Elective_Recovery.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Welcome\n",
    "\n",
    "This is an example notebook to allow more experienced users to explore the features of ASDM with a worked example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First import packages\n",
    "\n",
    "from ASDM.ASDM import Structure\n",
    "from ASDM.Utilities import plot_time_series\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "pd.options.display.max_columns = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 1) First we re-create the model manually\n",
    "\n",
    "This section creates a model (ASDM Structure object) from scratch and then plots it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is what an ASDM structure object looks like\n",
    "\n",
    "class PatientFlow(Structure):\n",
    "    def __init__(self):\n",
    "        super(PatientFlow, self).__init__()\n",
    "        self.add_stock(\"Waiting_list\", 100, in_flows=['GP_referrals'], out_flows=['Hospital_admission_rate'])\n",
    "        self.add_stock(\"In_hospital\", 30, in_flows=['Hospital_admission_rate'], out_flows=['Treatment_completion_rate'])\n",
    "        self.add_stock(\"In_community_care\", 100, in_flows=['Treatment_completion_rate'], out_flows=['Community_care_leaving_rate'])\n",
    "        \n",
    "        self.add_aux(\"Hospital_bed_capacity\", 30)\n",
    "        self.add_aux(\"Hospital_bed_vacancy\", \"Hospital_bed_capacity-In_hospital\")\n",
    "        self.add_aux(\"p_completion\", 0.1)\n",
    "        self.add_aux(\"p_leaving\", 0.1)\n",
    "\n",
    "        self.add_flow(\"GP_referrals\", 3)\n",
    "        self.add_flow(\n",
    "            \"Hospital_admission_rate\", \n",
    "            \"MIN(Waiting_list, MAX(Treatment_completion_rate, Hospital_bed_vacancy))\",  # mimicing the non-negative stock behaviour\n",
    "            )\n",
    "        self.add_flow(\n",
    "            \"Treatment_completion_rate\", \n",
    "            \"RBINOM(In_hospital, p_completion)\", \n",
    "            )\n",
    "        self.add_flow(\n",
    "            \"Community_care_leaving_rate\", \n",
    "            \"RBINOM(In_community_care, p_leaving)\", \n",
    "            )\n",
    "\n",
    "        # mapping SD functions to Python functions\n",
    "        self.custom_functions['MAX'] = max\n",
    "        self.custom_functions['MIN'] = min\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Object Created \n",
    "patient_flow_model = PatientFlow()\n",
    "\n",
    "# Last run cleared in case this is not a new model\n",
    "patient_flow_model.clear_last_run()\n",
    "\n",
    "# Simulation run with specified simulation time\n",
    "patient_flow_model.simulate(time=20, dt=1)\n",
    "\n",
    "# Now we can export the result as follows into a dictionary\n",
    "df_patient_flow = patient_flow_model.export_simulation_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Waiting_list</th>\n",
       "      <th>GP_referrals</th>\n",
       "      <th>In_hospital</th>\n",
       "      <th>p_completion</th>\n",
       "      <th>Treatment_completion_rate</th>\n",
       "      <th>Hospital_bed_capacity</th>\n",
       "      <th>Hospital_bed_vacancy</th>\n",
       "      <th>Hospital_admission_rate</th>\n",
       "      <th>In_community_care</th>\n",
       "      <th>p_leaving</th>\n",
       "      <th>Community_care_leaving_rate</th>\n",
       "      <th>Weeks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>98.50</td>\n",
       "      <td>0.1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>95.75</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98.25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.50</td>\n",
       "      <td>0.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Waiting_list  GP_referrals  In_hospital  p_completion  \\\n",
       "0        100.00           3.0         30.0           0.1   \n",
       "1         98.75           3.0         30.0           0.1   \n",
       "2         98.75           3.0         30.0           0.1   \n",
       "3         98.25           3.0         30.0           0.1   \n",
       "4         98.75           3.0         30.0           0.1   \n",
       "\n",
       "   Treatment_completion_rate  Hospital_bed_capacity  Hospital_bed_vacancy  \\\n",
       "0                        8.0                   30.0                   0.0   \n",
       "1                        3.0                   30.0                   0.0   \n",
       "2                        5.0                   30.0                   0.0   \n",
       "3                        1.0                   30.0                   0.0   \n",
       "4                        2.0                   30.0                   0.0   \n",
       "\n",
       "   Hospital_admission_rate  In_community_care  p_leaving  \\\n",
       "0                      8.0             100.00        0.1   \n",
       "1                      3.0              98.50        0.1   \n",
       "2                      5.0              95.75        0.1   \n",
       "3                      1.0              96.00        0.1   \n",
       "4                      2.0              94.50        0.1   \n",
       "\n",
       "   Community_care_leaving_rate  Weeks  \n",
       "0                         14.0      0  \n",
       "1                         14.0      1  \n",
       "2                          4.0      2  \n",
       "3                          7.0      3  \n",
       "4                          8.0      4  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To turn it into a dataframe you can do the following\n",
    "\n",
    "dataframe = pd.DataFrame(df_patient_flow)\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Time Series Plotting using the in-built plotter\n",
    "\n",
    "plot_time_series({\n",
    "    \"Waiting_list\": {\"Simulated\": df_patient_flow['Waiting_list']},\n",
    "    \"In_hospital\": {\"Simulated\": df_patient_flow['In_hospital']},\n",
    "    \"In_community_care\": {\"Simulated\": df_patient_flow['In_community_care']}\n",
    "    }, separate_ax=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 2 - Simple Model Compiled from Source, The Revised Goal Gap\n",
    "\n",
    "Imagine you are working in a trust which wants to simulate time to close a backlog of over 76 week waiters. You could do this with a simple goal-gap model.\n",
    "\n",
    "Here we will do this by compiling a model from Stell and plotting the results using plotly\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ENGINE Reading XMILE model from models/Revised_Goal_Gap2.stmx\n"
     ]
    }
   ],
   "source": [
    "# Compile the model from XMILE\n",
    "\n",
    "model = Structure(from_xmile='models/Revised_Goal_Gap2.stmx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clearing the last run and compiling exporting the result as before because the Structure already exists from the compile step\n",
    "\n",
    "model.clear_last_run()\n",
    "model.simulate()\n",
    "results = model.export_simulation_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['>_78_weeks', 'Goal_1', 'Gap_1', 'Adjustment', 'Intervention_Outflow', 'Closed_Long-_Wait_Pathways', 'Months'])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As this is a dictionary we can get the keys like this\n",
    "\n",
    "results.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "\n",
    "We have used plotly here to plot the results. First we convert to a dataframe then plot inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "variable=>_78_weeks<br>Months=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": ">_78_weeks",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": ">_78_weeks",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          1,
          1.25,
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          12
         ],
         "xaxis": "x",
         "y": [
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         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "legend": {
         "title": {
          "text": "Waiting Period"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
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          "barpolar": [
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              "solidity": 0.2
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            "type": "barpolar"
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          "carpet": [
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            "aaxis": {
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             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
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            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
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            "type": "carpet"
           }
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          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
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          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
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            "colorscale": [
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   "source": [
    "# Convert the dict to a dataframe\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "# Now specify the columns directly\n",
    "fig = px.line(df, x='Months', y=[\">_78_weeks\"])\n",
    "\n",
    "# Set up the figure layout\n",
    "fig.update_layout(\n",
    "    title='Treatment Waiting Times',\n",
    "    xaxis_title='Months',\n",
    "    yaxis_title='>_78_weeks',\n",
    "    legend_title='Waiting Period',\n",
    ")\n",
    "\n",
    "# And Plot\n",
    "fig.show()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 3) Hopefully the above was useful. Now we Load an extremely complex model called the Elective Recovery Complex Stella Model using ASDM and plot it\n",
    "\n",
    "You will find the complete dashboard in the .py file which is deployed online using Streamlit but here we just inspect the model after loading it as we did with the goal-gap model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ENGINE Reading XMILE model from models/Elective Recovery Model.stmx\n"
     ]
    }
   ],
   "source": [
    "# Compile the model from XMILE\n",
    "\n",
    "model = Structure(from_xmile='models/Elective Recovery Model.stmx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clearing the last run and compiling exporting the result\n",
    "\n",
    "model.clear_last_run()\n",
    "model.simulate()\n",
    "results = model.export_simulation_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot ASDM simulation DataFrame\n",
    "\n",
    "As before we plot the results but this time we are working in weeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
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   "source": [
    "# Convert to dataframe for easier plotting\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "# Now specify the columns desired in the plot\n",
    "fig = px.line(df, x='Weeks', y=[\"Waiting_more_than_12mths\", \"Waiting_6_to_12mths_for_treatment\", \"Waiting_12_to_24mths_for_treatment\"])\n",
    "\n",
    "# SEt layouts\n",
    "fig.update_layout(\n",
    "    title='Treatment Waiting Times',\n",
    "    xaxis_title='Weeks',\n",
    "    yaxis_title='Number of Patients',\n",
    "    legend_title='Waiting Period',\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, you have done it. This is just the beginning really. We recommend you head over to the Streamlit app to check out what you can do with ASDM and Streamlit: https://electiverecovery.streamlit.app/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Expected_population_rate_of_incidence_pw': '100',\n",
       " 'COVID_period': <ASDM.ASDM.GraphFunc at 0x2464a0bc460>,\n",
       " 'Percent_people_not_presenting_during_COVID': '50',\n",
       " 'Underlying_trend_in_health_needs': <ASDM.ASDM.GraphFunc at 0x2464a0bc730>,\n",
       " 'Period_of_return_in_wks': '156',\n",
       " 'Percent_of_need_referred_for_diagnostics': '80',\n",
       " 'Net_COVID_induced_changes_in_underlying_health_needs?': <ASDM.ASDM.GraphFunc at 0x2464a0bc6d0>,\n",
       " 'Pre_COVID_capacity_for_diagnostics': '20',\n",
       " 'Percent_of_unmet_need_returning': '50',\n",
       " 'Increased_percent_of_need_for_diagnostics_for_COVID_delayed_demand': '10',\n",
       " 'Average_pre_COVID_wait_for_diagnostics': '13',\n",
       " 'Reduced_diagnostic_capacity_during_COVID': '70',\n",
       " 'Average_wait_for_diagnostic_test': 'Waiting_for_diagnostics/Undergoing_diagnostic_tests',\n",
       " 'Percent_increase_in_diagnostic_capacity_post_COVID': '30',\n",
       " 'Timing_of_new_diagnostic_capacity': <ASDM.ASDM.GraphFunc at 0x2464a0bc790>,\n",
       " 'Percent_negative_test_results': '40',\n",
       " 'Pre_COVID_percent_of_positive_tests_urgent': '20',\n",
       " 'COVID_delayed_percent_of_positive_tests_urgent': '50',\n",
       " 'COVID_modified_percent_urgent': '(Pre_COVID_percent_of_positive_tests_urgent*(Proceeding_to_tests_without_COVID_delay/(Delayed_demand_to_wait_for_diagnostics+Proceeding_to_tests_without_COVID_delay)))+(COVID_delayed_percent_of_positive_tests_urgent*(Delayed_demand_to_wait_for_diagnostics/(Delayed_demand_to_wait_for_diagnostics+Proceeding_to_tests_without_COVID_delay)))',\n",
       " 'Total_treatment_capacity': 'IF TIME>New_capacity_week_available AND COVID_switch = 1 THEN (Baseline_treatment_capacity+Post_COVID_increase_in_treatment_capacity)ELSE Baseline_treatment_capacity',\n",
       " 'COVID_switch': '0',\n",
       " 'Switch_for_demographic_increase': '0',\n",
       " 'Total_waiting_for_diagnostics_or_treatment': 'Waiting_6mths_for_treatment+Waiting_6_to_12mths_for_treatment+Waiting_12_to_24mths_for_treatment+Waiting_over_24mths_for_treatment+Waiting_for_diagnostics',\n",
       " 'Post_COVID_increase_in_treatment_capacity': '3',\n",
       " 'Pre_COVID_treatment_capacity': '11',\n",
       " 'New_capacity_week_available': '208',\n",
       " 'Percent_reduction_in_treatment_capacity_during_COVID': '20',\n",
       " 'Baseline_treatment_capacity': 'Pre_COVID_treatment_capacity-(COVID_switch*(COVID_period/100)*((1-(Percent_reduction_in_treatment_capacity_during_COVID/100))*Pre_COVID_treatment_capacity))',\n",
       " 'percent_becoming_urgent_by_waiting_time_pa_lt6': '5',\n",
       " 'percent_becoming_urgent_by_waiting_time_pa_6to12': '10',\n",
       " 'percent_becoming_urgent_by_waiting_time_pa_12to24': '15',\n",
       " 'percent_becoming_urgent_by_waiting_time_pa_gt24': '20'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you want to change the aux equations you can find them here inside the model and re-assign them as we do with the app\n",
    "\n",
    "model.aux_equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hopefully this has been a helpful introduction to ASDM. Now you can go and start building your own ASDM/Streamlit models to deploy :)\n",
    "\n",
    "Good luck and hopefully catch you again soon"
   ]
  }
 ],
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