def __init__(self,
env_harshness,
env_harshness_death_mult=0.001,
migration_death_mult=0.05,
name='migration',
t=TimeAlways(),
i=IterAlways()):
super().__init__(
name,
t,
i,
group_qry=GroupQry(
cond=[lambda g: g.has_attr({'is-migrating': True})]))
self.env_harshness = env_harshness # [0=benign .. 1=harsh]
self.env_harshness_death_mult = env_harshness_death_mult
self.migration_death_mult = migration_death_mult
def __init__(self,
severity,
scale,
severity_death_mult=0.0001,
scale_migration_mult=0.01,
name='conflict',
t=TimeAlways(),
i=IterAlways()):
super().__init__(
name,
t,
i,
group_qry=GroupQry(
cond=[lambda g: g.has_attr({'is-migrating': False})]))
self.severity = severity # [0=benign .. 1=lethal]
self.scale = scale # [0=contained .. 1=wide-spread]
self.severity_death_mult = severity_death_mult
self.scale_migration_mult = scale_migration_mult
def make_sir(beta, gamma, t=TimeAlways(), i=IterAlways(), dt=0.1):
def f_sir_model(t, state):
'''
[1] Kermack WO & McKendrick AG (1927) A Contribution to the Mathematical Theory of Epidemics. Proceedings of the
Royal Society A. 115(772), 700--721.
http://www.public.asu.edu/~hnesse/classes/sir.html
'''
s, i, r = state
n = s + i + r
return [-beta * s * i / n, beta * s * i / n - gamma * i, gamma * i]
return ODESystemMass(f_sir_model, [
DotMap(attr={'flu': 's'}),
DotMap(attr={'flu': 'i'}),
DotMap(attr={'flu': 'r'})
],
t=t,
i=i,
dt=dt)
# sir_b1 = make_sir(0.50, 0.02, i=IterInt(1000,0), dt=0.1)
# 05 Two overlapping and time-unbounded SIR models (B1 starts with a fixed delay):
# sir_a1 = make_sir(0.10, 0.05, i=IterAlways(), dt=0.1)
# def make_sir_b1_fixed_delay(iter0):
# return make_sir(0.50, 0.02, i=IterInt(iter0,0), dt=0.1)
# 06 Two overlapping and time-unbounded SIR models (B1 starts with a random delay):
# sir_a1 = make_sir(0.10, 0.05, i=IterAlways(), dt=0.1)
# def make_sir_b1_random_delay(iter0=900, iter0_dist=gamma(a=5.0, loc=0.0, scale=35.0)):
# iter0_rnd = iter0_dist.rvs()
# # print(f'rand-iter: {iter0_rnd}')
# return make_sir(0.50, 0.02, i=IterInt(iter0 + iter0_rnd,0), dt=0.1)
# 07 Two overlapping and time-unbounded SIR models (B1 starts with a random delay and a random 'gamma' parameter):
sir_a1 = make_sir(0.10, 0.05, i=IterAlways(), dt=0.1)
def make_sir_b1_random_delay_gamma(iter0=900,
iter0_dist=gamma(a=5.0, loc=5.0,
scale=25.0),
gamma=uniform(loc=0.01, scale=0.14)):
iter0_rnd = iter0_dist.rvs()
gamma_rnd = gamma.rvs()
return make_sir(0.50, gamma_rnd, i=IterInt(iter0 + iter0_rnd, 0), dt=0.1)
# ----------------------------------------------------------------------------------------------------------------------
# The recurrent flu process: