def nodeUpdateRule(self, node, srcNetwork, dt):
# Read original node state.
srcState = node[1][self.STATE_ATTR_NAME]
# By default we have not changed states.
dstState = srcState
# Start out with a dictionary of zero neighbors in each state.
nNSt = dict(zip(self.nodeStateIds,[0]*len(self.nodeStateIds)))
# Calculate the actual numbers and update dictionary.
nNSt.update(attributeCount(neighbors_data_iter(srcNetwork, node[0]),
self.STATE_ATTR_NAME))
# Pick a random number.
eventp = numpy.random.random_sample()
# Go through each state name, and chose an action.
if srcState == 'SA':
# Allow for asymmetric transmission.
if eventp < self.beta_ba*nNSt['IB']*dt:
dstState = 'IA'
elif srcState == 'SB':
if eventp < self.beta_ab*nNSt['IA']*dt:
dstState = 'IB'
elif srcState == 'IA':
if eventp < self.gamma*dt:
dstState = 'RA'
elif srcState == 'IB':
if eventp < self.gamma*dt:
dstState = 'RB'
node[1][self.STATE_ATTR_NAME] = dstState
return node
def nodeUpdateRule(self, node, srcNetwork, dt):
# Read original node state.
srcState = node[1][self.STATE_ATTR_NAME]
# By default we have not changed states, so set
# the destination state to be the same as the source state.
dstState = srcState
# Start out with a dictionary of zero neighbors in each state.
nNSt = dict(zip(self.nodeStateIds,[0]*len(self.nodeStateIds)))
# Calculate the actual numbers and update dictionary.
nNSt.update(attributeCount(neighbors_data_iter(srcNetwork, node[0]),
self.STATE_ATTR_NAME))
# Pick a random number.
eventp = numpy.random.random_sample()
# Go through each state name, and chose an action.
if srcState == 'S':
if eventp < self.beta*nNSt['I']*dt:
dstState = 'I'
elif srcState == 'I':
if eventp < self.gamma*dt:
dstState = 'R'
node[1][self.STATE_ATTR_NAME] = dstState
return node
def nodeUpdateRule(self, node, srcNetwork, dt):
# Read original node state.
srcState = node[1][self.STATE_ATTR_NAME]
# By default we have not changed states, so set
# the destination state to be the same as the source state.
dstState = srcState
# Start out with a dictionary of zero neighbors in each state.
nNSt = dict(zip(self.nodeStateIds,[0]*len(self.nodeStateIds)))
# Calculate the actual numbers and update dictionary.
nNSt.update(attributeCount(neighbors_data_iter(srcNetwork, node[0]),
self.STATE_ATTR_NAME))
# Pick a random number.
eventp = numpy.random.random_sample()
# Go through each state name, and chose an action.
if srcState == 'S':
if eventp < ( self.beta*(nNSt['I'] +nNSt['J']) + srcNetwork.graph['fracJ'])*dt:
dstState = 'I'
elif srcState == 'I':
# Check recovery before super spreader.
if eventp < self.gamma*dt:
dstState = 'R'
elif eventp - self.gamma*dt < self.alpha*dt:
dstState = 'J'
self.Jcounter += 1
# Super spreaders are still infected and can recover.
elif srcState == 'J':
if eventp < self.gamma*dt:
dstState = 'R'
self.Jcounter -= 1
node[1][self.STATE_ATTR_NAME] = dstState
return node
def nodeUpdateRule(self, node, srcNetwork, dt):
# Read original node state.
srcState = node[1][self.STATE_ATTR_NAME]
# By default we have not changed states.
dstState = srcState
# Start out with a dictionary of zero neighbors in each state.
nNSt = dict(zip(self.nodeStateIds,[0]*len(self.nodeStateIds)))
# Calculate the actual numbers and update dictionary.
nNSt.update(attributeCount(neighbors_data_iter(srcNetwork, node[0]),
self.STATE_ATTR_NAME))
# Pick a random number.
eventp = numpy.random.random_sample()
# Chose an action:
#-------------------------------------------------#
# Move from Susceptible to Acute
if srcState == 'SA':
eventq = numpy.random.random_sample()
if eventp < (self.mu_a+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LA'
else:
dstState = 'SA'
elif eventq < self.nu_a*dt:
dstState = 'VA'
elif eventq < self.beta*(self.c*nNSt['AB']+nNSt['LB'])*dt:
dstState = 'AA'
elif srcState == 'SB':
eventq = numpy.random.random_sample()
if eventp < (self.mu_b+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LB'
else:
dstState = 'SB'
elif eventq < self.nu_b*dt:
dstState = 'VB'
elif eventq < self.beta*(self.c*nNSt['AA']+nNSt['LA'])*dt:
dstState = 'AB'
# Move from Acute to Latent (or migrate)
elif srcState == 'AA':
eventq = numpy.random.random_sample()
if eventp < (self.mu_a+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LA'
else:
dstState = 'SA'
elif eventq < self.eta*dt:
dstState = 'LA'
elif srcState == 'AB':
eventq = numpy.random.random_sample()
if eventp < (self.mu_b+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LB'
else:
dstState = 'SB'
elif eventq < self.eta*dt:
dstState = 'LB'
# Move from Latent to either Susceptible or Treated
elif srcState == 'LA':
eventq = numpy.random.random_sample()
if eventp < (self.tau+self.mu_a+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LA'
else:
dstState = 'SA'
elif eventq < self.gamma_a*dt:
dstState = 'TA'
elif srcState == 'LB':
eventq = numpy.random.random_sample()
if eventp < (self.tau+self.mu_b+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LB'
else:
dstState = 'SB'
elif eventq < self.gamma_b*dt:
dstState = 'TB'
# Move from Treated to Susceptible
elif srcState == 'TA':
eventq = numpy.random.random_sample()
if eventp < (self.mu_a+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LA'
else:
dstState = 'SA'
elif srcState == 'TB':
eventq = numpy.random.random_sample()
if eventp < (self.mu_b+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LB'
else:
dstState = 'SB'
# Vaccinated state
elif srcState == 'VA':
eventq = numpy.random.random_sample()
if eventp < (self.mu_a+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LA'
else:
dstState = 'SA'
elif srcState == 'VB':
eventq = numpy.random.random_sample()
if eventp < (self.mu_b+self.delta)*dt:
if eventq < self.alpha*dt:
dstState = 'LB'
else:
dstState = 'SB'
# update node with chosen state
node[1][self.STATE_ATTR_NAME] = dstState
return node
