TransitionPriors: Build a TransitionPriors object, which governs how...

In grantbrown/ABSEIR: Posterior Approximation to Fully Bayesian Spatial SEIR Models Using Multithreaded C++

Description

Build a TransitionPriors object, which governs how individuals move from the exposed to infectious and infectious to removed compartments.

Usage

TransitionPriors(
  mode = c("exponential", "weibull", "path_specific"),
  params = list()
)

Arguments

mode	The type of transition model to employ ("exponential" or "path_specific)
params	Additional parameters, specific to the type of transition model. See details section for additional information.

Details

The TransitionPriors component of spatial SEIR(S) models captures the process by which individuals move from the exposed to infectious compartment, and from the infectious to removed compartment. This component thus governs the duration of the latent and infectious periods of the disease of interest, on the discrete timescale employed.

Two different TransitionPriors configurations are currently offered: the exponential compartment membership model, and a path specific generalization. These may be specified manually using the TransitionPriors function, or with the specific ExponentialTransitionPriors and PathSpecificTransitionPriors functions.

The exponential version of this process requires four parameters:

• p_eiThe probability, at a given time point, that an exposed individual will become infectious

• p_irThe probability, at a given time point, that an infectious individual be removed from the infectious population

• p_ei_essAn effective number of samples, corresponding to the confidence in the chosen E to I transition probability

• p_ir_essAn effective number of samples, corresponding to the confidence in the chosen I to R transition probability

The path specific formulation requires fewer parameters, but more care is required in their specification.

• Z1A probability density function for the time individuals spend in the latent state.

• Z2A probability density function for the time individuals spend in the infectious state.

References

"A path-specific SEIR model for use with general latent and infectious time distributions." 2013. Porter, Aaron T, Oleson, Jacob J. Biometrics 69(1)

Examples

transitionPriors <- TransitionPriors("exponential", params=list(p_ei=
                                        1/5, p_ir=1/7, p_ei_ess=100, p_ir_ess=100))

transitionPriors <- TransitionPriors("path_specific", params = list(
                                        Z1 = function(x){dunif(x, 2, 10)},
                                        Z2 = function(x){dunif(x, 7, 24)}))

grantbrown/ABSEIR documentation built on Oct. 14, 2021, 2:32 p.m.