contagious_pop = Population('contagious', initial_contagious_par,
'number of people that can cause someone to become infected',
hidden=False, color='red')
# this value is only used if the transition is removed
trans_rate = Parameter('alpha', 0.4, 0., 2.,
'mean number of people that a contagious person infects ' +
'per day', hidden=True)
fast_delay = Delay('fast', 'fast', model=bc_model)
neg_binom_par = Parameter('neg_binom_p', 0.5, 0.001, 0.999,
'Dispersion parameter p for neg binom')
bc_model.add_connector(
Multiplier('infection cycle', [susceptible_pop, contagious_pop, total_pop],
infected_pop, trans_rate, fast_delay, bc_model,
distribution='nbinom', nbinom_par=neg_binom_par))
contagious_frac = Parameter('cont_frac', 0.9, 0., 1.,
'fraction of infected people that become contagious',
hidden=False)
contagious_delay_pars = {
'mean': Parameter('cont_delay_mean', 5., 0., 50.,
'mean time from being infected to becoming contagious'),
'sigma': Parameter('cont_delay_sigma', 3., 0.01, 20.,
'standard deviation of times from being infected to becoming contagious')
}
contagious_delay = Delay('cont_delay', 'norm', contagious_delay_pars, bc_model)
bc_model.add_connector(
'alpha',
0.4,
0.,
2.,
'mean number of people that a contagious person infects ' + 'per day',
hidden=True)
infection_delay = Delay('fast', 'fast', model=bc_model)
neg_binom_par = Parameter('neg_binom_p', 0.5, 0.001, 0.999,
'Dispersion parameter p for neg binom')
bc_model.add_connector(
Multiplier('infection cycle', [susceptible_pop, contagious_pop, total_pop],
infected_pop,
trans_rate,
infection_delay,
bc_model,
distribution='nbinom',
nbinom_par=neg_binom_par))
contagious_frac = Parameter(
'cont_frac',
0.9,
0.,
1.,
'fraction of infected people that become contagious',
hidden=False)
contagious_delay_pars = {
'mean':
Parameter('cont_delay_mean', 5., 0., 50.,
'mean time from being infected to becoming contagious'),
initial_contagious_par = Parameter('cont_0', 55., 0., 5000.,
'Number of contagious people at t0',
hidden=False)
contagious_pop = Population('contagious', initial_contagious_par,
'number of people that can cause someone to become infected',
hidden=False, color='red')
# this value is only used if the transition is removed
trans_rate = Parameter('alpha', 0.390, 0., 2.,
'mean number of people that a contagious person infects ' +
'per day', hidden=True)
infection_delay = Delay('fast', 'fast', model=bc_model)
bc_model.add_connector(
Multiplier('infection cycle', [susceptible_pop, contagious_pop, total_pop],
infected_pop, trans_rate, infection_delay, bc_model))
contagious_frac = Parameter('cont_frac', 0.9, 0., 1.,
'fraction of infected people that become contagious',
hidden=False)
contagious_delay_pars = {
'mean': Parameter('cont_delay_mean', 2., 0., 50.,
'mean time from being infected to becoming contagious'),
'sigma': Parameter('cont_delay_sigma', 1., 0.01, 20.,
'standard deviation of times from being infected to becoming contagious')
}
contagious_delay = Delay('cont_delay', 'norm', contagious_delay_pars, bc_model)
bc_model.add_connector(
Propagator('infected to contagious', infected_pop,
def test_class_Multiplier():
"""tests to ensure the behaviour class Multiplier"""
test_model = Model('test_model')
EPS = 1.
n1 = 50.
n2 = 20.
n3 = 2.
scale = 0.1
f_pops = [Population('f1_pop', n1), Population('f2_pop', n2), Population('f3_pop', n3)]
to_pop = Population('to_pop', 0.)
scale_par = Parameter('alpha', scale)
delay = Delay('fast', 'fast')
test_multiplier = Multiplier('test_multiplier', f_pops, to_pop, scale_par, delay, model=test_model)
test_model.add_connector(test_multiplier)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
# expectation:
expected = n1 * n2 / n3 * scale * time_step
to_pop.reset()
test_multiplier.set_distribution('poisson', None)
test_multiplier.update_expectation()
assert to_pop.future[0] == expected
# Poisson
n_rep = 1000
n_list = []
for i in range(n_rep):
to_pop.reset()
test_multiplier.update_data()
n_list.append(to_pop.future[0])
assert np.abs(np.mean(n_list) - expected) < EPS
assert np.abs(np.std(n_list) - np.sqrt(expected)) < EPS
# Negative binomial
p_nb = 0.2
nbinom_par = Parameter('nb', p_nb)
test_multiplier.set_distribution('nbinom', nbinom_par)
n_rep = 1000
n_list = []
for i in range(n_rep):
to_pop.reset()
test_multiplier.update_data()
n_list.append(to_pop.future[0])
assert np.abs(np.mean(n_list) - expected) < EPS
assert np.abs(np.std(n_list) - np.sqrt(expected / p_nb)) < EPS