'''
Set the parameters for FPsim, specifically for Ethiopia.
'''
import numpy as np
import pandas as pd
import sciris as sc
from scipy import interpolate as si
from .. import defaults as fpd
# %% Housekeeping
thisdir = sc.thispath(__file__) # For loading CSV files
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def scalar_pars():
scalar_pars = {
'location': 'ethiopia',
'postpartum_dur': 23,
'breastfeeding_dur_mu': 9.30485863, # Location parameter of gumbel distribution. Requires children's recode DHS file, see data_processing/breastfeedin_stats.R
'breastfeeding_dur_beta': 8.20149079, # Location parameter of gumbel distribution. Requires children's recode DHS file, see data_processing/breastfeedin_stats.R
'abortion_prob': 0.176, # From https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5568682/, % of all pregnancies calculated
'twins_prob': 0.011, # From https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0025239
'mcpr_norm_year': 2020, # Year to normalize MCPR trend to 1
}
return scalar_pars
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def data2interp(data, ages, normalize=False):
''' Convert unevenly spaced data into an even spline interpolation '''
model = si.interp1d(data[0], data[1])
interp = model(ages)
if normalize:
interp = np.minimum(1, np.maximum(0, interp))
return interp
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def filenames():
''' Data files for use with calibration, etc -- not needed for running a sim '''
files = {}
files['base'] = sc.thisdir(aspath=True) / 'ethiopia'
files['basic_dhs'] = 'basic_dhs.yaml' # From World Bank https://data.worldbank.org/indicator/SH.STA.MMRT?locations=ET
files['popsize'] = 'popsize.csv' # From UN World Population Prospects 2022: https://population.un.org/wpp/Download/Standard/Population/
files['mcpr'] = 'cpr.csv' # From UN Population Division Data Portal, married women 1970-1986, all women 1990-2030
files['tfr'] = 'tfr.csv' # From World Bank https://data.worldbank.org/indicator/SP.DYN.TFRT.IN?locations=ET
files['asfr'] = 'asfr.csv' # From UN World Population Prospects 2022: https://population.un.org/wpp/Download/Standard/Fertility/
files['ageparity'] = 'ageparity.csv' # Choose from either DHS 2016 or PMA 2022
files['spacing'] = 'birth_spacing_dhs.csv'
files['methods'] = 'mix.csv'
files['afb'] = 'afb.table.csv'
files['use'] = 'use.csv'
files['urban'] = 'urban.csv'
#subnational_data files
files['region'] = '/subnational_data/region.csv' ## From DHS 2016
files['asfr_region'] = '/subnational_data/asfr_region.csv' ## From DHS 2016
files['tfr_region'] = '/subnational_data/tfr_region.csv' ## From DHS 2016
files['methods_region'] = '/subnational_data/mix_region.csv' ## From DHS 2016
files['use_region'] = '/subnational_data/use_region.csv' ## From PMA 2019
files['barriers_region'] = '/subnational_data/barriers_region.csv' ## From PMA 2019
files['lactational_amenorrhea_region'] = '/subnational_data/lam_region.csv' ## From DHS 2016
files['sexual_activity_region'] = '/subnational_data/sexual_activity_region.csv' ## From DHS 2016
files['sexual_activity_pp_region'] = '/subnational_data/sexual_activity_pp_region.csv' ## From DHS 2016
files['debut_age_region'] = '/subnational_data/sexual_debut_region.csv' ## From DHS 2016
return files
# %% Demographics and pregnancy outcome
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def age_pyramid():
'''
Starting age bin, male population, female population
Data are from World Population Prospects
https://population.un.org/wpp/Download/Standard/Population/
'''
pyramid = np.array([[0, 2018504, 1986199], # Ethiopia 1962
[5, 1508878, 1515088],
[10, 1349237, 1359040],
[15, 1227673, 1215562],
[20, 1021618, 1018324],
[25, 862087, 864220],
[30, 727361, 732051],
[35, 612416, 620469],
[40, 510553, 521148],
[45, 423644, 434699],
[50, 345374, 360522],
[55, 276116, 297462],
[60, 182129, 224203],
[65, 117217, 162750],
[70, 77018, 111877],
[75, 40877, 66069],
[80, 21275, 40506],
], dtype=float)
return pyramid
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def region_proportions():
'''
Defines the proportion of the population in each region to establish the probability of living in a given region.
Uses 2016 Ethiopia DHS individual recode (v025) for region and V024 for urban to produce subnational estimates
'''
region_data = pd.read_csv(thisdir / 'ethiopia' / 'subnational_data' / 'region.csv')
region_dict = {}
region_dict['region'] = region_data['region'] # Return region names
region_dict['mean'] = region_data['mean'] # Return proportion living in each region
region_dict['urban'] = region_data['urban'] # Return proportion living in an urban area by region
return region_dict
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def age_mortality():
'''
Age-dependent mortality rates taken from UN World Population Prospects 2022. From probability of dying each year.
https://population.un.org/wpp/
Used CSV WPP2022_Life_Table_Complete_Medium_Female_1950-2021, Ethiopia, 2020
Used CSV WPP2022_Life_Table_Complete_Medium_Male_1950-2021, Ethiopia, 2020
Mortality rate trend from crude death rate per 1000 people, also from UN Data Portal, 1950-2030:
https://population.un.org/dataportal/data/indicators/59/locations/231/start/1950/end/2030/table/pivotbylocation
Projections go out until 2030, but the csv file can be manually adjusted to remove any projections and stop at your desired year
'''
data_year = 2020 # NORMED TO 2020 BASED ON ETHIOPIA PROBABILITY DATA
mortality_data = pd.read_csv(thisdir / 'ethiopia' / 'mortality_prob.csv')
mortality_trend = pd.read_csv(thisdir / 'ethiopia' / 'mortality_trend.csv')
mortality = {
'ages': mortality_data['age'].to_numpy(),
'm': mortality_data['male'].to_numpy(),
'f': mortality_data['female'].to_numpy()
}
mortality['year'] = mortality_trend['year'].to_numpy()
mortality['probs'] = mortality_trend['crude_death_rate'].to_numpy()
trend_ind = np.where(mortality['year'] == data_year)
trend_val = mortality['probs'][trend_ind]
mortality['probs'] /= trend_val # Normalize around data year for trending
m_mortality_spline_model = si.splrep(x=mortality['ages'],
y=mortality['m']) # Create a spline of mortality along known age bins
f_mortality_spline_model = si.splrep(x=mortality['ages'], y=mortality['f'])
m_mortality_spline = si.splev(fpd.spline_ages,
m_mortality_spline_model) # Evaluate the spline along the range of ages in the model with resolution
f_mortality_spline = si.splev(fpd.spline_ages, f_mortality_spline_model)
m_mortality_spline = np.minimum(1, np.maximum(0, m_mortality_spline)) # Normalize
f_mortality_spline = np.minimum(1, np.maximum(0, f_mortality_spline))
mortality['m_spline'] = m_mortality_spline
mortality['f_spline'] = f_mortality_spline
return mortality
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def maternal_mortality():
'''
From World Bank indicators for maternal mortality ratio (modeled estimate) per 100,000 live births:
https://data.worldbank.org/indicator/SH.STA.MMRT?locations=ET
'''
data = np.array([
[2000, 953],
[2001, 955],
[2002, 960],
[2003, 906],
[2004, 900],
[2005, 880],
[2006, 814],
[2007, 780],
[2008, 725],
[2009, 684],
[2010, 635],
[2011, 603],
[2012, 543],
[2013, 498],
[2014, 447],
[2015, 399],
[2016, 365],
[2017, 348],
[2018, 312],
[2019, 294],
[2020, 267],
])
maternal_mortality = {}
maternal_mortality['year'] = data[:, 0]
maternal_mortality['probs'] = data[:, 1] / 100000 # ratio per 100,000 live births
# maternal_mortality['ages'] = np.array([16, 17, 19, 22, 25, 50])
# maternal_mortality['age_probs'] = np.array([2.28, 1.63, 1.3, 1.12, 1.0, 1.0]) #need to be added
return maternal_mortality
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def infant_mortality():
'''
From World Bank indicators for infant mortality (< 1 year) for Ethiopia, per 1000 live births
From API_SP.DYN.IMRT.IN_DS2_en_csv_v2_5358355
Adolescent increased risk of infant mortality gradient taken
from Noori et al for Sub-Saharan African from 2014-2018. Odds ratios with age 23-25 as reference group:
https://www.medrxiv.org/content/10.1101/2021.06.10.21258227v1
'''
data = np.array([
[1966, 146.6],
[1967, 146.1],
[1968, 145.9],
[1969, 145.8],
[1970, 145.6],
[1971, 145.4],
[1972, 145.1],
[1973, 145],
[1974, 144.7],
[1975, 144.4],
[1976, 144.1],
[1977, 143.8],
[1978, 143.4],
[1979, 142.6],
[1980, 141.5],
[1981, 139.9],
[1982, 138],
[1983, 135.8],
[1984, 133.4],
[1985, 131],
[1986, 128.7],
[1987, 126.4],
[1988, 124.2],
[1989, 121.9],
[1990, 119.5],
[1991, 116.9],
[1992, 114],
[1993, 110.7],
[1994, 107.2],
[1995, 103.7],
[1996, 100.3],
[1997, 96.8],
[1998, 93.5],
[1999, 90.3],
[2000, 87],
[2001, 83.7],
[2002, 80.2],
[2003, 76.7],
[2004, 73.1],
[2005, 69.5],
[2006, 66.1],
[2007, 62.8],
[2008, 59.8],
[2009, 57],
[2010, 54.4],
[2011, 51.9],
[2012, 49.5],
[2013, 47.3],
[2014, 45.2],
[2015, 43.2],
[2016, 41.3],
[2017, 39.6],
[2018, 38],
[2019, 36.6],
[2020, 35.4],
[2021, 34.3]
])
infant_mortality = {}
infant_mortality['year'] = data[:, 0]
infant_mortality['probs'] = data[:, 1] / 1000 # Rate per 1000 live births, used after stillbirth is filtered out
infant_mortality['ages'] = np.array([16, 17, 19, 22, 25, 50])
infant_mortality['age_probs'] = np.array([2.28, 1.63, 1.3, 1.12, 1.0, 1.0])
return infant_mortality
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def miscarriage():
'''
Returns a linear interpolation of the likelihood of a miscarriage
by age, taken from data from Magnus et al BMJ 2019: https://pubmed.ncbi.nlm.nih.gov/30894356/
Data to be fed into likelihood of continuing a pregnancy once initialized in model
Age 0 and 5 set at 100% likelihood. Age 10 imputed to be symmetrical with probability at age 45 for a parabolic curve
'''
miscarriage_rates = np.array([[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50],
[1, 1, 0.569, 0.167, 0.112, 0.097, 0.108, 0.167, 0.332, 0.569, 0.569]])
miscarriage_interp = data2interp(miscarriage_rates, fpd.spline_preg_ages)
return miscarriage_interp
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def stillbirth():
'''
From Report of the UN Inter-agency Group for Child Mortality Estimation, 2020
https://childmortality.org/wp-content/uploads/2020/10/UN-IGME-2020-Stillbirth-Report.pdf
Age adjustments come from an extension of Noori et al., which were conducted June 2022.
'''
data = np.array([
[2000, 35.8],
[2010, 31.1],
[2019, 24.6],
])
stillbirth_rate = {}
stillbirth_rate['year'] = data[:, 0]
stillbirth_rate['probs'] = data[:, 1] / 1000 # Rate per 1000 total births
stillbirth_rate['ages'] = np.array([15, 16, 17, 19, 20, 28, 31, 36, 50])
stillbirth_rate['age_probs'] = np.array([3.27, 1.64, 1.85, 1.39, 0.89, 1.0, 1.5, 1.55, 1.78]) # odds ratios
return stillbirth_rate
# %% Fecundity
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def female_age_fecundity():
'''
Use fecundity rates from PRESTO study: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712257/
Fecundity rate assumed to be approximately linear from onset of fecundity around age 10 (average age of menses 12.5) to first data point at age 20
45-50 age bin estimated at 0.10 of fecundity of 25-27 yr olds
'''
fecundity = {
'bins': np.array([0., 5, 10, 15, 20, 25, 28, 31, 34, 37, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 99]),
'f': np.array([0., 0, 0, 65, 70.8, 79.3, 77.9, 76.6, 74.8, 67.4, 55.5, 7.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])}
fecundity[
'f'] /= 100 # Conceptions per hundred to conceptions per woman over 12 menstrual cycles of trying to conceive
fecundity_interp_model = si.interp1d(x=fecundity['bins'], y=fecundity['f'])
fecundity_interp = fecundity_interp_model(fpd.spline_preg_ages)
fecundity_interp = np.minimum(1, np.maximum(0, fecundity_interp)) # Normalize to avoid negative or >1 values
return fecundity_interp
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def fecundity_ratio_nullip():
'''
Returns an array of fecundity ratios for a nulliparous woman vs a gravid woman
from PRESTO study: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712257/
Approximates primary infertility and its increasing likelihood if a woman has never conceived by age
'''
fecundity_ratio_nullip = np.array([[0, 5, 10, 12.5, 15, 18, 20, 25, 30, 34, 37, 40, 45, 50],
[1, 1, 1, 1, 1, 1, 1, 0.96, 0.95, 0.71, 0.73, 0.42, 0.42, 0.42]])
fecundity_nullip_interp = data2interp(fecundity_ratio_nullip, fpd.spline_preg_ages)
return fecundity_nullip_interp
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def lactational_amenorrhea():
'''
Returns an array of the percent of breastfeeding women by month postpartum 0-11 months who meet criteria for LAM:
Exclusively breastfeeding (bf + water alone), menses have not returned. Extended out 5-11 months to better match data
as those women continue to be postpartum insusceptible.
From DHS Ethiopia 2016 calendar data
'''
data = np.array([
[0, 0.9892715],
[1, 0.8494371],
[2, 0.8265375],
[3, 0.730395],
[4, 0.666934],
[5, 0.4947101],
[6, 0.3667066],
[7, 0.2191162],
[8, 0.3135374],
[9, 0.1113177],
[10, 0.0981782],
[11, 0.0847675],
])
lactational_amenorrhea = {}
lactational_amenorrhea['month'] = data[:, 0]
lactational_amenorrhea['rate'] = data[:, 1]
return lactational_amenorrhea
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def lactational_amenorrhea_region():
'''
Returns an array of the percent of breastfeeding women by month postpartum 0-11 months who meet criteria for LAM, stratified by region
'''
lam_region = pd.read_csv(thisdir / 'ethiopia' / 'subnational_data' / 'lam_region.csv')
lam_dict = {}
lam_dict['region'] = lam_region['region'] # Return region names
lam_dict['month'] = lam_region['month'] # Return month postpartum
lam_dict['rate'] = lam_region['rate'] # Return percent of breastfeeding women
return lam_dict
# %% Pregnancy exposure
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def sexual_activity():
'''
Returns a linear interpolation of rates of female sexual activity, defined as
percentage women who have had sex within the last four weeks.
From STAT Compiler DHS https://www.statcompiler.com/en/
Using indicator "Timing of sexual intercourse"
Includes women who have had sex "within the last four weeks"
Excludes women who answer "never had sex", probabilities are only applied to agents who have sexually debuted
Data taken from 2018 DHS, no trend over years for now
Onset of sexual activity probabilities assumed to be linear from age 10 to first data point at age 15
Last value duplicated so that it interpolates out to 50 and then stops
'''
sexually_active = np.array([[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50],
[0, 0, 0, 15, 51.2, 69.4, 69.3, 68.6, 68.1, 59.7, 59.7]])
sexually_active[1] /= 100 # Convert from percent to rate per woman
activity_ages = sexually_active[0]
activity_interp_model = si.interp1d(x=activity_ages, y=sexually_active[1])
activity_interp = activity_interp_model(fpd.spline_preg_ages) # Evaluate interpolation along resolution of ages
return activity_interp
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def sexual_activity_region():
'''
Returns a linear interpolation of rates of female sexual activity, stratified by region
'''
sexually_active_region_data = pd.read_csv(thisdir / 'ethiopia' / 'subnational_data' / 'sexual_activity_region.csv')
sexually_active_region_dict = {}
sexually_active_region_dict['region'] = sexually_active_region_data.iloc[:, 0] # Return region names
sexually_active_region_dict['age'] = sexually_active_region_data.iloc[:, 1] # Return age
sexually_active_region_dict['perc'] = sexually_active_region_data.iloc[:, 2] / 100 # Return perc divide by 100 to convert to a rate
activity_ages_region = sexually_active_region_dict['age']
activity_interp_model_region = si.interp1d(x=activity_ages_region, y=sexually_active_region_dict['perc'])
activity_interp_region = activity_interp_model_region(fpd.spline_preg_ages)
return activity_interp_region
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def sexual_activity_pp():
'''
Returns an array of monthly likelihood of having resumed sexual activity within 0-35 months postpartum
Uses 2016 Ethiopia DHS individual recode (postpartum (v222), months since last birth, and sexual activity within 30 days.
Data is weighted.
Limited to 23 months postpartum (can use any limit you want 0-23 max)
Postpartum month 0 refers to the first month after delivery
'''
postpartum_sex = np.array([
[0, 0.19253],
[1, 0.31858],
[2, 0.49293],
[3, 0.64756],
[4, 0.78684],
[5, 0.67009],
[6, 0.75804],
[7, 0.79867],
[8, 0.84857],
[9, 0.80293],
[10, 0.89259],
[11, 0.8227],
[12, 0.85876],
[13, 0.83104],
[14, 0.77563],
[15, 0.79917],
[16, 0.79582],
[17, 0.84817],
[18, 0.77804],
[19, 0.80811],
[20, 0.82049],
[21, 0.77607],
[22, 0.79261],
[23, 0.8373],
])
postpartum_activity = {}
postpartum_activity['month'] = postpartum_sex[:, 0]
postpartum_activity['percent_active'] = postpartum_sex[:, 1]
return postpartum_activity
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def sexual_activity_pp_region():
'''
# Returns an additional array of monthly likelihood of having resumed sexual activity by region
'''
pp_activity_region = pd.read_csv(thisdir / 'ethiopia' / 'subnational_data' / 'sexual_activity_pp_region.csv')
pp_activity_region_dict = {}
pp_activity_region_dict['region'] = pp_activity_region['region'] # Return region names
pp_activity_region_dict['month'] = pp_activity_region['month'] # Return month postpartum
pp_activity_region_dict['perc'] = pp_activity_region['perc'] # Return likelihood of resumed sexual activity
return pp_activity_region_dict
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def debut_age():
'''
Returns an array of weighted probabilities of sexual debut by a certain age 10-45.
Data taken from DHS variable v531 (imputed age of sexual debut, imputed with data from age at first union)
Use sexual_debut_age_probs.py under locations/data_processing to output for other DHS countries
'''
sexual_debut = np.array([
[10, 0.00671240845335203],
[11, 0.00918135820646243],
[12, 0.0309770814788788],
[13, 0.059400507503726],
[14, 0.130291755291],
[15, 0.196183864268175],
[16, 0.130556610013873],
[17, 0.103290455840828],
[18, 0.110776245328648],
[19, 0.0530816775521274],
[20, 0.0588590881799291],
[21, 0.026991174849838],
[22, 0.0271788262050103],
[23, 0.0188626403851833],
[24, 0.0112214052863469],
[25, 0.0109271507351524],
[26, 0.00443999952806908],
[27, 0.00359275321149036],
[28, 0.00303477463739577],
[29, 0.0017573689141809],
[30, 0.00121215246872525],
[31, 0.000711491329468429],
[32, 0.000137332034070925],
[33, 0.000279848072025066],
[34, 7.17053090713206E-06],
[35, 9.65008015799441E-05],
[36, 8.46224502635213E-06],
[37, 3.97705796721265E-05],
[43, 0.00019012606885453]])
debut_age = {}
debut_age['ages'] = sexual_debut[:, 0]
debut_age['probs'] = sexual_debut[:, 1]
return debut_age
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def debut_age_region():
'''
# Returns an additional array of weighted probabilities of sexual debut by region
'''
sexual_debut_region_data = pd.read_csv(thisdir / 'ethiopia' / 'subnational_data' / 'sexual_debut_region.csv')
debut_age_region_dict = {}
debut_age_region_dict['region'] = sexual_debut_region_data['region'] # Return region names
debut_age_region_dict['age'] = sexual_debut_region_data['age'] # Return month postpartum
debut_age_region_dict['prob'] = sexual_debut_region_data['prob'] # Return weighted probabilities of sexual debut
return debut_age_region_dict
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def exposure_age():
'''
Returns an array of experimental factors to be applied to account for
residual exposure to either pregnancy or live birth by age. Exposure to pregnancy will
increase factor number and residual likelihood of avoiding live birth (mostly abortion,
also miscarriage), will decrease factor number
'''
exposure_correction_age = np.array([[0, 5, 10, 12.5, 15, 18, 20, 25, 30, 35, 40, 45, 50],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
exposure_age_interp = data2interp(exposure_correction_age, fpd.spline_preg_ages)
return exposure_age_interp
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def exposure_parity():
'''
Returns an array of experimental factors to be applied to account for residual exposure to either pregnancy
or live birth by parity.
'''
exposure_correction_parity = np.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 20],
[1, 1, 1, 1, 1, 1, 1, 0.8, 0.5, 0.3, 0.15, 0.10, 0.05, 0.01]])
exposure_parity_interp = data2interp(exposure_correction_parity, fpd.spline_parities)
return exposure_parity_interp
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def birth_spacing_pref():
'''
Returns an array of birth spacing preferences by closest postpartum month.
Applied to postpartum pregnancy likelihoods.
NOTE: spacing bins must be uniform!
'''
postpartum_spacing = np.array([
[0, 1],
[3, 1],
[6, 1],
[9, 1],
[12, 1],
[15, 1],
[18, 1],
[21, 1],
[24, 1],
[27, 1],
[30, 1],
[33, 1],
[36, 1],
])
# Calculate the intervals and check they're all the same
intervals = np.diff(postpartum_spacing[:, 0])
interval = intervals[0]
assert np.all(
intervals == interval), f'In order to be computed in an array, birth spacing preference bins must be equal width, not {intervals}'
pref_spacing = {}
pref_spacing['interval'] = interval # Store the interval (which we've just checked is always the same)
pref_spacing['n_bins'] = len(intervals) # Actually n_bins - 1, but we're counting 0 so it's OK
pref_spacing['months'] = postpartum_spacing[:, 0]
pref_spacing['preference'] = postpartum_spacing[:, 1] # Store the actual birth spacing data
return pref_spacing
# %% Contraceptive methods
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def methods():
'''
Names, indices, modern/traditional flag, and efficacies of contraceptive methods -- see also parameters.py
Efficacy from Guttmacher, fp_prerelease/docs/gates_review/contraceptive-failure-rates-in-developing-world_1.pdf
BTL failure rate from general published data
Pooled efficacy rates for all women in this study: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970461/
'''
# Define method data
data = { # Index, modern, efficacy
'None': [0, False, 0.000],
'Withdrawal': [1, False, 0.866],
'Other traditional': [2, False, 0.861],
# 1/2 periodic abstinence, 1/2 other traditional approx. Using rate from periodic abstinence
'Condoms': [3, True, 0.946],
'Pill': [4, True, 0.945],
'Injectables': [5, True, 0.983],
'Implants': [6, True, 0.994],
'IUDs': [7, True, 0.986],
'BTL': [8, True, 0.995],
'Other modern': [9, True, 0.880],
# SDM makes up about 1/2 of this, perfect use is 95% and typical is 88%. EC also included here, efficacy around 85% https : //www.aafp.org/afp/2004/0815/p707.html
}
keys = data.keys()
methods = {}
methods['map'] = {k: data[k][0] for k in keys}
methods['modern'] = {k: data[k][1] for k in keys}
methods['eff'] = {k: data[k][2] for k in keys}
# Age bins for different method switching matrices -- duplicated in defaults.py
methods['age_map'] = {
'<18': [0, 18],
'18-20': [18, 20],
'21-25': [20, 25],
'26-35': [25, 35],
'>35': [35, fpd.max_age + 1], # +1 since we're using < rather than <=
}
# Data on trend in CPR over time in from Ethiopia, in %.
# Taken from UN Population Division Data Portal, married women 1970-1986, all women 1990-2030
# https://population.un.org/dataportal/data/indicators/1/locations/231/start/1950/end/2040/table/pivotbylocation
# Projections go out until 2030, but the csv file can be manually adjusted to remove any projections and stop at your desired year
cpr_data = pd.read_csv(thisdir / 'ethiopia' / 'cpr.csv')
methods['mcpr_years'] = cpr_data['year'].to_numpy()
methods['mcpr_rates'] = cpr_data['cpr'].to_numpy() / 100 # convert from percent to rate
return methods
'''
For reference
def method_probs_senegal():
It does leave Senegal matrices in place in the Ethiopia file for now.
We may want to test with these as we work through scenarios and calibration.
Define "raw" (un-normalized, un-trended) matrices to give transitional probabilities
from 2018 DHS Senegal contraceptive calendar data.
Probabilities in this function are annual probabilities of initiating (top row), discontinuing (first column),
continuing (diagonal), or switching methods (all other entries).
Probabilities at postpartum month 1 are 1 month transitional probabilities
for starting a method after delivery.
Probabilities at postpartum month 6 are 5 month transitional probabilities
for starting or changing methods over the first 6 months postpartum.
Data from Senegal DHS contraceptive calendars, 2017 and 2018 combined
raw = {
# Main switching matrix: all non-postpartum women
'annual': {
'<18': np.array([
[0.9953, 0., 0.0002, 0.0012, 0.0002, 0.0017, 0.0014, 0.0001, 0., 0.],
[0., 1.0000, 0., 0., 0., 0., 0., 0., 0., 0.],
[0.0525, 0., 0.9475, 0., 0., 0., 0., 0., 0., 0.],
[0.307, 0., 0., 0.693, 0., 0., 0., 0., 0., 0.],
[0.5358, 0., 0., 0., 0.3957, 0.0685, 0., 0., 0., 0.],
[0.3779, 0., 0., 0., 0.0358, 0.5647, 0.0216, 0., 0., 0.],
[0.2003, 0., 0., 0., 0., 0., 0.7997, 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 1.0000, 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.0000]]),
'18-20': np.array([
[0.9774, 0., 0.0014, 0.0027, 0.0027, 0.0104, 0.0048, 0.0003, 0., 0.0003],
[0., 1.0000, 0., 0., 0., 0., 0., 0., 0., 0.],
[0.3216, 0., 0.6784, 0., 0., 0., 0., 0., 0., 0.],
[0.182, 0., 0., 0.818, 0., 0., 0., 0., 0., 0.],
[0.4549, 0., 0., 0., 0.4754, 0.0463, 0.0234, 0., 0., 0.],
[0.4389, 0., 0.0049, 0.0099, 0.0196, 0.5218, 0.0049, 0., 0., 0.],
[0.17, 0., 0., 0., 0., 0.0196, 0.8103, 0., 0., 0.],
[0.1607, 0., 0., 0., 0., 0., 0., 0.8393, 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0.4773, 0., 0., 0.4773, 0., 0., 0., 0., 0., 0.0453]]),
'21-25': np.array([
[0.9581, 0.0001, 0.0011, 0.0024, 0.0081, 0.0184, 0.0108, 0.0006, 0., 0.0004],
[0.4472, 0.5528, 0., 0., 0., 0., 0., 0., 0., 0.],
[0.2376, 0., 0.7624, 0., 0., 0., 0., 0., 0., 0.],
[0.1896, 0., 0.0094, 0.754, 0.0094, 0., 0.0188, 0., 0., 0.0188],
[0.3715, 0.003, 0.003, 0., 0.5703, 0.0435, 0.0088, 0., 0., 0.],
[0.3777, 0., 0.0036, 0.0036, 0.0258, 0.5835, 0.0036, 0.0024, 0., 0.],
[0.137, 0., 0., 0.003, 0.0045, 0.0045, 0.848, 0.003, 0., 0.],
[0.1079, 0., 0., 0., 0.0445, 0., 0.0225, 0.8251, 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0.3342, 0., 0., 0.1826, 0., 0., 0., 0., 0., 0.4831]]),
'26-35': np.array([
[0.9462, 0.0001, 0.0018, 0.0013, 0.0124, 0.0209, 0.0139, 0.003, 0.0001, 0.0002],
[0.0939, 0.8581, 0., 0., 0., 0.048, 0., 0., 0., 0.],
[0.1061, 0., 0.8762, 0.0051, 0.0025, 0.0025, 0.0051, 0.0025, 0., 0.],
[0.1549, 0., 0., 0.8077, 0.0042, 0.0125, 0.0083, 0.0083, 0., 0.0042],
[0.3031, 0.0016, 0.0021, 0.0021, 0.6589, 0.0211, 0.0053, 0.0053, 0., 0.0005],
[0.2746, 0., 0.0028, 0.002, 0.0173, 0.691, 0.0073, 0.0048, 0., 0.0003],
[0.1115, 0.0003, 0.0009, 0.0003, 0.0059, 0.0068, 0.8714, 0.0025, 0.0003, 0.],
[0.0775, 0., 0.0015, 0., 0.0058, 0.0044, 0.0044, 0.905, 0., 0.0015],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0.1581, 0., 0.0121, 0., 0., 0., 0., 0., 0., 0.8297]]),
'>35': np.array([
[0.9462, 0.0001, 0.0018, 0.0013, 0.0124, 0.0209, 0.0139, 0.003, 0.0001, 0.0002],
[0.0939, 0.8581, 0., 0., 0., 0.048, 0., 0., 0., 0.],
[0.1061, 0., 0.8762, 0.0051, 0.0025, 0.0025, 0.0051, 0.0025, 0., 0.],
[0.1549, 0., 0., 0.8077, 0.0042, 0.0125, 0.0083, 0.0083, 0., 0.0042],
[0.3031, 0.0016, 0.0021, 0.0021, 0.6589, 0.0211, 0.0053, 0.0053, 0., 0.0005],
[0.2746, 0., 0.0028, 0.002, 0.0173, 0.691, 0.0073, 0.0048, 0., 0.0003],
[0.1115, 0.0003, 0.0009, 0.0003, 0.0059, 0.0068, 0.8714, 0.0025, 0.0003, 0.],
[0.0775, 0., 0.0015, 0., 0.0058, 0.0044, 0.0044, 0.905, 0., 0.0015],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0.1581, 0., 0.0121, 0., 0., 0., 0., 0., 0., 0.8297]])
},
# Postpartum switching matrix, 1 to 6 months
'pp1to6': {
'<18': np.array([
[0.9014, 0., 0.0063, 0.001, 0.0126, 0.051, 0.0272, 0.0005, 0., 0.],
[0., 0.5, 0., 0., 0., 0., 0.5, 0., 0., 0.],
[0., 0., 1.0000, 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 1.0000, 0., 0., 0., 0., 0., 0.],
[0.4, 0., 0., 0., 0.6, 0., 0., 0., 0., 0.],
[0.0714, 0., 0., 0., 0., 0.9286, 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 1.0000, 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 1.0000, 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.0000]]),
'18-20': np.array([
[0.8775, 0.0007, 0.0026, 0.0033, 0.0191, 0.0586, 0.0329, 0.0046, 0., 0.0007],
[0., 1.0000, 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 1.0000, 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 1.0000, 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.75, 0.25, 0., 0., 0., 0.],
[0.0278, 0., 0., 0., 0., 0.9722, 0., 0., 0., 0.],
[0.0312, 0., 0., 0., 0., 0., 0.9688, 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 1.0000, 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.0000]]),
'21-25': np.array([
[0.8538, 0.0004, 0.0055, 0.0037, 0.0279, 0.0721, 0.0343, 0.0022, 0., 0.],
[0., 1.0000, 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0.9583, 0., 0., 0.0417, 0., 0., 0., 0.],
[0., 0., 0., 0.5, 0.25, 0.25, 0., 0., 0., 0.],
[0.0244, 0., 0., 0., 0.9512, 0.0244, 0., 0., 0., 0.],
[0.0672, 0., 0., 0., 0., 0.9328, 0., 0., 0., 0.],
[0.0247, 0., 0., 0., 0., 0.0123, 0.963, 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 1.0000, 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.0000]]),
'26-35': np.array([
[0.8433, 0.0008, 0.0065, 0.004, 0.029, 0.0692, 0.039, 0.0071, 0.0001, 0.001],
[0., 0.5, 0., 0., 0., 0., 0.5, 0., 0., 0.],
[0.027, 0., 0.9189, 0., 0., 0.027, 0.027, 0., 0., 0.],
[0.1667, 0., 0., 0.6667, 0., 0., 0.1667, 0., 0., 0.],
[0.0673, 0., 0., 0., 0.8654, 0.0288, 0.0385, 0., 0., 0.],
[0.0272, 0., 0.0039, 0., 0.0078, 0.9533, 0.0078, 0., 0., 0.],
[0.0109, 0., 0., 0., 0.0036, 0., 0.9855, 0., 0., 0.],
[0.0256, 0., 0., 0., 0., 0.0256, 0., 0.9487, 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.0000]]),
'>35': np.array([
[0.8433, 0.0008, 0.0065, 0.004, 0.029, 0.0692, 0.039, 0.0071, 0.0001, 0.001],
[0., 0.5, 0., 0., 0., 0., 0.5, 0., 0., 0.],
[0.027, 0., 0.9189, 0., 0., 0.027, 0.027, 0., 0., 0.],
[0.1667, 0., 0., 0.6667, 0., 0., 0.1667, 0., 0., 0.],
[0.0673, 0., 0., 0., 0.8654, 0.0288, 0.0385, 0., 0., 0.],
[0.0272, 0., 0.0039, 0., 0.0078, 0.9533, 0.0078, 0., 0., 0.],
[0.0109, 0., 0., 0., 0.0036, 0., 0.9855, 0., 0., 0.],
[0.0256, 0., 0., 0., 0., 0.0256, 0., 0.9487, 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1.0000, 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.0000]])
},
# Postpartum initiation vectors, 0 to 1 month
'pp0to1': {
'<18': np.array([0.9607, 0.0009, 0.0017, 0.0009, 0.0021, 0.0128, 0.0205, 0.0004, 0., 0.]),
'18-20': np.array([0.9525, 0.0006, 0.0017, 0.0006, 0.0028, 0.0215, 0.0198, 0.0006, 0., 0.]),
'21-25': np.array([0.9379, 0., 0.0053, 0.0009, 0.0083, 0.0285, 0.0177, 0.0013, 0., 0.]),
'26-35': np.array([0.9254, 0.0002, 0.0036, 0.0007, 0.0102, 0.0265, 0.0268, 0.004, 0.0022, 0.0004]),
'>35': np.array([0.9254, 0.0002, 0.0036, 0.0007, 0.0102, 0.0265, 0.0268, 0.004, 0.0022, 0.0004]),
}
}
return raw
'''
[docs]
def method_probs():
'''
Define "raw" (un-normalized, un-trended) matrices to give transitional probabilities
from PMA Ethiopia contraceptive calendar data.
Probabilities in this function are annual probabilities of initiating (top row), discontinuing (first column),
continuing (diagonal), or switching methods (all other entries).
Probabilities at postpartum month 1 are 1 month transitional probabilities
for starting a method after delivery.
Probabilities at postpartum month 6 are 5 month transitional probabilities
for starting or changing methods over the first 6 months postpartum.
Data from Ethiopia PMA contraceptive calendars, 2019-2020
Processed from matrices_ethiopia_pma_2019_20.csv using process_matrices.py
'''
raw = {
# Main switching matrix: all non-postpartum women
'annual': {
'<18': np.array([
[0.9236, 0.0004, 0.0017, 0.0007, 0.0042, 0.0596, 0.0076, 0.0006, 0. , 0.0017],
[0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0.1374, 0. , 0.8571, 0. , 0.0003, 0.0044, 0.0005, 0. , 0. , 0.0001],
[0.2815, 0.0001, 0.0003, 0.6995, 0.0007, 0.0164, 0.0012, 0.0001, 0. , 0.0003],
[0.4681, 0.0001, 0.0006, 0.0013, 0.3023, 0.1869, 0.003 , 0.0005, 0. , 0.0371],
[0.3267, 0.0001, 0.0017, 0.0005, 0.0043, 0.6549, 0.009 , 0.0023, 0. , 0.0005],
[0.1937, 0. , 0.0008, 0.0001, 0.0007, 0.0087, 0.7957, 0.0001, 0. , 0.0002],
[0.4158, 0.0001, 0.0004, 0.0002, 0.0011, 0.0142, 0.0017, 0.5662, 0. , 0.0004],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ],
[0.5988, 0.0001, 0.0006, 0.024 , 0.0186, 0.0258, 0.0027, 0.0002, 0. , 0.3293]]),
'18-20': np.array([
[0.8783, 0.0002, 0.0021, 0.0017, 0.0115, 0.0845, 0.0194, 0.0016, 0. , 0.0006],
[0.0599, 0.9362, 0.0001, 0.0001, 0.0004, 0.0027, 0.0006, 0.0001, 0. , 0. ],
[0.1874, 0. , 0.8002, 0.0002, 0.0012, 0.0088, 0.002 , 0.0002, 0. , 0.0001],
[0.8023, 0.0001, 0.0012, 0.117 , 0.0068, 0.0498, 0.0109, 0.009 , 0. , 0.0028],
[0.2326, 0. , 0.0003, 0.0003, 0.7384, 0.025 , 0.0026, 0.0003, 0. , 0.0004],
[0.279 , 0. , 0.0003, 0.0004, 0.0069, 0.6868, 0.0219, 0.0045, 0. , 0.0002],
[0.2046, 0. , 0.0002, 0.0018, 0.0058, 0.0224, 0.7647, 0.0002, 0. , 0.0001],
[0.3239, 0. , 0.0004, 0.0004, 0.0372, 0.1151, 0.0048, 0.5179, 0. , 0.0002],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ],
[0.8974, 0.0002, 0.0017, 0.0016, 0.0093, 0.0693, 0.0156, 0.0013, 0. , 0.0036]]),
'21-25': np.array([
[0.8704, 0.0008, 0.0026, 0.0011, 0.0106, 0.0794, 0.0291, 0.0047, 0. , 0.0012],
[0.1535, 0.8357, 0.0002, 0.0001, 0.0009, 0.0067, 0.0024, 0.0004, 0. , 0.0001],
[0.0473, 0. , 0.8681, 0. , 0.0333, 0.0408, 0.0047, 0.0004, 0. , 0.0053],
[0.0851, 0. , 0.0001, 0.8887, 0.0006, 0.0237, 0.0014, 0.0003, 0. , 0.0001],
[0.3553, 0.0002, 0.0027, 0.0002, 0.4916, 0.1088, 0.0297, 0.0111, 0. , 0.0003],
[0.2505, 0.0001, 0.0014, 0.0002, 0.0118, 0.7151, 0.0152, 0.0052, 0. , 0.0005],
[0.1413, 0.0001, 0.0002, 0.0001, 0.0027, 0.0409, 0.8127, 0.0019, 0. , 0.0001],
[0.0687, 0. , 0.0001, 0. , 0.0007, 0.0038, 0.0265, 0.9002, 0. , 0.0001],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ],
[0.7042, 0.0004, 0.0254, 0.0005, 0.0214, 0.061 , 0.0145, 0.0024, 0. , 0.1703]]),
'26-35': np.array([
[0.875 , 0.0001, 0.0014, 0.0008, 0.0078, 0.0827, 0.0261, 0.0058, 0.0002, 0.0001],
[0.0664, 0.8974, 0.0001, 0. , 0.0005, 0.0342, 0.0012, 0.0003, 0. , 0. ],
[0.1795, 0. , 0.8028, 0.0001, 0.0008, 0.0081, 0.0081, 0.0005, 0. , 0. ],
[0.1613, 0. , 0.0002, 0.7594, 0.001 , 0.0613, 0.0027, 0.0009, 0. , 0.013 ],
[0.3407, 0. , 0.0018, 0.0002, 0.4605, 0.1452, 0.0463, 0.0052, 0.0001, 0. ],
[0.2059, 0.0002, 0.0016, 0.0002, 0.0094, 0.7561, 0.0203, 0.0059, 0.0004, 0. ],
[0.1098, 0. , 0.0007, 0. , 0.0014, 0.0176, 0.8692, 0.001 , 0. , 0.0002],
[0.0641, 0. , 0.0013, 0. , 0.0003, 0.0028, 0.0009, 0.9306, 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ],
[0.3603, 0. , 0.0003, 0.0002, 0.0017, 0.0172, 0.0052, 0.0338, 0. , 0.5813]]),
'>35': np.array([
[0.9342, 0.0001, 0.0009, 0.0001, 0.0034, 0.0482, 0.0118, 0.001 , 0. , 0.0002],
[0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0.0867, 0. , 0.9104, 0. , 0.0002, 0.0021, 0.0005, 0. , 0. , 0. ],
[0.1769, 0. , 0.0001, 0.8162, 0.0003, 0.0053, 0.0011, 0.0001, 0. , 0. ],
[0.1985, 0. , 0.0141, 0. , 0.6921, 0.066 , 0.0075, 0.0183, 0. , 0.0033],
[0.1747, 0.0003, 0.0005, 0. , 0.0061, 0.8054, 0.0117, 0.0012, 0. , 0. ],
[0.0998, 0. , 0.0013, 0.0002, 0.0061, 0.0315, 0.861 , 0.0001, 0. , 0. ],
[0.1299, 0. , 0.0002, 0. , 0.0148, 0.0037, 0.0008, 0.8504, 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ],
[0.0957, 0. , 0. , 0. , 0.0002, 0.0023, 0.0006, 0. , 0. , 0.9012]])
},
# Postpartum switching matrix, 1 to 6 months
'pp1to6': {
'<18': np.array([
[0.8316, 0. , 0.0046, 0. , 0.0069, 0.1376, 0.018 , 0.0012,0. , 0. ],
[0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0.0135, 0. , 0 , 0 , 0.1861, 0.8004, 0. , 0. ,0. , 0. ],
[0.0036, 0. , 0. , 0. , 0. , 0.9964, 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,1. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 1. ]]),
'18-20': np.array([
[0.7971, 0. , 0.0027, 0. , 0.0046, 0.1534, 0.0383, 0.004, 0. , 0. ],
[0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. ,0. , 0. ],
[0.2151, 0. , 0. , 0. , 0.0337, 0.7016, 0.0496, 0. ,0. , 0 ],
[0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,1. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 1. ]]),
'21-25': np.array([
[0.807 , 0.0005, 0.0011, 0.0011, 0.0187, 0.1338, 0.0307, 0.0067,0. , 0.0004],
[0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0.3813, 0.6187, 0. , 0. ,0. , 0. ],
[0.0722, 0. , 0. , 0. , 0. , 0.9278, 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,1. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 1. ]]),
'26-35': np.array([
[0.8582, 0.0007, 0.0019, 0.0001, 0.0118, 0.1083, 0.0151, 0.0039, 0, 0],
[0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0.2954, 0. , 0. , 0. , 0.6214, 0. , 0. , 0.0832,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0.989 , 0. , 0.0067,0.0043, 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,1. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 1. ]]),
'>35': np.array([
[0.8897, 0. , 0.0035, 0.0004, 0.0012, 0.0733, 0.0194, 0.0099,0. , 0.0025],
[0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. ,0. , 0. ],
[0.0568, 0. , 0. , 0. , 0. , 0.9432, 0. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. ,0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,1. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 1. ]])
},
# Postpartum initiation vectors, 0 to 1 month
'pp0to1': {
'<18': np.array([0.95, 0., 0., 0., 0.0031, 0.0383, 0.0073, 0.0013, 0., 0.]),
'18-20': np.array([0.9382, 0., 0., 0., 0.0015, 0.0466, 0.0118, 0.0019, 0., 0.]),
'21-25': np.array([0.9583, 0.0006, 0., 0., 0.0003, 0.0343, 0.0053, 0.0013, 0., 0.]),
'26-35': np.array([0.9693, 0., 0.0005, 0., 0.0032, 0.0195, 0.0044, 0.0025, 0.0005, 0.]),
'>35': np.array([0.9556, 0., 0., 0., 0.0009, 0.0298, 0.0028, 0.0048, 0.006, 0.]),
}
}
return raw
[docs]
def barriers():
''' Reasons for nonuse -- taken from Ethiopia PMA 2019. '''
barriers = sc.odict({ #updated based on PMA cross-sectional data
'No need': 58.5,
'Opposition': 16.6,
'Knowledge': 1.28,
'Access': 2.73,
'Health': 20.9,
})
barriers[:] /= barriers[:].sum() # Ensure it adds to 1
return barriers
[docs]
def barriers_region():
'''
Returns reasons for nonuse by region
'''
reasons_region = pd.read_csv(thisdir / 'ethiopia' / 'subnational_data' / 'barriers_region.csv')
reasons_region_dict = {}
reasons_region_dict['region'] = reasons_region['region'] # Return region names
reasons_region_dict['barrier'] = reasons_region['barrier'] # Return the reason for nonuse
reasons_region_dict['perc'] = reasons_region['perc'] # Return retuned the percentage
return reasons_region_dict
[docs]
def urban_proportion():
"""Load information about the proportion of people who live in an urban setting"""
urban_data = pd.read_csv(thisdir / 'ethiopia' / 'urban.csv')
return urban_data["mean"][0] # Return this value as a float
# %% Make and validate parameters
[docs]
def make_pars(use_empowerment=None, use_education=None, use_partnership=None, use_subnational=None, seed=None):
'''
Take all parameters and construct into a dictionary
'''
# Scalar parameters and filenames
pars = scalar_pars()
pars['filenames'] = filenames()
# Demographics and pregnancy outcome
pars['age_pyramid'] = age_pyramid()
pars['age_mortality'] = age_mortality()
pars['urban_prop'] = urban_proportion()
pars['maternal_mortality'] = maternal_mortality()
pars['infant_mortality'] = infant_mortality()
pars['miscarriage_rates'] = miscarriage()
pars['stillbirth_rate'] = stillbirth()
# Fecundity
pars['age_fecundity'] = female_age_fecundity()
pars['fecundity_ratio_nullip'] = fecundity_ratio_nullip()
pars['lactational_amenorrhea'] = lactational_amenorrhea()
# Pregnancy exposure
pars['sexual_activity'] = sexual_activity()
pars['sexual_activity_pp'] = sexual_activity_pp()
pars['debut_age'] = debut_age()
pars['exposure_age'] = exposure_age()
pars['exposure_parity'] = exposure_parity()
pars['spacing_pref'] = birth_spacing_pref()
# Contraceptive methods
pars['methods'] = methods()
pars['methods']['raw'] = method_probs()
pars['barriers'] = barriers()
# Regional parameters
if use_subnational:
pars['region'] = region_proportions() # This function returns extrapolated and raw data
pars['lactational_amenorrhea_region'] = lactational_amenorrhea_region()
pars['sexual_activity_region'] = sexual_activity_region()
pars['sexual_activity_pp_region'] = sexual_activity_pp_region()
pars['debut_age_region'] = debut_age_region()
pars['barriers_region'] = barriers_region()
kwargs = locals()
not_implemented_args = ['use_empowerment', 'use_education', 'use_partnership']
true_args = [key for key in not_implemented_args if kwargs[key] is True]
if true_args:
errmsg = f"{true_args} not implemented yet for {pars['location']}"
raise NotImplementedError(errmsg)
return pars
