def __init__(self, diagnostic):
self.diagnostic = diagnostic
self.probInfectedRVG = None # list of dirichlet distributions for moving out of the infected state
self.probAlphaRVG = None # list of beta distributions for the alpha probability if the diagnostic test is NSB
self.weeklyStateCostRVG = [
] # list of gamma distributions for the annual cost of states
self.singleStateCostRVG = [
] # list of gamma distributions for the single cost of states
# create Dirichlet distributions for transition probabilities
self.probInfectedRVG = RVGs.Dirichlet(a=[
Data.P_INFECTED_CLEARED, Data.P_INFECTED_TBD, Data.P_INFECTED_TBM
])
# crate beta distribution for the alpha probability
if self.diagnostic == Diagnostic.SOC:
fit_output = MM.get_beta_params(mean=Data.P_DX_SOC,
st_dev=Data.P_DX_SOC / 5)
else:
fit_output = MM.get_beta_params(mean=Data.P_DX_NSB,
st_dev=Data.P_DX_NSB / 5)
self.probAlphaRVG = RVGs.Beta(a=fit_output["a"], b=fit_output["b"])
# create gamma distributions for annual state cost
for cost in Data.WEEKLY_STATE_COST:
# if cost is zero, add a constant 0, otherwise add a gamma distribution
if cost == 0:
self.weeklyStateCostRVG.append(RVGs.Constant(value=0))
else:
# find shape and scale of the assumed gamma distribution
# no data available to estimate the standard deviation, so we assumed st_dev=cost / 5
fit_output = MM.get_gamma_params(mean=cost, st_dev=cost / 5)
# append the distribution
self.weeklyStateCostRVG.append(
RVGs.Gamma(a=fit_output["a"],
loc=0,
scale=fit_output["scale"]))
# create gamma distributions for single state cost
if self.diagnostic == Diagnostic.SOC:
single_cost = Data.SOC_ONE_TIME_COST
else:
single_cost = Data.NSB_ONE_TIME_COST
for cost in single_cost:
# if cost is zero, add a constant 0, otherwise add a gamma distribution
if cost == 0:
self.singleStateCostRVG.append(RVGs.Constant(value=0))
else:
# find shape and scale of the assumed gamma distribution
# no data available to estimate the standard deviation, so we assumed st_dev=cost / 5
fit_output = MM.get_gamma_params(mean=cost, st_dev=cost / 5)
# append the distribution
self.singleStateCostRVG.append(
RVGs.Gamma(a=fit_output["a"],
loc=0,
scale=fit_output["scale"]))
def __init__(self, therapy):
self.therapy = therapy
self.probMatrixRVG = [
] # list of dirichlet distributions for transition probabilities
self.lnRelativeRiskRVG = None # normal distribution for the natural log of the treatment relative risk
self.annualStateCostRVG = [
] # list of gamma distributions for the annual cost of states
self.annualStateUtilityRVG = [
] # list of beta distributions for the annual utility of states
# create Dirichlet distributions for transition probabilities
j = 0
for prob in Data.PROB_MATRIX:
self.probMatrixRVG.append(RVGs.Dirichlet(a=prob[j:]))
j += 1
# treatment relative risk
rr_ci = [1.2, 3] # confidence interval of the treatment relative risk
# find the mean and st_dev of the normal distribution assumed for ln(RR)
# sample mean ln(RR)
mean_ln_rr = math.log(Data.TREATMENT_RR)
# sample standard deviation of ln(RR)
std_ln_rr = \
(math.log(rr_ci[1]) - math.log(rr_ci[0])) / (2 * stat.norm.ppf(1 - 0.05 / 2))
# create a normal distribution for ln(RR)
self.lnRelativeRiskRVG = RVGs.Normal(loc=mean_ln_rr, scale=std_ln_rr)
# create gamma distributions for annual state cost
for cost in Data.ANNUAL_STATE_COST_NO:
# if cost is zero, add a constant 0, otherwise add a gamma distribution
if cost == 0:
self.annualStateCostRVG.append(RVGs.Constant(value=0))
else:
# find shape and scale of the assumed gamma distribution
# no data available to estimate the standard deviation, so we assumed st_dev=cost / 5
fit_output = MM.get_gamma_params(mean=cost, st_dev=cost / 5)
# append the distribution
self.annualStateCostRVG.append(
RVGs.Gamma(a=fit_output["a"],
loc=0,
scale=fit_output["scale"]))
# create beta distributions for annual state utility
for utility in Data.ANNUAL_STATE_UTILITY_0:
# if utility is zero, add a constant 0, otherwise add a beta distribution
if utility == 0:
self.annualStateCostRVG.append(RVGs.Constant(value=0))
else:
# find alpha and beta of the assumed beta distribution
# no data available to estimate the standard deviation, so we assumed st_dev=cost / 4
fit_output = MM.get_beta_params(mean=utility,
st_dev=utility / 4)
# append the distribution
self.annualStateUtilityRVG.append(
RVGs.Beta(a=fit_output["a"], b=fit_output["b"]))
def test_gamma(rnd, a, loc=0, scale=1):
# gamma random variate generator
gamma_dist = RVGs.Gamma(a, loc, scale)
# obtain samples
samples = get_samples(gamma_dist, rnd)
# report mean and variance
print_test_results('Gamma',
samples,
expectation=a * scale + loc,
variance=a * scale**2)
def __init__(self):
self.hospdaycost = None
self.fadcost = None
self.sadcost = None
self.icucost = None
self.sequtility = None # annual state utilities
self.hosputility = None
self.rsvinfection = None # list of dirichlet distributions for transition probabilities
self.lnRelativeRiskRVG = None
self.probnhpd = None
# normal distribution for the natural log of the treatment relative risk
self.probhops = None
self.probICU = None
self.probhpd = None
self.probseq = None
self.weight = None
self.hoptime = None
self.icutime = None
# hospital cost
# per unit P cost
# utility
hua = MM.get_beta_params(mean=Data.hospital_utility, st_dev=0.07)
self.hosputility = RVGs.Beta(a=hua["a"], b=hua["b"])
nhu = MM.get_beta_params(mean=Data.rsv_nh_u, st_dev=0.07)
# append the distribution
self.rsvinfection = RVGs.Beta(a=nhu["a"], b=nhu["b"])
seq = MM.get_beta_params(mean=Data.Asthma_utility, st_dev=0.07)
# append the distribution
self.sequtility = RVGs.Beta(a=seq["a"], b=seq["b"])
##### cost
hos = MM.get_gamma_params(mean=Data.cost_hos, st_dev=149)
# append the distribution
self.hospdaycost = RVGs.Gamma(a=hos["a"],
loc=0,
scale=hos["scale"])
icu = MM.get_gamma_params(mean=Data.ICU, st_dev=271)
# append the distribution
self.icucost = RVGs.Gamma(a=icu["a"],
loc=0,
scale=icu["scale"])
sad = MM.get_gamma_params(mean=Data.sadcost, st_dev=5)
# append the distribution
self.sadcost = RVGs.Gamma(a=sad["a"],
loc=0,
scale=icu["scale"])
fad = MM.get_gamma_params(mean=Data.fadcost, st_dev=6)
# append the distribution
self.fadcost = RVGs.Gamma(a=fad["a"],
loc=0,
scale=icu["scale"])
##### prob
hp = MM.get_beta_params(mean=Data.hos_rate, st_dev=0.023)
# append the distribution
self.probhops = RVGs.Beta(a=hp["a"], b=hp["b"])
hu = MM.get_beta_params(mean=Data.prob_ICU, st_dev=0.0967)
# append the distribution
self.probICU = RVGs.Beta(a=hu["a"], b=hu["b"])
dea = MM.get_beta_params(mean=Data.Hos_MORTALITY_PROB, st_dev=0.0253)
# append the distribution
self.probhpd = RVGs.Beta(a=dea["a"], b=dea["b"])
se = MM.get_beta_params(mean=Data.prob_seq, st_dev=0.115)
# append the distribution
self.probseq = RVGs.Beta(a=se["a"], b=se["b"])
de = MM.get_beta_params(mean=Data.death_rate, st_dev=0.0004836235)
self.probnhpd = RVGs.Beta(a=de["a"], b=de["b"])
### stay time
hoss = MM.get_gamma_params(mean=Data.hoptime, st_dev=3.1)
# append the distribution
self.hoptime = RVGs.Gamma(a=hoss["a"],
loc=0,
scale=hoss["scale"])
ic = MM.get_gamma_params(mean=Data.icutime, st_dev=3.8)
# append the distribution
self.icutime = RVGs.Gamma(a=ic["a"],
loc=0,
scale=ic["scale"])
## weight
we = MM.get_gamma_params(mean=Data.weight, st_dev=392)
# append the distribution
self.weight = RVGs.Gamma(a=we["a"],
loc=0,
scale=we["scale"])
rr_ci = [0.181, 0.634] # confidence interval of the treatment relative risk
# find the mean and st_dev of the normal distribution assumed for ln(RR)
# sample mean ln(RR)
mean_ln_rr = math.log(0.453)
# sample standard deviation of ln(RR)
std_ln_rr = \
(math.log(rr_ci[1]) - math.log(rr_ci[0])) / (2 * stat.norm.ppf(1 - 0.05 / 2))
# create a normal distribution for ln(RR)
self.lnRelativeRiskRVG = RVGs.Normal(loc=mean_ln_rr,
scale=std_ln_rr)
fixed_scale=2) # fit
print("Fitting BetaBinomial:", dictResults)
# 4 Binomial
dist = RVGs.Binomial(100, 0.3, 1)
dat_bin = np.array(get_samples(dist, np.random))
dictResults = Fit.fit_binomial(dat_bin, 'Data', fixed_location=1) # fit
print("Fitting Binomial:", dictResults)
# 5 Empirical (for int data)
dat_em = np.random.poisson(30, 1000)
dictResults = Fit.fit_empirical(dat_em, 'Data', bin_size=2.5) # fit
print("Fitting Empirical:", dictResults)
# 6 fitting a gamma distribution
dist = RVGs.Gamma(10, 1, 2)
dat_gamma = np.array(get_samples(dist, np.random)) # generate data
dictResults = Fit.fit_gamma(dat_gamma, 'Data', fixed_location=1) # fit
print("Fitting Gamma:", dictResults)
# 7 GammaPoisson
dist = RVGs.GammaPoisson(a=2, gamma_scale=4, loc=1, scale=2)
dat_gamma_poisson = np.array(get_samples(dist, np.random))
dictResults = Fit.fit_gamma_poisson(dat_gamma_poisson,
'Data',
fixed_location=1,
fixed_scale=2) # fit
print("Fitting GammaPoisson:", dictResults)
# 8 Geometric
dist = RVGs.Geometric(0.3, 1)
