T7 - Calibration¶
Tutorial 2 demonstrated how to run the model and plot the outputs. But it’s entirely possible that the model outputs won’t look like the data for the country that you wish to model. The default parameter values included in HPVsim are intended as points of departure to be iteratively refined via calibration. The process of model calibration involves finding the model parameters that are the most likely explanation for the observed data. This tutorial gives an introduction to the Fit object and some recipes for optimization approaches.
Click here to open an interactive version of this notebook.
Data types supported by HPVsim¶
Data on HPV and cervical disease comes in many different formats. When using HPVsim, the goal is typically to produce population-level estimates of epidemic outputs like: - age-specific incidence of cancer or high-grade lesions in one or more years; - number of cases of cancer or high-grade lesions reported in one or more years; - HPV prevalence over time; - lifetime incidence of HPV; - the distribution of genotypes in detected cases of cancer/high-grade lesions; - sexual behavior metrics like the average age at first marriage, duration of relationships, or number of lifetime partners.
After running HPVsim, estimates all of these variables are included within the results dictionary. To plot them alongside data, the easiest method is to use the Calibration object.
The Calibration object¶
Calibration objects contain the following ingredients: - an hpv.Sim() instance with details of the model configuration; - two lists of parameters to vary, one for parameters that vary by genotype and one for those that don’t; - dataframes that hold the calibration targets, which are typically added as csv files; - a list of any additional results to plot; - settings that are passed to the Optuna package[LINK], an open source hyperparameter optimization framework that automates calibration
for HPVsim.
We have included Optuna as a built-in calibration option as we have found that it works reasonably well, but it is also possible to use other methods; we will discuss this a little further down.
The example below illustrates the general idea of calibration, and can be adapted for different use cases:
[1]:
# Import HPVsim
import hpvsim as hpv
# Configure a simulation with some parameters
pars = dict(n_agents=10e3, start=1980, end=2020, dt=0.25, location='nigeria')
sim = hpv.Sim(pars)
# Specify some parameters to adjust during calibration.
# The parameters in the calib_pars dictionary don't vary by genotype,
# whereas those in the genotype_pars dictionary do. Both kinds are
# given in the order [best, lower_bound, upper_bound].
calib_pars = dict(
beta=[0.05, 0.010, 0.20],
)
genotype_pars = dict(
hpv16=dict(
sev_fn=dict(k=[0.5, 0.2, 1.0]),
dur_episomal=dict(par1=[6, 4, 12])
),
hpv18=dict(
sev_fn=dict(k=[0.5, 0.2, 1.0]),
dur_episomal=dict(par1=[6, 4, 12])
)
)
# List the datafiles that contain data that we wish to compare the model to:
datafiles=['nigeria_cancer_cases.csv',
'nigeria_cancer_types.csv']
# List extra results that we don't have data on, but wish to include in the
# calibration object so we can plot them.
results_to_plot = ['cancer_incidence', 'asr_cancer_incidence']
# Create the calibration object, run it, and plot the results
calib = hpv.Calibration(
sim,
calib_pars=calib_pars,
genotype_pars=genotype_pars,
extra_sim_result_keys=results_to_plot,
datafiles=datafiles,
total_trials=3, n_workers=1
)
calib.calibrate(die=True)
calib.plot(res_to_plot=4);
HPVsim 1.2.6 (2023-09-22) — © 2023 by IDM
Loading location-specific demographic data for "nigeria"
Initializing sim with 10000 agents
Loading location-specific data for "nigeria"
/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/v1.2.6/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
[I 2023-09-22 03:24:57,141] A new study created in RDB with name: hpvsim_calibration
Could not delete study, skipping...
'Record does not exist.'
Removed existing calibration hpvsim_calibration.db
Initializing sim (resetting people) with 10000 agents
Loading location-specific data for "nigeria"
Running 1980.0 ( 0/164) (0.00 s) ———————————————————— 1%
Running 1982.5 (10/164) (0.27 s) •——————————————————— 7%
Running 1985.0 (20/164) (0.51 s) ••—————————————————— 13%
Running 1987.5 (30/164) (0.76 s) •••————————————————— 19%
Running 1990.0 (40/164) (1.00 s) •••••——————————————— 25%
Running 1992.5 (50/164) (1.23 s) ••••••—————————————— 31%
Running 1995.0 (60/164) (1.48 s) •••••••————————————— 37%
Running 1997.5 (70/164) (1.72 s) ••••••••———————————— 43%
Running 2000.0 (80/164) (1.98 s) •••••••••——————————— 49%
Running 2002.5 (90/164) (2.23 s) •••••••••••————————— 55%
Running 2005.0 (100/164) (2.49 s) ••••••••••••———————— 62%
Running 2007.5 (110/164) (2.74 s) •••••••••••••——————— 68%
Running 2010.0 (120/164) (3.01 s) ••••••••••••••—————— 74%
Running 2012.5 (130/164) (3.31 s) •••••••••••••••————— 80%
Running 2015.0 (140/164) (3.59 s) •••••••••••••••••——— 86%
Running 2017.5 (150/164) (3.89 s) ••••••••••••••••••—— 92%
[I 2023-09-22 03:25:01,670] Trial 0 finished with value: 6.71923539388181 and parameters: {'hpv16_sev_fn_k': 0.5394640931447512, 'hpv16_dur_episomal_par1': 9.078960582207792, 'hpv18_sev_fn_k': 0.6259662922332976, 'hpv18_dur_episomal_par1': 4.264990863342565, 'beta': 0.010072235765342517}. Best is trial 0 with value: 6.71923539388181.
Running 2020.0 (160/164) (4.21 s) •••••••••••••••••••— 98%
Simulation summary:
86,193 infections
0 dysplasias
0 pre-cins
76,138 cin1s
66,082 cin2s
71,110 cin3s
608,382 cins
8,619 cancers
0 cancer detections
12,929 cancer deaths
0 detected cancer deaths
71,828 reinfections
0 reactivations
619,759,680 number susceptible
531,526 number infectious
109,178 number with inactive infection
206,054,048 number with no cellular changes
3,727,866 number with episomal infection
0 number with transformation
109,178 number with cancer
640,704 number infected
3,837,044 number with abnormal cells
0 number with latent infection
10,774 number with precin
200,400 number with cin1
252,116 number with cin2
929,453 number with cin3
1,374,785 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)
8 cancer incidence (/100,000)
7,520,377 births
2,065,051 other deaths
-509,978 migration
13 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)
12 cancer mortality
206,054,048 number alive
0 crude death rate
0 crude birth rate
0.09 hpv prevalence (/100)
0 pre-cin prevalence (/100,000)
0 cin1 prevalence (/100,000)
0 cin2 prevalence (/100,000)
0 cin3 prevalence (/100,000)
Initializing sim (resetting people) with 10000 agents
Loading location-specific data for "nigeria"
Running 1980.0 ( 0/164) (0.00 s) ———————————————————— 1%
Running 1982.5 (10/164) (0.30 s) •——————————————————— 7%
Running 1985.0 (20/164) (0.62 s) ••—————————————————— 13%
Running 1987.5 (30/164) (0.96 s) •••————————————————— 19%
Running 1990.0 (40/164) (1.34 s) •••••——————————————— 25%
Running 1992.5 (50/164) (1.73 s) ••••••—————————————— 31%
Running 1995.0 (60/164) (2.17 s) •••••••————————————— 37%
Running 1997.5 (70/164) (2.62 s) ••••••••———————————— 43%
Running 2000.0 (80/164) (3.11 s) •••••••••——————————— 49%
Running 2002.5 (90/164) (3.64 s) •••••••••••————————— 55%
Running 2005.0 (100/164) (4.20 s) ••••••••••••———————— 62%
Running 2007.5 (110/164) (4.78 s) •••••••••••••——————— 68%
Running 2010.0 (120/164) (5.44 s) ••••••••••••••—————— 74%
Running 2012.5 (130/164) (6.13 s) •••••••••••••••————— 80%
Running 2015.0 (140/164) (6.85 s) •••••••••••••••••——— 86%
Running 2017.5 (150/164) (7.57 s) ••••••••••••••••••—— 92%
Running 2020.0 (160/164) (8.39 s) •••••••••••••••••••— 98%
[I 2023-09-22 03:25:10,541] Trial 1 finished with value: 672.0420563058792 and parameters: {'hpv16_sev_fn_k': 0.25562957833986943, 'hpv16_dur_episomal_par1': 10.51033026674225, 'hpv18_sev_fn_k': 0.4929810692360094, 'hpv18_dur_episomal_par1': 9.562440670153968, 'beta': 0.17908650438739113}. Best is trial 0 with value: 6.71923539388181.
Simulation summary:
62,547,702 infections
0 dysplasias
0 pre-cins
18,970,457 cin1s
7,019,018 cin2s
4,551,013 cin3s
73,655,163 cins
1,119,078 cancers
0 cancer detections
813,091 cancer deaths
0 detected cancer deaths
53,597,951 reinfections
0 reactivations
909,384,128 number susceptible
95,630,896 number infectious
7,525,406 number with inactive infection
236,899,808 number with no cellular changes
107,863,192 number with episomal infection
17,957 number with transformation
7,525,406 number with cancer
103,156,312 number infected
115,388,600 number with abnormal cells
0 number with latent infection
10,380,563 number with precin
28,492,680 number with cin1
27,930,984 number with cin2
43,784,112 number with cin3
79,518,472 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
2.29 hpv incidence (/100)
0 cin1 incidence (/100,000)
0 cin2 incidence (/100,000)
0 cin3 incidence (/100,000)
0 dysplasia incidence (/100,000)
873 cancer incidence (/100,000)
7,513,194 births
2,655,476 other deaths
-351,957 migration
1,036 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)
599 cancer mortality
236,899,808 number alive
0 crude death rate
0 crude birth rate
13.46 hpv prevalence (/100)
0 pre-cin prevalence (/100,000)
0 cin1 prevalence (/100,000)
0 cin2 prevalence (/100,000)
0 cin3 prevalence (/100,000)
Initializing sim (resetting people) with 10000 agents
Loading location-specific data for "nigeria"
Running 1980.0 ( 0/164) (0.00 s) ———————————————————— 1%
Running 1982.5 (10/164) (0.30 s) •——————————————————— 7%
Running 1985.0 (20/164) (0.62 s) ••—————————————————— 13%
Running 1987.5 (30/164) (0.96 s) •••————————————————— 19%
Running 1990.0 (40/164) (1.34 s) •••••——————————————— 25%
Running 1992.5 (50/164) (1.72 s) ••••••—————————————— 31%
Running 1995.0 (60/164) (2.15 s) •••••••————————————— 37%
Running 1997.5 (70/164) (2.61 s) ••••••••———————————— 43%
Running 2000.0 (80/164) (3.10 s) •••••••••——————————— 49%
Running 2002.5 (90/164) (3.63 s) •••••••••••————————— 55%
Running 2005.0 (100/164) (4.19 s) ••••••••••••———————— 62%
Running 2007.5 (110/164) (4.76 s) •••••••••••••——————— 68%
Running 2010.0 (120/164) (5.40 s) ••••••••••••••—————— 74%
Running 2012.5 (130/164) (6.07 s) •••••••••••••••————— 80%
Running 2015.0 (140/164) (6.78 s) •••••••••••••••••——— 86%
Running 2017.5 (150/164) (7.51 s) ••••••••••••••••••—— 92%
Running 2020.0 (160/164) (8.32 s) •••••••••••••••••••— 98%
[I 2023-09-22 03:25:19,330] Trial 2 finished with value: 629.8990287569075 and parameters: {'hpv16_sev_fn_k': 0.6663691303460332, 'hpv16_dur_episomal_par1': 6.760801483311111, 'hpv18_sev_fn_k': 0.3628395377812048, 'hpv18_dur_episomal_par1': 11.363119965827, 'beta': 0.14628931970527712}. Best is trial 0 with value: 6.71923539388181.
Simulation summary:
55,530,121 infections
0 dysplasias
0 pre-cins
18,237,095 cin1s
7,711,439 cin2s
5,322,445 cin3s
78,487,743 cins
1,049,405 cancers
0 cancer detections
751,319 cancer deaths
0 detected cancer deaths
47,018,519 reinfections
0 reactivations
911,000,256 number susceptible
92,599,048 number infectious
7,094,438 number with inactive infection
235,484,080 number with no cellular changes
105,499,336 number with episomal infection
1,437 number with transformation
7,094,438 number with cancer
99,693,488 number infected
112,593,784 number with abnormal cells
0 number with latent infection
9,879,923 number with precin
20,975,172 number with cin1
23,503,516 number with cin2
46,530,092 number with cin3
75,493,248 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
2.03 hpv incidence (/100)
0 cin1 incidence (/100,000)
0 cin2 incidence (/100,000)
0 cin3 incidence (/100,000)
0 dysplasia incidence (/100,000)
829 cancer incidence (/100,000)
7,520,377 births
2,468,724 other deaths
-538,709 migration
973 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)
562 cancer mortality
235,484,080 number alive
0 crude death rate
0 crude birth rate
13.11 hpv prevalence (/100)
0 pre-cin prevalence (/100,000)
0 cin1 prevalence (/100,000)
0 cin2 prevalence (/100,000)
0 cin3 prevalence (/100,000)
Loading saved results...
Removed temporary file tmp_calibration_00000.obj
Loaded trial 0
Removed temporary file tmp_calibration_00001.obj
Loaded trial 1
Removed temporary file tmp_calibration_00002.obj
Loaded trial 2
Making results structure...
Processed 3 trials; 0 failed
Deleted study hpvsim_calibration in sqlite:///hpvsim_calibration.db
Removed existing calibration hpvsim_calibration.db
/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/v1.2.6/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/v1.2.6/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/v1.2.6/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/v1.2.6/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/v1.2.6/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/v1.2.6/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
This isn’t a great fit yet! In general, it will probably be necessary to run many more trials that the 3 we ran here. Moreover, careful consideration should be given to the parameters that you want to adjust during calibration. In HPVsim we have taken the approach that any parameter can be adjusted. As we learn more about which parameters make most sense to calibrate, we will add details here. We would also enourage users to share their experiences with calibration and parameter searches.
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