def simulate(self, sim_length):
""" simulate the patient over the specified simulation length """
# random number generator for this patient
self._rng = rndClasses.RNG(self._id)
k = 0 # current time step
# while the patient is alive and simulation length is not yet reached
while self._stateMonitor.get_if_alive(
) and k * self._delta_t < sim_length:
# find the transition probabilities of the future states
trans_probs = self._param.get_transition_prob(
self._stateMonitor.get_current_state())
# create an empirical distribution
empirical_dist = rndClasses.Empirical(trans_probs)
# sample from the empirical distribution to get a new state
# (returns an integer from {0, 1, 2, ...})
new_state_index = empirical_dist.sample(self._rng)
# update health state
self._stateMonitor.update(k, P.HealthStats(new_state_index))
# increment time step
k += 1
def simulate(self, sim_length):
""" simulate the patient over the specified simulation length """
# random number generator for this patient
self._rng = rndClasses.RNG(self._id) # from now on use random number generator from support library
k = 0
while self._stateMonitor.get_if_alive() and k*self._delta_t < sim_length:
trans_prob = self._param.get_transition_prob(self._stateMonitor.get_current_state())
empirical_dist = rndClasses.Empirical(trans_prob)
new_state_index = empirical_dist.sample(self._rng)
self._stateMonitor.update(k, P.HealthStats(new_state_index))
k += 1
def simulate(self, sim_length):
self._rng = RndClasses.RNG(self._id)
t = 0
while self._healthStateMonitor.get_if_alive(
) and t * self._delta_t < sim_length:
trans_prob = self._param.get_prob_matrix(
self._healthStateMonitor.get_current_state())
empirical_dist = RndClasses.Empirical(trans_prob)
new_state_index = empirical_dist.sample(self._rng)
self._healthStateMonitor.update(
t, Parameters.HealthStates(new_state_index))
t += 1
def test_empirical(rnd, prob):
# empirical random variate generator
empirical_dist = RVGs.Empirical(prob)
# obtain samples
samples = get_samples(empirical_dist, rnd)
# report mean and variance
if type(prob) == list:
prob = np.array(prob)
outcome = np.array(range(len(prob)))
mean = sum(outcome*prob)
var = sum((outcome**2)*prob) - mean**2
print_test_results('Empirical', samples,
expectation=mean,
variance=var)
def simulate(self, sim_length):
""" simulate the patient over the specified simulation length """
# random number generator for this patient
self._rng = rndClasses.RNG(self._id)
k = 0 # current time step
# while the patient is alive and simulation length is not yet reached
while self.healthstat!=3 and k < sim_length:
# find the transition probabilities of the future states
trans_probs = TRANS_MATRIX[self.healthstat]
# create an empirical distribution
empirical_dist = rndClasses.Empirical(trans_probs)
# sample from the empirical distribution to get a new state
# (returns an integer from {0, 1, 2, ...})
new_state_index = empirical_dist.sample(self._rng)
# update health state
self.healthstat =new_state_index[0]
# increment time step
k += 1
self.survival=k+1
def simulate_fiveshort(self, sim_length_short):
""" simulate the patient over the specified simulation length """
# random number generator for this patient
self._rng = rndClasses.RNG(self._id)
if self.vaccine == 0:
k = 0
# while the patient is alive and simulation length is not yet reached
while (self.healthstat != 8) and k * ma.delta_t < sim_length_short:
# find the transition probabilities of the future states
trans_probs = ma.prob_matrix[0][self.healthstat]
# create an empirical distribution
empirical_dist = rndClasses.Empirical(trans_probs)
# sample from the empirical distribution to get a new state
# (returns an integer from {0, 1, 2, ...})
new_state_index = empirical_dist.sample(self._rng)
# caculate cost and utality
cost = ma.cost_matrix[
self.
healthstat] + ma.salary * ma.work_loss_day[self.healthstat]
utility = ma.utility_matrix[self.healthstat] * ma.delta_t
# update total discounted cost and utility (corrected for the half-cycle effect)
self.totalDiscountCost += \
EconCls.pv(cost, ma.discount_rate * ma.delta_t, k + 1)
self.totalDiscountUtility += \
EconCls.pv(utility, ma.discount_rate * ma.delta_t, k + 1)
# update diseases:
if self.healthstat == HealthStats.Pneumoniae.value:
self.pneumonaie += 1
if self.healthstat == HealthStats.Meningitis.value:
self.meningitis += 1
if self.healthstat == HealthStats.AOM_T.value or self.healthstat == HealthStats.AOM_NT.value:
self.aom += 1
# update disability number
if self.healthstat == HealthStats.Disability.value:
self._ndisability = 1
# update deafness number
if self.healthstat == HealthStats.Deaf.value:
self._ndeaf = 1
# update health state
self.healthstat = new_state_index[0]
#update number of deahts
if self.healthstat == HealthStats.DEATH.value:
self._ndeath = 1
# increment time step
k += 1
if self.vaccine == 1:
k = 0
while (self.healthstat != 8) and k * ma.delta_t < sim_length_short:
# find the transition probabilities of the future states
trans_probsv = ma.prob_matrix_vaccine[0][self.healthstat]
# create an empirical distribution
empirical_distv = rndClasses.Empirical(trans_probsv)
# sample from the empirical distribution to get a new state
# (returns an integer from {0, 1, 2, ...})
new_state_indexv = empirical_distv.sample(self._rng)
# caculate cost and utality
cost = ma.cost_matrix[
self.
healthstat] + ma.salary * ma.work_loss_day[self.healthstat]
utility = ma.utility_matrix[self.healthstat] * ma.delta_t
# update total discounted cost and utility (corrected for the half-cycle effect)
self.totalDiscountCost += \
EconCls.pv(cost, ma.discount_rate * ma.delta_t, k + 1)
self.totalDiscountUtility += \
EconCls.pv(utility, ma.discount_rate * ma.delta_t, k + 1)
# update diseases:
if self.healthstat == HealthStats.Pneumoniae.value:
self.pneumonaie += 1
if self.healthstat == HealthStats.Meningitis.value:
self.meningitis += 1
if self.healthstat == HealthStats.AOM_T.value or self.healthstat == HealthStats.AOM_NT.value:
self.aom += 1
# update disability number
if self.healthstat == HealthStats.Disability.value:
self._ndisability = 1
# update deafness number
if self.healthstat == HealthStats.Deaf.value:
self._ndeaf = 1
# update health state
self.healthstat = new_state_indexv[0]
#update number of deahts
if self.healthstat == HealthStats.DEATH.value:
self._ndeath = 1
if k == 3:
self.shot += 1
self.totalDiscountCost+= \
EconCls.pv(ma.vaccine_administration+ma.vaccine_cost, ma.discount_rate * ma.delta_t, k + 1)
if k == 5:
self.shot += 1
self.totalDiscountCost+= \
EconCls.pv(ma.vaccine_administration+ma.vaccine_cost, ma.discount_rate * ma.delta_t, k + 1)
if k == 11:
self.shot += 1
self.totalDiscountCost+= \
EconCls.pv(ma.vaccine_administration+ma.vaccine_cost, ma.discount_rate * ma.delta_t, k + 1)
# increment time step
k += 1
if self.healthstat == 3:
for i in (6, 16):
self.totalDiscountCost += EconCls.pv(2746, ma.discount_rate,
i + 1)
