Remove more information about this tool

Author: jlubken

src/app.py

@@ -6,7 +6,6 @@
 from penn_chime.presentation import (
     display_download_link,
     display_header,
-    display_more_info,
     display_sidebar,
     hide_menu_style,
     write_definitions,
@@ -34,10 +33,6 @@
 
 display_header(st, m, p)
 
-if st.checkbox("Show more info about this tool"):
-    notes = "The total size of the susceptible population will be the entire catchment area for our hospitals."
-    display_more_info(st=st, model=m, parameters=p, defaults=d, notes=notes)
-
 st.subheader("New Admissions")
 st.markdown("Projected number of **daily** COVID-19 admissions. \n\n _NOTE: Now including estimates of prior admissions for comparison._")
 admits_chart = build_admits_chart(alt=alt, admits_floor_df=m.admits_floor_df, max_y_axis=p.max_y_axis)

src/chime_dash/app/components/tool_details.py

@@ -10,7 +10,6 @@
 from chime_dash.app.components.base import Component
 from chime_dash.app.components.footer import Footer
 from chime_dash.app.components.header import Header
-from chime_dash.app.components.tool_details import ToolDetails
 from chime_dash.app.components.intro import Intro
 from chime_dash.app.components.visualizations import Visualizations
 
@@ -50,7 +49,6 @@ def handle_model_change_helper(pars_json):
                     max_y_axis=pars.max_y_axis,
                 )
             result.extend(self.components["intro"].build(model, pars))
-            result.extend(self.components["tool_details"].build(model, pars))
             for df_key in ["admits_df", "census_df", "sim_sir_w_date_df"]:
                 df = None
                 if model:
@@ -59,11 +57,6 @@ def handle_model_change_helper(pars_json):
             return result
 
         super().__init__(language, defaults, [
-            ChimeCallback(  # If user toggles show_additional_projections, show/hide the additional intro content
-                changed_elements=OrderedDict(show_tool_details="value"),
-                dom_updates=OrderedDict(more_intro_wrapper="hidden"),
-                callback_fn=Index.toggle_tool_details
-            ),
             ChimeCallback(  # If user toggles show_tables, show/hide tables
                 changed_elements=OrderedDict(show_tables="value"),
                 dom_updates=OrderedDict(
@@ -77,7 +70,6 @@ def handle_model_change_helper(pars_json):
                 changed_elements=OrderedDict(pars="children"),
                 dom_updates=OrderedDict(
                     intro="children",
-                    more_intro="children",
                     new_admissions_graph="figure",
                     new_admissions_table="children",
                     new_admissions_download="href",
@@ -94,7 +86,6 @@ def handle_model_change_helper(pars_json):
         self.components = OrderedDict(
             header=Header(language, defaults),
             intro=Intro(language, defaults),
-            tool_details=ToolDetails(language, defaults),
             visualizations=Visualizations(language, defaults),
             footer=Footer(language, defaults),
         )
@@ -113,7 +104,6 @@ def get_html(self):
                 children=
                 self.components["header"].html
                 + self.components["intro"].html
-                + self.components["tool_details"].html
             )]
             + self.components["visualizations"].html
             + [Container(

src/chime_dash/app/pages/index.py

@@ -93,7 +93,6 @@
     },
     max_y_axis_value={"type": "number", "min": 10, "step": 10, "value": None},
     show_tables={"type": "switch", "value": False},
-    show_tool_details={"type": "switch", "value": False},
 )
 
 # Different kind of inputs store different kind of "values"

src/chime_dash/app/pages/sidebar.py

@@ -62,8 +62,8 @@ def display_header(st, m, p):
     Penn Medicine to assist hospitals and public health officials with hospital capacity planning.*"""
     )
     st.markdown(
-        """**Notice**: *There is a high 
-    degree of uncertainty about the details of COVID-19 infection, transmission, and the effectiveness of social distancing 
+        """**Notice**: *There is a high
+    degree of uncertainty about the details of COVID-19 infection, transmission, and the effectiveness of social distancing
     measures. Long-term projections made using this simplified model of outbreak progression should be treated with extreme caution.*
     """
     )
@@ -372,103 +372,6 @@ def display_sidebar(st, d: Parameters) -> Parameters:
     )
 
 
-def display_more_info(
-    st, model: Model, parameters: Parameters, defaults: Parameters, notes: str = "",
-):
-    """a lot of streamlit writing to screen."""
-    st.subheader(
-        "[Discrete-time SIR modeling](https://mathworld.wolfram.com/SIRModel.html) of infections/recovery"
-    )
-    st.markdown(
-        """The model consists of individuals who are either _Susceptible_ ($S$), _Infected_ ($I$), or _Recovered_ ($R$).
-
-The epidemic proceeds via a growth and decline process. This is the core model of infectious disease spread and has been in use in epidemiology for many years."""
-    )
-    st.markdown("""The dynamics are given by the following 3 equations.""")
-
-    st.latex("S_{t+1} = (-\\beta S_t I_t) + S_t")
-    st.latex("I_{t+1} = (\\beta S_t I_t - \\gamma I_t) + I_t")
-    st.latex("R_{t+1} = (\\gamma I_t) + R_t")
-
-    st.markdown(
-        """To project the expected impact to Penn Medicine, we estimate the terms of the model.
-
-To do this, we use a combination of estimates from other locations, informed estimates based on logical reasoning, and best guesses from the American Hospital Association.
-
-
-### Parameters
-
-The model's parameters, $\\beta$ and $\\gamma$, determine the virulence of the epidemic.
-
-$$\\beta$$ can be interpreted as the _effective contact rate_:
-"""
-    )
-    st.latex("\\beta = \\tau \\times c")
-
-    st.markdown(
-        """which is the transmissibility ($\\tau$) multiplied by the average number of people exposed ($$c$$).  The transmissibility is the basic virulence of the pathogen.  The number of people exposed $c$ is the parameter that can be changed through social distancing.
-
-
-$\\gamma$ is the inverse of the mean recovery time, in days.  I.e.: if $\\gamma = 1/{recovery_days}$, then the average infection will clear in {recovery_days} days.
-
-An important descriptive parameter is the _basic reproduction number_, or $R_0$.  This represents the average number of people who will be infected by any given infected person.  When $R_0$ is greater than 1, it means that a disease will grow.  Higher $R_0$'s imply more rapid growth.  It is defined as """.format(
-            recovery_days=int(parameters.infectious_days)
-        )
-    )
-    st.latex("R_0 = \\beta /\\gamma")
-
-    st.markdown(
-        """
-
-$R_0$ gets bigger when
-
-- there are more contacts between people
-- when the pathogen is more virulent
-- when people have the pathogen for longer periods of time
-
-A doubling time of {doubling_time} days and a recovery time of {recovery_days} days imply an $R_0$ of {r_naught:.2f}.
-
-#### Effect of social distancing
-
-After the beginning of the outbreak, actions to reduce social contact will lower the parameter $c$.  If this happens at
-time $t$, then the number of people infected by any given infected person is $R_t$, which will be lower than $R_0$.
-
-A {relative_contact_rate:.0%} reduction in social contact would increase the time it takes for the outbreak to double,
-to {doubling_time_t:.2f} days from {doubling_time:.2f} days, with a $R_t$ of {r_t:.2f}.
-
-#### Using the model
-
-We need to express the two parameters $\\beta$ and $\\gamma$ in terms of quantities we can estimate.
-
-- $\\gamma$:  the CDC is recommending 14 days of self-quarantine, we'll use $\\gamma = 1/{recovery_days}$.
-- To estimate $$\\beta$$ directly, we'd need to know transmissibility and social contact rates.  since we don't know these things, we can extract it from known _doubling times_.  The AHA says to expect a doubling time $T_d$ of 7-10 days. That means an early-phase rate of growth can be computed by using the doubling time formula:
-""".format(
-            doubling_time=parameters.doubling_time,
-            recovery_days=parameters.infectious_days,
-            r_naught=model.r_naught,
-            relative_contact_rate=parameters.relative_contact_rate,
-            doubling_time_t=model.doubling_time_t,
-            r_t=model.r_t,
-        )
-    )
-    st.latex("g = 2^{1/T_d} - 1")
-
-    st.markdown(
-        """
-- Since the rate of new infections in the SIR model is $g = \\beta S - \\gamma$, and we've already computed $\\gamma$, $\\beta$ becomes a function of the initial population size of susceptible individuals.
-$$\\beta = (g + \\gamma)$$.
-
-
-### Initial Conditions
-
-- {notes} \n
-""".format(
-            notes=notes
-        )
-    )
-    return None
-
-
 def write_definitions(st):
     st.subheader("Guidance on Selecting Inputs")
     st.markdown(