diff --git a/Dockerfile b/Dockerfile
index e64d1547..3c7f0812 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,5 +1,8 @@
 FROM python:3.7.7-slim-buster
 ENV PARAMETERS=./defaults/webapp.cfg
+ENV ELASTIC_APM_ENABLED false
+ENV ELASTIC_APM_SERVICE_NAME chime_local
+ENV ELASTIC_APM_SERVER_URL http://apm-server:8200
 WORKDIR /app
 COPY README.md .
 COPY setup.cfg .
diff --git a/setup.py b/setup.py
index 0394d775..dd011e72 100644
--- a/setup.py
+++ b/setup.py
@@ -35,7 +35,8 @@
         "streamlit",
         "gspread",
         "oauth2client",
-        "python-i18n"
+        "python-i18n",
+        "elastic-apm"
     ],
     classifiers=[
         "Programming Language :: Python :: 3",
diff --git a/src/APM.md b/src/APM.md
new file mode 100644
index 00000000..5a8a97a2
--- /dev/null
+++ b/src/APM.md
@@ -0,0 +1,23 @@
+# Elastic APM
+
+This application has been instrumented with Elastic APM. 
+
+In order to [configure](https://www.elastic.co/guide/en/apm/agent/python/current/configuration.html) this application environment variables should be 
+used in the configMap of the `app.yaml` file.
+
+
+Custom instrumentation begins in the main method in `st_app.py` with the call
+
+```
+client = Client()
+client.begin_transaction('main_page')
+```
+and ends with `client.end_transaction('main_page')`
+
+The sidebar menu and charts are instrumented with [custom instrumentations](https://www.elastic.co/guide/en/apm/agent/python/current/api.html). 
+
+
+The `display_sidebar` has its internal functions instrumented
+with the `with` notation.
+
+The `charts` and `display_header` are instrumented with the `annotation` method. 
\ No newline at end of file
diff --git a/src/penn_chime/model/sir.py b/src/penn_chime/model/sir.py
index 91909c61..368dd708 100644
--- a/src/penn_chime/model/sir.py
+++ b/src/penn_chime/model/sir.py
@@ -195,9 +195,9 @@ def get_argmin_doubling_time(self, p: Parameters, dts):
             intrinsic_growth_rate = get_growth_rate(i_dt)
             self.beta = get_beta(intrinsic_growth_rate, self.gamma, self.susceptible, 0.0)
             self.beta_t = get_beta(intrinsic_growth_rate, self.gamma, self.susceptible, p.relative_contact_rate)
-
+    
             raw = self.run_projection(p, self.gen_policy(p))
-
+    
             # Skip values the would put the fit past peak
             peak_admits_day = raw["admits_hospitalized"].argmax()
             if peak_admits_day < 0:
@@ -228,7 +228,7 @@ def gen_policy(self, p: Parameters) -> Sequence[Tuple[float, int]]:
             (self.beta,   pre_mitigation_days),
             (self.beta_t, post_mitigation_days),
         ]
-
+    
     def run_projection(self, p: Parameters, policy: Sequence[Tuple[float, int]]):
         raw = sim_sir(
             self.susceptible,
@@ -245,12 +245,10 @@ def run_projection(self, p: Parameters, policy: Sequence[Tuple[float, int]]):
 
         return raw
 
-
 def get_loss(current_hospitalized, predicted) -> float:
     """Squared error: predicted vs. actual current hospitalized."""
     return (current_hospitalized - predicted) ** 2.0
 
-
 def get_argmin_ds(census, current_hospitalized: float) -> float:
     # By design, this forbids choosing a day after the peak
     # If that's a problem, see #381
@@ -258,7 +256,6 @@ def get_argmin_ds(census, current_hospitalized: float) -> float:
     losses = (census[:peak_day] - current_hospitalized) ** 2.0
     return losses.argmin()
 
-
 def get_beta(
     intrinsic_growth_rate: float,
     gamma: float,
@@ -271,14 +268,12 @@ def get_beta(
         * (1.0 - relative_contact_rate)
     )
 
-
 def get_growth_rate(doubling_time: Optional[float]) -> float:
     """Calculates average daily growth rate from doubling time."""
     if doubling_time is None or doubling_time == 0.0:
         return 0.0
     return (2.0 ** (1.0 / doubling_time) - 1.0)
 
-
 def sir(
     s: float, i: float, r: float, beta: float, gamma: float, n: float
 ) -> Tuple[float, float, float]:
@@ -289,7 +284,6 @@ def sir(
     scale = n / (s_n + i_n + r_n)
     return s_n * scale, i_n * scale, r_n * scale
 
-
 def sim_sir(
     s: float, i: float, r: float, gamma: float, i_day: int, policies: Sequence[Tuple[float, int]]
 ):
@@ -334,7 +328,6 @@ def sim_sir(
         "ever_infected": i_a + r_a
     }
 
-
 def build_sim_sir_w_date_df(
     raw_df: pd.DataFrame,
     current_date: datetime,
@@ -350,7 +343,6 @@ def build_sim_sir_w_date_df(
         }
     })
 
-
 def build_floor_df(df, keys, prefix):
     """Build floor sim sir w date."""
     return pd.DataFrame({
@@ -362,7 +354,6 @@ def build_floor_df(df, keys, prefix):
         }
     })
 
-
 def calculate_dispositions(
     raw: Dict,
     rates: Dict[str, float],
@@ -373,7 +364,6 @@ def calculate_dispositions(
         raw["ever_" + key] = raw["ever_infected"] * rate * market_share
         raw[key] = raw["ever_infected"] * rate * market_share
 
-
 def calculate_admits(raw: Dict, rates):
     """Build admits dataframe from dispositions."""
     for key in rates.keys():
@@ -384,7 +374,6 @@ def calculate_admits(raw: Dict, rates):
         raw["admits_"+key] = admit
         raw[key] = admit
 
-
 def calculate_census(
     raw: Dict,
     lengths_of_stay: Dict[str, int],
diff --git a/src/penn_chime/view/charts.py b/src/penn_chime/view/charts.py
index 394f226f..4bfaba46 100644
--- a/src/penn_chime/view/charts.py
+++ b/src/penn_chime/view/charts.py
@@ -4,10 +4,11 @@
 import pandas as pd
 import i18n
 import numpy as np
+import elasticapm
 
 from ..constants import DATE_FORMAT
 
-
+@elasticapm.capture_span()
 def build_admits_chart(
     *, alt, admits_floor_df: pd.DataFrame, max_y_axis: Optional[int] = None
 ) -> Chart:
@@ -51,7 +52,7 @@ def build_admits_chart(
         .interactive()
     )
 
-
+@elasticapm.capture_span()
 def build_census_chart(
     *, alt, census_floor_df: pd.DataFrame, max_y_axis: Optional[int] = None
 ) -> Chart:
@@ -95,7 +96,7 @@ def build_census_chart(
         .interactive()
     )
 
-
+@elasticapm.capture_span()
 def build_sim_sir_w_date_chart(
     *, alt, sim_sir_w_date_floor_df: pd.DataFrame, max_y_axis: Optional[int] = None
 ) -> Chart:
@@ -139,6 +140,7 @@ def build_sim_sir_w_date_chart(
         .interactive()
     )
 
+@elasticapm.capture_span()
 def build_table(
     *, df: pd.DataFrame, labels: Dict[str, str], modulo: int = 1
 ) -> pd.DataFrame:
diff --git a/src/penn_chime/view/st_app.py b/src/penn_chime/view/st_app.py
index bd3dd94d..6ae703af 100644
--- a/src/penn_chime/view/st_app.py
+++ b/src/penn_chime/view/st_app.py
@@ -6,6 +6,8 @@
 import streamlit as st  # type: ignore
 import i18n  # type: ignore
 
+from elasticapm import Client
+
 i18n.set('filename_format', '{locale}.{format}')
 i18n.set('locale', 'en')
 i18n.set('fallback', 'en')
@@ -28,6 +30,10 @@
 
 
 def main():
+    #client = Client(server_url='http://apm-server:8200',service_name='chime_local')
+    #client = Client(server_url='${ELASTIC_APM_SERVER_URL}',service_name='${ELASTIC_APM_SERVICE_NAME}')
+    client = Client()
+    client.begin_transaction('main_page')
     # This is somewhat dangerous:
     # Hide the main menu with "Rerun", "run on Save", "clear cache", and "record a screencast"
     # This should not be hidden in prod, but removed
@@ -37,8 +43,9 @@ def main():
     d = Parameters.create(os.environ, [])
     p = display_sidebar(st, d)
     m = Sir(p)
-
+    
     display_header(st, m, p)
+   
 
     st.subheader(i18n.t("app-new-admissions-title"))
     st.markdown(i18n.t("app-new-admissions-text"))
@@ -73,3 +80,4 @@ def main():
         df=m.sim_sir_w_date_df,
     )
     display_footer(st)
+    client.end_transaction('main_page')
diff --git a/src/penn_chime/view/st_display.py b/src/penn_chime/view/st_display.py
index 33ce9c62..a0e06ac3 100644
--- a/src/penn_chime/view/st_display.py
+++ b/src/penn_chime/view/st_display.py
@@ -18,6 +18,8 @@
 from ..utils import dataframe_to_base64
 from .spreadsheet import spreadsheet
 
+import elasticapm
+
 hide_menu_style = """
         <style>
         #MainMenu {visibility: hidden;}
@@ -29,9 +31,8 @@
 # Text #
 ########
 
-
+@elasticapm.capture_span()
 def display_header(st, m, p):
-
     infected_population_warning_str = (
         i18n.t("presentation-infected-population-warning")
         if m.infected > p.population
@@ -150,7 +151,7 @@ def __init__(self, st_obj, label, value=None, key=None):
         kwargs = dict(key=key)
         super().__init__(st_obj.checkbox, label, value, kwargs)
 
-
+#@elasticapm.capture_span()
 def display_sidebar(st, d: Parameters) -> Parameters:
     # Initialize variables
     # these functions create input elements and bind the values they are set to
@@ -159,120 +160,137 @@ def display_sidebar(st, d: Parameters) -> Parameters:
 
     st_obj = st.sidebar
     # used_widget_key = st.get_last_used_widget_key ( )
-
-    current_hospitalized_input = NumberInput(
-        st_obj,
-        i18n.t("presentation-current-hospitalized"),
-        min_value=0,
-        value=d.current_hospitalized,
-        step=1,
-        format="%i",
-    )
-    n_days_input = NumberInput(
-        st_obj,
-        i18n.t("presentation-n-days"),
-        min_value=1,
-        max_value=30,
-        value=d.n_days,
-        step=1,
-        format="%i",
-    )
-    doubling_time_input = NumberInput(
-        st_obj,
-        i18n.t("presentation-doubling-time"),
-        min_value=0.5,
-        value=d.doubling_time,
-        step=0.25,
-        format="%f",
-    )
-    current_date_input = DateInput(
-        st_obj, i18n.t("presentation-current-date"), value=d.current_date,
-    )
-    date_first_hospitalized_input = DateInput(
-        st_obj, i18n.t("presentation-date-first-hospitalized"),
-        value=d.date_first_hospitalized,
-    )
-    mitigation_date_input = DateInput(
-        st_obj, i18n.t("presentation-mitigation-date"),
-        value=d.mitigation_date
-    )
-    relative_contact_pct_input = PercentInput(
-        st_obj,
-        i18n.t("presentation-relative-contact-rate"),
-        min_value=0.0,
-        max_value=100.0,
-        value=d.relative_contact_rate,
-        step=1.0,
-    )
-    hospitalized_pct_input = PercentInput(
-        st_obj,
-        i18n.t("presentation-hospitalized-rate"),
-        value=d.hospitalized.rate,
-        min_value=FLOAT_INPUT_MIN,
-        max_value=100.0
-    )
-    icu_pct_input = PercentInput(
-        st_obj,
-        i18n.t("presentation-icu-rate"),
-        min_value=0.0,
-        value=d.icu.rate,
-        step=0.05
-    )
-    ventilated_pct_input = PercentInput(
-        st_obj, i18n.t("presentation-ventilated-rate"), value=d.ventilated.rate,
-    )
-    hospitalized_days_input = NumberInput(
-        st_obj,
-        i18n.t("presentation-hospitalized-days"),
-        min_value=1,
-        value=d.hospitalized.days,
-        step=1,
-        format="%i",
-    )
-    icu_days_input = NumberInput(
-        st_obj,
-        i18n.t("presentation-icu-days"),
-        min_value=1,
-        value=d.icu.days,
-        step=1,
-        format="%i",
-    )
-    ventilated_days_input = NumberInput(
-        st_obj,
-        i18n.t("presentation-ventilated-days"),
-        min_value=1,
-        value=d.ventilated.days,
-        step=1,
-        format="%i",
-    )
-    market_share_pct_input = PercentInput(
-        st_obj,
-        i18n.t("presentation-market-share"),
-        min_value=0.5,
-        value=d.market_share,
-    )
-    population_input = NumberInput(
-        st_obj,
-        i18n.t("presentation-population"),
-        min_value=1,
-        value=(d.population),
-        step=1,
-        format="%i",
-    )
-    infectious_days_input = NumberInput(
-        st_obj,
-        i18n.t("presentation-infectious-days"),
-        min_value=1,
-        value=d.infectious_days,
-        step=1,
-        format="%i",
-    )
-    max_y_axis_set_input = CheckboxInput(
-        st_obj, i18n.t("presentation-max-y-axis-set")
-    )
-    max_y_axis_input = NumberInput(
-        st_obj, i18n.t("presentation-max-y-axis"), value=500, format="%i", step=25
-    )
+    with elasticapm.capture_span('current_hospitalized_input'):
+        current_hospitalized_input = NumberInput(
+            st_obj,
+            i18n.t("presentation-current-hospitalized"),
+            min_value=0,
+            value=d.current_hospitalized,
+            step=1,
+            format="%i",
+        )
+    with elasticapm.capture_span('n_days_input'):
+        n_days_input = NumberInput(
+            st_obj,
+            i18n.t("presentation-n-days"),
+            min_value=1,
+            max_value=30,
+            value=d.n_days,
+            step=1,
+            format="%i",
+        )
+    with elasticapm.capture_span('doubling_time_input'):
+        doubling_time_input = NumberInput(
+            st_obj,
+            i18n.t("presentation-doubling-time"),
+            min_value=0.5,
+            value=d.doubling_time,
+            step=0.25,
+            format="%f",
+        )
+    with elasticapm.capture_span('current_date_input'):    
+        current_date_input = DateInput(
+            st_obj, i18n.t("presentation-current-date"), value=d.current_date,
+        )
+    with elasticapm.capture_span('date_first_hospitalized_input'):    
+        date_first_hospitalized_input = DateInput(
+            st_obj, i18n.t("presentation-date-first-hospitalized"),
+            value=d.date_first_hospitalized,
+        )
+    with elasticapm.capture_span('mitigation_date_input'):
+        mitigation_date_input = DateInput(
+            st_obj, i18n.t("presentation-mitigation-date"),
+            value=d.mitigation_date
+        )
+    with elasticapm.capture_span('relative_contact_pct_input'):    
+        relative_contact_pct_input = PercentInput(
+            st_obj,
+            i18n.t("presentation-relative-contact-rate"),
+            min_value=0.0,
+            max_value=100.0,
+            value=d.relative_contact_rate,
+            step=1.0,
+        )
+    with elasticapm.capture_span('hospitalized_pct_input'):
+        hospitalized_pct_input = PercentInput(
+            st_obj,
+            i18n.t("presentation-hospitalized-rate"),
+            value=d.hospitalized.rate,
+            min_value=FLOAT_INPUT_MIN,
+            max_value=100.0
+        )
+    with elasticapm.capture_span('icu_pct_input'):
+        icu_pct_input = PercentInput(
+            st_obj,
+            i18n.t("presentation-icu-rate"),
+            min_value=0.0,
+            value=d.icu.rate,
+            step=0.05
+        )
+    with elasticapm.capture_span('ventilated_pct_input'):
+        ventilated_pct_input = PercentInput(
+            st_obj, i18n.t("presentation-ventilated-rate"), value=d.ventilated.rate,
+        )
+    with elasticapm.capture_span('hospitalized_days_input'):
+        hospitalized_days_input = NumberInput(
+            st_obj,
+            i18n.t("presentation-hospitalized-days"),
+            min_value=1,
+            value=d.hospitalized.days,
+            step=1,
+            format="%i",
+        )
+    with elasticapm.capture_span('icu_days_input'):
+        icu_days_input = NumberInput(
+            st_obj,
+            i18n.t("presentation-icu-days"),
+            min_value=1,
+            value=d.icu.days,
+            step=1,
+            format="%i",
+        )
+    with elasticapm.capture_span('ventilated_days_input'):
+        ventilated_days_input = NumberInput(
+            st_obj,
+            i18n.t("presentation-ventilated-days"),
+            min_value=1,
+            value=d.ventilated.days,
+            step=1,
+            format="%i",
+        )
+    with elasticapm.capture_span('market_share_pct_input'):
+        market_share_pct_input = PercentInput(
+            st_obj,
+            i18n.t("presentation-market-share"),
+            min_value=0.5,
+            value=d.market_share,
+        )
+    with elasticapm.capture_span('population_input'):
+        population_input = NumberInput(
+            st_obj,
+            i18n.t("presentation-population"),
+            min_value=1,
+            value=(d.population),
+            step=1,
+            format="%i",
+        )
+    with elasticapm.capture_span('infectious_days_input'):
+        infectious_days_input = NumberInput(
+            st_obj,
+            i18n.t("presentation-infectious-days"),
+            min_value=1,
+            value=d.infectious_days,
+            step=1,
+            format="%i",
+        )
+    with elasticapm.capture_span('max_y_axis_set_input'):
+        max_y_axis_set_input = CheckboxInput(
+            st_obj, i18n.t("presentation-max-y-axis-set")
+        )
+    with elasticapm.capture_span('max_y_axis_input'):
+        max_y_axis_input = NumberInput(
+            st_obj, i18n.t("presentation-max-y-axis"), value=500, format="%i", step=25
+        )
 
     # Build in desired order
     st.sidebar.markdown(