Add a utility for generating the observation kernel, along with unit …

…test and update the `./test` env

EpiAware/src/EpiAware.jl

@@ -33,6 +33,7 @@ using Distributions,
 export scan,
     create_discrete_pmf,
     growth_rate_to_reproductive_ratio,
+    generate_observation_kernel,
     EpiModel,
     log_daily_infections,
     random_walk

EpiAware/src/epimodel.jl

@@ -36,13 +36,7 @@ struct EpiModel{T<:Real} <: AbstractEpiModel
         @assert sum(gen_int) ≈ 1 "Generation interval must sum to 1"
         @assert sum(delay_int) ≈ 1 "Delay interval must sum to 1"
         #construct observation delay kernel
-        K = zeros(time_horizon, time_horizon) |> SparseMatrixCSC
-        for i = 1:time_horizon, j = 1:time_horizon
-            m = i - j
-            if m >= 0 && m <= (length(delay_int) - 1)
-                K[i, j] = delay_int[m+1]
-            end
-        end
+        K = generate_observation_kernel(delay_int, time_horizon)
 
         new{eltype(gen_int)}(
             gen_int,
@@ -70,13 +64,7 @@ struct EpiModel{T<:Real} <: AbstractEpiModel
 
         #construct observation delay kernel
         #Recall first element is zero delay
-        K = zeros(time_horizon, time_horizon) |> SparseMatrixCSC
-        for i = 1:time_horizon, j = 1:time_horizon
-            m = i - j
-            if m >= 0 && m <= (length(delay_int) - 1)
-                K[i, j] = delay_int[m+1]
-            end
-        end
+        K = generate_observation_kernel(delay_int, time_horizon)
 
         new{eltype(gen_int)}(
             gen_int,

EpiAware/src/utilities.jl

@@ -145,3 +145,27 @@ function mean_cc_neg_bin(μ, α)
     r = μ^2 / ex_σ²
     return NegativeBinomial(r, p)
 end
+
+
+"""
+    generate_observation_kernel(delay_int, time_horizon)
+
+Generate an observation kernel matrix based on the given delay interval and time horizon.
+
+# Arguments
+- `delay_int::Vector{Float64}`: The delay PMF vector.
+- `time_horizon::Int`: The number of time steps of the observation period.
+
+# Returns
+- `K::SparseMatrixCSC{Float64, Int}`: The observation kernel matrix.
+"""
+function generate_observation_kernel(delay_int, time_horizon)
+    K = zeros(eltype(delay_int), time_horizon, time_horizon) |> SparseMatrixCSC
+    for i = 1:time_horizon, j = 1:time_horizon
+        m = i - j
+        if m >= 0 && m <= (length(delay_int) - 1)
+            K[i, j] = delay_int[m+1]
+        end
+    end
+    return K
+end

EpiAware/test/Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.10.0"
 manifest_format = "2.0"
-project_hash = "0b01aa91e53bb772f02a49192dfa1019eaa23f4b"
+project_hash = "0dea5a2fa6648a3a05ed8cb24ee73213ffe76d33"
 
 [[deps.ADTypes]]
 git-tree-sha1 = "41c37aa88889c171f1300ceac1313c06e891d245"

EpiAware/test/Project.toml

@@ -2,6 +2,7 @@
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
 TestItems = "1c621080-faea-4a02-84b6-bbd5e436b8fe"

EpiAware/test/test_utilities.jl

@@ -81,7 +81,7 @@ end
 
 end
 
-@testitem"Testing growth_rate_to_reproductive_ratio function" begin
+@testitem "Testing growth_rate_to_reproductive_ratio function" begin
     #Test that zero exp growth rate imples R0 = 1
     @testset "Test case 1" begin
         r = 0
@@ -99,3 +99,17 @@ end
     end
 
 end
+
+@testitem "Testing generate_observation_kernel function" begin
+    using SparseArrays
+    @testset "Test case 1" begin
+        delay_int = [0.2, 0.5, 0.3]
+        time_horizon = 5
+        expected_K = SparseMatrixCSC(
+            [0.2 0 0 0 0 0.5 0.2 0 0 0 0.3 0.5 0.2 0 0 0 0.3 0.5 0.2 0 0 0 0.3 0.5 0.2],
+        )
+        K = generate_observation_kernel(delay_int, time_horizon)
+        @test K == expected_K
+    end
+
+end