T6 - Using analyzers¶
Analyzers are objects that do not change the behavior of a simulation, but just report on its internal state, almost always something to do with sim.people. This tutorial takes you through some of the built-in analyzers and gives a brief example of how to build your own.
Click here to open an interactive version of this notebook.
Results by age¶
By far the most common reason to use an analyzer is to report results by age. The results in sim.results already include results disaggregated by age, e.g. sim.results['cancers_by_age'], but these results use standardized age bins which may not match the age bins for available data on cervical cancers. Age-specific outputs can be customized using an analyzer to match the age bins of the data. The following example shows how to set this up:
[1]:
import numpy as np
import sciris as sc
import hpvsim as hpv
# Create some parameters, setting beta (per-contact transmission probability) higher
# to create more cancers for illutration
pars = dict(beta=0.5, n_agents=50e3, start=1970, n_years=50, dt=1., location='tanzania')
# Also set initial HPV prevalence to be high, again to generate more cancers
pars['init_hpv_prev'] = {
'age_brackets' : np.array([ 12, 17, 24, 34, 44, 64, 80, 150]),
'm' : np.array([ 0.0, 0.75, 0.9, 0.45, 0.1, 0.05, 0.005, 0]),
'f' : np.array([ 0.0, 0.75, 0.9, 0.45, 0.1, 0.05, 0.005, 0]),
}
# Create the age analyzers.
az1 = hpv.age_results(
result_args=sc.objdict(
hpv_prevalence=sc.objdict( # The keys of this dictionary are any results you want by age, and can be any key of sim.results
years=2019, # List the years that you want to generate results for
edges=np.array([0., 15., 20., 25., 30., 40., 45., 50., 55., 65., 100.]),
),
hpv_incidence=sc.objdict(
years=2019,
edges=np.array([0., 15., 20., 25., 30., 40., 45., 50., 55., 65., 100.]),
),
cancer_incidence=sc.objdict(
years=2019,
edges=np.array([0.,20.,25.,30.,40.,45.,50.,55.,65.,100.]),
),
cancer_mortality=sc.objdict(
years=2019,
edges=np.array([0., 20., 25., 30., 40., 45., 50., 55., 65., 100.]),
)
)
)
sim = hpv.Sim(pars, genotypes=[16, 18], analyzers=[az1])
sim.run()
a = sim.get_analyzer()
a.plot();
HPVsim 1.2.0 (2023-05-31) — © 2023 by IDM
HPVsim data: at least one file missing: {'metadata': False, 'age_dist': False, 'birth': False, 'death': False, 'life_expectancy': False}
————————————————————————————————————
Downloading preprocessed HPVsim data
————————————————————————————————————
Note: this automatic download only happens once, when HPVsim is first run.
Downloading 1 URL(s)...
Downloading https://github.com/amath-idm/hpvsim_data/blob/main/hpvsim_data_v1.1.zip?raw=true...
Saving to /home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/checkouts/v1.2.0/docs/tutorials/files/tmp_hpvsim_data_v1.1.zip.zip...
Time to download https://github.com/amath-idm/hpvsim_data/blob/main/hpvsim_data_v1.1.zip?raw=true: 0.617 s
Time to download 1 URLs: 0.617 s
Removed "/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/checkouts/v1.2.0/docs/tutorials/files/tmp_hpvsim_data_v1.1.zip.zip"
Data downloaded.
Loading location-specific demographic data for "tanzania"
/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/v1.2.0/lib/python3.9/site-packages/sciris/sc_fileio.py:172: UserWarning: Fixing known unpickling deprecation "No module named 'pandas.core.indexes.numeric'"
obj = _unpickler(filestr, **kw, **kwargs) # Unpickle the data
Initializing sim with 50000 agents
Loading location-specific data for "tanzania"
/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/v1.2.0/lib/python3.9/site-packages/sciris/sc_fileio.py:172: UserWarning: Fixing known unpickling deprecation "No module named 'pandas.core.indexes.numeric'"
obj = _unpickler(filestr, **kw, **kwargs) # Unpickle the data
Running 1970.0 ( 0/51) (1.09 s) ———————————————————— 2%
Running 1980.0 (10/51) (2.01 s) ••••———————————————— 22%
Running 1990.0 (20/51) (3.24 s) ••••••••———————————— 41%
Running 2000.0 (30/51) (4.74 s) ••••••••••••———————— 61%
Running 2010.0 (40/51) (6.72 s) ••••••••••••••••———— 80%
Running 2020.0 (50/51) (9.68 s) •••••••••••••••••••• 100%
Simulation summary:
1,071 infections
0 dysplasias
0 pre-cins
0 cin1s
1,232 cin2s
375 cin3s
2,570 cins
107 cancers
0 cancer detections
54 cancer deaths
0 detected cancer deaths
535 reinfections
0 reactivations
121,557,432 number susceptible
4,150 number infectious
750 number with inactive infection
60,774,880 number with no cellular changes
428,527 number with episomal infection
0 number with transformation
750 number with cancer
4,900 number infected
429,277 number with abnormal cells
0 number with latent infection
535 number with precin
16,573 number with cin1
9,853 number with cin2
17,805 number with cin3
44,232 number with detectable dysplasia
0 number with detected cancer
0 number screened
0 number treated for precancerous lesions
0 number treated for cancer
0 number vaccinated
0 number given therapeutic vaccine
0.00 hpv incidence (/100)
0 cin1 incidence (/100,000)
0 cin2 incidence (/100,000)
0 cin3 incidence (/100,000)
0 dysplasia incidence (/100,000)
0 cancer incidence (/100,000)
2,132,060 births
302,124 other deaths
-34,271 migration
1 age-adjusted cervical cancer incidence (/100,000)
0 age-adjusted cervical cancer mortality
0 newly vaccinated
0 cumulative number vaccinated
0 new doses
0 cumulative doses
0 new therapeutic vaccine doses
0 newly received therapeutic vaccine
0 cumulative therapeutic vaccine doses
0 total received therapeutic vaccine
0 new screens
0 newly screened
0 new cin treatments
0 newly treated for cins
0 new cancer treatments
0 newly treated for cancer
0 cumulative screens
0 cumulative number screened
0 cumulative cin treatments
0 cumulative number treated for cins
0 cumulative cancer treatments
0 cumulative number treated for cancer
0 detected cancer incidence (/100,000)
0 cancer mortality
60,774,880 number alive
0 crude death rate
0 crude birth rate
0.00 hpv prevalence (/100)
0 pre-cin prevalence (/100,000)
0 cin1 prevalence (/100,000)
0 cin2 prevalence (/100,000)
0 cin3 prevalence (/100,000)
It’s also possible to plot these results alongside data.
[2]:
az2 = hpv.age_results(
result_args=sc.objdict(
cancers=sc.objdict(
datafile='example_cancer_cases.csv',
),
)
)
sim = hpv.Sim(pars, genotypes=[16, 18], analyzers=[az2])
sim.run()
a = sim.get_analyzer()
a.plot();
Loading location-specific demographic data for "tanzania"
Initializing sim with 50000 agents
Loading location-specific data for "tanzania"
Running 1970.0 ( 0/51) (0.10 s) ———————————————————— 2%
Running 1980.0 (10/51) (1.01 s) ••••———————————————— 22%
Running 1990.0 (20/51) (2.17 s) ••••••••———————————— 41%
Running 2000.0 (30/51) (3.67 s) ••••••••••••———————— 61%
Running 2010.0 (40/51) (5.66 s) ••••••••••••••••———— 80%
Running 2020.0 (50/51) (8.46 s) •••••••••••••••••••• 100%
Simulation summary:
1,071 infections
0 dysplasias
0 pre-cins
0 cin1s
1,232 cin2s
375 cin3s
2,570 cins
107 cancers
0 cancer detections
54 cancer deaths
0 detected cancer deaths
535 reinfections
0 reactivations
121,557,432 number susceptible
4,150 number infectious
750 number with inactive infection
60,774,880 number with no cellular changes
428,527 number with episomal infection
0 number with transformation
750 number with cancer
4,900 number infected
429,277 number with abnormal cells
0 number with latent infection
535 number with precin
16,573 number with cin1
9,853 number with cin2
17,805 number with cin3
44,232 number with detectable dysplasia
0 number with detected cancer
0 number screened
0 number treated for precancerous lesions
0 number treated for cancer
0 number vaccinated
0 number given therapeutic vaccine
0.00 hpv incidence (/100)
0 cin1 incidence (/100,000)
0 cin2 incidence (/100,000)
0 cin3 incidence (/100,000)
0 dysplasia incidence (/100,000)
0 cancer incidence (/100,000)
2,132,060 births
302,124 other deaths
-34,271 migration
1 age-adjusted cervical cancer incidence (/100,000)
0 age-adjusted cervical cancer mortality
0 newly vaccinated
0 cumulative number vaccinated
0 new doses
0 cumulative doses
0 new therapeutic vaccine doses
0 newly received therapeutic vaccine
0 cumulative therapeutic vaccine doses
0 total received therapeutic vaccine
0 new screens
0 newly screened
0 new cin treatments
0 newly treated for cins
0 new cancer treatments
0 newly treated for cancer
0 cumulative screens
0 cumulative number screened
0 cumulative cin treatments
0 cumulative number treated for cins
0 cumulative cancer treatments
0 cumulative number treated for cancer
0 detected cancer incidence (/100,000)
0 cancer mortality
60,774,880 number alive
0 crude death rate
0 crude birth rate
0.00 hpv prevalence (/100)
0 pre-cin prevalence (/100,000)
0 cin1 prevalence (/100,000)
0 cin2 prevalence (/100,000)
0 cin3 prevalence (/100,000)
These results are not particularly well matched to the data, but we will deal with this in the calibration tutorial later.
Snapshots¶
Snapshots both take “pictures” of the sim.people object at specified points in time. This is because while most of the information from sim.people is retrievable at the end of the sim from the stored events, it’s much easier to see what’s going on at the time. The following example leverages a snapshot in order to create a figure demonstrating age mixing patterns among sexual contacts:
[3]:
snap = hpv.snapshot(timepoints=['2020'])
sim = hpv.Sim(pars, analyzers=snap)
sim.run()
a = sim.get_analyzer()
people = a.snapshots[0]
# Plot age mixing
import pylab as pl
import matplotlib as mpl
fig, ax = pl.subplots(nrows=1, ncols=1, figsize=(5, 4))
fc = people.contacts['m']['age_f'] # Get the age of female contacts in marital partnership
mc = people.contacts['m']['age_m'] # Get the age of male contacts in marital partnership
h = ax.hist2d(fc, mc, bins=np.linspace(0, 75, 16), density=True, norm=mpl.colors.LogNorm())
ax.set_xlabel('Age of female partner')
ax.set_ylabel('Age of male partner')
fig.colorbar(h[3], ax=ax)
ax.set_title('Marital age mixing')
pl.show();
Loading location-specific demographic data for "tanzania"
Initializing sim with 50000 agents
Loading location-specific data for "tanzania"
Running 1970.0 ( 0/51) (0.10 s) ———————————————————— 2%
Running 1980.0 (10/51) (1.23 s) ••••———————————————— 22%
Running 1990.0 (20/51) (2.78 s) ••••••••———————————— 41%
Running 2000.0 (30/51) (5.10 s) ••••••••••••———————— 61%
Running 2010.0 (40/51) (7.99 s) ••••••••••••••••———— 80%
Running 2020.0 (50/51) (11.53 s) •••••••••••••••••••• 100%
Simulation summary:
1,606 infections
0 dysplasias
0 pre-cins
0 cin1s
1,419 cin2s
295 cin3s
4,257 cins
0 cancers
0 cancer detections
214 cancer deaths
0 detected cancer deaths
1,339 reinfections
0 reactivations
182,339,280 number susceptible
5,114 number infectious
884 number with inactive infection
60,774,108 number with no cellular changes
438,059 number with episomal infection
0 number with transformation
884 number with cancer
5,998 number infected
438,943 number with abnormal cells
0 number with latent infection
0 number with precin
22,544 number with cin1
7,202 number with cin2
16,172 number with cin3
45,624 number with detectable dysplasia
0 number with detected cancer
0 number screened
0 number treated for precancerous lesions
0 number treated for cancer
0 number vaccinated
0 number given therapeutic vaccine
0.00 hpv incidence (/100)
0 cin1 incidence (/100,000)
0 cin2 incidence (/100,000)
0 cin3 incidence (/100,000)
0 dysplasia incidence (/100,000)
0 cancer incidence (/100,000)
2,132,060 births
301,375 other deaths
-35,342 migration
0 age-adjusted cervical cancer incidence (/100,000)
0 age-adjusted cervical cancer mortality
0 newly vaccinated
0 cumulative number vaccinated
0 new doses
0 cumulative doses
0 new therapeutic vaccine doses
0 newly received therapeutic vaccine
0 cumulative therapeutic vaccine doses
0 total received therapeutic vaccine
0 new screens
0 newly screened
0 new cin treatments
0 newly treated for cins
0 new cancer treatments
0 newly treated for cancer
0 cumulative screens
0 cumulative number screened
0 cumulative cin treatments
0 cumulative number treated for cins
0 cumulative cancer treatments
0 cumulative number treated for cancer
0 detected cancer incidence (/100,000)
1 cancer mortality
60,774,108 number alive
0 crude death rate
0 crude birth rate
0.00 hpv prevalence (/100)
0 pre-cin prevalence (/100,000)
0 cin1 prevalence (/100,000)
0 cin2 prevalence (/100,000)
0 cin3 prevalence (/100,000)
Age pyramids¶
Age pyramids, like snapshots, take a picture of the people at a given point in time, and then bin them into age groups by sex. These can also be plotted alongside data:
[4]:
# Create some parameters
pars = dict(n_agents=50e3, start=2000, n_years=30, dt=0.5)
# Make the age pyramid analyzer
age_pyr = hpv.age_pyramid(
timepoints=['2010', '2020'],
datafile='south_africa_age_pyramid.csv',
edges=np.linspace(0, 100, 21))
# Make the sim, run, get the analyzer, and plot
sim = hpv.Sim(pars, location='south africa', analyzers=age_pyr)
sim.run()
a = sim.get_analyzer()
fig = a.plot(percentages=True);
Loading location-specific demographic data for "south africa"
Initializing sim with 50000 agents
Loading location-specific data for "south africa"
Dates provided in the age pyramid datafile ({'1990.0', '2000.0', '2020.0', '2010.0'}) are not the same as the age pyramid dates that were requested (['2010.0' '2020.0']).
Plots will only show requested dates, not all dates in the datafile.
Running 2000.0 ( 0/62) (0.10 s) ———————————————————— 2%
Running 2005.0 (10/62) (0.87 s) •••————————————————— 18%
Running 2010.0 (20/62) (1.66 s) ••••••—————————————— 34%
Running 2015.0 (30/62) (2.50 s) ••••••••••—————————— 50%
Running 2020.0 (40/62) (3.39 s) •••••••••••••——————— 66%
Running 2025.0 (50/62) (4.29 s) ••••••••••••••••———— 82%
Running 2030.0 (60/62) (5.22 s) •••••••••••••••••••— 98%
Simulation summary:
238,627 infections
0 dysplasias
0 pre-cins
31,972 cin1s
12,397 cin2s
10,720 cin3s
118,102 cins
2,051 cancers
0 cancer detections
2,610 cancer deaths
0 detected cancer deaths
182,699 reinfections
0 reactivations
193,742,736 number susceptible
456,467 number infectious
18,643 number with inactive infection
64,434,588 number with no cellular changes
4,921,401 number with episomal infection
93 number with transformation
18,643 number with cancer
475,110 number infected
4,940,044 number with abnormal cells
0 number with latent infection
61,241 number with precin
270,226 number with cin1
88,460 number with cin2
142,803 number with cin3
498,693 number with detectable dysplasia
0 number with detected cancer
0 number screened
0 number treated for precancerous lesions
0 number treated for cancer
0 number vaccinated
0 number given therapeutic vaccine
0.04 hpv incidence (/100)
0 cin1 incidence (/100,000)
0 cin2 incidence (/100,000)
0 cin3 incidence (/100,000)
0 dysplasia incidence (/100,000)
6 cancer incidence (/100,000)
1,272,366 births
601,228 other deaths
-96,942 migration
6 age-adjusted cervical cancer incidence (/100,000)
0 age-adjusted cervical cancer mortality
0 newly vaccinated
0 cumulative number vaccinated
0 new doses
0 cumulative doses
0 new therapeutic vaccine doses
0 newly received therapeutic vaccine
0 cumulative therapeutic vaccine doses
0 total received therapeutic vaccine
0 new screens
0 newly screened
0 new cin treatments
0 newly treated for cins
0 new cancer treatments
0 newly treated for cancer
0 cumulative screens
0 cumulative number screened
0 cumulative cin treatments
0 cumulative number treated for cins
0 cumulative cancer treatments
0 cumulative number treated for cancer
0 detected cancer incidence (/100,000)
8 cancer mortality
64,434,588 number alive
0 crude death rate
0 crude birth rate
0.24 hpv prevalence (/100)
0 pre-cin prevalence (/100,000)
0 cin1 prevalence (/100,000)
0 cin2 prevalence (/100,000)
0 cin3 prevalence (/100,000)
