def plot_survival_curves_and_histograms(sim_outcomes_mono, sim_outcomes_combo):
""" draws the survival curves and the histograms of time until HIV deaths
:param sim_outcomes_mono: outcomes of a cohort simulated under mono therapy
:param sim_outcomes_combo: outcomes of a cohort simulated under combination therapy
"""
# get survival curves of both treatments
survival_curves = [
sim_outcomes_mono.nLivingPatients, sim_outcomes_combo.nLivingPatients
]
# graph survival curve
PathCls.graph_sample_paths(sample_paths=survival_curves,
title='Survival curve',
x_label='Simulation time step (year)',
y_label='Number of alive patients',
legends=['Mono Therapy', 'Combination Therapy'])
# histograms of survival times
set_of_survival_times = [
sim_outcomes_mono.survivalTimes, sim_outcomes_combo.survivalTimes
]
# graph histograms
Figs.graph_histograms(data_sets=set_of_survival_times,
title='Histogram of patient survival time',
x_label='Survival time (year)',
y_label='Counts',
bin_width=1,
legends=['Mono Therapy', 'Combination Therapy'],
transparency=0.6)
def plot_survival_curves_and_histograms(sim_outcomes_NO, sim_outcomes_ASP):
# get survival curves of both treatments
survival_curves = [
sim_outcomes_NO.nLivingPatients, sim_outcomes_ASP.nLivingPatients
]
# graph survival curve
PathCls.graph_sample_paths(sample_paths=survival_curves,
title='Survival curve',
x_label='Simulation time step (year)',
y_label='Number of alive patients',
legends=['No Therapy', 'Aspirin Therapy'])
# histograms of survival times
set_of_survival_times = [
sim_outcomes_NO.survivalTimes, sim_outcomes_ASP.survivalTimes
]
# graph histograms
Figs.graph_histograms(data_sets=set_of_survival_times,
title='Histogram of patient survival time',
x_label='Survival time (year)',
y_label='Counts',
bin_width=1,
legends=['No Therapy', 'Aspirin Therapy'],
transparency=0.6)
def extract_outcomes(self, simulated_patients):
""" extracts outcomes of a simulated cohort
:param simulated_patients: a list of simulated patients"""
# record patient outcomes
for patient in simulated_patients:
# survival time
if not (patient.stateMonitor.survivalTime is None):
self.survivalTimes.append(patient.stateMonitor.survivalTime)
# discounted cost and number of patients alive
self.costs.append(
patient.stateMonitor.costUtilityMonitor.totalDiscountedCost)
self.numPatientsAlive.append(patient.stateMonitor.numAlive)
# self.utilities.append(patient.stateMonitor.costUtilityMonitor.totalDiscountedUtility)
# summary statistics
self.statSurvivalTime = Stat.SummaryStat('Survival time',
self.survivalTimes)
self.statCost = Stat.SummaryStat('Discounted cost', self.costs)
self.statAlive = Stat.SummaryStat('Number of patients alive',
self.numPatientsAlive)
# self.statUtility = Stat.SummaryStat('Discounted utility', self.utilities)
# survival curve
self.nLivingPatients = Path.PrevalencePathBatchUpdate(
name='# of living patients',
initial_size=len(simulated_patients),
times_of_changes=self.survivalTimes,
increments=[-1] * len(self.survivalTimes))
def extract_outcomes(self, simulated_patients):
""" extracts outcomes of a simulated cohort
:param simulated_patients: a list of simulated patients"""
# record survival time and time until STROKES
for patient in simulated_patients:
if not (patient.stateMonitor.survivalTime is None):
self.survivalTimes.append(patient.stateMonitor.survivalTime)
if patient.stateMonitor.ifDevelopedSTROKES:
self.timesToSTROKES.append(patient.stateMonitor.timeToSTROKES)
if patient.stateMonitor.ifDevelopedSTROKES:
self.Num_stroke.append(patient.stateMonitor.num_strokes)
# calculate mean survival time
self.meanSurvivalTime = sum(self.survivalTimes) / len(self.survivalTimes)
# calculate mean time to STROKES
self.meanTimeToSTROKES = sum(self.timesToSTROKES)/len(self.timesToSTROKES)
# calculate mean number of STROKES
self.MeanNumberSTROKES=sum(self.Num_stroke)/len(self.Num_stroke)
# survival curve
self.nLivingPatients = PathCls.PrevalencePathBatchUpdate(
name='# of living patients',
initial_size=len(simulated_patients),
times_of_changes=self.survivalTimes,
increments=[-1]*len(self.survivalTimes)
)
def plot_survival_curves_and_histograms(multi_cohort_outcomes_mono, multi_cohort_outcomes_combo):
""" plot the survival curves and the histograms of survival times
:param multi_cohort_outcomes_mono: outcomes of a multi-cohort simulated under mono therapy
:param multi_cohort_outcomes_combo: outcomes of a multi-cohort simulated under combination therapy
"""
# get survival curves of both treatments
sets_of_survival_curves = [
multi_cohort_outcomes_mono.survivalCurves,
multi_cohort_outcomes_combo.survivalCurves
]
# graph survival curve
PathCls.graph_sets_of_sample_paths(
sets_of_sample_paths=sets_of_survival_curves,
title='Survival Curves',
x_label='Simulation Time Step (year)',
y_label='Number of Patients Alive',
legends=['Mono Therapy', 'Combination Therapy'],
transparency=0.4,
color_codes=['green', 'blue']
)
# histograms of survival times
set_of_survival_times = [
multi_cohort_outcomes_mono.meanSurvivalTimes,
multi_cohort_outcomes_combo.meanSurvivalTimes
]
# graph histograms
Figs.graph_histograms(
data_sets=set_of_survival_times,
title='Histograms of Average Patient Survival Time',
x_label='Survival Time (year)',
y_label='Counts',
bin_width=0.25,
x_range=[5.25, 17.75],
legends=['No Therapy', 'Aspirin Therapy'],
color_codes=['green', 'blue'],
transparency=0.5
)
def plot_survival_curves_and_histograms(multi_cohort_outcomes_SOC, multi_cohort_outcomes_NSB):
""" plot the survival curves and the histograms of survival times
:param multi_cohort_outcomes_SOC: outcomes of a multi-cohort simulated under mono therapy
:param multi_cohort_outcomes_NSB: outcomes of a multi-cohort simulated under combination therapy
"""
# get survival curves of both treatments
sets_of_survival_curves = [
multi_cohort_outcomes_SOC.survivalCurves,
multi_cohort_outcomes_NSB.survivalCurves
]
# graph survival curve
PathCls.graph_sets_of_sample_paths(
sets_of_sample_paths=sets_of_survival_curves,
title='Survival Curves',
x_label='Simulation Time Step (week)',
y_label='Number of Patients Alive',
legends=['SOC Diagnostic', 'NSB Diagnostic'],
transparency=0.4,
color_codes=['green', 'blue']
)
def extract_outcomes(self, simulated_patients):
for patient in simulated_patients:
if not (patient.stateMonitor.survivalTime is None):
self.survivalTimes.append(patient.stateMonitor.survivalTime)
self.costs.append(
patient.stateMonitor.costUtilityMonitor.totalDiscountedCost)
self.utilities.append(
patient.stateMonitor.costUtilityMonitor.totalDiscountedUtility)
self.statSurvivalTime = Stat.SummaryStat('Survival time',
self.survivalTimes)
self.statCost = Stat.SummaryStat('Discounted cost', self.costs)
self.statUtility = Stat.SummaryStat('Discounted utility',
self.utilities)
self.nLivingPatients = Path.PrevalencePathBatchUpdate(
name='# of living patients',
initial_size=len(simulated_patients),
times_of_changes=self.survivalTimes,
increments=[-1] * len(self.survivalTimes))
pop_size=D.POP_SIZE,
parameters=P.ParametersFixed(diagnostic=P.Diagnostic.NSB))
# simulate the cohort over the specified time steps
myCohort_NSB.simulate(sim_length=D.SIM_LENGTH)
# print the outcomes of this simulated cohort
Support.print_outcomes(sim_outcomes=myCohort_NSB.cohortOutcomes,
diagnostic_name=P.Diagnostic.NSB)
# histograms of the simulated cohort
Support.print_histograms(sim_outcomes=myCohort_NSB.cohortOutcomes,
diagnostic_name=P.Diagnostic.NSB)
PathCls.graph_sample_path(sample_path=myCohort.cohortOutcomes.nLivingPatients,
title='Survival Curve (Standard of Care Diagnostic)',
x_label='Time-Step (Week)',
y_label='Number Survived')
PathCls.graph_sample_path(
sample_path=myCohort_NSB.cohortOutcomes.nLivingPatients,
title='Survival Curve (SOC + Urine LAM)',
x_label='Time-Step (Week)',
y_label='Number Survived')
survival_curves = [
myCohort.cohortOutcomes.nLivingPatients,
myCohort_NSB.cohortOutcomes.nLivingPatients
]
# graph survival curve
# graph survival curve
def print_histograms(sim_outcomes, diagnostic_name):
# plot the sample path (survival curve)
PathCls.graph_sample_path(sample_path=sim_outcomes.nLivingPatients,
title=f'Survival Curve {diagnostic_name}',
x_label='Time-Step (Week)',
y_label='Number Survived')
# plot the histogram of survival times
Figs.graph_histogram(
data=sim_outcomes.survivalTimes,
title=f'Histogram of Patient Survival Time {diagnostic_name}',
x_label='Survival Time (Week)',
y_label='Count',
bin_width=1)
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTEDtoHOSP_TBD,
# title=f'Histogram of INFECTED to HOSP_TBD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTEDtoHOSP_TBM,
# title=f'Histogram of INFECTED to HOSP_TBM {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTEDtoDX_TBD,
# title=f'Histogram of INFECTED to DX_TBD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTEDtoCLEARED,
# title=f'Histogram of INFECTED to CLEARED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesCLEAREDtoDEAD,
# title=f'Histogram of CLEARED to DEAD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBDtoDEAD,
# title=f'Histogram of HOSP_TBD to DEAD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBDtoDX_TBD,
# title=f'Histogram of HOSP_TBD to DX_TBD {diagnostic_name}',
# x_label='Survival Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBMtoDEAD,
# title=f'Histogram of HOSP_TBM to DEAD {diagnostic_name}',
# x_label='Survival Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBMtoDX_TBM,
# title=f'Histogram of HOSP_TBM to DX_TBM {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBDtoDEAD,
# title=f'Histogram of DX_TBD to DEAD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBDtoCLEARED,
# title=f'Histogram of DX_TBD to CLEARED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBMtoDEAD,
# title=f'Histogram of DX_TBM to DEAD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBMtoCLEARED,
# title=f'Histogram of DX_TBM to CLEARED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesINFECTED,
# title=f'Histogram of INFECTED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBM,
# title=f'Histogram of HOSP_TBM {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesHOSP_TBD,
# title=f'Histogram of HOSP_TBD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBD,
# title=f'Histogram of DX_TBD {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesDX_TBM,
# title=f'Histogram of DX_TBM {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
#
# Figs.graph_histogram(
# data=sim_outcomes.timesCLEARED,
# title=f'Histogram of CLEARED {diagnostic_name}',
# x_label='Time (Week)',
# y_label='Count',
# bin_width=1)
# Figs.graph_histogram(
# data=sim_outcomes.costs,
# title=f'Histogram of Cost {diagnostic_name}',
# x_label='Cost (Dollars)',
# y_label='Count',
# bin_width=1)
Figs.graph_histogram(
data=sim_outcomes.costsPresenting,
title=f'Histogram of Cost (Patients Presenting) {diagnostic_name}',
x_label='Cost (Dollars)',
y_label='Count',
bin_width=1)
from SimPy import SamplePathClasses as Path
# a sample path with initial size = 1
path1 = Path.PrevalencePathRealTimeUpdate('Path 1', 0, sim_rep=1)
# record the observations
path1.record(1.5, 2)
path1.record(2, -1)
path1.record(5, 0)
# second sample path with initial size = 1
path2 = Path.PrevalencePathRealTimeUpdate('Path 2', 0, sim_rep=1)
# record the observations
path2.record(0.5, 4)
path2.record(1.8, -2)
path2.record(5.5, 1)
# third sample path with initial size = 1
path3 = Path.PrevalencePathBatchUpdate('Path 3',
0,
times_of_changes=[1.5, 2, 5],
increments=[2, -1, 0],
sim_rep=1)
# plot path 1 only
Path.graph_sample_path(sample_path=path1,
title='Plotting a single sample path',
x_label='time',
y_label='observed value',
legend='Path 1',
color_code='r')
calibrated_model_SOC.simulate(
num_of_simulated_cohorts=CalibSets.NUM_SIM_COHORTS,
cohort_size=CalibSets.SIM_POP_SIZE,
sim_length=CalibSets.SIM_LENGTH,
diagnostic=P.Diagnostic.SOC)
calibrated_model_NSB.simulate(
num_of_simulated_cohorts=CalibSets.NUM_SIM_COHORTS,
cohort_size=CalibSets.SIM_POP_SIZE,
sim_length=CalibSets.SIM_LENGTH,
diagnostic=P.Diagnostic.NSB)
# plot the sample paths
Path.graph_sample_paths(sample_paths=calibrated_model_SOC.multiCohorts.
multiCohortOutcomes.survivalCurves,
title='Survival Curves',
x_label='Time (Week)',
y_label='Number Survived',
transparency=0.5)
Path.graph_sample_paths(sample_paths=calibrated_model_NSB.multiCohorts.
multiCohortOutcomes.survivalCurves,
title='Survival Curves',
x_label='Time (Week)',
y_label='Number Survived',
transparency=0.5)
# plot the histogram of mean survival time
# Fig.graph_histogram(
# data=calibrated_model.multiCohorts.multiCohortOutcomes.meanSurvivalTimes,
# title='Histogram of Mean Survival Time',
# x_label='Mean Survival Time (Week)',
# selected therapy
therapy_none = P.Therapies.NO
# create a cohort
myCohort_0 = Cls.Cohort(id=0,
pop_size=D.POP_SIZE,
parameters=P.ParametersFixed(therapy=therapy_none))
# simulate the cohort over the specified time steps
myCohort_0.simulate(sim_length=D.SIM_LENGTH)
# plot the sample path (survival curve)
Path.graph_sample_path(
sample_path=myCohort_0.cohortOutcomes.nLivingPatients,
title='Survival Curvel for No Therapy',
x_label='Time-Step (Year)',
y_label='Number Survived')
# plot the histogram of survival times
Fig.graph_histogram(
data=myCohort_0.cohortOutcomes.survivalTimes,
title='Histogram of Patient Survival Times for No Therapy',
x_label='Survival Time (Year)',
y_label='Count',
bin_width=1)
# print the outcomes of this simulated cohort
Support.print_outcomes(sim_outcomes=myCohort_0.cohortOutcomes,therapy_name=therapy_none)
def extract_outcomes(self, simulated_patients):
""" extracts outcomes of a simulated cohort
:param simulated_patients: a list of simulated patients"""
# record survival time
for patient in simulated_patients:
# survival time
if not (patient.stateMonitor.survivalTime is None):
self.survivalTimes.append(patient.stateMonitor.survivalTime)
# USE THIS SECTION IF NO INFORMATION ABOUT TIMES; USE THE BELOW SECTION IF WANT INFO ON TIMES
if patient.stateMonitor.ifHOSP_TBD:
self.nHOSP_TBD += 1
if patient.stateMonitor.ifHOSP_TBM:
self.nHOSP_TBM += 1
if patient.stateMonitor.ifDX_TBD:
self.nDX_TBD += 1
if patient.stateMonitor.ifDX_TBM:
self.nDX_TBM += 1
if patient.stateMonitor.ifCLEARED:
self.nCLEARED += 1
if patient.stateMonitor.ifDEAD:
self.nDEAD += 1
# USE THIS SECTION IF WANT INFORMATION ABOUT TIME FOR EACH TRANSITION; USE ABOVE IF DON'T WANT
# if patient.stateMonitor.ifHOSP_TBM:
# self.timesINFECTEDtoHOSP_TBM.append(patient.stateMonitor.timeINFECTEDtoHOSP_TBM)
# self.nHOSP_TBM += 1
# if patient.stateMonitor.ifHOSP_TBD:
# self.timesINFECTEDtoHOSP_TBD.append(patient.stateMonitor.timeINFECTEDtoHOSP_TBD)
# self.nHOSP_TBD += 1
# if patient.stateMonitor.ifDX_TBM:
# self.timesHOSP_TBMtoDX_TBM.append(patient.stateMonitor.timeHOSP_TBMtoDX_TBM)
# self.nDX_TBM += 1
# if patient.stateMonitor.ifDX_TBD and patient.stateMonitor.ifHOSP_TBD:
# self.timesHOSP_TBDtoDX_TBD.append(patient.stateMonitor.timeHOSP_TBDtoDX_TBD)
# self.nDX_TBD += 1
# if patient.stateMonitor.ifDX_TBD and (patient.stateMonitor.ifHOSP_TBD is False): # and \
# # (patient.stateMonitor.ifHOSP_TBD is False) and (patient.stateMonitor.ifDEAD is False):
# self.timesINFECTEDtoDX_TBD.append(patient.stateMonitor.timeINFECTEDtoDX_TBD)
# self.nDX_TBD += 1
# if patient.stateMonitor.ifCLEARED and patient.stateMonitor.ifDX_TBD:
# self.timesDX_TBDtoCLEARED.append(patient.stateMonitor.timeDX_TBDtoCLEARED)
# self.nCLEARED += 1
# if patient.stateMonitor.ifCLEARED and patient.stateMonitor.ifDX_TBM:
# self.timesDX_TBMtoCLEARED.append(patient.stateMonitor.timeDX_TBMtoCLEARED)
# self.nCLEARED += 1
# if patient.stateMonitor.ifCLEARED and (patient.stateMonitor.ifDX_TBD is False) and \
# (patient.stateMonitor.ifDX_TBM is False):
# self.timesINFECTEDtoCLEARED.append(patient.stateMonitor.timeINFECTEDtoCLEARED)
# self.nCLEARED += 1
# # times from HOSP_TBM to DEAD
# if patient.stateMonitor.ifDEAD and patient.stateMonitor.ifHOSP_TBM and \
# (patient.stateMonitor.ifCLEARED is False) and (patient.stateMonitor.ifHOSP_TBD is False) and \
# (patient.stateMonitor.ifDX_TBD is False) and (patient.stateMonitor.ifDX_TBM is False):
# self.timesHOSP_TBMtoDEAD.append(patient.stateMonitor.timeHOSP_TBMtoDEAD)
# self.nDEAD += 1
# # times from HOSP_TBD to DEAD
# if patient.stateMonitor.ifDEAD and patient.stateMonitor.ifHOSP_TBD and \
# (patient.stateMonitor.ifCLEARED is False) and (patient.stateMonitor.ifHOSP_TBM is False) and \
# (patient.stateMonitor.ifDX_TBM is False) and (patient.stateMonitor.ifDX_TBD is False):
# self.timesHOSP_TBDtoDEAD.append(patient.stateMonitor.timeHOSP_TBDtoDEAD)
# self.nDEAD += 1
# # get rid of cleared and the two hosps
# if patient.stateMonitor.ifDEAD and patient.stateMonitor.ifDX_TBM and \
# (patient.stateMonitor.ifCLEARED is False) and (patient.stateMonitor.timeHOSP_TBMtoDEAD is None) and\
# (patient.stateMonitor.ifHOSP_TBD is False) and (patient.stateMonitor.ifDX_TBD is False):
# self.timesDX_TBMtoDEAD.append(patient.stateMonitor.timeDX_TBMtoDEAD)
# self.nDEAD += 1
# if patient.stateMonitor.ifDEAD and patient.stateMonitor.ifDX_TBD and \
# (patient.stateMonitor.ifCLEARED is False) and (patient.stateMonitor.timeHOSP_TBDtoDEAD is None) and\
# (patient.stateMonitor.ifHOSP_TBM is False) and (patient.stateMonitor.ifDX_TBM is False):
# self.timesDX_TBDtoDEAD.append(patient.stateMonitor.timeDX_TBDtoDEAD)
# self.nDEAD += 1
# if patient.stateMonitor.ifDEAD and patient.stateMonitor.ifCLEARED and \
# (patient.stateMonitor.timeHOSP_TBMtoDEAD is None) and (patient.stateMonitor.timeHOSP_TBDtoDEAD is None) and \
# (patient.stateMonitor.timeDX_TBMtoDEAD is None) and (patient.stateMonitor.timeDX_TBDtoDEAD is None):
# self.timesCLEAREDtoDEAD.append(patient.stateMonitor.timeCLEAREDtoDEAD)
# self.nDEAD += 1
# discounted cost and discounted utility
self.costs.append(
patient.stateMonitor.costUtilityMonitor.totalDiscountedCost)
# self.utilities.append(patient.stateMonitor.costUtilityMonitor.totalDiscountedUtility)
# discounted cost of all the patients who present to healthcare
if not patient.stateMonitor.ifINFECTEDtoCLEARED:
self.costsPresenting.append(
patient.stateMonitor.costUtilityMonitor.totalDiscountedCost
)
if patient.stateMonitor.survivalTime is None:
self.listYLLPresenting.append(0)
else:
self.listYLLPresenting.append(
62.77 - (patient.stateMonitor.survivalTime / 52))
if patient.stateMonitor.mortality56Day:
self.nMortality56Day += 1
# total YLL due to TB infection
if not patient.stateMonitor.ifCLEARED:
if not (patient.stateMonitor.survivalTime is None):
self.totalYLL += 62.77 - (
patient.stateMonitor.survivalTime / 52)
self.listYLL.append(62.77 -
(patient.stateMonitor.survivalTime /
52))
else:
self.listYLL.append(0)
if patient.stateMonitor.mortality2Months:
self.nMortality2Months += 1
if patient.stateMonitor.mortality2Years:
self.nMortality2Years += 1
if patient.stateMonitor.mortality5Years:
self.nMortality5Years += 1
if patient.stateMonitor.mortality1Year:
self.nMortality1Year += 1
# Gather Data
self.nHospitalized = self.nHOSP_TBD + self.nHOSP_TBM
self.mortality56Day = self.nMortality56Day / D.POP_SIZE
self.mortality2Months = self.nMortality2Months / D.POP_SIZE
self.mortality1Year = self.nMortality1Year / D.POP_SIZE
self.mortality2Years = self.nMortality2Years / D.POP_SIZE
self.mortality5Years = self.nMortality5Years / D.POP_SIZE
# self.timesINFECTED = self.timesINFECTEDtoCLEARED + self.timesINFECTEDtoDX_TBD + self.timesINFECTEDtoHOSP_TBM + \
# self.timesINFECTEDtoHOSP_TBD
# self.timesHOSP_TBD = self.timesHOSP_TBDtoDEAD + self.timesHOSP_TBDtoDX_TBD
# self.timesHOSP_TBM = self.timesHOSP_TBMtoDEAD + self.timesHOSP_TBMtoDX_TBM
# self.timesDX_TBM = self.timesDX_TBMtoCLEARED + self.timesDX_TBMtoDEAD
# self.timesDX_TBD = self.timesDX_TBDtoCLEARED + self.timesDX_TBDtoDEAD
# self.timesCLEARED = self.timesCLEAREDtoDEAD
# Summary Statistics
self.statSurvivalTime = Stat.SummaryStat('Survival time',
self.survivalTimes)
# self.statTimeINFECTEDtoHOSP_TBD = Stat.SummaryStat('Infected to HOSP_TBD', self.timesINFECTEDtoHOSP_TBD)
# self.statTimeINFECTEDtoHOSP_TBM = Stat.SummaryStat('Infected to HOSP_TBM', self.timesINFECTEDtoHOSP_TBM)
# self.statTimeINFECTEDtoDX_TBD = Stat.SummaryStat('Infected to DX_TBD', self.timesINFECTEDtoDX_TBD)
# self.statTimeINFECTEDtoCLEARED = Stat.SummaryStat('Infected to Cleared', self.timesINFECTEDtoCLEARED)
# self.statTimeCLEAREDtoDEAD = Stat.SummaryStat('Cleared to Dead', self.timesCLEAREDtoDEAD)
# self.statTimeHOSP_TBDtoDEAD = Stat.SummaryStat('HOSP_TBD to Dead', self.timesHOSP_TBDtoDEAD)
# self.statTimeHOSP_TBDtoDX_TBD = Stat.SummaryStat('HOSP_TBD to DX_TBD', self.timesHOSP_TBDtoDX_TBD)
# self.statTimeHOSP_TBMtoDEAD = Stat.SummaryStat('HOSP_TBM to DEAD', self.timesHOSP_TBMtoDEAD)
# self.statTimeHOSP_TBMtoDX_TBM = Stat.SummaryStat('HOSP_TBM to DX_TBM', self.timesHOSP_TBMtoDX_TBM)
# self.statTimeDX_TBDtoDEAD = Stat.SummaryStat('DX_TBD to Dead', self.timesDX_TBDtoDEAD)
# self.statTimeDX_TBDtoCLEARED = Stat.SummaryStat('DX_TBD to Cleared', self.timesDX_TBDtoCLEARED)
# self.statTimeDX_TBMtoDEAD = Stat.SummaryStat('DX_TBM to Dead', self.timesDX_TBMtoDEAD)
# self.statTimeDX_TBMtoCLEARED = Stat.SummaryStat('DX_TBM to Cleared', self.timesDX_TBMtoCLEARED)
#
# self.statTimesINFECTED = Stat.SummaryStat('Infected', self.timesINFECTED)
# self.statTimesHOSP_TBM = Stat.SummaryStat('HOSP_TBM', self.timesHOSP_TBM)
# self.statTimesHOSP_TBD = Stat.SummaryStat('HOSP_TBD', self.timesHOSP_TBD)
# self.statTimesDX_TBD = Stat.SummaryStat('DX_TBD', self.timesDX_TBD)
# self.statTimesDX_TBM = Stat.SummaryStat('DX_TBM', self.timesDX_TBM)
# self.statTimesCLEARED = Stat.SummaryStat('Cleared', self.timesCLEARED)
self.statCost = Stat.SummaryStat('Discounted cost', self.costs)
# self.statCostPresenting = Stat.SummaryStat('Discounted cost (Presenting)', self.costsPresenting)
# self.statUtility = Stat.SummaryStat('Discounted utility', self.utilities)
# survival curve
self.nLivingPatients = Path.PrevalencePathBatchUpdate(
name='# of living patients',
initial_size=len(simulated_patients),
times_of_changes=self.survivalTimes,
increments=[-1] * len(self.survivalTimes))
import SimPy.RandomVariantGenerators as RVGs
import SimPy.SamplePathClasses as Path
import SimPy.FigureSupport as Fig
# create multiple cohort
multiCohort = Cls.MultiCohort(
ids=range(D.N_COHORTS),
pop_size=D.POP_SIZE,
parameters=P.ParametersFixed(diagnostic=P.Diagnostic.SOC))
multiCohort.simulate(sim_length=D.SIM_LENGTH)
# plot the sample paths
Path.graph_sample_paths(
sample_paths=multiCohort.multiCohortOutcomes.survivalCurves,
title='Survival Curves',
x_label='Time-Step (Week)',
y_label='Number Survived',
transparency=0.5)
# plot the histogram of average survival time
Fig.graph_histogram(data=multiCohort.multiCohortOutcomes.meanSurvivalTimes,
title='Histogram of Mean Survival Time',
x_label='Mean Survival Time (Week)',
y_label='Count')
# print the outcomes of this simulated cohort
Support.print_outcomes(multi_cohort_outcomes=multiCohort.multiCohortOutcomes,
diagnostic=P.Diagnostic.SOC)
# create multiple cohort
multiCohort_NSB = Cls.MultiCohort(