latency_period_mean=latency_period_mean,
latency_period_stdev=latency_period_stdev,
incubation_period_mean=incubation_period_mean,
incubation_period_stdev=incubation_period_stdev,
disease_period_mean=disease_period_mean,
disease_period_stdev=disease_period_stdev,
asymptomatic_isolation_rate=asymptomatic_isolation_rate,
symptomatic_isolation_rate=symptomatic_isolation_rate,
daily_interaction_count=daily_interaction_count,
contagion_probability=contagion_probability))
sum = 0.0
for k in range(epochs):
model = CovidModel()
location = SimpleLocation(0, model, population_size)
statistics = BasicStatistics(model)
model.add_location(location)
model.add_listener(statistics)
for i in range(simulation_cycles):
model.step()
sum += statistics.death[-1]
baseline_deaths = sum / epochs
# Parameter variation
ME = float(sys.argv[1])
difference = [None] * len(ICR)
for i in range(len(ICR)):
difference[i] = [None] * len(ICS)
for j in range(len(ICS)):
sum = 0.0
sc7_model = CovidModel()
np.random.seed(seed)
setup_city_layout(sc7_model, population_size)
sc7_model.add_listener(Propaganda(sc7_model, 1))
sc7_model.add_listener(RemovePolicy(sc7_model, SocialPolicy.LOCKDOWN_RETAIL, 30))
sc7_model.add_listener(RemovePolicy(sc7_model, SocialPolicy.LOCKDOWN_FACTORY, 60))
sc7_model.add_listener(RemovePolicy(sc7_model, SocialPolicy.LOCKDOWN_OFFICE, 90))
sc7_model.add_listener(RemovePolicy(sc7_model, SocialPolicy.LOCKDOWN_ELEMENTARY_SCHOOL, 90))
sc7_model.add_listener(RemovePolicy(sc7_model, SocialPolicy.LOCKDOWN_MIDDLE_SCHOOL, 90))
sc7_model.add_listener(RemovePolicy(sc7_model, SocialPolicy.LOCKDOWN_HIGH_SCHOOL, 120))
scenario[sc]['parameters'] = sc7_parameters
scenario[sc]['model'] = sc7_model
################################################################################
# Simulation of all scenarios
for sc in scenario:
#for sc in [1, 2, 3, 4, 5, 6, 7, 8]:
#print("--------------------------------------------------------------------------------")
print(f"Running scenario {sc}")
set_parameters(scenario[sc]['parameters'])
model = scenario[sc]['model']
model.reset_randomizer(seed)
statistics = BasicStatistics(model)
model.add_listener(statistics)
for i in range(simulation_cycles):
model.step()
statistics.export_chart("scenario" + str(sc) + ".png")
statistics.export_csv("scenario" + str(sc) + ".csv")
for value in var_values:
infections_in_restaurants[value] = []
for k in range(multiple_runs):
params = copy.deepcopy(basic_parameters)
params.params[var_name] = value
set_parameters(params)
model = CovidModel()
setup_city_layout(model, population_size)
model.add_listener(Propaganda(model, 30))
model.add_listener(
RemovePolicy(model, SocialPolicy.LOCKDOWN_ELEMENTARY_SCHOOL, 30))
model.add_listener(
RemovePolicy(model, SocialPolicy.LOCKDOWN_MIDDLE_SCHOOL, 60))
model.add_listener(
RemovePolicy(model, SocialPolicy.LOCKDOWN_HIGH_SCHOOL, 90))
statistics = BasicStatistics(model)
model.add_listener(statistics)
debug = DebugUtils(model)
logger().model = model
for i in range(simulation_cycles):
model.step()
statistics.export_chart(f'scenario_{var_name}_{value}_{k}.png')
statistics.export_csv(f'scenario_{var_name}_{value}_{k}.csv')
debug.update_infection_status()
infections_in_restaurants[value].append(debug.count_restaurant)
print(infections_in_restaurants)