def test_multirun(do_plot=do_plot): # If being run via pytest, turn off
sc.heading('Multirun test')
n_days = 60
# Method 1 -- Note: this runs 3 simulations, not 3x3!
iterpars = {
'beta': [0.015, 0.025, 0.035],
'iso_factor': [0.1, 0.5, 0.9],
}
sim = cv.Sim(n_days=n_days, pop_size=pop_size)
sims = cv.multi_run(sim=sim, iterpars=iterpars, verbose=verbose)
# Method 2 -- run a list of sims
simlist = []
for i in range(len(iterpars['beta'])):
sim = cv.Sim(n_days=n_days,
pop_size=pop_size,
beta=iterpars['beta'][i],
iso_factor=iterpars['iso_factor'][i])
simlist.append(sim)
sims2 = cv.multi_run(sim=simlist, verbose=verbose)
# Method 3 -- shortcut for parallelization
s1 = cv.Sim(n_days=n_days, pop_size=pop_size)
s2 = s1.copy()
s1, s2 = cv.parallel(s1, s2).sims
assert np.allclose(s1.summary[:],
s2.summary[:],
rtol=0,
atol=0,
equal_nan=True)
# Run in serial for debugging
cv.multi_run(sim=cv.Sim(n_days=n_days, pop_size=pop_size),
n_runs=2,
parallel=False)
if do_plot:
for sim in sims + sims2:
sim.plot()
return sims
''' Demonstrate the vaccine intervention with subtargeting '''
import covasim as cv
import numpy as np
# Define the vaccine subtargeting
def vaccinate_by_age(sim):
young = cv.true(sim.people.age < 50) # cv.true() returns indices of people matching this condition, i.e. people under 50
middle = cv.true((sim.people.age >= 50) * (sim.people.age < 75)) # Multiplication means "and" here
old = cv.true(sim.people.age > 75)
inds = sim.people.uid # Everyone in the population -- equivalent to np.arange(len(sim.people))
vals = np.ones(len(sim.people)) # Create the array
vals[young] = 0.1 # 10% probability for people <50
vals[middle] = 0.5 # 50% probability for people 50-75
vals[old] = 0.9 # 90% probbaility for people >75
output = dict(inds=inds, vals=vals)
return output
# Define the vaccine
vaccine = cv.simple_vaccine(days=20, rel_sus=0.8, rel_symp=0.06, subtarget=vaccinate_by_age)
if __name__ == '__main__':
# Create, run, and plot the simulations
sim1 = cv.Sim(label='Baseline')
sim2 = cv.Sim(interventions=vaccine, label='With age-targeted vaccine')
msim = cv.parallel([sim1, sim2])
msim.plot()
'''
Illustrate sinusoidally varying transmission via change beta
'''
import numpy as np
import sciris as sc
import covasim as cv
pars = sc.objdict(
beta=0.008,
n_agents=50e3,
n_days=180,
verbose=0,
)
beta_days = np.arange(pars.n_days)
beta_vals = np.cos(2 * np.pi * beta_days / 90)**2 + 0.5
beta = cv.change_beta(beta_days, beta_vals, do_plot=False)
s1 = cv.Sim(pars, label='Normal')
s2 = cv.Sim(pars, interventions=beta, label='Waves')
if __name__ == '__main__':
msim = cv.parallel(s1, s2)
msim.plot(['r_eff'])
pl.xlabel('Day')
pl.ylabel('Number infected')
pl.title('Number of elderly people with active COVID')
return
if __name__ == '__main__':
# Define and run the baseline simulation
pars = dict(
pop_size=50e3,
pop_infected=100,
n_days=90,
verbose=0,
)
orig_sim = cv.Sim(pars, label='Default')
# Define the intervention and the scenario sim
protect = protect_elderly(start_day='2020-04-01',
end_day='2020-05-01',
rel_sus=0.1) # Create intervention
sim = cv.Sim(pars, interventions=protect, label='Protect the elderly')
# Run and plot
msim = cv.parallel([orig_sim, sim])
msim.plot()
# Plot intervention
protect = msim.sims[1].get_intervention(
protect_elderly) # Find intervention by type
protect.plot()
'''
Demonstrate change beta and clip edges interventions
'''
import covasim as cv
# Define baseline parameters and sim
pars = dict(
start_day = '2020-03-01',
end_day = '2020-06-01',
pop_type = 'hybrid',
)
orig_sim = cv.Sim(pars, label='Baseline')
# Define sim with change_beta
cb = cv.change_beta(days=['2020-04-15', '2020-05-01', '2020-05-15'], changes=[0.2, 1.5, 0.7])
sim = cv.Sim(pars, interventions=cb, label='With beta changes')
if __name__ == '__main__':
# Run and plot
msim = cv.parallel(orig_sim, sim)
msim.plot()
def update(self, people):
''' Update the contacts '''
pop_size = len(people)
n_new = self.contact_data[people.t] # Pull out today's contacts
self['p1'] = np.array(cv.choose_r(max_n=pop_size, n=n_new), dtype=cv.default_int) # Choose with replacement
self['p2'] = np.array(cv.choose_r(max_n=pop_size, n=n_new), dtype=cv.default_int) # Paired contact
self['beta'] = np.ones(n_new, dtype=cv.default_float) # Per-contact transmission (just 1.0)
return
# Define some simple parameters
pars = sc.objdict(
pop_size = 1000,
n_days = 90,
)
# Set up and run the simulation
base_sim = cv.Sim(pars, label='Default simulation')
sim = cv.Sim(pars, dynam_layer=True, label='Dynamic layers')
sim.initialize()
# Update to custom layer
for key in sim.layer_keys():
contact_data = np.random.randint(pars.pop_size*10, pars.pop_size*20, size=pars.n_days+1) # Generate a number of contacts for today
sim.people.contacts[key] = CustomLayer(sim.people.contacts[key], contact_data=contact_data)
# Run and plot
if __name__ == '__main__':
msim = cv.parallel(base_sim, sim)
msim.plot()
''' Illustrate testing options '''
import covasim as cv
# Define the testing interventions
tn_data = cv.test_num('data')
tn_fixed = cv.test_num(daily_tests=500, start_day='2020-03-01')
tp = cv.test_prob(symp_prob=0.2, asymp_prob=0.001, start_day='2020-03-01')
# Define the default parameters
pars = dict(
pop_size=50e3,
pop_infected=100,
start_day='2020-02-01',
end_day='2020-03-30',
)
# Define the simulations
sim1 = cv.Sim(pars,
datafile='example_data.csv',
interventions=tn_data,
label='Number of tests from data')
sim2 = cv.Sim(pars,
interventions=tn_fixed,
label='Constant daily number of tests')
sim3 = cv.Sim(pars, interventions=tp, label='Probability-based testing')
# Run and plot results
msim = cv.parallel(sim1, sim2, sim3)
msim.plot(
to_plot=['new_infections', 'new_tests', 'new_diagnoses', 'cum_diagnoses'])
'''
Illustrate waning immunity
'''
import numpy as np
import covasim as cv
import pylab as pl
# Create sims with and without waning immunity
sim_nowaning = cv.Sim(n_days=120, use_waning=False, label='No waning immunity')
sim_waning = cv.Sim(n_days=120, label='Waning immunity')
# Now create an alternative sim with faster decay for neutralizing antibodies
sim_fasterwaning = cv.Sim(label='Faster waning immunity',
n_days=120,
nab_decay=dict(form='nab_growth_decay',
growth_time=21,
decay_rate1=0.07,
decay_time1=47,
decay_rate2=0.02,
decay_time2=106))
if __name__ == '__main__':
msim = cv.parallel(sim_nowaning, sim_waning, sim_fasterwaning)
msim.plot()
