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