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 1.2.2 (2023-08-11) — © 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/v1.2.2/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 2023-08-11 05:45:06,459] 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) (0.29 s) •——————————————————— 7%
Running 1985.0 (20/164) (0.59 s) ••—————————————————— 13%
Running 1987.5 (30/164) (0.91 s) •••————————————————— 19%
Running 1990.0 (40/164) (1.24 s) •••••——————————————— 25%
Running 1992.5 (50/164) (1.59 s) ••••••—————————————— 31%
Running 1995.0 (60/164) (1.96 s) •••••••————————————— 37%
Running 1997.5 (70/164) (2.35 s) ••••••••———————————— 43%
Running 2000.0 (80/164) (2.79 s) •••••••••——————————— 49%
Running 2002.5 (90/164) (3.20 s) •••••••••••————————— 55%
Running 2005.0 (100/164) (3.66 s) ••••••••••••———————— 62%
Running 2007.5 (110/164) (4.13 s) •••••••••••••——————— 68%
Running 2010.0 (120/164) (4.67 s) ••••••••••••••—————— 74%
Running 2012.5 (130/164) (5.19 s) •••••••••••••••————— 80%
Running 2015.0 (140/164) (5.75 s) •••••••••••••••••——— 86%
Running 2017.5 (150/164) (6.33 s) ••••••••••••••••••—— 92%
Running 2020.0 (160/164) (7.00 s) •••••••••••••••••••— 98%
[I 2023-08-11 05:45:13,926] Trial 0 finished with value: 394.99399620526003 and parameters: {'hpv16_sev_fn_k': 0.634979687858372, 'hpv16_dur_episomal_par1': 5.967176541623256, 'hpv18_sev_fn_k': 0.37321178914119574, 'hpv18_dur_episomal_par1': 9.342082368052539, 'beta': 0.08804687567505717}. Best is trial 0 with value: 394.99399620526003.
Simulation summary:
39,332,939 infections
0 dysplasias
0 pre-cins
14,397,177 cin1s
6,001,936 cin2s
3,672,558 cin3s
60,063,178 cins
661,535 cancers
0 cancer detections
442,460 cancer deaths
0 detected cancer deaths
32,394,369 reinfections
0 reactivations
788,971,840 number susceptible
71,123,952 number infectious
4,359,234 number with inactive infection
223,822,096 number with no cellular changes
86,243,720 number with episomal infection
0 number with transformation
4,359,234 number with cancer
75,483,192 number infected
90,602,952 number with abnormal cells
0 number with latent infection
8,102,183 number with precin
17,107,958 number with cin1
17,008,120 number with cin2
29,875,364 number with cin3
55,591,180 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
1.66 hpv incidence (/100)
0 cin1 incidence (/100,000)
0 cin2 incidence (/100,000)
0 cin3 incidence (/100,000)
0 dysplasia incidence (/100,000)
563 cancer incidence (/100,000)
7,534,743 births
2,225,227 other deaths
-646,451 migration
688 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)
363 cancer mortality
223,822,096 number alive
0 crude death rate
0 crude birth rate
10.59 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) (0.63 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) (2.15 s) •••••••————————————— 37%
Running 1997.5 (70/164) (2.59 s) ••••••••———————————— 43%
Running 2000.0 (80/164) (3.08 s) •••••••••——————————— 49%
Running 2002.5 (90/164) (3.60 s) •••••••••••————————— 55%
Running 2005.0 (100/164) (4.16 s) ••••••••••••———————— 62%
Running 2007.5 (110/164) (4.73 s) •••••••••••••——————— 68%
Running 2010.0 (120/164) (5.36 s) ••••••••••••••—————— 74%
Running 2012.5 (130/164) (6.04 s) •••••••••••••••————— 80%
Running 2015.0 (140/164) (6.75 s) •••••••••••••••••——— 86%
Running 2017.5 (150/164) (7.50 s) ••••••••••••••••••—— 92%
Running 2020.0 (160/164) (8.29 s) •••••••••••••••••••— 98%
[I 2023-08-11 05:45:22,696] Trial 1 finished with value: 690.1211136402767 and parameters: {'hpv16_sev_fn_k': 0.6457935804636264, 'hpv16_dur_episomal_par1': 8.808199447844352, 'hpv18_sev_fn_k': 0.4160786129446836, 'hpv18_dur_episomal_par1': 9.43243026169334, 'beta': 0.13731999784768498}. Best is trial 0 with value: 394.99399620526003.
Simulation summary:
53,281,908 infections
0 dysplasias
0 pre-cins
17,668,936 cin1s
6,944,318 cin2s
5,531,463 cin3s
75,900,502 cins
1,149,246 cancers
0 cancer detections
790,107 cancer deaths
0 detected cancer deaths
45,215,638 reinfections
0 reactivations
912,515,840 number susceptible
91,230,728 number infectious
7,676,244 number with inactive infection
236,062,288 number with no cellular changes
104,317,768 number with episomal infection
718 number with transformation
7,676,244 number with cancer
98,906,960 number infected
111,994,008 number with abnormal cells
0 number with latent infection
8,958,372 number with precin
18,721,214 number with cin1
21,772,462 number with cin2
49,557,636 number with cin3
75,110,392 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
1.95 hpv incidence (/100)
0 cin1 incidence (/100,000)
0 cin2 incidence (/100,000)
0 cin3 incidence (/100,000)
0 dysplasia incidence (/100,000)
910 cancer incidence (/100,000)
7,506,012 births
2,806,314 other deaths
-215,484 migration
1,070 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)
590 cancer mortality
236,062,288 number alive
0 crude death rate
0 crude birth rate
12.88 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) (2.63 s) ••••••••———————————— 43%
Running 2000.0 (80/164) (3.12 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) (5.42 s) ••••••••••••••—————— 74%
Running 2012.5 (130/164) (6.07 s) •••••••••••••••————— 80%
Running 2015.0 (140/164) (6.77 s) •••••••••••••••••——— 86%
Running 2017.5 (150/164) (7.47 s) ••••••••••••••••••—— 92%
Running 2020.0 (160/164) (8.26 s) •••••••••••••••••••— 98%
[I 2023-08-11 05:45:31,415] Trial 2 finished with value: 696.2126726839457 and parameters: {'hpv16_sev_fn_k': 0.9387166807628995, 'hpv16_dur_episomal_par1': 8.212697501109254, 'hpv18_sev_fn_k': 0.8462926147843874, 'hpv18_dur_episomal_par1': 6.9666343638332835, 'beta': 0.1660871346653611}. Best is trial 0 with value: 394.99399620526003.
Simulation summary:
51,062,429 infections
0 dysplasias
0 pre-cins
17,892,321 cin1s
7,489,491 cin2s
6,496,830 cin3s
80,096,685 cins
1,159,302 cancers
0 cancer detections
871,272 cancer deaths
0 detected cancer deaths
42,371,258 reinfections
0 reactivations
881,378,432 number susceptible
82,018,088 number infectious
7,947,035 number with inactive infection
233,577,776 number with no cellular changes
97,496,992 number with episomal infection
0 number with transformation
7,947,035 number with cancer
89,965,128 number infected
105,444,040 number with abnormal cells
0 number with latent infection
10,009,932 number with precin
11,286,312 number with cin1
13,924,549 number with cin2
50,196,188 number with cin3
65,637,744 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
1.93 hpv incidence (/100)
0 cin1 incidence (/100,000)
0 cin2 incidence (/100,000)
0 cin3 incidence (/100,000)
0 dysplasia incidence (/100,000)
941 cancer incidence (/100,000)
7,520,377 births
2,557,072 other deaths
-445,333 migration
1,119 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)
664 cancer mortality
233,577,776 number alive
0 crude death rate
0 crude birth rate
11.70 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
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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