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pars_data.py
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"""
Compilation of sexual behavior data and assumptions for three countries
with high HPV burden, for use in HPVsim.
"""
#%% Initialization
import numpy as np
use_dhs = False # Whether to use DHS data on age of debut directly
# Initialize objects with per-country results
debut = dict()
layer_probs = dict()
mixing = dict()
screening_coverage = dict()
screening_start = dict()
vaccination_coverage = dict()
vaccination_start = dict()
condoms = dict()
#%% INDIA
if use_dhs:
debut['india'] = dict(
f=dict(dist='normal', par1=17.8, par2=2.),# DHS 2006
m=dict(dist='normal', par1=18.0, par2=2.)) # No data for males, assumption
else:
debut['india'] = dict(
f=dict(dist='normal', par1=14.8, par2=2.),# No data, assumption
m=dict(dist='normal', par1=17.0, par2=2.)) # No data, assumption
layer_probs['india'] = dict(
m=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0, 0.126, 0.599, 0.873, 0.936, 0.93, 0.90, 0.865, 0.55, 0.4, 0.4, 0.4, 0.4, 0.4], # Share of females of each age who are married
[0, 0, 0, 0.017, 0.205, 0.575, 0.835, 0.934, 0.952, 0.952, 0.5, 0.5, 0.5, 0.4, 0.4, 0.3]] # Share of males of each age who are married
),
c=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0.10, 0.7, 0.8, 0.6, 0.6, 0.5, 0.4, 0.3, 0.2, 0.15, 0.1, 0.05, 0.05, 0.01], # Share of females of each age having casual relationships
[0, 0, 0.05, 0.7, 0.8, 0.6, 0.6, 0.5, 0.5, 0.4, 0.3, 0.2, 0.15, 0.1, 0.01, 0.01]], # Share of males of each age having casual relationships
),
o=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0.01, 0.05, 0.05, 0.04, 0.03, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], # Share of females of each age having one-off relationships
[0, 0, 0.01, 0.01, 0.01, 0.02, 0.03, 0.04, 0.05, 0.05, 0.03, 0.02, 0.01, 0.01, 0.01, 0.01]], # Share of males of each age having one-off relationships
),
)
mixing['india'] = dict(
m=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .1, .1, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, .5, .1, .5, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 1, .5, .5, .5, .5, .1, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, .5, 1, 1, .5, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, .5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, .1, 1, 1, 2, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, .1, 1, 1, 1, 1, 2, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, .1, 1, 1, 1, 1, 2, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, .1, .5, 1, 1, 1, 2, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
]),
c=np.array([
# 0, 5,10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, 1, 1, 1, 1.0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .5, 1, 1, 1, 1.0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, 0, 1, 1, 1, 1.0, 1.0, 0.5, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 0, .5, 1, 1, 1, .5, 0.5, 0.5, 0, 0, 0, 0, 0, 0],
[35, 0, 0, 0, .5, 1, 1, 1, 1, .5, 0.5, 0.5, 0, 0, 0, 0, 0],
[40, 0, 0, 0, .5, .5, 1, 1, 1, 1, .5, 0.5, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0.5, 0.5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
]),
o=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .5, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, 0, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 0, 0, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, 0, 0, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, 0, 0, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
]),
)
# Intervention coverage
screening_start['india'] = '2003'
screening_coverage['india'] = 0.03
vaccination_start['india'] = 2018
vaccination_coverage['india'] = 0
condoms['india'] = 0.056 # 2015 estimate
#%% NIGERIA
if use_dhs:
debut['nigeria'] = dict(
f=dict(dist='normal', par1=17.7, par2=2.),# DHS 2013
m=dict(dist='normal', par1=18.2, par2=2.)) # No data for males, assumption
else:
debut['nigeria'] = dict(
f=dict(dist='normal', par1=14.8, par2=2.),# No data, assumption
m=dict(dist='normal', par1=17.0, par2=2.)) # No data, assumption
layer_probs['nigeria'] = dict(
m=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0, 0.253, 0.699, 0.887, 0.951, 0.7, 0.75, 0.65, 0.55, 0.4, 0.4, 0.4, 0.4, 0.4], # Share of females of each age who are married
[0, 0, 0, 0.01, 0.1, 0.5, 0.6, 0.7, 0.70, 0.70, 0.70, 0.8, 0.7, 0.6, 0.5, 0.6]] # Share of males of each age who are married
),
c=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0.10, 0.7, 0.8, 0.6, 0.6, 0.5, 0.2, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], # Share of females of each age having casual relationships
[0, 0, 0.05, 0.7, 0.8, 0.6, 0.6, 0.5, 0.5, 0.4, 0.3, 0.1, 0.05, 0.01, 0.01, 0.01]], # Share of males of each age having casual relationships
),
o=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0.01, 0.05, 0.05, 0.04, 0.03, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], # Share of females of each age having one-off relationships
[0, 0, 0.01, 0.01, 0.01, 0.02, 0.03, 0.04, 0.05, 0.05, 0.03, 0.02, 0.01, 0.01, 0.01, 0.01]], # Share of males of each age having one-off relationships
),
)
mixing['nigeria'] = dict(
m=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .1, .1, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, .5, .1, .5, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 1, .5, .5, .5, .5, .1, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, .5, 1, 1, .5, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, .5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, .1, 1, 1, 2, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, .1, 1, 1, 1, 1, 2, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, .1, 1, 1, 1, 1, 2, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, .1, .5, 1, 1, 1, 2, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
]),
c=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, 1, 1, 1, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .5, 1, 1, 1, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, 0, 1, 1, 1, 1.0, 1.0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 0, .5, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, 0, .5, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, 0, .5, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, 0.5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
]),
o=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .5, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, 0, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 0, 0, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, 0, 0, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, 0, 0, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
]),
)
# Intervention coverage
screening_start['nigeria'] = '2019'
screening_coverage['nigeria'] = 0
vaccination_start['nigeria'] = 2018
vaccination_coverage['nigeria'] = 0
condoms['nigeria'] = 0.0375 # 2018 estimate
#%% TANZANIA
if use_dhs:
debut['tanzania'] = dict(
f=dict(dist='normal', par1=17.3, par2=2.),
m=dict(dist='normal', par1=18.2, par2=2.)) # DHS 2015-16
else:
debut['tanzania'] = dict(
f=dict(dist='normal', par1=14.8, par2=2.),# No data, assumption
m=dict(dist='normal', par1=17.0, par2=2.)) # No data, assumption
layer_probs['tanzania'] = dict(
m=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0, 0.253, 0.699, 0.887, 0.951, 0.7, 0.75, 0.65, 0.55, 0.4, 0.4, 0.4, 0.4, 0.4], # Share of females of each age who are married
[0, 0, 0, 0.01, 0.1, 0.5, 0.6, 0.7, 0.70, 0.70, 0.70, 0.8, 0.7, 0.6, 0.5, 0.6]] # Share of males of each age who are married
),
c=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0.10, 0.7, 0.8, 0.6, 0.6, 0.5, 0.2, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], # Share of females of each age having casual relationships
[0, 0, 0.05, 0.7, 0.8, 0.6, 0.6, 0.5, 0.5, 0.4, 0.3, 0.1, 0.05, 0.01, 0.01, 0.01]], # Share of males of each age having casual relationships
),
o=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0.01, 0.05, 0.05, 0.04, 0.03, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], # Share of females of each age having one-off relationships
[0, 0, 0.01, 0.01, 0.01, 0.02, 0.03, 0.04, 0.05, 0.05, 0.03, 0.02, 0.01, 0.01, 0.01, 0.01]], # Share of males of each age having one-off relationships
),
)
mixing['tanzania'] = dict(
m=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .1, .1, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, .5, .1, .5, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 1, .5, .5, .5, .5, .1, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, .5, 1, 1, .5, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, .5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, .1, 1, 1, 2, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, .1, 1, 1, 1, 1, 2, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, .1, 1, 1, 1, 1, 2, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, .1, .5, 1, 1, 1, 2, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
]),
c=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, 1, 1, 1, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .5, 1, 1, 1, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, 0, 1, 1, 1, 1.0, 1.0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 0, .5, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, 0, .5, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, 0, .5, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, 0.5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
]),
o=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .5, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, 0, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 0, 0, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, 0, 0, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, 0, 0, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
]),
)
# Intervention coverage
screening_start['tanzania'] = '2003'
screening_coverage['tanzania'] = 0
vaccination_start['tanzania'] = 2018
vaccination_coverage['tanzania'] = 0
condoms['tanzania'] = 0.024 # DHS 2015-2016