def __init__(self, therapy):
# selected therapy
self._therapy = therapy
# simulation time step
self._delta_t = Data.DELTA_T
self._adjDiscountRate = Data.DISCOUNT * Data.DELTA_T
# initial health state
self._initialHealthState = HealthStats.WELL
##THIS IS CHECKED
# cost treatment per preg
if self._therapy == Therapies.BASELINE:
self._annualTreatmentCost = 0
elif self._therapy == Therapies.SUPPLIES_NO_TRAINING:
self._annualTreatmentCost = Data.COST_SUPPLIES
elif self._therapy == Therapies.BETTER_TRAINING:
self._annualTreatmentCost = Data.COST_TRAINING
else:
self._annualTreatmentCost = Data.COST_TRAINING + Data.COST_SUPPLIES
# transition probability matrix of the selected therapy
self._prob_matrix = []
self._continuous_matrix = []
# calculate transition probabilities depending of which therapy options is in use
# if therapy == Therapies.BASELINE:
# self._prob_matrix = Data.BASELINE_MATRIX
# elif therapy == Therapies.SUPPLIES_NO_TRAINING:
# self._prob_matrix = Data.SUPPLIES_NO_TRAINING_MATRIX
# elif therapy == Therapies.BETTER_TRAINING:
# self._prob_matrix = Data.BETTER_TRAINING_MATRIX
# else:
# self._prob_matrix = Data.BETTER_SUPPLIES_AND_TRAINING_MATRIX
#checked above
# calculate transition probabilities depending of which therapy options is in use
if therapy == Therapies.BASELINE:
self._continuous_matrix = SupportLibrary.discrete_to_continuous(
Data.BASELINE_MATRIX, Data.DELTA_T)
elif therapy == Therapies.SUPPLIES_NO_TRAINING:
self._continuous_matrix = SupportLibrary.discrete_to_continuous(
Data.SUPPLIES_NO_TRAINING_MATRIX, Data.DELTA_T)
elif therapy == Therapies.BETTER_TRAINING:
self._continuous_matrix = SupportLibrary.discrete_to_continuous(
Data.BETTER_TRAINING_MATRIX, Data.DELTA_T)
else:
self._continuous_matrix = SupportLibrary.discrete_to_continuous(
Data.BETTER_SUPPLIES_AND_TRAINING_MATRIX, Data.DELTA_T)
self._prob_matrix, p = SupportLibrary.continuous_to_discrete(
self._continuous_matrix, Data.DELTA_T)
#annual state cvsots and utilities
self._annualStateCosts = Data.HEALTH_COST
self._annualStateUtilities = Data.HEALTH_UTILITY
# adjusted discount rate
self._adjDiscountRate = Data.DISCOUNT_RATE * Data.DELTA_T
def add_background_mortality(prob_matrix):
# find the transition rate matrix
rate_matrix = MarkovCls.discrete_to_continuous(prob_matrix, 1)
# add mortality rates
for s in HealthStats:
if s not in [HealthStats.HIV_DEATH, HealthStats.BACKGROUND_DEATH]:
rate_matrix[s.value][HealthStats.BACKGROUND_DEATH.value] \
= -np.log(1 - Data.ANNUAL_PROB_BACKGROUND_MORT)
# convert back to transition probability matrix
prob_matrix[:], p = MarkovCls.continuous_to_discrete(rate_matrix, Data.DELTA_T)
def add_background_mortality(prob_matrix):
rate_matrix, p = MarkovCls.discrete_to_continuous(prob_matrix, 1)
#add all cause mortality rates
for s in HealthStats:
if s not in [HealthStats.STROKEDEATH]:
rate_matrix[s.value][HealthStats.value] \
= -np.log(1-Data.ANNUAL_ALL_CAUSE_MORT)
# convert back to transition probability matrix
prob_matrix = MarkovCls.continuous_to_discrete(rate_matrix, Data.DELTA_T)
print ('All-Cause Mortality. Upper bound of the probabilty of two transitions within delta_t:', p)
def add_background_mortality(prob_matrix):
# find the transition rate matrix
rate_matrix = MarkovCls.discrete_to_continuous(prob_matrix, 1)
# add mortality rates
for s in HealthStats:
# add background rates to non-death states (background mortality rate for death-state is assumed 0)
if s not in [HealthStats.DEATH, HealthStats.BACKGROUND_DEATH]:
rate_matrix[s.value][HealthStats.BACKGROUND_DEATH.value] = -np.log(
1 - Data.ANNUAL_PROB_BACKGROUND_MORT)
# convert back to transition probability matrix
prob_matrix[:], p = MarkovCls.continuous_to_discrete(
rate_matrix, Data.DELTA_T)
print('Upper bound on the probability of two transitions within delta_t:',
p)