Updates to HTM Swtiching Method to Fix Issue #1411 (#1413)

tbhallett · tdm32 · marghe-molaro · web-flow · commit c9e65ca53083 · 2024-07-29T10:34:53.000+01:00
* updates from paper analyses for HIV, TB and malaria * remove unused import statements * fix imports * update filepath for malaria resource file * remove test_hiv_tb_scenarios.py * updated test_healthsystem.py: test_manipulation_of_service_availability as small population size over 7 days will not schedule VMMC through HIV module. Remove HIV_Prevention_Circumcision in assert statement for HIV services delivered in one week * change ipt_coverage in TB logger as conflicts with existing parameter * updated ResourceFile_Improved_Healthsystem_And_Healthcare_Seeking.xlsx with updated NTP2019 TB data * remove test code * remove test code * delete tmp resourcefiles * malaria code use person_id consistently instead of individual_id * use individual_id for demography.do_death() * style change to avoid conflicts with master * style change to avoid conflicts with master * fix conflicts with master * fix conflicts with master * merge in master * check property hv_date_last_ART correctly set * Manually add PostnatalCare_Comprehensive to policy priorities * edit fix * add schisto high infection as conditional predictor for bladder cancer * fix conditional predictor for active TB - should check presence of CardioMetabolicDisorders * add 'ss' prefix to properties from schisto module referenced in bladder_cancer.py * edit praziquantel code in schisto.py to use value from CMST in place of donated * add parameter rr_depr_hiv for risk of depression with HIV infection * tidy up linear models in depression, include conditional predictors for hiv infection and add comments * move hv_inf into conditional predictor for depression in initial population * convert lm for incident cancer (site_confined) to model with conditional predictors. Include HIV as risk factor. * add parameter rr_site_confined_hiv to other_adult_cancers.py * update other_adult_cancers write-up to include HIV as risk factor * update Depression.docx to include HIV as risk factor for depression * edit HIV in depression to include only HIV cases not virally suppressed * update other_adult_cancers.py linear model to include HIV as risk factor only if not virally suppressed * edit: HIV remains as risk factor for depression independent of treatment status * include HIV as risk factor for low grade dysplasia (oesophageal cancer). Update ResourceFile_Oesophageal_Cancer.xlsx * update linear model for low grade dysplasia to include HIV as conditional risk factor * update OesophagealCancer.docx write-up to include HIV risk * add condition hiv diagnosed for increased risk of depression * remove hiv as risk factor for oesophageal cancer * remove parameter for hiv as risk factor for oesophageal cancer * update OesophagealCancer.docx to remove hiv as risk factor * update value for weighted risk of other_adult_cancers with unsuppressed HIV * add rr_hiv to linear model. update ResourceFile_cmd_condition_onset.xlsx with rr_hiv, leave value=1.0 if no effect of hiv * update resourcefiles for CMD include rr_hiv for all, no effect of majority of processes * refactoring: * put the helper function for switching scenario into same file a ScenarioSwitcher class * put tests for class and helper function together (next step will be to rename and mock-up extended functionality) * add diabetes as risk for active TB and relapse. add params to ResourceFile_TB.xlsx replace linearmodels dict which was accidentally removed in depression.py * add diabetes as risk factor for tb death * add diabetes as risk factor for PLHIV with active TB and on TB treatment * add diabetes as risk factor for PLHIV with active TB and on TB treatment * set up run to check calibration of deaths and disability * add predictor high-intensity S. haematobium infection to risk of bladder cancer in initial population * add predictor high-intensity S. haematobium infection to risk of HIV acquisition * fix indenting in HSI_Hiv_StartOrContinueTreatment * add hv_date_treated abd hv_date_last_ART to baseline_art * convert linear model in CMD to include conditional predictors * delete resourcefile created in error * comment out path-specific changes to analysis_cause_of_death_and_disability_calibrations.py * fix CMD error if Hiv not registered * initial sketch of structure * tidy-up and fix tests * commments * remove parameter rr_bcg_inf from tb.py * edit comment in initialise_simulation * fix parameter name error * update parameters * test runs * edit and fix flake8 errors * fix failing test * update ResourceFile_Improved_Healthsystem_And_Healthcare_Seeking.xlsx * update ResourceFile_PriorityRanking_ALLPOLICIES.xlsx * updated ResourceFile_Improved_Healthsystem_And_Healthcare_Seeking.xlsx * scenario file * starting work on the scenario file * design and HR scenarios * scenario for HRH * roll back changes to ScenarioSwitcher * Revert "roll back changes to ScenarioSwitcher" This reverts commit 49d14e7. * sketch out of prposed changed for scenario_switcher * sketch out of prposed changed for scenario_switcher * sketch out of prposed changed for scenario_switcher * sketch out of prposed changed for scenario_switcher * sketch out of prposed changed for scenario_switcher * Add ResourceFile_Consumables_Item_Designations.csv * create special scenarios for consumables availability based on the designation of the consumable item * comment and fix imports * draft of scenario file * linting * increase length of scenarios and reduce reps * rename * correct error in specifiction of dynamic scaling scenario * remove trailing commas that casts return as tuple * Initially only consider baseline and perfect healthcare seeking scenarios, to get upper limit on RAM requirements * Submit remaining scenarios * direct to self.module (rather than self) * edit check for last ART when new dispensation occurs * update schisto risk on HIV to only include women * add scale-up parameter for htm programs add event to hiv module to switch parameters include new keywords in hiv module for scale-up separately for hiv, tb and malaria add new sheet in ResourceFile_HIV.xlsx containing new scale-up parameter values * add catch for malaria rdt_testing_rates post-2024 * malaria parameters scale-up included * restore `scenario_comparison_of_horizontal_and_vertical_programs` (removing comments used to select certain scenarios to be run). * first draft of figures * set up test for HTM scenario scale-up * add tests for HTM scale-up * check resourcefiles updated * check the usage of '' versus "" * reset to single quotes to match PR #1273 * remove unneeded resource files * remove unneeded resource files * edit filepaths * isort for imports * edit ResourceFile_Improved_Healthsystem_And_Healthcare_Seeking.xlsx to remove 2024 missing value in malaria Rate_rdt_testing * fix filename for test_HTMscaleup.py * update scenario file * update scenario file * include "scale_to_effective_capabilities": True * linting * linting * reduce number of draws * set scaleup parameters separately in each module use parameters to select scaleup instead of module arguments update resourcefiles * set scaleup parameters separately in each module use parameters to select scaleup instead of module arguments update resourcefiles * update resourcefiles * update resourcefiles * set up script to test scenarios - scale-up of HTM programs * test runs * add scenario switch to malaria.py * cherry-pick inadvertently updated files and revert * isort fixes * Delete resources/~$ResourceFile_HIV.xlsx * Delete resources/~$ResourceFile_TB.xlsx * Delete resources/malaria/~$ResourceFile_malaria.xlsx * fix failing tests * rollback changes to calibration_analyses/scenarios/long_run_all_diseases.py * fix error in filename * revert timedelta format to 'M' * add todos * merge in master * set up test runs for scale-up * edit scenario start date * change parameter scaleup_start_date to an integer value used in DateOffset, as timestamp is not json serializable for analysis runs * change scale-up event scheduling to use new DateOffset parameter * test runs * set up test runs * set up test runs * fix failing tests * Update tests/test_HTMscaleup.py Co-authored-by: Tim Hallett <39991060+tbhallett@users.noreply.github.com> * address comments on PR review * isort fixes * isort fixes * change np.timedelta in enhanced_lifestyle.py back to original * remove json file * call it 'htm_scenario_analysis' rather than just 'scenario_analysis' * update comment * roll back change in test_tb.py * remove .py extension for clarity * roll back incidental change * linting and editing string for clarity * roll back incidental changes * defaults for healthsystem ok -- no need to step through each option * remove inadvertent duplication in code * remove comment * use dict for ease of accessing * parameter to be the YEAR (int) of the change to fit with the convention used in other modules (instead of years since the beginning of the simulation) * remove comment * refactor module method for clarity * refactor to prevent same name being used for events specific to different modules * specify year for scale-up in analysis file * rename; add note; remove comments for HTM scenarios * refacotring * rename scenario class for hss elements * new scenario for combinations of vertical and horizontal programs * minor refactor * update docstring * renaming * add @Property decorator * plot DALYS averted relative to Baseline - broken down by major cause (HIV, TB, MALARIA) * remove unused parameter prob_start_art_after_hiv_test * set up test runs * update linear models for tb and malaria after changing parameters * set up test runs * set up test runs * remove unused parameter from ResourceFile_HIV.xlsx * remove annual_testing_rate_children as not used * add scale-up parameter probability_of_being_retained_on_art_every_3_months, increase to 1 * set cons_availability to all and set off larger runs * test runs * change spec. of runs to be smaller pop size and fewer runs * roll back changes that are not related to this PR * delete file added by accident * roll back change (I think this is mistake) * refactor * remove clause `if p["do_scale_up"]` as not neccessary * make "_build_linear_models" function, called from initialise_population and update_parameter_for_program_scaleup --------- Co-authored-by: tdm32 <t.mangal@imperial.ac.uk> Co-authored-by: Tara <37845078+tdm32@users.noreply.github.com> Co-authored-by: Margherita Molaro <48129834+marghe-molaro@users.noreply.github.com>
diff --git a/resources/ResourceFile_HIV.xlsx b/resources/ResourceFile_HIV.xlsx
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:913d736db7717519270d61824a8855cbfd4d6e61a73b7ce51e2c3b7915b011ff
-size 161597
+oid sha256:f57142efaf515d74f8290238ce1abad7b99871f9195623112892b3bb535bf634
+size 161721
diff --git a/src/scripts/hiv/projections_jan2023/analysis_logged_deviance.py b/src/scripts/hiv/projections_jan2023/analysis_logged_deviance.py
@@ -34,8 +34,8 @@
 
 # %% Run the simulation
 start_date = Date(2010, 1, 1)
-end_date = Date(2014, 1, 1)
-popsize = 1000
+end_date = Date(2022, 1, 1)
+popsize = 5000
 
 # scenario = 1
 
@@ -87,8 +87,8 @@
 )
 
 # set the scenario
-# sim.modules["Hiv"].parameters["beta"] = 0.129671
-# sim.modules["Tb"].parameters["scaling_factor_WHO"] = 1.5
+sim.modules["Hiv"].parameters["do_scaleup"] = True
+sim.modules["Hiv"].parameters["scaleup_start_year"] = 2019
 # sim.modules["Tb"].parameters["scenario"] = scenario
 # sim.modules["Tb"].parameters["scenario_start_date"] = Date(2010, 1, 1)
 # sim.modules["Tb"].parameters["scenario_SI"] = "z"
diff --git a/src/scripts/htm_scenario_analyses/analysis_htm_scaleup.py b/src/scripts/htm_scenario_analyses/analysis_htm_scaleup.py
@@ -51,9 +51,9 @@ def __init__(self):
         super().__init__()
         self.seed = 0
         self.start_date = Date(2010, 1, 1)
-        self.end_date = Date(2020, 1, 1)
-        self.pop_size = 75_000
-        self.number_of_draws = 5
+        self.end_date = Date(2025, 1, 1)
+        self.pop_size = 5_000
+        self.number_of_draws = 2
         self.runs_per_draw = 1
 
     def log_configuration(self):
@@ -86,7 +86,7 @@ def modules(self):
         ]
 
     def draw_parameters(self, draw_number, rng):
-        scaleup_start_year = 2012
+        scaleup_start_year = 2019
 
         return {
             'Hiv': {
diff --git a/src/scripts/htm_scenario_analyses/scenario_plots.py b/src/scripts/htm_scenario_analyses/scenario_plots.py
@@ -23,6 +23,7 @@
 datestamp = datetime.date.today().strftime("__%Y_%m_%d")
 
 outputspath = Path("./outputs")
+# outputspath = Path("./outputs/t.mangal@imperial.ac.uk")
 
 
 # 0) Find results_folder associated with a given batch_file (and get most recent [-1])
@@ -32,7 +33,7 @@
 make_graph_file_name = lambda stub: results_folder / f"{stub}.png"  # noqa: E731
 
 # look at one log (so can decide what to extract)
-log = load_pickled_dataframes(results_folder)
+log = load_pickled_dataframes(results_folder, draw=1)
 
 # get basic information about the results
 info = get_scenario_info(results_folder)
@@ -55,14 +56,14 @@ def get_num_deaths_by_cause_label(_df):
         .size()
 
 
-TARGET_PERIOD = (Date(2015, 1, 1), Date(2020, 1, 1))
+TARGET_PERIOD = (Date(2020, 1, 1), Date(2025, 1, 1))
 
 num_deaths_by_cause_label = extract_results(
         results_folder,
         module='tlo.methods.demography',
         key='death',
         custom_generate_series=get_num_deaths_by_cause_label,
-        do_scaling=True
+        do_scaling=False
     )
 
 
@@ -103,9 +104,9 @@ def summarise_deaths_for_one_cause(results_folder, label):
 tb_deaths = summarise_deaths_for_one_cause(results_folder, 'TB (non-AIDS)')
 malaria_deaths = summarise_deaths_for_one_cause(results_folder, 'Malaria')
 
-draw_labels = ['No scale-up', 'HIV, scale-up', 'TB scale-up', 'Malaria scale-up']
+draw_labels = ['No scale-up', 'HIV, scale-up', 'TB scale-up', 'Malaria scale-up', 'HTM scale-up']
 
-colors = sns.color_palette("Set1", 4) # Blue, Orange, Green, Red
+colors = sns.color_palette("Set1", 5) # Blue, Orange, Green, Red
 
 
 # Create subplots
@@ -116,19 +117,19 @@ def summarise_deaths_for_one_cause(results_folder, label):
     axs[0].plot(aids_deaths.index, aids_deaths[col], label=draw_labels[i], color=colors[i])
 axs[0].set_title('HIV/AIDS')
 axs[0].legend()
-axs[0].axvline(x=2015, color='gray', linestyle='--')
+axs[0].axvline(x=2019, color='gray', linestyle='--')
 
 # Plot for df2
 for i, col in enumerate(tb_deaths.columns):
     axs[1].plot(tb_deaths.index, tb_deaths[col], color=colors[i])
 axs[1].set_title('TB')
-axs[1].axvline(x=2015, color='gray', linestyle='--')
+axs[1].axvline(x=2019, color='gray', linestyle='--')
 
 # Plot for df3
 for i, col in enumerate(malaria_deaths.columns):
     axs[2].plot(malaria_deaths.index, malaria_deaths[col], color=colors[i])
 axs[2].set_title('Malaria')
-axs[2].axvline(x=2015, color='gray', linestyle='--')
+axs[2].axvline(x=2019, color='gray', linestyle='--')
 
 for ax in axs:
     ax.set_xlabel('Years')
diff --git a/src/tlo/methods/hiv.py b/src/tlo/methods/hiv.py
@@ -472,10 +472,13 @@ def read_parameters(self, data_folder):
         )
 
     def pre_initialise_population(self):
-        """
-        * Establish the Linear Models
-        *
-        """
+        """Do things required before the population is created
+        * Build the LinearModels"""
+        self._build_linear_models()
+
+    def _build_linear_models(self):
+        """Establish the Linear Models"""
+
         p = self.parameters
 
         # ---- LINEAR MODELS -----
@@ -1099,42 +1102,41 @@ def initialise_simulation(self, sim):
         )
 
     def update_parameters_for_program_scaleup(self):
-
         p = self.parameters
         scaled_params = p["scaleup_parameters"]
 
-        if p["do_scaleup"]:
+        # scale-up HIV program
+        # reduce risk of HIV - applies to whole adult population
+        p["beta"] = p["beta"] * scaled_params["reduction_in_hiv_beta"]
 
-            # scale-up HIV program
-            # reduce risk of HIV - applies to whole adult population
-            p["beta"] = p["beta"] * scaled_params["reduction_in_hiv_beta"]
+        # increase PrEP coverage for FSW after HIV test
+        p["prob_prep_for_fsw_after_hiv_test"] = scaled_params["prob_prep_for_fsw_after_hiv_test"]
 
-            # increase PrEP coverage for FSW after HIV test
-            p["prob_prep_for_fsw_after_hiv_test"] = scaled_params["prob_prep_for_fsw_after_hiv_test"]
+        # prep poll for AGYW - target to the highest risk
+        # increase retention to 75% for FSW and AGYW
+        p["prob_prep_for_agyw"] = scaled_params["prob_prep_for_agyw"]
+        p["probability_of_being_retained_on_prep_every_3_months"] = scaled_params["probability_of_being_retained_on_prep_every_3_months"]
 
-            # prep poll for AGYW - target to the highest risk
-            # increase retention to 75% for FSW and AGYW
-            p["prob_prep_for_agyw"] = scaled_params["prob_prep_for_agyw"]
-            p["probability_of_being_retained_on_prep_every_3_months"] = scaled_params["probability_of_being_retained_on_prep_every_3_months"]
+        # perfect retention on ART
+        p["probability_of_being_retained_on_art_every_3_months"] = scaled_params["probability_of_being_retained_on_art_every_3_months"]
 
-            # increase probability of VMMC after hiv test
-            p["prob_circ_after_hiv_test"] = scaled_params["prob_circ_after_hiv_test"]
+        # increase probability of VMMC after hiv test
+        p["prob_circ_after_hiv_test"] = scaled_params["prob_circ_after_hiv_test"]
 
-            # increase testing/diagnosis rates, default 2020 0.03/0.25 -> 93% dx
-            p["hiv_testing_rates"]["annual_testing_rate_children"] = scaled_params["annual_testing_rate_children"]
-            p["hiv_testing_rates"]["annual_testing_rate_adults"] = scaled_params["annual_testing_rate_adults"]
+        # increase testing/diagnosis rates, default 2020 0.03/0.25 -> 93% dx
+        p["hiv_testing_rates"]["annual_testing_rate_adults"] = scaled_params["annual_testing_rate_adults"]
 
-            # ANC testing - value for mothers and infants testing
-            p["prob_hiv_test_at_anc_or_delivery"] = scaled_params["prob_hiv_test_at_anc_or_delivery"]
-            p["prob_hiv_test_for_newborn_infant"] = scaled_params["prob_hiv_test_for_newborn_infant"]
+        # ANC testing - value for mothers and infants testing
+        p["prob_hiv_test_at_anc_or_delivery"] = scaled_params["prob_hiv_test_at_anc_or_delivery"]
+        p["prob_hiv_test_for_newborn_infant"] = scaled_params["prob_hiv_test_for_newborn_infant"]
 
-            # prob ART start if dx, this is already 95% at 2020
-            p["prob_start_art_after_hiv_test"] = scaled_params["prob_start_art_after_hiv_test"]
+        # viral suppression rates
+        # adults already at 95% by 2020
+        # change all column values
+        p["prob_start_art_or_vs"]["virally_suppressed_on_art"] = scaled_params["virally_suppressed_on_art"]
 
-            # viral suppression rates
-            # adults already at 95% by 2020
-            # change all column values
-            p["prob_start_art_or_vs"]["virally_suppressed_on_art"] = scaled_params["virally_suppressed_on_art"]
+        # update exising linear models to use new scaled-up paramters
+        self._build_linear_models()
 
     def on_birth(self, mother_id, child_id):
         """
diff --git a/src/tlo/methods/malaria.py b/src/tlo/methods/malaria.py
@@ -327,13 +327,16 @@ def read_parameters(self, data_folder):
         )
 
     def pre_initialise_population(self):
-        """
-        * Establish the Linear Models
+        """Do things required before the population is created
+        * Build the LinearModels"""
+        self._build_linear_models()
+
+    def _build_linear_models(self):
+        """Establish the Linear Models
 
         if HIV is registered, the conditional predictors will apply
         otherwise only IPTp will affect risk of clinical/severe malaria
         """
-
         p = self.parameters
 
         # ---- LINEAR MODELS -----
@@ -656,52 +659,52 @@ def initialise_simulation(self, sim):
         )
 
     def update_parameters_for_program_scaleup(self):
-
         p = self.parameters
         scaled_params = p["scaleup_parameters"]
 
-        if p["do_scaleup"]:
+        # scale-up malaria program
+        # increase testing
+        # prob_malaria_case_tests=0.4 default
+        p["prob_malaria_case_tests"] = scaled_params["prob_malaria_case_tests"]
 
-            # scale-up malaria program
-            # increase testing
-            # prob_malaria_case_tests=0.4 default
-            p["prob_malaria_case_tests"] = scaled_params["prob_malaria_case_tests"]
+        # gen pop testing rates
+        # annual Rate_rdt_testing=0.64 at 2023
+        p["rdt_testing_rates"]["Rate_rdt_testing"] = scaled_params["rdt_testing_rates"]
 
-            # gen pop testing rates
-            # annual Rate_rdt_testing=0.64 at 2023
-            p["rdt_testing_rates"]["Rate_rdt_testing"] = scaled_params["rdt_testing_rates"]
+        # treatment reaches XX
+        # no default between testing and treatment, governed by tx availability
 
-            # treatment reaches XX
-            # no default between testing and treatment, governed by tx availability
+        # coverage IPTp reaches XX
+        # given during ANC visits and MalariaIPTp Event which selects ALL eligible women
 
-            # coverage IPTp reaches XX
-            # given during ANC visits and MalariaIPTp Event which selects ALL eligible women
+        # treatment success reaches 1 - default is currently 1 also
+        p["prob_of_treatment_success"] = scaled_params["prob_of_treatment_success"]
 
-            # treatment success reaches 1 - default is currently 1 also
-            p["prob_of_treatment_success"] = scaled_params["prob_of_treatment_success"]
+        # bednet and ITN coverage
+        # set IRS for 4 high-risk districts
+        # lookup table created in malaria read_parameters
+        # produces self.itn_irs called by malaria poll to draw incidence
+        # need to overwrite this
+        highrisk_distr_num = p["highrisk_districts"]["district_num"]
 
-            # bednet and ITN coverage
-            # set IRS for 4 high-risk districts
-            # lookup table created in malaria read_parameters
-            # produces self.itn_irs called by malaria poll to draw incidence
-            # need to overwrite this
-            highrisk_distr_num = p["highrisk_districts"]["district_num"]
+        # Find indices where District_Num is in highrisk_distr_num
+        mask = self.itn_irs['irs_rate'].index.get_level_values('District_Num').isin(
+            highrisk_distr_num)
 
-            # Find indices where District_Num is in highrisk_distr_num
-            mask = self.itn_irs['irs_rate'].index.get_level_values('District_Num').isin(
-                highrisk_distr_num)
+        # IRS values can be 0 or 0.8 - no other value in lookup table
+        self.itn_irs['irs_rate'].loc[mask] = scaled_params["irs_district"]
 
-            # IRS values can be 0 or 0.8 - no other value in lookup table
-            self.itn_irs['irs_rate'].loc[mask] = scaled_params["irs_district"]
+        # set ITN for all districts
+        # Set these values to 0.7 - this is the max value possible in lookup table
+        # equivalent to 0.7 of all pop sleeping under bednet
+        # household coverage could be 100%, but not everyone in household sleeping under bednet
+        self.itn_irs['itn_rate'] = scaled_params["itn_district"]
 
-            # set ITN for all districts
-            # Set these values to 0.7 - this is the max value possible in lookup table
-            # equivalent to 0.7 of all pop sleeping under bednet
-            # household coverage could be 100%, but not everyone in household sleeping under bednet
-            self.itn_irs['itn_rate'] = scaled_params["itn_district"]
+        # itn rates for 2019 onwards
+        p["itn"] = scaled_params["itn"]
 
-            # itn rates for 2019 onwards
-            p["itn"] = scaled_params["itn"]
+        # update exising linear models to use new scaled-up parameters
+        self._build_linear_models()
 
     def on_birth(self, mother_id, child_id):
         df = self.sim.population.props
diff --git a/src/tlo/methods/tb.py b/src/tlo/methods/tb.py
@@ -470,9 +470,13 @@ def read_parameters(self, data_folder):
         )
 
     def pre_initialise_population(self):
-        """
-        * Establish the Linear Models
-        """
+        """Do things required before the population is created
+        * Build the LinearModels"""
+        self._build_linear_models()
+
+    def _build_linear_models(self):
+        """Establish the Linear Models"""
+
         p = self.parameters
 
         # risk of active tb
@@ -885,29 +889,29 @@ def initialise_simulation(self, sim):
             )
 
     def update_parameters_for_program_scaleup(self):
-
         p = self.parameters
         scaled_params = p["scaleup_parameters"]
 
-        if p["do_scaleup"]:
+        # scale-up TB program
+        # use NTP treatment rates
+        p["rate_testing_active_tb"]["treatment_coverage"] = scaled_params["tb_treatment_coverage"]
 
-            # scale-up TB program
-            # use NTP treatment rates
-            p["rate_testing_active_tb"]["treatment_coverage"] = scaled_params["tb_treatment_coverage"]
+        # increase tb treatment success rates
+        p["prob_tx_success_ds"] = scaled_params["tb_prob_tx_success_ds"]
+        p["prob_tx_success_mdr"] = scaled_params["tb_prob_tx_success_mdr"]
+        p["prob_tx_success_0_4"] = scaled_params["tb_prob_tx_success_0_4"]
+        p["prob_tx_success_5_14"] = scaled_params["tb_prob_tx_success_5_14"]
 
-            # increase tb treatment success rates
-            p["prob_tx_success_ds"] = scaled_params["tb_prob_tx_success_ds"]
-            p["prob_tx_success_mdr"] = scaled_params["tb_prob_tx_success_mdr"]
-            p["prob_tx_success_0_4"] = scaled_params["tb_prob_tx_success_0_4"]
-            p["prob_tx_success_5_14"] = scaled_params["tb_prob_tx_success_5_14"]
+        # change first-line testing for TB to xpert
+        p["first_line_test"] = scaled_params["first_line_test"]
+        p["second_line_test"] = scaled_params["second_line_test"]
 
-            # change first-line testing for TB to xpert
-            p["first_line_test"] = scaled_params["first_line_test"]
-            p["second_line_test"] = scaled_params["second_line_test"]
+        # increase coverage of IPT
+        p["ipt_coverage"]["coverage_plhiv"] = scaled_params["ipt_coverage_plhiv"]
+        p["ipt_coverage"]["coverage_paediatric"] = scaled_params["ipt_coverage_paediatric"]
 
-            # increase coverage of IPT
-            p["ipt_coverage"]["coverage_plhiv"] = scaled_params["ipt_coverage_plhiv"]
-            p["ipt_coverage"]["coverage_paediatric"] = scaled_params["ipt_coverage_paediatric"]
+        # update exising linear models to use new scaled-up paramters
+        self._build_linear_models()
 
     def on_birth(self, mother_id, child_id):
         """Initialise properties for a newborn individual
