V1

app.py

@@ -1,4 +1,249 @@
 import streamlit as st
+import altair as alt
+import numpy as np
+import pandas as pd
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.markdown(
+    """
+        <style>
+@font-face {
+  font-family: 'Tangerine';
+  font-style: normal;
+  font-weight: 400;
+  src: url(https://fonts.gstatic.com/s/tangerine/v12/IurY6Y5j_oScZZow4VOxCZZM.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+
+    html, body, [class*="css"]  {
+    font-family: 'Public Sans', sans-serif;
+    # font-size: 1rem;
+    }
+    </style>
+
+    """,
+    unsafe_allow_html=True,
+)
+
+
+# Define params
+st.subheader("Configuration")
+col1, col2 = st.columns(2)
+
+# Chances of developing symptoms (per day)
+with col1:
+    symptoms_chance = st.slider(
+        'Chances of developing symptoms if infected (per day)', min_value=0.0, max_value=1.0, value=0.5, step=0.01)
+
+# Days spent inf asympt
+with col1:
+    mean_days_inf_asympt = st.slider(
+        'Mean number of days as infectious asymptomatic (without routine testing)', min_value=1, max_value=14, value=4, step=1)
+    base_p00 = 1-(1/mean_days_inf_asympt)
+    base_p01 = (1-symptoms_chance)*(1/mean_days_inf_asympt)
+    base_p03 = (symptoms_chance)*(1/mean_days_inf_asympt)
+
+# Days spent inf asympt
+with col2:
+    mean_days_inf_sympt = st.slider(
+        'Mean number of days as infectious symptomatic (when testing on symptoms only)', min_value=1, max_value=14, value=2, step=1)
+    base_p11 = 1-(1/mean_days_inf_sympt)
+    base_p12 = (1/mean_days_inf_sympt)
+
+# Wearable efficiency
+    efficiency = st.radio(
+        "Performance of device",
+        ('Standard', 'Conservative'))
+# with col2:
+#     wear_efficiency = st.slider(
+#         'Sensitivity of device', min_value=0.0, max_value=1.0, value=0.2, step=0.01)  # 👈 this is a widget
+
+
+
+# # Calculate
+# test_efficiency = np.linspace(1, 30, 30)
+# days_inf = np.zeros((len(test_efficiency)))
+# temp_df = []
+# for tau_count, t_e in enumerate(test_efficiency):
+#     tau = 1/t_e
+#     pi = wear_efficiency
+#     # Transition matrix
+#     p = np.array([
+#         [base_p00*(1-tau)*(1-pi), base_p01*(1-tau) *
+#          (1-pi), 1-(1-tau)*(1-pi), base_p03*(1-tau)*(1-pi)],
+#         [0, base_p11*(1-tau)*(1-pi), base_p12*(1+tau+pi-tau*pi), 0.0],
+#                 [0, 0, 1.0, 0.0],
+#                 [0, 0, 0.0, 1.0]
+#                 ])
+
+#     m1 = 1/(1-p[0,0])
+#     m2 = 1/(1-p[1,1])
+#     p2 = p[0,1]/(p[0,1]+p[0,2]+p[0,3])
+#     days_inf[int(tau_count)] = m1 + p2*m2
+#     routine_tests_required = 30 * days_inf[2]
+
+# Cost case
+sens_list_standard = {0.0: 0.0,
+             0.005: 0.05,
+             0.014: 0.1,
+             0.021: 0.15,
+             0.05: 0.295,
+             0.1: 0.434,
+             0.2: 0.6,
+             0.3: 0.72,
+             0.4: 0.79,
+             0.5: 0.86,
+             0.6: 0.9,
+             0.7: 0.925,
+             0.8: 0.97,
+             0.9: 0.99,
+             1.0: 1.0}
+
+sens_list_conservative = {
+    0:  0,
+    0.012: 0.050,
+    0.026: 0.105,
+    0.049: 0.149,
+    0.072: 0.198,
+    0.096: 0.248,
+    0.120: 0.297,
+    0.146: 0.347,
+    0.184: 0.396,
+    0.222: 0.446,
+    0.255: 0.495,
+    0.300: 0.545,
+    0.349: 0.594,
+    0.401: 0.644,
+    0.467: 0.693,
+    0.547: 0.743,
+    0.621: 0.792,
+    0.699: 0.842,
+    0.787: 0.891,
+    0.868: 0.941,
+    1: 1
+}
+
+if efficiency == 'Standard':
+    sens_list = sens_list_standard
+else:
+    sens_list = sens_list_conservative
+
+def roc_func(x):
+    return sens_list[x]
+
+def roc_random(x):
+    return x
+
+test_efficiency = np.array([7, 30, 10000])
+# FPR = np.linspace(0, 1, 11)
+# FPR = [0.0, 0.005, 0.016, 0.021, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+# FPR = list(sens_list.keys())
+# days_inf = np.zeros((len(test_efficiency), len(FPR)))
+
+# for tau_count, t_e in enumerate(test_efficiency):
+#     tau = 1/t_e
+#     for fi_count, fi in enumerate(FPR):
+#         pi = roc_func(fi)
+#         alpha = tau + pi - (tau*pi)
+#         m1 = 4/(1+3*alpha)
+#         m2 = 2/(1 + alpha)
+#         p2 = 1/2 * (1 - alpha) / (1 + 3 * alpha)
+#         days_inf[int(tau_count), int(fi_count)] = m1 + p2*m2
+
+# Calculate
+test_efficiency = np.array([7, 30, 10000])
+FPR = list(sens_list.keys())
+days_inf = np.zeros((len(test_efficiency), len(FPR)))
+temp_df = []
+for tau_count, t_e in enumerate(test_efficiency):
+    tau = 1/t_e
+    for fi_count, fi in enumerate(FPR):
+        pi = roc_func(fi)
+        # Transition matrix
+        p = np.array([
+            [base_p00*(1-tau)*(1-pi), base_p01*(1-tau) *
+            (1-pi), 1-(1-tau)*(1-pi), base_p03*(1-tau)*(1-pi)],
+            [0, base_p11*(1-tau)*(1-pi), base_p12*(1+tau+pi-tau*pi), 0.0],
+            [0, 0, 1.0, 0.0],
+            [0, 0, 0.0, 1.0]
+        ])
+
+        m1 = 1/(1-p[0, 0])
+        m2 = 1/(1-p[1, 1])
+        p2 = p[0, 1]/(p[0, 1]+p[0, 2]+p[0, 3])
+        days_inf[int(tau_count), int(fi_count)] = m1 + p2*m2
+        routine_tests_required = 30 * days_inf[2]
+        # print(routine_tests_required)
+
+# No wearable case
+no_wearables = []
+tau = 1/10000
+for fi_count, fi in enumerate(FPR):
+    pi = roc_random(fi)
+    # Transition matrix
+    p = np.array([
+        [base_p00*(1-tau)*(1-pi), base_p01*(1-tau) *
+         (1-pi), 1-(1-tau)*(1-pi), base_p03*(1-tau)*(1-pi)],
+        [0, base_p11*(1-tau)*(1-pi), base_p12*(1+tau+pi-tau*pi), 0.0],
+        [0, 0, 1.0, 0.0],
+        [0, 0, 0.0, 1.0]
+    ])
+
+    m1 = 1/(1-p[0, 0])
+    m2 = 1/(1-p[1, 1])
+    p2 = p[0, 1]/(p[0, 1]+p[0, 2]+p[0, 3])
+    no_wearables.append(m1 + p2*m2)
+
+cost = np.array(FPR)*30
+no_wearable_cost = cost
+# for i in range(len(test_efficiency)):0
+
+wearable_cost = (1-(1-np.array(FPR))*(1-1/test_efficiency[2]))*30
+wearable_days_inf = days_inf[2]
+
+# Create chart
+chart_data = pd.DataFrame(
+    {'Tests required per month': no_wearable_cost,
+        'Routine testing': no_wearables,
+        'Wearable-triggered testing': wearable_days_inf})
+
+# st.line_chart(chart_data)
+
+chart_data_melted = chart_data.melt('Tests required per month')
+print(chart_data_melted)
+chart = (
+    alt.Chart(
+        data=chart_data_melted,
+        title="",
+        height=400,
+    )
+    .mark_line()
+    # .encode(
+    #     x=alt.X('Tests required per month',
+    #             scale=alt.Scale(domain=[0, 30])),
+    #     y=alt.Y('Average case infectious days',
+    #             scale=alt.Scale(domain=[0, 6])),
+    #     # color=alt.value("#162d88"),
+    #     color=alt.Color("name:N"),
+    #     strokeWidth=alt.value(6),
+    # )
+    .encode(
+        x='Tests required per month',
+        y=alt.Y('value:Q', axis=alt.Axis(title='Average case infectious days')),
+        # y='value:Q',
+        color='variable:N',
+        strokeWidth=alt.value(6)
+    )
+    .configure_axis(
+        labelFontSize=20,
+        titleFontSize=20
+    )
+
+)
+
+st.subheader("Outcome")
+st.altair_chart(chart, use_container_width=True)
+
+# col1, col2, col3 = st.columns(3)
+# col1.metric("Tests required per month", int(routine_tests_required), "1.2")
+# col2.metric("Tests saved", "9", "-8%")
+# col3.metric("Humidity", "86%", "4%")