def generate_disease_data(condition, cov):
""" Generate csv files with gold-standard disease data,
and somewhat good, somewhat dense disease data, as might be expected from a
condition that is carefully studied in the literature
"""
age_len = dismod3.MAX_AGE
ages = np.arange(age_len, dtype='float')
# incidence rate
i0 = .005 + .02 * mc.invlogit((ages - 44) / 3)
#i0 = np.maximum(0., .001 * (-.125 + np.ones_like(ages) + (ages / age_len)**2.))
# remission rate
#r = 0. * ages
r = .1 * np.ones_like(ages)
# excess-mortality rate
#f_init = .085 * (ages / 100) ** 2.5
SMR = 3. * np.ones_like(ages) - ages / age_len
# all-cause mortality-rate
mort = dismod3.get_disease_model('all-cause_mortality')
#age_intervals = [[a, a+9] for a in range(0, dismod3.MAX_AGE-4, 10)] + [[0, 100] for ii in range(1)]
age_intervals = [[a, a] for a in range(0, dismod3.MAX_AGE, 1)]
# TODO: take age structure from real data
sparse_intervals = dict([[
region,
random.sample(age_intervals,
(ii**3 * len(age_intervals)) / len(countries_for)**3 / 1)
] for ii, region in enumerate(countries_for)])
dense_intervals = dict(
[[region, random.sample(age_intervals,
len(age_intervals) / 2)]
for ii, region in enumerate(countries_for)])
gold_data = []
noisy_data = []
for ii, region in enumerate(sorted(countries_for)):
if region == 'world':
continue
print region
sys.stdout.flush()
# introduce unexplained regional variation
#i = i0 * (1 + float(ii) / 21)
# or not
i = i0
for year in [1990, 2005]:
for sex in ['male', 'female']:
param_type = 'all-cause_mortality'
key = dismod3.gbd_key_for(param_type, region, year, sex)
m_all_cause = mort.mortality(key, mort.data)
# calculate excess-mortality rate from smr
f = (SMR - 1.) * m_all_cause
## compartmental model (bins S, C, D, M)
import scipy.linalg
from dismod3 import NEARLY_ZERO
from dismod3.utils import trim
SCDM = np.zeros([4, age_len])
p = np.zeros(age_len)
m = np.zeros(age_len)
SCDM[0, 0] = 1.
SCDM[1, 0] = 0.
SCDM[2, 0] = 0.
SCDM[3, 0] = 0.
p[0] = SCDM[1, 0] / (SCDM[0, 0] + SCDM[1, 0] + NEARLY_ZERO)
m[0] = trim(m_all_cause[0] - f[0] * p[0], NEARLY_ZERO,
1 - NEARLY_ZERO)
for a in range(age_len - 1):
A = [[-i[a] - m[a], r[a], 0., 0.],
[i[a], -r[a] - m[a] - f[a], 0., 0.],
[m[a], m[a], 0., 0.], [0., f[a], 0., 0.]]
SCDM[:, a + 1] = np.dot(scipy.linalg.expm(A), SCDM[:, a])
p[a + 1] = SCDM[1, a + 1] / (SCDM[0, a + 1] +
SCDM[1, a + 1] + NEARLY_ZERO)
m[a + 1] = m_all_cause[a + 1] - f[a + 1] * p[a + 1]
# duration = E[time in bin C]
hazard = r + m + f
pr_not_exit = np.exp(-hazard)
X = np.empty(len(hazard))
X[-1] = 1 / hazard[-1]
for ii in reversed(range(len(X) - 1)):
X[ii] = (pr_not_exit[ii] *
(X[ii + 1] + 1)) + (1 / hazard[ii] *
(1 - pr_not_exit[ii]) -
pr_not_exit[ii])
country = countries_for[region][0]
params = dict(age_intervals=age_intervals,
condition=condition,
gbd_region=region,
country=country,
year=year,
sex=sex,
effective_sample_size=1000)
params['age_intervals'] = [[0, 99]]
generate_and_append_data(gold_data, 'prevalence data', p,
**params)
generate_and_append_data(gold_data, 'incidence data', i,
**params)
generate_and_append_data(gold_data, 'excess-mortality data', f,
**params)
generate_and_append_data(gold_data, 'remission data', r,
**params)
generate_and_append_data(gold_data, 'duration data', X,
**params)
# TODO: use this approach to age standardize all gold data, and then change it to get iX as a direct sum
params['age_intervals'] = [[0, 99]]
iX = i * X * (1 - p) * regional_population(key)
generate_and_append_data(gold_data, 'incidence_x_duration', iX,
**params)
params['effective_sample_size'] = 1000
params['cov'] = 0.
params['age_intervals'] = age_intervals
generate_and_append_data(noisy_data, 'prevalence data', p,
**params)
generate_and_append_data(noisy_data, 'excess-mortality data',
f, **params)
generate_and_append_data(noisy_data, 'remission data', r,
**params)
generate_and_append_data(noisy_data, 'incidence data', i,
**params)
col_names = sorted(data_dict_for_csv(gold_data[0]).keys())
f_file = open(OUTPUT_PATH + '%s_gold.tsv' % condition, 'w')
csv_f = csv.writer(f_file, dialect='excel-tab')
csv_f.writerow(col_names)
for d in gold_data:
dd = data_dict_for_csv(d)
csv_f.writerow([dd[c] for c in col_names])
f_file.close()
f_name = OUTPUT_PATH + '%s_data.tsv' % condition
f_file = open(f_name, 'w')
csv_f = csv.writer(f_file, dialect='excel-tab')
csv_f.writerow(col_names)
for d in noisy_data:
dd = data_dict_for_csv(d)
csv_f.writerow([dd[c] for c in col_names])
f_file.close()
# upload data file
from dismod3.disease_json import dismod_server_login, twc, DISMOD_BASE_URL
dismod_server_login()
twc.go(DISMOD_BASE_URL + 'dismod/data/upload/')
twc.formvalue(1, 'tab_separated_values', open(f_name).read())
# TODO: find or set the model number for this model, set the
# expert priors and covariates, merge the covariate data into the
# model, and add the "ground truth" to the disease json
try:
url = twc.submit()
except Exception, e:
print e
if __name__ == "__main__":
dm_egypt = hep_c_fit(["egypt"], [1990, 2005], egypt_flag=True)
dm_na_me = hep_c_fit(["north_africa_middle_east"], [1990, 2005])
# combine prevalence curves for egypt and rest of north africa
for y in [1990, 2005]:
for s in ["male", "female"]:
key = "prevalence+egypt+%d+%s" % (y, s)
prev_1 = neg_binom_model.calc_rate_trace(dm_egypt, key, dm_egypt.vars[key])
pop_1 = neg_binom_model.population_by_age[("EGY", str(y), s)]
key = "prevalence+north_africa_middle_east+%d+%s" % (y, s)
prev_0 = neg_binom_model.calc_rate_trace(dm_na_me, key, dm_na_me.vars[key])
pop_0 = neg_binom_model.regional_population(key)
# generate population weighted average
prev = (prev_0 * (pop_0 - pop_1) + prev_1 * pop_1) / pop_0
neg_binom_model.store_mcmc_fit(dm_na_me, key, None, prev)
# generate plots of results
dismod3.tile_plot_disease_model(dm_na_me, [key], defaults={"ymax": 0.15, "alpha": 0.5})
dm_na_me.savefig("dm-%d-posterior-na_me_w_egypt.%f.png" % (dm_na_me.id, random()))
# save results
dismod3.post_disease_model(dm_na_me)
dm = hep_c_fit(
"caribbean latin_america_tropical latin_america_andean latin_america_central latin_america_southern".split(),
[1990, 2005],
def generate_disease_data(condition, cov):
""" Generate csv files with gold-standard disease data,
and somewhat good, somewhat dense disease data, as might be expected from a
condition that is carefully studied in the literature
"""
age_len = dismod3.MAX_AGE
ages = np.arange(age_len, dtype='float')
# incidence rate
i0 = .005 + .02 * mc.invlogit((ages - 44) / 3)
#i0 = np.maximum(0., .001 * (-.125 + np.ones_like(ages) + (ages / age_len)**2.))
# remission rate
#r = 0. * ages
r = .1 * np.ones_like(ages)
# excess-mortality rate
#f_init = .085 * (ages / 100) ** 2.5
SMR = 3. * np.ones_like(ages) - ages / age_len
# all-cause mortality-rate
mort = dismod3.get_disease_model('all-cause_mortality')
#age_intervals = [[a, a+9] for a in range(0, dismod3.MAX_AGE-4, 10)] + [[0, 100] for ii in range(1)]
age_intervals = [[a, a] for a in range(0, dismod3.MAX_AGE, 1)]
# TODO: take age structure from real data
sparse_intervals = dict([[region, random.sample(age_intervals, (ii**3 * len(age_intervals)) / len(countries_for)**3 / 1)] for ii, region in enumerate(countries_for)])
dense_intervals = dict([[region, random.sample(age_intervals, len(age_intervals)/2)] for ii, region in enumerate(countries_for)])
gold_data = []
noisy_data = []
for ii, region in enumerate(sorted(countries_for)):
if region == 'world':
continue
print region
sys.stdout.flush()
# introduce unexplained regional variation
#i = i0 * (1 + float(ii) / 21)
# or not
i = i0
for year in [1990, 2005]:
for sex in ['male', 'female']:
param_type = 'all-cause_mortality'
key = dismod3.gbd_key_for(param_type, region, year, sex)
m_all_cause = mort.mortality(key, mort.data)
# calculate excess-mortality rate from smr
f = (SMR - 1.) * m_all_cause
## compartmental model (bins S, C, D, M)
import scipy.linalg
from dismod3 import NEARLY_ZERO
from dismod3.utils import trim
SCDM = np.zeros([4, age_len])
p = np.zeros(age_len)
m = np.zeros(age_len)
SCDM[0,0] = 1.
SCDM[1,0] = 0.
SCDM[2,0] = 0.
SCDM[3,0] = 0.
p[0] = SCDM[1,0] / (SCDM[0,0] + SCDM[1,0] + NEARLY_ZERO)
m[0] = trim(m_all_cause[0] - f[0] * p[0], NEARLY_ZERO, 1-NEARLY_ZERO)
for a in range(age_len - 1):
A = [[-i[a]-m[a], r[a] , 0., 0.],
[ i[a] , -r[a]-m[a]-f[a], 0., 0.],
[ m[a], m[a] , 0., 0.],
[ 0., f[a], 0., 0.]]
SCDM[:,a+1] = np.dot(scipy.linalg.expm(A), SCDM[:,a])
p[a+1] = SCDM[1,a+1] / (SCDM[0,a+1] + SCDM[1,a+1] + NEARLY_ZERO)
m[a+1] = m_all_cause[a+1] - f[a+1] * p[a+1]
# duration = E[time in bin C]
hazard = r + m + f
pr_not_exit = np.exp(-hazard)
X = np.empty(len(hazard))
X[-1] = 1 / hazard[-1]
for ii in reversed(range(len(X)-1)):
X[ii] = (pr_not_exit[ii] * (X[ii+1] + 1)) + (1 / hazard[ii] * (1 - pr_not_exit[ii]) - pr_not_exit[ii])
country = countries_for[region][0]
params = dict(age_intervals=age_intervals, condition=condition, gbd_region=region,
country=country, year=year, sex=sex, effective_sample_size=1000)
params['age_intervals'] = [[0,99]]
generate_and_append_data(gold_data, 'prevalence data', p, **params)
generate_and_append_data(gold_data, 'incidence data', i, **params)
generate_and_append_data(gold_data, 'excess-mortality data', f, **params)
generate_and_append_data(gold_data, 'remission data', r, **params)
generate_and_append_data(gold_data, 'duration data', X, **params)
# TODO: use this approach to age standardize all gold data, and then change it to get iX as a direct sum
params['age_intervals'] = [[0,99]]
iX = i * X * (1-p) * regional_population(key)
generate_and_append_data(gold_data, 'incidence_x_duration', iX, **params)
params['effective_sample_size'] = 1000
params['cov'] = 0.
params['age_intervals'] = age_intervals
generate_and_append_data(noisy_data, 'prevalence data', p, **params)
generate_and_append_data(noisy_data, 'excess-mortality data', f, **params)
generate_and_append_data(noisy_data, 'remission data', r, **params)
generate_and_append_data(noisy_data, 'incidence data', i, **params)
col_names = sorted(data_dict_for_csv(gold_data[0]).keys())
f_file = open(OUTPUT_PATH + '%s_gold.tsv' % condition, 'w')
csv_f = csv.writer(f_file, dialect='excel-tab')
csv_f.writerow(col_names)
for d in gold_data:
dd = data_dict_for_csv(d)
csv_f.writerow([dd[c] for c in col_names])
f_file.close()
f_name = OUTPUT_PATH + '%s_data.tsv' % condition
f_file = open(f_name, 'w')
csv_f = csv.writer(f_file, dialect='excel-tab')
csv_f.writerow(col_names)
for d in noisy_data:
dd = data_dict_for_csv(d)
csv_f.writerow([dd[c] for c in col_names])
f_file.close()
# upload data file
from dismod3.disease_json import dismod_server_login, twc, DISMOD_BASE_URL
dismod_server_login()
twc.go(DISMOD_BASE_URL + 'dismod/data/upload/')
twc.formvalue(1, 'tab_separated_values', open(f_name).read())
# TODO: find or set the model number for this model, set the
# expert priors and covariates, merge the covariate data into the
# model, and add the "ground truth" to the disease json
try:
url = twc.submit()
except Exception, e:
print e
