( (node, "S"), (node, "I"), infection_rate, (node, "I"), (node, "I") ),
)