commit all

EpiAware/src/abstract-types.jl

@@ -1,10 +1,8 @@
 abstract type AbstractModel end
 
 """
-    abstract type AbstractEpiModel <: AbstractModel end
-
 The abstract supertype for all structs that define a model for generating unobserved/latent
-    infections.
+infections.
 """
 abstract type AbstractEpiModel <: AbstractModel end
 
@@ -17,8 +15,8 @@ abstract type AbstractLatentModel <: AbstractModel end
 abstract type AbstractObservationModel <: AbstractModel end
 
 @doc raw"""
-Generate unobserved/latent infections based on the given `epi_model <: AbstractEpimodel`
-    and a latent process path ``Z_t``.
+Constructor function for unobserved/latent infections based on the type of
+`epi_model <: AbstractEpimodel` and a latent process path ``Z_t``.
 
 The `generate_latent_infs` function implements a model of generating unobserved/latent
 infections conditional on a latent process. Which model of generating unobserved/latent
@@ -29,9 +27,10 @@ defined for the given `epi_model`, then `EpiAware` will return a warning and ret
 ## Interface to `Turing.jl` probablilistic programming language (PPL)
 
 Apart from the no implementation fallback method, the `generate_latent_infs` implementation
-function should be a constructor function for a
+function returns a constructor function for a
     [`DynamicPPL.Model`](https://turinglang.org/DynamicPPL.jl/stable/api/#DynamicPPL.Model)
-    object.
+object where the unobserved/latent infections are a generated quantity. Priors for model
+parameters are fields of `epi_model`.
 """
 function generate_latent_infs(epi_model::AbstractEpiModel, Z_t)
     @warn "No concrete implementation for `generate_latent_infs` is defined."

EpiAware/src/epimodels/directinfections.jl

@@ -11,14 +11,15 @@ I_t = g(\hat{I}_0 + Z_t).
 ```
 
 where ``g`` is a transformation function and the unconstrained initial infections
-    ``\hat{I}_0`` are sampled from a prior distribution, `initialisation_prior` which must
-be supplied to the `DirectInfections` constructor. The default `initialisation_prior` is
-the standard Normal `Distributions.Normal()`.
+    ``\hat{I}_0`` are sampled from a prior distribution.
 
-## Constructor
+`DirectInfections` are constructed by passing an `EpiData` object `data` and an
+`initialisation_prior` for the prior distribution of ``\hat{I}_0``. The default
+`initialisation_prior` is `Normal()`.
 
-`DirectInfections` can be constructed by passing an `EpiData` object and subtype of
-[`Distributions.Sampleable`](https://juliastats.org/Distributions.jl/latest/types/#Sampleable).
+## Constructors
+
+- `DirectInfections(; data, initialisation_prior)`
 
 ## Example usage with `generate_latent_infs`
 

EpiAware/src/epimodels/expgrowthrate.jl

@@ -12,14 +12,15 @@ I_t = g(\hat{I}_0) \exp(Z_t).
 ```
 
 where ``g`` is a transformation function and the unconstrained initial infections
-    ``\hat{I}_0`` are sampled from a prior distribution, `initialisation_prior` which must
-be supplied to the `DirectInfections` constructor. The default `initialisation_prior` is
-the standard Normal `Distributions.Normal()`.
+    ``\hat{I}_0`` are sampled from a prior distribution.
+
+`ExpGrowthRate` are constructed by passing an `EpiData` object `data` and an
+`initialisation_prior` for the prior distribution of ``\hat{I}_0``. The default
+`initialisation_prior` is `Normal()`.
 
 ## Constructor
 
-`ExpGrowthRate` can be constructed by passing an `EpiData` object and and subtype of
-[`Distributions.Sampleable`](https://juliastats.org/Distributions.jl/latest/types/#Sampleable).
+- `ExpGrowthRate(; data, initialisation_prior)`.
 
 ## Example usage with `generate_latent_infs`
 

EpiAware/src/epimodels/renewal.jl

@@ -16,10 +16,8 @@ I_t &= g(\hat{I}_0) \exp(r(\mathcal{R}_1) t), \qquad t \leq 0.
 ```
 
 where ``g`` is a transformation function and the unconstrained initial infections
-``\hat{I}_0`` are sampled from a prior distribution, `initialisation_prior` which must
-be supplied to the `DirectInfections` constructor. The default `initialisation_prior` is
-the standard Normal `Distributions.Normal()`. The discrete generation interval is given by
-``g_i``.
+``\hat{I}_0`` are sampled from a prior distribution. The discrete generation interval is
+given by ``g_i``.
 
 ``r(\mathcal{R}_1)`` is the exponential growth rate implied by ``\mathcal{R}_1)``
 using the implicit relationship between the exponential growth rate and the reproduction
@@ -29,10 +27,13 @@ number.
 \mathcal{R} \sum_{j \geq 1} g_j \exp(- r j)= 1.
 ```
 
+`Renewal` are constructed by passing an `EpiData` object `data` and an
+`initialisation_prior` for the prior distribution of ``\hat{I}_0``. The default
+`initialisation_prior` is `Normal()`.
+
 ## Constructor
 
-`Renewal` can be constructed by passing an `EpiData` object and and subtype of
-[`Distributions.Sampleable`](https://juliastats.org/Distributions.jl/latest/types/#Sampleable).
+- `Renewal(; data, initialisation_prior)`.
 
 ## Example usage with `generate_latent_infs`
 
@@ -82,6 +83,8 @@ I_t = generated_quantities(latent_inf, θ)
 end
 
 @doc """
+    function (epi_model::Renewal)(recent_incidence, Rt)
+
 Callable on a `Renewal` struct for compute new incidence based on recent incidence and Rt.
 
 ## Mathematical specification