Constants

Dismod-AT makes assumptions about the order of variables. In some cases, it has relaxed those assumptions over time, but we retain these as conventions.

class cascade_at.dismod.constants.RateEnum(value)

These are the five underlying rates.

pini = 0

Initial prevalence of the condition at birth, as a fraction of one.

iota = 1

Incidence rate for leaving susceptible to become diseased.

rho = 2

Remission from disease to susceptible.

chi = 3

Excess mortality rate.

omega = 4

Other-cause mortality rate.

class cascade_at.dismod.constants.IntegrandEnum(value)

These are all of the integrands Dismod-AT supports, and they will have exactly these IDs when serialized.

Sincidence = 0

Susceptible incidence, where the denominator is the number of susceptibles. Corresponds to iota.

remission = 1

Remission rate, corresponds to rho.

mtexcess = 2

Excess mortality rate, corresponds to chi.

mtother = 3

Other-cause mortality, corresponds to omega.

mtwith = 4

Mortality rate for those with condition.

susceptible = 5

Fraction of susceptibles out of total population.

withC = 6

Fraction of population with the disease. Total pop is the denominator.

prevalence = 7

Fraction of those alive with the disease, so S+C is denominator.

Tincidence = 8

Total-incidence, where denominator is susceptibles and with-condition.

mtspecific = 9

Cause-specific mortality rate, so mx_c.

mtall = 10

All-cause mortality rate, mx.

mtstandard = 11

Standardized mortality ratio.

relrisk = 12

Relative risk.

incidence = -99

This integrand should never be used, but we need it when we are converting from the epi database measures initially

class cascade_at.dismod.constants.DensityEnum(value)

The distributions supported by Dismod-AT. They always have these ids.

uniform = 0

Uniform Distribution

gaussian = 1

Gaussian Distribution

laplace = 2

Laplace Distribution

students = 3

Students-t Distribution

log_gaussian = 4

Log-Gaussian Distribution

log_laplace = 5

Log-Laplace Distribution

log_students = 6

Log-Students-t Distribution

class cascade_at.dismod.constants.WeightEnum(value)

Dismod-AT allows arbitrary weights, which are functions of space and time, defined by bilinear interpolations on grids. These weights are used to average rates over age and time intervals. Given this problem, there are three kinds of weights that are relevant.

constant = 0

This weight is constant everywhere at 1. This is the no-weight weight.

susceptible = 1

For measures that are integrals over population without the condition.

with_condition = 2

For measures that are integrals over those with the disease.

total = 3

For measures where the denominator is the whole population.

constants.INTEGRAND_TO_WEIGHT = {'Sincidence': <WeightEnum.susceptible: 1>, 'Tincidence': <WeightEnum.total: 3>, 'mtall': <WeightEnum.total: 3>, 'mtexcess': <WeightEnum.with_condition: 2>, 'mtother': <WeightEnum.total: 3>, 'mtspecific': <WeightEnum.total: 3>, 'mtstandard': <WeightEnum.constant: 0>, 'mtwith': <WeightEnum.with_condition: 2>, 'prevalence': <WeightEnum.total: 3>, 'relrisk': <WeightEnum.constant: 0>, 'remission': <WeightEnum.with_condition: 2>, 'susceptible': <WeightEnum.constant: 0>, 'withC': <WeightEnum.constant: 0>}

Each integrand has a natural association with a particular weight because it is a count of events with one of four denominators: constant, susceptibles, with-condition, or the total population.