def examplev2(): pars = {'use_waning': True} variants = [cv.variant('b117', days=30, n_imports=10)] sim = cv.Sim(pars=pars, variants=variants) # length of our base campaign duration = 30 # estimate per-day probability needed for a coverage of 30% prob = cv.historical_vaccinate_prob.estimate_prob(duration=duration, coverage=0.30) print('using per-day probability of ', prob) # estimate per-day probability needed for a coverage of 30% prob2 = cv.historical_vaccinate_prob.estimate_prob(duration=2 * duration, coverage=0.30) scenarios = { 'base': { 'name': 'baseline', 'pars': {} }, 'scen1': { 'name': 'historical_vaccinate', 'pars': { 'interventions': [ cv.historical_vaccinate_prob(vaccine='pfizer', days=np.arange(-duration, 0), prob=prob) ] } }, 'scen2': { 'name': 'vaccinate', 'pars': { 'interventions': [ cv.vaccinate_prob(vaccine='pfizer', days=np.arange(0, 30), prob=prob) ] } }, 'scen3': { 'name': 'historical_vaccinate into sim', 'pars': { 'interventions': [ cv.historical_vaccinate_prob(vaccine='pfizer', days=np.arange(-30, 30), prob=prob2) ] } }, } scens = cv.Scenarios(sim=sim, scenarios=scenarios) scens.run() scens.plot()
def test_vaccines(do_plot=False): sc.heading('Testing vaccines...') p1 = cv.variant('sa variant', days=20, n_imports=20) pfizer = cv.vaccinate_prob(vaccine='pfizer', days=30) sim = cv.Sim(base_pars, use_waning=True, variants=p1, interventions=pfizer) sim.run() if do_plot: sim.plot('overview-variant') return sim
def test_vaccine_1variant(do_plot=False, do_show=True, do_save=False): sc.heading('Test vaccination with a single variant') pars = sc.mergedicts(base_pars, { 'beta': 0.015, 'n_days': 120, }) pfizer = cv.vaccinate_prob(days=[20], vaccine='pfizer') sim = cv.Sim(use_waning=True, pars=pars, interventions=pfizer) sim.run() return sim
def test_vaccine_1variant_scen(do_plot=False, do_show=True, do_save=False): sc.heading('Run a basic sim with 1 variant, pfizer vaccine') # Define baseline parameters n_runs = 3 base_sim = cv.Sim(use_waning=True, pars=base_pars) # Vaccinate 75+, then 65+, then 50+, then 18+ on days 20, 40, 60, 80 base_sim.vxsubtarg = sc.objdict() base_sim.vxsubtarg.age = [75, 65, 50, 18] base_sim.vxsubtarg.prob = [.05, .05, .05, .05] base_sim.vxsubtarg.days = subtarg_days = [20, 40, 60, 80] pfizer = cv.vaccinate_prob(days=subtarg_days, vaccine='pfizer', subtarget=vacc_subtarg) # Define the scenarios scenarios = { 'baseline': { 'name': 'No Vaccine', 'pars': {} }, 'pfizer': { 'name': 'Pfizer starting on day 20', 'pars': { 'interventions': [pfizer], } }, } metapars = {'n_runs': n_runs} scens = cv.Scenarios(sim=base_sim, metapars=metapars, scenarios=scenarios) scens.run() to_plot = sc.objdict({ 'New infections': ['new_infections'], 'Cumulative infections': ['cum_infections'], 'New reinfections': ['new_reinfections'], # 'Cumulative reinfections': ['cum_reinfections'], }) if do_plot: scens.plot(do_save=do_save, do_show=do_show, fig_path='results/test_basic_vaccination.png', to_plot=to_plot) return scens
def test_vaccine_1dose(do_plot=False, do_show=True, do_save=False): # Create some base parameters pars = sc.mergedicts(base_pars, { 'beta': 0.015, 'n_days': 120, }) janssen = cv.vaccinate_prob(vaccine='janssen', days=[0]) sim = cv.Sim(use_waning=True, pars=pars, interventions=janssen) sim.run() to_plot = sc.objdict({ 'New infections': ['new_infections'], 'Cumulative infections': ['cum_infections'], 'New reinfections': ['new_reinfections'], }) if do_plot: sim.plot(do_save=do_save, do_show=do_show, fig_path='results/test_reinfection.png', to_plot=to_plot)
def make_sim(use_defaults=False, do_plot=False, **kwargs): ''' Define a default simulation for testing the baseline -- use hybrid and include interventions to increase coverage. If run directly (not via pytest), also plot the sim by default. ''' # Define the interventions tp = cv.test_prob(start_day=20, symp_prob=0.1, asymp_prob=0.01) vx = cv.vaccinate_prob('pfizer', days=30, prob=0.1) cb = cv.change_beta(days=40, changes=0.5) ct = cv.contact_tracing(trace_probs=0.3, start_day=50) # Define the parameters pars = dict( use_waning=True, # Whether or not to use waning and NAb calculations pop_size=20e3, # Population size pop_infected= 100, # Number of initial infections -- use more for increased robustness pop_type= 'hybrid', # Population to use -- "hybrid" is random with household, school,and work structure n_days=60, # Number of days to simulate verbose=0, # Don't print details of the run rand_seed=2, # Set a non-default seed interventions=[cb, tp, ct, vx], # Include the most common interventions ) pars = sc.mergedicts(pars, kwargs) # Create the sim if use_defaults: sim = cv.Sim() else: sim = cv.Sim(pars) # Optionally plot if do_plot: s2 = sim.copy() s2.run() s2.plot() return sim
def test_synthpops(): sim = cv.Sim(use_waning=True, **sc.mergedicts(base_pars, dict(pop_size=5000, pop_type='synthpops'))) sim.popdict = cv.make_synthpop(sim, with_facilities=True, layer_mapping={'LTCF': 'f'}) sim.reset_layer_pars() # Vaccinate 75+, then 65+, then 50+, then 18+ on days 20, 40, 60, 80 sim.vxsubtarg = sc.objdict() sim.vxsubtarg.age = [75, 65, 50, 18] sim.vxsubtarg.prob = [.05, .05, .05, .05] sim.vxsubtarg.days = subtarg_days = [20, 40, 60, 80] pfizer = cv.vaccinate_prob(days=subtarg_days, vaccine='pfizer', subtarget=vacc_subtarg) sim['interventions'] += [pfizer] sim.run() return sim
def test_vaccine_target_eff(): sc.heading('Testing vaccine with pre-specified efficacy...') target_eff_1 = 0.7 target_eff_2 = 0.95 default_pars = cv.parameters.get_vaccine_dose_pars(default=True) test_pars = dict(doses=2, interval=21, target_eff=[target_eff_1, target_eff_2]) vacc_pars = sc.mergedicts(default_pars, test_pars) # construct analyzer to select placebo arm class placebo_arm(cv.Analyzer): def __init__(self, day, trial_size, **kwargs): super().__init__(**kwargs) self.day = day self.trial_size = trial_size return def initialize(self, sim=None): self.placebo_inds = [] self.initialized = True return def apply(self, sim): if sim.t == self.day: eligible = cv.true(~np.isfinite(sim.people.date_exposed) & ~sim.people.vaccinated) self.placebo_inds = eligible[cv.choose(len(eligible), min(self.trial_size, len(eligible)))] return pars = dict( rand_seed = 1, # Note: results may be sensitive to the random seed pop_size = 20_000, beta = 0.01, n_days = 90, verbose = -1, use_waning = True, ) # Define vaccine arm trial_size = 4_000 start_trial = 20 def subtarget(sim): ''' Select people who are susceptible ''' if sim.t == start_trial: eligible = cv.true(~np.isfinite(sim.people.date_exposed)) inds = eligible[cv.choose(len(eligible), min(trial_size // 2, len(eligible)))] else: inds = [] return {'vals': [1.0 for ind in inds], 'inds': inds} # Initialize vx = cv.vaccinate_prob(vaccine=vacc_pars, days=[start_trial], label='target_eff', prob=0.0, subtarget=subtarget) sim = cv.Sim( pars = pars, interventions = vx, analyzers = placebo_arm(day=start_trial, trial_size=trial_size // 2) ) # Run sim.run() print('Vaccine efficiency:') results = sc.objdict() vacc_inds = cv.true(sim.people.vaccinated) # Find trial arm indices, those who were vaccinated placebo_inds = sim['analyzers'][0].placebo_inds assert (len(set(vacc_inds).intersection(set(placebo_inds))) == 0) # Check that there is no overlap # Calculate vaccine efficacy against infection VE_inf = 1 - (np.isfinite(sim.people.date_exposed[vacc_inds]).sum() / np.isfinite(sim.people.date_exposed[placebo_inds]).sum()) # Calculate vaccine efficacy against symptoms VE_symp = 1 - (np.isfinite(sim.people.date_symptomatic[vacc_inds]).sum() / np.isfinite(sim.people.date_symptomatic[placebo_inds]).sum()) # Calculate vaccine efficacy against severe disease VE_sev = 1 - (np.isfinite(sim.people.date_severe[vacc_inds]).sum() / np.isfinite(sim.people.date_severe[placebo_inds]).sum()) results['inf'] = VE_inf results['symp'] = VE_symp results['sev'] = VE_sev print(f'Against: infection: {VE_inf * 100:0.2f}%, symptoms: {VE_symp * 100:0.2f}%, severity: {VE_sev * 100:0.2f}%') # Check that actual efficacy is within 6 %age points of target errormsg = f'Expected VE to be about {target_eff_2}, but it is {VE_symp}. Check different random seeds; this test is highly sensitive.' assert round(abs(VE_symp-target_eff_2),2)<=0.1, errormsg nab_init = sim['vaccine_pars']['target_eff']['nab_init'] boost = sim['vaccine_pars']['target_eff']['nab_boost'] print(f'Initial NAbs: {nab_init}') print(f'Boost: {boost}') return sim
''' Illustrate simple vaccine usage ''' import covasim as cv # Create some base parameters pars = dict( beta = 0.015, n_days = 90, ) # Define probability based vaccination pfizer = cv.vaccinate_prob(vaccine='pfizer', days=20, prob=0.8) # Create and run the sim sim = cv.Sim(pars=pars, interventions=pfizer) sim.run() sim.plot(['new_infections', 'cum_infections', 'new_doses', 'cum_doses'])
def make_sim(seed, beta, calibration=True, future_symp_test=None, scenario=None, vx_scenario=None, end_day='2021-08-31', verbose=0): # Set the parameters #total_pop = 67.86e6 # UK population size total_pop = 55.98e6 # UK population size pop_size = 100e3 # Actual simulated population pop_scale = int(total_pop / pop_size) pop_type = 'hybrid' pop_infected = 1000 beta = beta asymp_factor = 2 contacts = {'h': 3.0, 's': 20, 'w': 20, 'c': 20} beta_layer = {'h': 3.0, 's': 1.0, 'w': 0.6, 'c': 0.3} if end_day is None: end_day = '2021-05-05' pars = sc.objdict( use_waning=True, pop_size=pop_size, pop_infected=pop_infected, pop_scale=pop_scale, pop_type=pop_type, start_day=start_day, end_day=end_day, beta=beta, asymp_factor=asymp_factor, contacts=contacts, rescale=True, rand_seed=seed, verbose=verbose, ) sim = cv.Sim(pars=pars, datafile=data_path, location='uk') #sim['prognoses']['sus_ORs'][0] = 0.5 # ages 0-10 #sim['prognoses']['sus_ORs'][1] = 1.0 # ages 11-20 # ADD BETA INTERVENTIONS #sbv is transmission in schools and assumed to be 63%=0.7*90% assuming that masks are used and redyce it by 30% #from June 2021 we will asume that it is 50% as a combination of large scale isolation of bubbles - found via seeking optimal value sbv = 0.63 sbv_new = 0.63 beta_past = sc.odict({ '2020-02-14': [1.00, 1.00, 0.90, 0.90], '2020-03-16': [1.00, 0.90, 0.80, 0.80], #first lockdown starts '2020-03-23': [1.00, 0.02, 0.20, 0.20], #first lockdown ends '2020-06-01': [1.00, 0.23, 0.40, 0.40], '2020-06-15': [1.00, 0.38, 0.50, 0.50], '2020-07-22': [1.15, 0.00, 0.30, 0.50], '2020-07-29': [1.15, 0.00, 0.30, 0.70], '2020-08-12': [1.15, 0.00, 0.30, 0.70], '2020-07-19': [1.15, 0.00, 0.30, 0.70], '2020-07-26': [1.15, 0.00, 0.30, 0.70], #schools start in Sep 2020 '2020-09-02': [1.15, sbv, 0.50, 0.70], '2020-10-01': [1.15, sbv, 0.40, 0.70], '2020-10-16': [1.15, sbv, 0.40, 0.70], #schools holiday Oct 2020 '2020-10-26': [1.15, 0.00, 0.30, 0.60], #2nd lockdown starts '2020-11-05': [1.15, sbv, 0.30, 0.40], '2020-11-14': [1.15, sbv, 0.30, 0.40], '2020-11-21': [1.15, sbv, 0.30, 0.40], '2020-11-30': [1.15, sbv, 0.30, 0.40], '2020-12-05': [1.15, sbv, 0.30, 0.40], #2nd lockdown ends and opening for Christmas '2020-12-10': [1.50, sbv, 0.40, 0.80], '2020-12-17': [1.50, sbv, 0.40, 0.80], '2020-12-24': [1.50, 0.00, 0.40, 0.60], '2020-12-26': [1.50, 0.00, 0.40, 0.70], '2020-12-31': [1.50, 0.00, 0.20, 0.70], '2021-01-01': [1.50, 0.00, 0.20, 0.70], #3rd lockdown starts '2021-01-04': [1.10, 0.14, 0.20, 0.40], '2021-01-11': [1.05, 0.14, 0.20, 0.40], '2021-01-18': [1.05, 0.14, 0.30, 0.30], '2021-01-30': [1.05, 0.14, 0.30, 0.30], '2021-02-08': [1.05, 0.14, 0.30, 0.30], '2021-02-15': [1.05, 0.00, 0.20, 0.20], '2021-02-22': [1.05, 0.14, 0.30, 0.30], #3rd lockdown ends and reopening starts in 4 steps #schools open in March 2021 - step 1 part 1 '2021-03-08': [1.05, sbv, 0.30, 0.40], '2021-03-15': [1.05, sbv, 0.30, 0.40], '2021-03-22': [1.05, sbv, 0.30, 0.40], #stay at home rule finishes - step 1 part 2 '2021-03-29': [1.05, 0.00, 0.40, 0.50], '2021-04-01': [1.05, 0.00, 0.30, 0.50], #further relaxation measures - step 2 '2021-04-12': [1.05, 0.00, 0.30, 0.40], '2021-04-19': [1.05, sbv, 0.30, 0.40], '2021-04-26': [1.05, sbv, 0.30, 0.40], '2021-05-03': [1.05, sbv, 0.30, 0.40], '2021-05-10': [1.05, sbv, 0.30, 0.40], #some further relaxation - step 3 '2021-05-17': [1.05, sbv, 0.30, 0.50], '2021-05-21': [1.05, sbv, 0.30, 0.50], #May half-term '2021-05-31': [1.05, 0.00, 0.30, 0.40], #slight relaxation after Spring half-term #but delay Step 3 until 19/07/2021 '2021-06-07': [1.05, sbv, 0.30, 0.50], '2021-06-14': [1.05, sbv, 0.30, 0.50], '2021-06-21': [1.05, sbv, 0.30, 0.50], '2021-06-28': [1.05, sbv, 0.30, 0.50], '2021-07-05': [1.25, sbv, 0.30, 0.50], '2021-07-12': [1.25, sbv, 0.30, 0.50], '2021-07-19': [1.25, 0.00, 0.30, 0.50], '2021-07-26': [1.25, 0.00, 0.30, 0.50], '2021-08-02': [1.25, 0.00, 0.30, 0.50], }) if not calibration: ##no schools until 8th March but assue 20% (1 in 5) in schools between 04/01-22/02; ##model transmission remaining at schools as 14% (to account for 30% reduction due to school measures) ## reopening schools on 8th March, society stage 1 29th March, society stage 2 12th April, ## society some more (stage 3) 17th May and everything (stage 4) 21st June 2021. ## Projecting until end of August 2021. if scenario == 'Roadmap_Step3': beta_scens = sc.odict({ '2021-06-21': [1.05, sbv, 0.40, 0.80], '2021-06-28': [1.25, sbv, 0.40, 0.80], '2021-07-05': [1.25, sbv, 0.40, 0.80], '2021-07-12': [1.25, sbv, 0.40, 0.80], '2021-07-19': [1.25, 0.00, 0.40, 0.80], '2021-07-26': [1.25, 0.00, 0.40, 0.80], '2021-08-02': [1.25, 0.00, 0.40, 0.80], }) elif scenario == 'Roadmap_delayed_Step3': beta_scens = sc.odict({ '2021-06-21': [1.25, sbv, 0.30, 0.50], '2021-06-28': [1.25, sbv, 0.30, 0.50], '2021-07-05': [1.25, sbv, 0.30, 0.50], '2021-07-12': [1.25, sbv, 0.30, 0.50], '2021-07-19': [1.25, 0.00, 0.40, 0.80], '2021-07-26': [1.25, 0.00, 0.40, 0.80], '2021-08-02': [1.25, 0.00, 0.40, 0.80], }) beta_dict = sc.mergedicts(beta_past, beta_scens) else: beta_dict = beta_past beta_days = list(beta_dict.keys()) h_beta = cv.change_beta(days=beta_days, changes=[c[0] for c in beta_dict.values()], layers='h') s_beta = cv.change_beta(days=beta_days, changes=[c[1] for c in beta_dict.values()], layers='s') w_beta = cv.change_beta(days=beta_days, changes=[c[2] for c in beta_dict.values()], layers='w') c_beta = cv.change_beta(days=beta_days, changes=[c[3] for c in beta_dict.values()], layers='c') # Add B.1.1351 strain from August 2020; n_imports, rel_beta and rel_severe_beta from calibration b1351 = cv.variant('b1351', days=np.arange(sim.day('2020-08-10'), sim.day('2020-08-20')), n_imports=3000) b1351.p['rel_beta'] = 1.2 b1351.p['rel_severe_prob'] = 0.4 sim['variants'] += [b1351] # Add Alpha strain from October 2020; n_imports, rel_beta and rel_severe_beta from calibration b117 = cv.variant('b117', days=np.arange(sim.day('2020-10-20'), sim.day('2020-10-30')), n_imports=3000) b117.p['rel_beta'] = 1.8 b117.p['rel_severe_prob'] = 0.4 sim['variants'] += [b117] # Add Delta strain starting middle of April 2021; n_imports, rel_beta and rel_severe_beta from calibration b16172 = cv.variant('b16172', days=np.arange(sim.day('2021-04-15'), sim.day('2021-04-20')), n_imports=4000) b16172.p['rel_beta'] = 2.9 b16172.p['rel_severe_prob'] = 0.2 sim['variants'] += [b16172] interventions = [h_beta, w_beta, s_beta, c_beta] # ADD TEST AND TRACE INTERVENTIONS tc_day = sim.day( '2020-03-16' ) #intervention of some testing (tc) starts on 16th March and we run until 1st April when it increases te_day = sim.day( '2020-04-01' ) #intervention of some testing (te) starts on 1st April and we run until 1st May when it increases tt_day = sim.day( '2020-05-01' ) #intervention of increased testing (tt) starts on 1st May tti_day = sim.day( '2020-06-01' ) #intervention of tracing and enhanced testing (tti) starts on 1st June tti_day_july = sim.day( '2020-07-01' ) #intervention of tracing and enhanced testing (tti) at different levels starts on 1st July tti_day_august = sim.day( '2020-08-01' ) #intervention of tracing and enhanced testing (tti) at different levels starts on 1st August tti_day_sep = sim.day('2020-09-01') tti_day_oct = sim.day('2020-10-01') tti_day_nov = sim.day('2020-11-01') tti_day_dec = sim.day('2020-12-01') tti_day_jan = sim.day('2021-01-01') tti_day_feb = sim.day('2021-02-01') tti_day_march = sim.day('2021-03-08') tti_day_june21 = sim.day('2021-06-20') tti_day_july21 = sim.day('2021-07-19') tti_day_august21 = sim.day('2021-08-02') tti_day_sep21 = sim.day('2021-09-07') s_prob_april = 0.012 s_prob_may = 0.012 s_prob_june = 0.04769 s_prob_july = 0.04769 s_prob_august = 0.04769 s_prob_sep = 0.07769 s_prob_oct = 0.07769 s_prob_nov = 0.07769 s_prob_dec = 0.07769 s_prob_jan = 0.08769 s_prob_march = 0.08769 #for reopening in June #s_prob_june21 = 0.19769 #for reopening in July s_prob_june21 = 0.08769 #for reopening in July #s_prob_july21 = 0.195069 s_prob_july21 = 0.08769 s_prob_august21 = 0.08769 s_prob_sep21 = 0.03769 #0.114=70%; 0.149=80%; 0.205=90% if future_symp_test is None: future_symp_test = s_prob_jan t_delay = 1.0 #isolation may-june iso_vals = [{k: 0.2 for k in 'hswc'}] #isolation july iso_vals1 = [{k: 0.4 for k in 'hswc'}] #isolation september iso_vals2 = [{k: 0.6 for k in 'hswc'}] #isolation october iso_vals3 = [{k: 0.6 for k in 'hswc'}] #isolation november iso_vals4 = [{k: 0.2 for k in 'hswc'}] #isolation december iso_vals5 = [{k: 0.5 for k in 'hswc'}] #isolation March 2021 ####changed to 0.2 for fitting iso_vals6 = [{k: 0.5 for k in 'hswc'}] #isolation from 20 June 2021 reduced iso_vals7 = [{k: 0.7 for k in 'hswc'}] #isolation from 16 July 2021 increased ####chnaged to 0.2 for fitting iso_vals8 = [{k: 0.3 for k in 'hswc'}] #isolation from August 2021 iso_vals9 = [{k: 0.5 for k in 'hswc'}] #isolation from Sep 2021 iso_vals10 = [{k: 0.5 for k in 'hswc'}] #testing and isolation intervention interventions += [ cv.test_prob(symp_prob=0.009, asymp_prob=0.0, symp_quar_prob=0.0, start_day=tc_day, end_day=te_day - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_april, asymp_prob=0.0, symp_quar_prob=0.0, start_day=te_day, end_day=tt_day - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_may, asymp_prob=0.00076, symp_quar_prob=0.0, start_day=tt_day, end_day=tti_day - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_june, asymp_prob=0.00076, symp_quar_prob=0.0, start_day=tti_day, end_day=tti_day_july - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_july, asymp_prob=0.00076, symp_quar_prob=0.0, start_day=tti_day_july, end_day=tti_day_august - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_august, asymp_prob=0.0028, symp_quar_prob=0.0, start_day=tti_day_august, end_day=tti_day_sep - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_sep, asymp_prob=0.0028, symp_quar_prob=0.0, start_day=tti_day_sep, end_day=tti_day_oct - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_oct, asymp_prob=0.0028, symp_quar_prob=0.0, start_day=tti_day_oct, end_day=tti_day_nov - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_nov, asymp_prob=0.0063, symp_quar_prob=0.0, start_day=tti_day_nov, end_day=tti_day_dec - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_dec, asymp_prob=0.0063, symp_quar_prob=0.0, start_day=tti_day_dec, end_day=tti_day_jan - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_jan, asymp_prob=0.0063, symp_quar_prob=0.0, start_day=tti_day_jan, end_day=tti_day_feb - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_jan, asymp_prob=0.008, symp_quar_prob=0.0, start_day=tti_day_feb, end_day=tti_day_march - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_march, asymp_prob=0.008, symp_quar_prob=0.0, start_day=tti_day_march, end_day=tti_day_june21 - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_june21, asymp_prob=0.008, symp_quar_prob=0.0, start_day=tti_day_june21, end_day=tti_day_july21 - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_july21, asymp_prob=0.004, symp_quar_prob=0.0, start_day=tti_day_july21, end_day=tti_day_august21 - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_august21, asymp_prob=0.004, symp_quar_prob=0.0, start_day=tti_day_august21, end_day=tti_day_sep21 - 1, test_delay=t_delay), cv.test_prob(symp_prob=s_prob_sep21, asymp_prob=0.008, symp_quar_prob=0.0, start_day=tti_day_sep21, test_delay=t_delay), cv.contact_tracing(trace_probs={ 'h': 1, 's': 0.8, 'w': 0.8, 'c': 0.1 }, trace_time={ 'h': 0, 's': 1, 'w': 1, 'c': 2 }, start_day='2020-06-01', end_day='2023-07-12', quar_period=10), #cv.contact_tracing(trace_probs={'h': 1, 's': 0.8, 'w': 0.8, 'c': 0.1}, # trace_time={'h': 0, 's': 1, 'w': 1, 'c': 2}, # start_day='2021-07-12', end_day='2021-07-20', # quar_period=10), #cv.contact_tracing(trace_probs={'h': 1, 's': 0.8, 'w': 0.8, 'c': 0.1}, # trace_time={'h': 0, 's': 1, 'w': 1, 'c': 2}, # start_day='2021-07-20', end_day='2022-07-19', # quar_period=10), #cv.contact_tracing(trace_probs={'h': 1, 's': 0.5, 'w': 0.5, 'c': 0.05}, # trace_time={'h': 0, 's': 1, 'w': 1, 'c': 2}, # start_day='2021-03-08', # quar_period=5), cv.dynamic_pars({'iso_factor': { 'days': te_day, 'vals': iso_vals }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_july, 'vals': iso_vals1 }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_sep, 'vals': iso_vals2 }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_oct, 'vals': iso_vals3 }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_nov, 'vals': iso_vals4 }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_dec, 'vals': iso_vals5 }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_march, 'vals': iso_vals6 }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_june21, 'vals': iso_vals7 }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_july21, 'vals': iso_vals8 }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_august21, 'vals': iso_vals9 }}), cv.dynamic_pars( {'iso_factor': { 'days': tti_day_sep21, 'vals': iso_vals10 }}) ] #cv.dynamic_pars({'rel_crit_prob': {'days': tti_day_vac, 'vals': 1.2}}), #cv.dynamic_pars({'rel_severe_prob': {'days': tti_day_dec, 'vals': 0.7}}), #cv.dynamic_pars({'rel_death_prob': {'days': tti_day_dec, 'vals': 1.2}})] #cv.vaccine(days=[0,14], rel_sus=0.4, rel_symp=0.2, cumulative=[0.7, 0.3])] dose_pars = cvp.get_vaccine_dose_pars()['az'] dose_pars['interval'] = 7 * 8 variant_pars = cvp.get_vaccine_variant_pars()['az'] az_vaccine = sc.mergedicts({'label': 'az_uk'}, sc.mergedicts(dose_pars, variant_pars)) dose_pars = cvp.get_vaccine_dose_pars()['pfizer'] dose_pars['interval'] = 7 * 8 variant_pars = cvp.get_vaccine_variant_pars()['pfizer'] pfizer_vaccine = sc.mergedicts({'label': 'pfizer_uk'}, sc.mergedicts(dose_pars, variant_pars)) # Loop over vaccination in different ages for age in vx_ages: vaccine = az_vaccine if (age > 40 and age < 65) else pfizer_vaccine subtarget = subtargets[vx_scen][age] vx_start_day = sim.day(vx_rollout[age]['start_day']) vx_end_day = vx_start_day + vx_duration days = np.arange(vx_start_day, vx_end_day) #vx = cv.vaccinate(vaccine=vaccine, subtarget=subtarget, days=days) vx = cv.vaccinate_prob(vaccine=vaccine, days=days, prob=0.01) interventions += [vx] analyzers = [] # add daily age stats analyzer analyzers += [cv.daily_age_stats(edges=[0, 30, 65, 80, 100])] #analyzers += [cv.age_histogram(datafile='uk_stats_by_age.xlsx', edges=np.concatenate([np.linspace(0, 90, 19),np.array([100])]))] # Finally, update the parameters sim.update_pars(interventions=interventions, analyzers=analyzers) # Change death and critical probabilities # interventions += [cv.dynamic_pars({'rel_death_prob':{'days':sim.day('2020-07-01'), 'vals':0.6}})] # Finally, update the parameters #sim.update_pars(interventions=interventions) for intervention in sim['interventions']: intervention.do_plot = False sim.initialize() return sim
trial_size = 4000 start_trial = 20 def subtarget(sim): ''' Select people who are susceptible ''' if sim.t == start_trial: eligible = cv.true(~np.isfinite(sim.people.date_exposed)) inds = eligible[cv.choose(len(eligible), min(trial_size//2, len(eligible)))] else: inds = [] return {'vals': [1.0 for ind in inds], 'inds': inds} # Initialize sims = [] for vaccine in vaccines: vx = cv.vaccinate_prob(vaccine=vaccine, days=[start_trial], prob=0.0, subtarget=subtarget) sim = cv.Sim( label=vaccine, use_waning=True, pars=pars, interventions=vx, analyzers=placebo_arm(day=start_trial, trial_size=trial_size//2) ) sims.append(sim) # Run # Run msim = cv.MultiSim(sims) msim.run(keep_people=True) results = sc.objdict()
def test_vaccine_2variants_scen(do_plot=False, do_show=True, do_save=False): sc.heading( 'Run a basic sim with b117 variant on day 10, pfizer vaccine day 20') # Define baseline parameters n_runs = 3 base_sim = cv.Sim(use_waning=True, pars=base_pars) # Vaccinate 75+, then 65+, then 50+, then 18+ on days 20, 40, 60, 80 base_sim.vxsubtarg = sc.objdict() base_sim.vxsubtarg.age = [75, 65, 50, 18] base_sim.vxsubtarg.prob = [.01, .01, .01, .01] base_sim.vxsubtarg.days = subtarg_days = [60, 150, 200, 220] jnj = cv.vaccinate_prob(days=subtarg_days, vaccine='j&j', subtarget=vacc_subtarg) b1351 = cv.variant('b1351', days=10, n_imports=20) p1 = cv.variant('p1', days=100, n_imports=100) # Define the scenarios scenarios = { 'baseline': { 'name': 'B1351 on day 10, No Vaccine', 'pars': { 'variants': [b1351] } }, 'b1351': { 'name': 'B1351 on day 10, J&J starting on day 60', 'pars': { 'interventions': [jnj], 'variants': [b1351], } }, 'p1': { 'name': 'B1351 on day 10, J&J starting on day 60, p1 on day 100', 'pars': { 'interventions': [jnj], 'variants': [b1351, p1], } }, } metapars = {'n_runs': n_runs} scens = cv.Scenarios(sim=base_sim, metapars=metapars, scenarios=scenarios) scens.run(debug=debug) to_plot = sc.objdict({ 'New infections': ['new_infections'], 'Cumulative infections': ['cum_infections'], 'New reinfections': ['new_reinfections'], # 'Cumulative reinfections': ['cum_reinfections'], }) if do_plot: scens.plot(do_save=do_save, do_show=do_show, fig_path='results/test_vaccine_b1351.png', to_plot=to_plot) return scens