hgps::StaticLinearModel Class Reference

Implements the static linear model type. More...

#include <static_linear_model.h>

Inheritance diagram for hgps::StaticLinearModel:

Collaboration diagram for hgps::StaticLinearModel:

Public Member Functions
	StaticLinearModel (std::shared_ptr< RiskFactorSexAgeTable > expected, std::shared_ptr< std::unordered_map< core::Identifier, double > > expected_trend, std::shared_ptr< std::unordered_map< core::Identifier, int > > trend_steps, std::shared_ptr< std::unordered_map< core::Identifier, double > > expected_trend_boxcox, const std::vector< core::Identifier > &names, const std::vector< LinearModelParams > &models, const std::vector< core::DoubleInterval > &ranges, const std::vector< double > &lambda, const std::vector< double > &stddev, const Eigen::MatrixXd &cholesky, const std::vector< LinearModelParams > &policy_models, const std::vector< core::DoubleInterval > &policy_ranges, const Eigen::MatrixXd &policy_cholesky, std::shared_ptr< std::vector< LinearModelParams > > trend_models, std::shared_ptr< std::vector< core::DoubleInterval > > trend_ranges, std::shared_ptr< std::vector< double > > trend_lambda, double info_speed, const std::unordered_map< core::Identifier, std::unordered_map< core::Gender, double > > &rural_prevalence, const std::unordered_map< core::Income, LinearModelParams > &income_models, double physical_activity_stddev, TrendType trend_type, std::shared_ptr< std::unordered_map< core::Identifier, double > > expected_income_trend=nullptr, std::shared_ptr< std::unordered_map< core::Identifier, double > > expected_income_trend_boxcox=nullptr, std::shared_ptr< std::unordered_map< core::Identifier, int > > income_trend_steps=nullptr, std::shared_ptr< std::vector< LinearModelParams > > income_trend_models=nullptr, std::shared_ptr< std::vector< core::DoubleInterval > > income_trend_ranges=nullptr, std::shared_ptr< std::vector< double > > income_trend_lambda=nullptr, std::shared_ptr< std::unordered_map< core::Identifier, double > > income_trend_decay_factors=nullptr, bool is_continuous_income_model=false, const LinearModelParams &continuous_income_model=LinearModelParams{}, core::IncomeCategoryLayout income_category_layout={}, const std::unordered_map< core::Identifier, PhysicalActivityModel > &physical_activity_models={}, const std::vector< IncomeStratumExpectedTableEntry > &income_stratum_expected_tables={}, bool income_stratum_adjustment_enabled=false, std::size_t adjustment_income_stratum_count=0u, bool has_active_policies=true, const std::vector< LinearModelParams > &logistic_models={}, core::Gender gender2_indicator=core::Gender::male)
	Initialises a new instance of the StaticLinearModel class.
RiskFactorModelType	type () const noexcept override
	Gets the model type identifier.
std::string	name () const noexcept override
	Gets the model name.
bool	is_continuous_income_model () const noexcept
	Check if using continuous income model (FINCH approach)
void	generate_risk_factors (RuntimeContext &context) override
	Generates the initial risk factors for a population and newborns.
void	update_risk_factors (RuntimeContext &context) override
	Update risk factors for population.
Public Member Functions inherited from hgps::RiskFactorAdjustableModel
	RiskFactorAdjustableModel (std::shared_ptr< RiskFactorSexAgeTable > expected, std::shared_ptr< std::unordered_map< core::Identifier, double > > expected_trend, std::shared_ptr< std::unordered_map< core::Identifier, int > > trend_steps, TrendType trend_type=TrendType::Null, std::shared_ptr< std::unordered_map< core::Identifier, double > > expected_income_trend=nullptr, std::shared_ptr< std::unordered_map< core::Identifier, double > > expected_income_trend_decay_factors=nullptr)
	Constructs a new RiskFactorAdjustableModel instance.
virtual double	get_expected (RuntimeContext &context, core::Gender sex, int age, const core::Identifier &factor, OptionalRange range, bool apply_trend) const
	Gets a person's expected risk factor value.
void	adjust_risk_factors (RuntimeContext &context, const std::vector< core::Identifier > &factors, OptionalRanges ranges, bool apply_trend, const RiskFactorSexAgeTable *expected_override=nullptr, std::optional< std::size_t > income_stratum_filter=std::nullopt, std::vector< IncomeStratumAdjustmentExampleRow > *debug_example_rows=nullptr) const
	Adjust risk factors such that mean sim value matches expected value.
int	get_trend_steps (const core::Identifier &factor) const
	Gets the number of time steps to apply the trend.
const std::shared_ptr< std::unordered_map< core::Identifier, double > > &	get_expected_trend () const noexcept
	Gets the expected trend values.
void	set_logistic_factors (const std::unordered_set< core::Identifier > &logistic_factors)
	Sets the logistic factors for simulated mean calculation.
Public Member Functions inherited from hgps::RiskFactorModel
virtual	~RiskFactorModel ()=default
	Destroys a RiskFactorModel instance.

Detailed Description

Implements the static linear model type.

The static model is used to initialise the virtual population.

Constructor & Destructor Documentation

StaticLinearModel()

hgps::StaticLinearModel::StaticLinearModel	(	std::shared_ptr< RiskFactorSexAgeTable >	expected,
		std::shared_ptr< std::unordered_map< core::Identifier, double > >	expected_trend,
		std::shared_ptr< std::unordered_map< core::Identifier, int > >	trend_steps,
		std::shared_ptr< std::unordered_map< core::Identifier, double > >	expected_trend_boxcox,
		const std::vector< core::Identifier > &	names,
		const std::vector< LinearModelParams > &	models,
		const std::vector< core::DoubleInterval > &	ranges,
		const std::vector< double > &	lambda,
		const std::vector< double > &	stddev,
		const Eigen::MatrixXd &	cholesky,
		const std::vector< LinearModelParams > &	policy_models,
		const std::vector< core::DoubleInterval > &	policy_ranges,
		const Eigen::MatrixXd &	policy_cholesky,
		std::shared_ptr< std::vector< LinearModelParams > >	trend_models,
		std::shared_ptr< std::vector< core::DoubleInterval > >	trend_ranges,
		std::shared_ptr< std::vector< double > >	trend_lambda,
		double	info_speed,
		const std::unordered_map< core::Identifier, std::unordered_map< core::Gender, double > > &	rural_prevalence,
		const std::unordered_map< core::Income, LinearModelParams > &	income_models,
		double	physical_activity_stddev,
		TrendType	trend_type,
		std::shared_ptr< std::unordered_map< core::Identifier, double > >	expected_income_trend = nullptr,
		std::shared_ptr< std::unordered_map< core::Identifier, double > >	expected_income_trend_boxcox = nullptr,
		std::shared_ptr< std::unordered_map< core::Identifier, int > >	income_trend_steps = nullptr,
		std::shared_ptr< std::vector< LinearModelParams > >	income_trend_models = nullptr,
		std::shared_ptr< std::vector< core::DoubleInterval > >	income_trend_ranges = nullptr,
		std::shared_ptr< std::vector< double > >	income_trend_lambda = nullptr,
		std::shared_ptr< std::unordered_map< core::Identifier, double > >	income_trend_decay_factors = nullptr,
		bool	is_continuous_income_model = false,
		const LinearModelParams &	continuous_income_model = LinearModelParams{},
		core::IncomeCategoryLayout	income_category_layout = {},
		const std::unordered_map< core::Identifier, PhysicalActivityModel > &	physical_activity_models = {},
		const std::vector< IncomeStratumExpectedTableEntry > &	income_stratum_expected_tables = {},
		bool	income_stratum_adjustment_enabled = false,
		std::size_t	adjustment_income_stratum_count = 0u,
		bool	has_active_policies = true,
		const std::vector< LinearModelParams > &	logistic_models = {},
		core::Gender	gender2_indicator = core::Gender::male
	)

Initialises a new instance of the StaticLinearModel class.

Parameters

expected	The expected risk factor values by sex and age
expected_trend	The expected trend of risk factor values
trend_steps	The number of time steps to apply the trend
expected_trend_boxcox	The expected boxcox factor
names	The risk factor names
models	The linear models used to compute a person's risk factor values
ranges	The value range of each risk factor
lambda	The lambda values of the risk factors
stddev	The standard deviations of the risk factors
cholesky	Cholesky decomposition of the risk factor correlation matrix
policy_models	The linear models used to compute a person's intervention policies
policy_ranges	The value range of each intervention policy
policy_cholesky	Cholesky decomposition of the intervention policy covariance matrix
trend_models	The linear models used to compute a person's risk factor trends
trend_ranges	The value range of each risk factor trend
trend_lambda	The lambda values of the risk factor trends
info_speed	The information speed of risk factor updates
rural_prevalence	Rural sector prevalence for age groups and sex
income_models	The income models for each income category
physical_activity_stddev	The standard deviation of the physical activity
trend_type	The type of trend to apply (Null, UPFTrend, or IncomeTrend)
expected_income_trend	The expected income trend of risk factor values
expected_income_trend_boxcox	The expected income trend boxcox factor
income_trend_steps	The number of time steps to apply the income trend
income_trend_models	The linear models used to compute income trends
income_trend_ranges	The value range of each income trend
income_trend_lambda	The lambda values of the income trends
income_trend_decay_factors	The exponential decay factors for income trends
is_continuous_income_model	Whether this model uses continuous income calculation (FINCH approach)
continuous_income_model	The continuous income model parameters (if using FINCH approach)
income_category_layout	Final income category buckets from project_requirements

Exceptions

HgpsException

for invalid arguments

Parameters

physical_activity_models
physical_activity_models	Physical activity models for both India (simple) and FINCH (continuous) approaches
income_stratum_expected_tables
income_stratum_expected_tables	Optional per-stratum expected tables (ordered as in config)
income_stratum_adjustment_enabled
income_stratum_adjustment_enabled	Whether per-stratum factors-mean adjustment is enabled
adjustment_income_stratum_count
adjustment_income_stratum_count	Number of rank buckets used for adjustment strata
logistic_models
logistic_models	Logistic regression models for two-stage modeling (optional) Empty models indicate no logistic regression for that risk factor

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Member Function Documentation

generate_risk_factors()

void hgps::StaticLinearModel::generate_risk_factors ( RuntimeContext &  context )

overridevirtual

Generates the initial risk factors for a population and newborns.

Parameters

context

The simulation run-time context

Implements hgps::RiskFactorModel.

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is_continuous_income_model()

bool hgps::StaticLinearModel::is_continuous_income_model ( ) const

noexcept

Check if using continuous income model (FINCH approach)

Returns

true if continuous income model is enabled

name()

std::string hgps::StaticLinearModel::name ( ) const

overridevirtualnoexcept

Gets the model name.

Returns

The human-readable model name

Implements hgps::RiskFactorModel.

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type()

RiskFactorModelType hgps::StaticLinearModel::type ( ) const

overridevirtualnoexcept

Gets the model type identifier.

Returns

The module type identifier

Implements hgps::RiskFactorModel.

update_risk_factors()

void hgps::StaticLinearModel::update_risk_factors ( RuntimeContext &  context )

overridevirtual

Update risk factors for population.

Parameters

context

The simulation run-time context

Implements hgps::RiskFactorModel.

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The documentation for this class was generated from the following files:

• HealthGPS/static_linear_model.h
• HealthGPS/static_linear_model.cpp