Add acknowledgement

Author: cjbayesian

app.py

@@ -176,7 +176,7 @@ def sim_sir(S, I, R, beta, gamma, n_days, beta_decay=None):
 projection = pd.DataFrame.from_dict(data_dict)
 
 st.subheader("New Admissions")
-st.markdown("The number of **daily** new COVID-19 admissions at Penn hospitals")
+st.markdown("Projected number of **daily** COVID-19 admissions at Penn hospitals")
 
 # New cases
 projection_admits = projection.iloc[:-1, :] - projection.shift(1)
@@ -202,7 +202,7 @@ def sim_sir(S, I, R, beta, gamma, n_days, beta_decay=None):
 admits_table.index = range(admits_table.shape[0])
 admits_table = admits_table.fillna(0)
 
-if st.checkbox("Show Admissions Data"):
+if st.checkbox("Show Projected Admissions in tabular form"):
     st.dataframe(admits_table)
 
 st.subheader("Admitted Patients (Census)")
@@ -244,13 +244,13 @@ def sim_sir(S, I, R, beta, gamma, n_days, beta_decay=None):
 census_table.loc[0, :] = 0
 census_table = census_table.dropna()
 
-if st.checkbox("Show Census Data"):
+if st.checkbox("Show Projected Census in tabular form"):
     st.dataframe(census_table)
 
 st.markdown(
     """**Click the checkbox below to view additional data generated by this simulation**"""
 )
-if st.checkbox("Show Additional Data"):
+if st.checkbox("Show Additional Projections"):
     st.subheader("Projected Hospitalization Demand at Penn Medicine Facilities")
     st.markdown(
         "The number of COVID-19 patients projected to require hospitalization at Penn"
@@ -292,7 +292,9 @@ def sim_sir(S, I, R, beta, gamma, n_days, beta_decay=None):
     if st.checkbox("Show Raw SIR Similation Data"):
         st.dataframe(infect_table)
 
-st.subheader("References")
+st.subheader("References & Acknowledgements")
 st.markdown(
-    """* AHA Webinar, Feb 26, James Lawler, MD, an associate professor University of Nebraska Medical Center, What Healthcare Leaders Need To Know: Preparing for the COVID-19"""
+    """* AHA Webinar, Feb 26, James Lawler, MD, an associate professor University of Nebraska Medical Center, What Healthcare Leaders Need To Know: Preparing for the COVID-19
+* We would like to recognize the valuable assistance in consultation and review of model assumptions by Michael Z. Levy, PhD, Associate Professor of Epidemiology, Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine 
+    """
 )