beef up sampling to test

Also removed unnecessary call to `fetch`

pipeline/src/constructors/make_observation_model.jl

@@ -1,5 +1,5 @@
 """
-Constructs an observation model for the given pipeline. This is the defualt method.
+Constructs an observation model for the given pipeline. This is the default method.
 
 # Arguments
 - `pipeline::AbstractEpiAwarePipeline`: The pipeline for which the observation model is constructed.
@@ -18,3 +18,23 @@ function make_observation_model(pipeline::AbstractEpiAwarePipeline)
     obs = LatentDelay(dayofweek_logit_ascert, delay_distribution)
     return obs
 end
+
+const negC = -1e15
+"""
+Soft minimum function for a smooth transition from `x -> x` to a maximum value of 1e15.
+"""
+_softmin(x) = -logaddexp(negC, -x)
+
+function make_observation_model(pipeline::AbstractRtwithoutRenewalPipeline)
+    default_params = make_default_params(pipeline)
+    #Model for ascertainment based on day of the week
+    dayofweek_logit_ascert = ascertainment_dayofweek(
+        NegativeBinomialError(cluster_factor_prior = HalfNormal(default_params["cluster_factor"]));
+        transform = (x, y) -> _softmin.(x .* y))
+
+    #Default continuous-time model for latent delay in observations
+    delay_distribution = make_delay_distribution(pipeline)
+    #Model for latent delay in observations
+    obs = LatentDelay(dayofweek_logit_ascert, delay_distribution)
+    return obs
+end

pipeline/test/pipeline/test_pipelinefunctions.jl

@@ -13,16 +13,14 @@ end
 
 @testset "do_inference tests" begin
     function make_inference(pipeline)
-        truthdata_dg_task = do_truthdata(pipeline)
-        truthdata = fetch.(truthdata_dg_task)
+        truthdata = do_truthdata(pipeline)
         do_inference(truthdata[1], pipeline)
     end
 
     for pipetype in [SmoothOutbreakPipeline, MeasuresOutbreakPipeline,
         SmoothEndemicPipeline, RoughEndemicPipeline]
-        pipeline = pipetype(; ndraws = 20, nchains = 1, testmode = true)
-        inference_results_tsk = make_inference(pipeline)
-        inference_results = fetch.(inference_results_tsk)
+        pipeline = pipetype(; ndraws = 1000, nchains = 1, testmode = true)
+        inference_results = make_inference(pipeline)
         @test length(inference_results) == 1
         @test all([result["inference_results"] isa EpiAwareObservables
                    for result in inference_results])