Add calculate processes function to pipeline (#314)

* calculate_processes function

* unit tests

* add expected_Rt so not to repeat

* change to using first element of `rt` due possible weekly variation

* calculate_processes function

* unit tests

* add expected_Rt so not to repeat

* change to using first element of `rt` due possible weekly variation

* move infection seeding to a new function

pipeline/Project.toml

@@ -15,10 +15,12 @@ DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
 EpiAware = "b2eeebe4-5992-4301-9193-7ebc9f62c855"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

pipeline/src/EpiAwarePipeline.jl

@@ -12,12 +12,15 @@ module EpiAwarePipeline
 
 using CSV, Dagger, DataFramesMeta, Dates, Distributions, DocStringExtensions, DrWatson,
       EpiAware, Plots, Statistics, ADTypes, AbstractMCMC, Plots, JLD2, MCMCChains, Turing,
-      DynamicPPL, LogExpFunctions, RCall
+      DynamicPPL, LogExpFunctions, RCall, LinearAlgebra
 
 # Exported pipeline types
 export AbstractEpiAwarePipeline, EpiAwarePipeline, RtwithoutRenewalPipeline,
        RtwithoutRenewalPriorPipeline, EpiAwareExamplePipeline
 
+# Exported utility functions
+export calculate_processes
+
 # Exported configuration types
 export TruthSimulationConfig, InferenceConfig
 
@@ -47,6 +50,7 @@ export plot_truth_data, plot_Rt
 
 include("docstrings.jl")
 include("pipeline/pipeline.jl")
+include("utils/utils.jl")
 include("constructors/constructors.jl")
 include("simulate/simulate.jl")
 include("infer/infer.jl")

pipeline/src/utils/calculate_processes.jl

@@ -0,0 +1,79 @@
+"""
+Internal function for calculating the log of the infections with an informative
+    error message if the infections are not positive definite.
+"""
+function _calc_log_infections(I_t)
+    @assert all(I_t .> 0) "Infections must be positive definite."
+    log.(I_t)
+end
+
+"""
+Internal function for calculating the exponential growth rate with an informative
+    error message if the infections are not positive definite.
+"""
+function _calc_rt(I_t, I0)
+    @assert all(I_t .> 0) "Infections must be positive definite."
+    @assert I0>0 "Initial infections must be positive definite."
+    log.([I0; I_t]) .- log(I0) |> diff
+end
+
+"""
+Internal function for seeding the infections. Method dispatches on the pipeline
+type to determine the seeding method. This is the default seeding method which
+assumes backward exponential growth with initial infections `I0` from initial
+estimate of `rt`.
+
+"""
+function _infection_seeding(I_t, I0, data::EpiData, pipeline::AbstractEpiAwarePipeline)
+    n = length(data.gen_int)
+    init_rt = _calc_rt(I_t[1:2], I0) |> x -> x[1]
+    [I0 * exp(-init_rt * (n - i)) for i in 1:n]
+end
+
+"""
+Internal function for calculating the _instantaneous_ reproduction number `Rt`
+using the method of [Fraser (2007)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000758).
+Left truncation handling method is determined by the pipeline type. The default
+left truncation in `I_t` is handled by extending the series with backwards exponential
+growth from the initial infections `I0` and the exponential growth rate `init_rt`.
+
+# Arguments
+- `I_t`: Incident infections.
+- `I0`: Initial infections at time zero.
+- `init_rt`: Initial exponential growth rate.
+- `data::EpiData`: An instance of the `EpiData` type containing generation interval data.
+- `pipeline::AbstractEpiAwarePipeline`: An instance of the `AbstractEpiAwarePipeline` type.
+"""
+function _calc_Rt(I_t, I0, data::EpiData, pipeline::AbstractEpiAwarePipeline)
+    @assert all(I_t .> 0) "Log infections must be positive definite."
+    @assert I0>0 "Initial infections must be positive definite."
+
+    aug_I_t = vcat(_infection_seeding(I_t, I0, data, pipeline), I_t)
+
+    Rt = expected_Rt(data, aug_I_t)
+
+    return Rt
+end
+
+"""
+Calculate the log of infections `log_I_t`, exponential growth values `rt`, and
+instaneous reproductive number `Rt` for a given time series of infections. The
+reproductive number calculation deals with left truncation in `I_t` by extending
+`I_t` with backwards exponential growth using the mean exponential growth rate
+from the first 7 time steps of `rt`.
+
+# Arguments
+- `I_t`: An array representing the time series of infections.
+- `I0`: The initial number of infections.
+- `pmf`: The probability mass function used to calculate Rt.
+
+# Returns
+A named tuple containing the calculated values for `log_I_t`, `rt`, and `Rt`.
+
+"""
+function calculate_processes(I_t, I0, data::EpiData, pipeline::AbstractEpiAwarePipeline)
+    log_I_t = _calc_log_infections(I_t)
+    rt = _calc_rt(I_t, I0)
+    Rt = _calc_Rt(I_t, I0, data, pipeline)
+    return (; log_I_t, rt, Rt)
+end

pipeline/src/utils/utils.jl

@@ -0,0 +1 @@
+include("calculate_processes.jl")

pipeline/test/end-to-end/test_prior_predictive.jl

@@ -15,5 +15,6 @@ using Test
 
     inference_results = generate_inference_results(
         truthdata, inference_config, pipeline; tspan, inference_method)
+
     @test inference_results["inference_results"] isa EpiAwareObservables
 end

pipeline/test/runtests.jl

@@ -4,6 +4,7 @@ quickactivate(@__DIR__(), "EpiAwarePipeline")
 # Run tests
 include("pipeline/test_pipelinetypes.jl");
 include("pipeline/test_pipelinefunctions.jl");
+include("utils/test_calculate_processes.jl");
 include("constructors/test_constructors.jl");
 include("simulate/test_TruthSimulationConfig.jl");
 include("simulate/test_SimulationConfig.jl");

pipeline/test/utils/test_calculate_processes.jl

@@ -0,0 +1,32 @@
+using Test
+
+@testset "calculate_processes" begin
+    using EpiAware, EpiAwarePipeline
+    using Random
+    rng = MersenneTwister(1234)
+    I0 = 10.0
+    rt = randn(rng, 20)
+    I_t = cumsum(rt) .+ log(I0) .|> exp
+    pmf = [1.0]
+    pipeline = EpiAwareExamplePipeline()
+
+    data = EpiData(pmf, exp)
+
+    result = calculate_processes(I_t, I0, data, pipeline)
+
+    # Check if the log of infections is calculated correctly
+    @testset "Log of infections" begin
+        expected_log_I_t = log.(I_t)
+        @test result.log_I_t ≈ expected_log_I_t
+    end
+
+    # Check if the exponential growth rate is calculated correctly
+    @testset "Exponential growth rate" begin
+        @test result.rt ≈ rt
+    end
+
+    # In this special case (pmf = [1.0]), the Rt = exp(rt)
+    @testset "Instantaneous reproduction number" begin
+        @test result.Rt ≈ exp.(rt)
+    end
+end