/
football.py
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/
football.py
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import os
import stochpy
import numpy as numpy
workingdir = os.getcwd()
# Simulation parameters
start_time = 0.0
end_time = 109
n_runs = 1000
scenarios = 10
# Model output storage arrays
results = numpy.empty([n_runs*scenarios,3], dtype = object)
def LowControl(iteration, scen):
model = stochpy.SSA()
model.Model(model_file='no_football.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.25)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 0
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
def HighControl(iteration, scen):
model = stochpy.SSA()
model.Model(model_file='no_football.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.30)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 1
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
def LowBetaLowPrevLowMix(iteration,scen):
model = stochpy.SSA()
model.Model(model_file='football_lowmix_lowprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.25)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 2
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
def LowBetaLowPrevHighMix(iteration,scen):
model = stochpy.SSA()
model.Model(model_file='football_highmix_lowprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.25)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 3
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
def LowBetaHighPrevLowMix(iteration,scen):
model = stochpy.SSA()
model.Model(model_file='football_lowmix_highprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.25)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 4
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
def LowBetaHighPrevHighMix(iteration,scen):
model = stochpy.SSA()
model.Model(model_file='football_highmix_highprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.25)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 5
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
def HighBetaHighPrevLowMix(iteration,scen):
model = stochpy.SSA()
model.Model(model_file='football_lowmix_highprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.30)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 6
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
def HighBetaHighPrevHighMix(iteration,scen):
model = stochpy.SSA()
model.Model(model_file='football_highmix_highprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.30)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 7
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
def HighBetaLowPrevLowMix(iteration,scen):
model = stochpy.SSA()
model.Model(model_file='football_lowmix_lowprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.30)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 8
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
def HighBetaLowPrevHighMix(iteration,scen):
model = stochpy.SSA()
model.Model(model_file='football_highmix_lowprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta',0.30)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration+(n_runs*scen),0] = 9
results[iteration+(n_runs*scen),1] = cases
results[iteration+(n_runs*scen),2] = symptom
for i in range(0,n_runs):
print("*** Iteration %i of %i ***" % (i+1,n_runs))
LowControl(i,0)
HighControl(i,1)
LowBetaLowPrevLowMix(i,2)
LowBetaLowPrevHighMix(i,3)
LowBetaHighPrevLowMix(i,4)
LowBetaHighPrevHighMix(i,5)
HighBetaHighPrevLowMix(i,6)
HighBetaHighPrevHighMix(i,7)
HighBetaLowPrevLowMix(i,8)
HighBetaLowPrevHighMix(i,9)
numpy.savetxt('wsu_football.csv',results,delimiter=',',header="Scenario,Cases,Symptomatic",comments='')