diff --git a/examples/sir_hmc.py b/examples/sir_hmc.py
index 2fc83c85bd..a9fa427301 100644
--- a/examples/sir_hmc.py
+++ b/examples/sir_hmc.py
@@ -341,8 +341,8 @@ def heuristic_init(args, data):
 
 def infer_hmc_cont(model, args, data):
     if args.dct:
-        model = poutine.reparam(model, {"S_aux": DiscreteCosineReparam(),
-                                        "I_aux": DiscreteCosineReparam()})
+        rep = DiscreteCosineReparam()
+        model = poutine.reparam(model, {"S_aux": rep, "I_aux": rep})
     init_values = heuristic_init(args, data)
     return _infer_hmc(args, data, model, init_values=init_values)
 
@@ -478,11 +478,14 @@ def predict(args, data, samples, truth=None):
     particle_plate = pyro.plate("particles", args.num_samples, dim=-1)
 
     # First we sample discrete auxiliary variables from the continuous
-    # variables sampled in vectorized_model. Here infer_discrete runs a
-    # forward-filter backward-sample algorithm. We'll add these new samples to
-    # the existing dict of samples.
+    # variables sampled in vectorized_model. This samples only time steps
+    # [0:duration]. Here infer_discrete runs a forward-filter backward-sample
+    # algorithm. We'll add these new samples to the existing dict of samples.
     model = poutine.condition(continuous_model, samples)
     model = particle_plate(model)
+    if args.dct:  # Apply the same reparameterizer as during inference.
+        rep = DiscreteCosineReparam()
+        model = poutine.reparam(model, {"S_aux": rep, "I_aux": rep})
     model = infer_discrete(model, first_available_dim=-2)
     with poutine.trace() as tr:
         model(data, args.population)
@@ -490,8 +493,9 @@ def predict(args, data, samples, truth=None):
                for name, site in tr.trace.nodes.items()
                if site["type"] == "sample"}
 
-    # Next we'll run the forward generative process in discrete_model. Again
-    # we'll update the dict of samples.
+    # Next we'll run the forward generative process in discrete_model. This
+    # samples time steps [duration:duration+forecast]. Again we'll update the
+    # dict of samples.
     extended_data = list(data) + [None] * args.forecast
     model = poutine.condition(discrete_model, samples)
     model = particle_plate(model)
@@ -501,7 +505,8 @@ def predict(args, data, samples, truth=None):
                for name, site in tr.trace.nodes.items()
                if site["type"] == "sample"}
 
-    # Concatenate sequential time series into tensors.
+    # Finally we'll concatenate the sequentially sampled values into contiguous
+    # tensors. This operates on the entire time interval [0:duration+forecast].
     for key in ("S", "I", "S2I", "I2R"):
         pattern = key + "_[0-9]+"
         series = [value
diff --git a/tests/test_examples.py b/tests/test_examples.py
index 196534e0fa..2cea6778f9 100644
--- a/tests/test_examples.py
+++ b/tests/test_examples.py
@@ -74,10 +74,10 @@
     'rsa/schelling.py --num-samples=10',
     'rsa/schelling_false.py --num-samples=10',
     'rsa/semantic_parsing.py --num-samples=10',
-    'sir_hmc.py -w=2 -n=4 -d=2 -m=1 --enum',
-    'sir_hmc.py -w=2 -n=4 -d=2 -p=10000 --sequential',
-    'sir_hmc.py -w=2 -n=4 -d=100 -p=10000 -f 2',
-    'sir_hmc.py -w=2 -n=4 -d=100 -p=10000 --dct',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=2 -m=1 --enum',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=2 -p=10000 --sequential',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=100 -p=10000 -f 2',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=100 -p=10000 -f 2 --dct',
     'smcfilter.py --num-timesteps=3 --num-particles=10',
     'sparse_gamma_def.py --num-epochs=2 --eval-particles=2 --eval-frequency=1 --guide custom',
     'sparse_gamma_def.py --num-epochs=2 --eval-particles=2 --eval-frequency=1 --guide auto',
@@ -119,10 +119,10 @@
     'hmm.py --num-steps=1 --truncate=10 --model=4 --tmc --tmc-num-samples=2 --cuda',
     'hmm.py --num-steps=1 --truncate=10 --model=5 --tmc --tmc-num-samples=2 --cuda',
     'hmm.py --num-steps=1 --truncate=10 --model=6 --tmc --tmc-num-samples=2 --cuda',
-    'sir_hmc.py -w=2 -n=4 -d=2 -m=1 --enum --cuda',
-    'sir_hmc.py -w=2 -n=4 -d=2 -p=10000 --sequential --cuda',
-    'sir_hmc.py -w=2 -n=4 -d=100 -p=10000 --cuda',
-    'sir_hmc.py -w=2 -n=4 -d=100 -p=10000 --dct --cuda',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=2 -m=1 --enum --cuda',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=2 -p=10000 --sequential --cuda',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=100 -p=10000 --cuda',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=100 -p=10000 --dct --cuda',
     'vae/vae.py --num-epochs=1 --cuda',
     'vae/ss_vae_M2.py --num-epochs=1 --cuda',
     'vae/ss_vae_M2.py --num-epochs=1 --aux-loss --cuda',
@@ -160,10 +160,10 @@ def xfail_jit(*args):
     'lda.py --num-steps=2 --num-words=100 --num-docs=100 --num-words-per-doc=8 --jit',
     'minipyro.py --backend=pyro --jit',
     'minipyro.py --jit',
-    'sir_hmc.py -w=2 -n=4 -d=2 -m=1 --enum --jit',
-    'sir_hmc.py -w=2 -n=4 -d=2 -p=10000 --sequential --jit',
-    'sir_hmc.py -w=2 -n=4 -p=10000 --jit',
-    'sir_hmc.py -w=2 -n=4 -p=10000 --dct --jit',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=2 -m=1 --enum --jit',
+    'sir_hmc.py -t=2 -w=2 -n=4 -d=2 -p=10000 --sequential --jit',
+    'sir_hmc.py -t=2 -w=2 -n=4 -p=10000 --jit',
+    'sir_hmc.py -t=2 -w=2 -n=4 -p=10000 --dct --jit',
     xfail_jit('vae/ss_vae_M2.py --num-epochs=1 --aux-loss --jit'),
     'vae/ss_vae_M2.py --num-epochs=1 --enum-discrete=parallel --jit',
     'vae/ss_vae_M2.py --num-epochs=1 --enum-discrete=sequential --jit',