jessen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EGR 305/ECON 211 Engineering Systems Optimization and Economy\n",
    "## Final Project Code\n",
    "> Team: Adway, Chenglin, Yutong\n",
    "> \n",
    "> Code Author: Chenglin\n",
    "> \n",
    "> NetID: cz155\n",
    "> \n",
    "> Date: 4th Dec., 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Assume one QALY worths $100,000\n",
    "\n",
    "# the inputs are \n",
    "# 1. willingness_(vaccine brand)_(which dose)\n",
    "# 2. different_outcome_(age group): the percentage of people will have mild symptoms, be hospitalized, and be dead\n",
    "# 3. (vaccine brand)_(age group)_transition_(which dose): the percentage of people will go to the less severe case if they were to get vaccinated\n",
    "# 4. QALY_loss_(age group): the loss in QALY (based on how severe the outcome is)\n",
    "\n",
    "# the output is\n",
    "# QALY_(vaccine brand)_(age group)_(region)\n",
    "# utility_QALY_(vaccine brand)_(age group)_(region)\n",
    "\n",
    "population_A = [0.23, 0.57, 0.10, 0.07, 0.03]\n",
    "\n",
    "QALY = 100000\n",
    "\n",
    "\n",
    "# 0-19, 20-59, 60-69, 70-79, 80+\n",
    "# based on the other regions' data\n",
    "\n",
    "# here is the data for 5 age groups' people's willingness to get vaccinated (one dose, two dose (if applicable), not vaccinated)\n",
    "\n",
    "willingness_pfizer_first_dose = np.array([0.05, 0.2, 0.08, 0.11, 0.15])\n",
    "willingness_pfizer_second_dose = np.array([0.8, 0.6, 0.87, 0.8, 0.62])\n",
    "willingness_pfizer_no_dose = 1 - willingness_pfizer_first_dose - willingness_pfizer_second_dose\n",
    "\n",
    "# x = np.array([i for i in range(1, len(willingness_pfizer_first_dose) + 1)])\n",
    "age_group = ['0-19', '20-59', '60-69', '70-79', '80+']\n",
    "\n",
    "willingness_oxford_first_dose = []\n",
    "willingness_oxford_second_dose = []\n",
    "\n",
    "willingness_jenssen_first_dose = []\n",
    "\n",
    "willingness_sinovac_first_dose = []\n",
    "willingness_sinovac_second_dose = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the willingness to get vaccinated for Pfizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def utility_of_QALY(QALY):\n",
    "    return np.sqrt(QALY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_expectation(ageGroup_index, willingness_vaccineBrand_whichDose, different_outcome_ageGroup, QALY_loss_ageGroup, vaccineBrand_ageGroup_transition_whichDose):\n",
    "    if len(willingness_vaccineBrand_whichDose) == 3:\n",
    "        result = \\\n",
    "        willingness_vaccineBrand_whichDose[0][ageGroup_index] * (different_outcome_ageGroup[0] * QALY_loss_ageGroup[0] + \n",
    "                                        different_outcome_ageGroup[1] * QALY_loss_ageGroup[1] + \n",
    "                                        different_outcome_ageGroup[2] * QALY_loss_ageGroup[2]) + \\\n",
    "        \\\n",
    "        willingness_vaccineBrand_whichDose[1][ageGroup_index] * (different_outcome_ageGroup[0] * vaccineBrand_ageGroup_transition_whichDose[0][0] * 1 + \n",
    "                                            different_outcome_ageGroup[0] * (1 - vaccineBrand_ageGroup_transition_whichDose[0][0]) * QALY_loss_ageGroup[0] + \n",
    "                                            different_outcome_ageGroup[1] * vaccineBrand_ageGroup_transition_whichDose[0][1] * QALY_loss_ageGroup[0] + \n",
    "                                            different_outcome_ageGroup[1] * (1 - vaccineBrand_ageGroup_transition_whichDose[0][1]) * QALY_loss_ageGroup[1] + \n",
    "                                            different_outcome_ageGroup[2] * vaccineBrand_ageGroup_transition_whichDose[0][2] * QALY_loss_ageGroup[1] + \n",
    "                                            different_outcome_ageGroup[2] * (1 - vaccineBrand_ageGroup_transition_whichDose[0][2]) * QALY_loss_ageGroup[2]) + \\\n",
    "        \\\n",
    "        willingness_vaccineBrand_whichDose[2][ageGroup_index] * (different_outcome_ageGroup[0] * vaccineBrand_ageGroup_transition_whichDose[1][0] * 1 + \n",
    "                                            different_outcome_ageGroup[0] * (1 - vaccineBrand_ageGroup_transition_whichDose[1][0]) * QALY_loss_ageGroup[0] + \n",
    "                                            different_outcome_ageGroup[1] * vaccineBrand_ageGroup_transition_whichDose[1][1] * QALY_loss_ageGroup[0] + \n",
    "                                            different_outcome_ageGroup[1] * (1 - vaccineBrand_ageGroup_transition_whichDose[1][1]) * QALY_loss_ageGroup[1] + \n",
    "                                            different_outcome_ageGroup[2] * vaccineBrand_ageGroup_transition_whichDose[1][2] * QALY_loss_ageGroup[1] + \n",
    "                                            different_outcome_ageGroup[2] * (1 - vaccineBrand_ageGroup_transition_whichDose[1][2]) * QALY_loss_ageGroup[2])\n",
    "    else:\n",
    "        result = \\\n",
    "        willingness_vaccineBrand_whichDose[0][ageGroup_index] * (different_outcome_ageGroup[0] * QALY_loss_ageGroup[0] + \n",
    "                                        different_outcome_ageGroup[1] * QALY_loss_ageGroup[1] + \n",
    "                                        different_outcome_ageGroup[2] * QALY_loss_ageGroup[2]) + \\\n",
    "        \\\n",
    "        willingness_vaccineBrand_whichDose[1][ageGroup_index] * (different_outcome_ageGroup[0] * vaccineBrand_ageGroup_transition_whichDose[0][0] * 1 + \n",
    "                                            different_outcome_ageGroup[0] * (1 - vaccineBrand_ageGroup_transition_whichDose[0][0]) * QALY_loss_ageGroup[0] + \n",
    "                                            different_outcome_ageGroup[1] * vaccineBrand_ageGroup_transition_whichDose[0][1] * QALY_loss_ageGroup[0] + \n",
    "                                            different_outcome_ageGroup[1] * (1 - vaccineBrand_ageGroup_transition_whichDose[0][1]) * QALY_loss_ageGroup[1] + \n",
    "                                            different_outcome_ageGroup[2] * vaccineBrand_ageGroup_transition_whichDose[0][2] * QALY_loss_ageGroup[1] + \n",
    "                                            different_outcome_ageGroup[2] * (1 - vaccineBrand_ageGroup_transition_whichDose[0][2]) * QALY_loss_ageGroup[2])\n",
    "    if_not_vaccinated = different_outcome_ageGroup[0] * QALY_loss_ageGroup[0] + \\\n",
    "                        different_outcome_ageGroup[1] * QALY_loss_ageGroup[1] + \\\n",
    "                        different_outcome_ageGroup[2] * QALY_loss_ageGroup[2]\n",
    "    return utility_of_QALY(result), utility_of_QALY(if_not_vaccinated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pfizer willingness\n",
    "\n",
    "willingness_first_dose = np.array([0, 0.70, 0.75, 0.60, 0.55])\n",
    "willingness_no_dose = 1 - willingness_first_dose\n",
    "willingness_vaccineBrand_whichDose = [willingness_no_dose, willingness_first_dose]\n",
    "\n",
    "plt.stackplot(age_group, willingness_first_dose, \n",
    "              willingness_no_dose, labels = ['one dose', 'not vaccinated'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Percentage * 1')\n",
    "plt.title(\"People's willingness to get the Jessen vaccine\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:00<00:00, 146662.70it/s]\n"
     ]
    }
   ],
   "source": [
    "# Pfizer 0-19\n",
    "\n",
    "different_outcome_ageGroup = [0.99999, 0.000009, 0.000001] # children are less influenced by COVID\n",
    "QALY_loss_ageGroup = [0.92, 0.75, 0]\n",
    "\n",
    "# Pfizer seems to be more effective on children\n",
    "\n",
    "MC = 10000\n",
    "pfizer_0_19 = []\n",
    "\n",
    "for i in tqdm(range(0, MC)):\n",
    "    vaccineBrand_ageGroup_transition_whichDose = [[np.random.triangular(0.2, 0.3, 0.4), np.random.triangular(0.5, 0.58, 0.65), np.random.triangular(0.993, 0.999, 1)]]\n",
    "\n",
    "    this_pfizer_0_19, this_non_0_19 = calculate_expectation(0, willingness_vaccineBrand_whichDose, different_outcome_ageGroup, QALY_loss_ageGroup, vaccineBrand_ageGroup_transition_whichDose)\n",
    "    pfizer_0_19.append(this_pfizer_0_19)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pfizer_0_19)\n",
    "plt.xlabel('Utility of QUALY for 0_19 (age), Pfizer Vaccine')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of E(U(QUALY)) for 0_19 (age), Pfizer Vaccine')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9591650275109076\n",
      "0.9815517096804868\n",
      "4344.620877668648\n"
     ]
    }
   ],
   "source": [
    "print(this_non_0_19)\n",
    "print(np.mean(pfizer_0_19))\n",
    "QALY_gained_0_19 = QALY * (np.mean(pfizer_0_19) ** 2 - this_non_0_19 ** 2)\n",
    "print(QALY_gained_0_19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:00<00:00, 111006.24it/s]\n"
     ]
    }
   ],
   "source": [
    "# Pfizer 20-59\n",
    "\n",
    "willingness_vaccineBrand_whichDose = [willingness_no_dose, willingness_first_dose]\n",
    "different_outcome_ageGroup = [0.9999, 0.00009, 0.00001]\n",
    "QALY_loss_ageGroup = [0.95, 0.8, 0]\n",
    "\n",
    "MC = 10000\n",
    "pfizer_20_59 = []\n",
    "\n",
    "for i in tqdm(range(0, MC)):\n",
    "    vaccineBrand_ageGroup_transition_whichDose = [[np.random.triangular(0.3, 0.4, 0.5), np.random.triangular(0.6, 0.68, 0.75), np.random.triangular(0.99, 0.995, 1)], \n",
    "                                                [np.random.triangular(0.5, 0.6, 0.7), np.random.triangular(0.9, 0.95, 1), np.random.triangular(0.999, 0.9999, 1)]]\n",
    "\n",
    "    this_pfizer_20_59, this_non_20_59 = calculate_expectation(1, willingness_vaccineBrand_whichDose, different_outcome_ageGroup, QALY_loss_ageGroup, vaccineBrand_ageGroup_transition_whichDose)\n",
    "    pfizer_20_59.append(this_pfizer_20_59)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pfizer_20_59)\n",
    "plt.xlabel('Utility of QUALY for 20-59 (age), Jessen Vaccine')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of E(U(QUALY)) for 20-59 (age), Jessen Vaccine')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9746676356584331\n",
      "0.9859023624380183\n",
      "2202.6468260865518\n"
     ]
    }
   ],
   "source": [
    "print(this_non_20_59)\n",
    "print(np.mean(pfizer_20_59))\n",
    "QALY_gained_20_59 = QALY * (np.mean(pfizer_20_59) ** 2 - this_non_20_59 ** 2)\n",
    "print(QALY_gained_20_59)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'willingness_second_dose' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-99f6b419667d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Pfizer 60-69\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mwillingness_vaccineBrand_whichDose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mwillingness_no_dose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwillingness_first_dose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwillingness_second_dose\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdifferent_outcome_ageGroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.92\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0799\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0001\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mQALY_loss_ageGroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.82\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'willingness_second_dose' is not defined"
     ]
    }
   ],
   "source": [
    "# Pfizer 60-69\n",
    "\n",
    "willingness_vaccineBrand_whichDose = [willingness_no_dose, willingness_first_dose]\n",
    "different_outcome_ageGroup = [0.92, 0.0799, 0.0001]\n",
    "QALY_loss_ageGroup = [0.82, 0.6, 0]\n",
    "\n",
    "MC = 10000\n",
    "pfizer_60_69 = []\n",
    "\n",
    "for i in tqdm(range(0, MC)):\n",
    "    vaccineBrand_ageGroup_transition_whichDose = [[np.random.triangular(0.4, 0.5, 0.6), np.random.triangular(0.68, 0.78, 0.85), np.random.triangular(0.82, 0.89, 1)], \n",
    "                                                [np.random.triangular(0.6, 0.7, 0.8), np.random.triangular(0.84, 0.92, 1), np.random.triangular(0.86, 0.94, 1)]]\n",
    "\n",
    "    this_pfizer_60_69, this_non_60_69 = calculate_expectation(2, willingness_vaccineBrand_whichDose, different_outcome_ageGroup, QALY_loss_ageGroup, vaccineBrand_ageGroup_transition_whichDose)\n",
    "    pfizer_60_69.append(this_pfizer_60_69)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pfizer_60_69)\n",
    "plt.xlabel('Utility of QUALY for 60-69 (age), Jessen Vaccine')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of E(U(QUALY)) for 60-69 (age), Jessen Vaccine')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8957343356151979\n",
      "0.9617756183636662\n",
      "12267.234007881256\n"
     ]
    }
   ],
   "source": [
    "print(this_non_60_69)\n",
    "print(np.mean(pfizer_60_69))\n",
    "QALY_gained_60_69 = QALY * (np.mean(pfizer_60_69) ** 2 - this_non_60_69 ** 2)\n",
    "print(QALY_gained_60_69)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:00<00:00, 96197.43it/s]\n"
     ]
    }
   ],
   "source": [
    "# Pfizer 70-79\n",
    "\n",
    "willingness_vaccineBrand_whichDose = [willingness_no_dose, willingness_first_dose, willingness_second_dose]\n",
    "different_outcome_ageGroup = [0.84, 0.159, 0.001]\n",
    "QALY_loss_ageGroup = [0.76, 0.54, 0]\n",
    "\n",
    "MC = 10000\n",
    "pfizer_70_79 = []\n",
    "\n",
    "for i in tqdm(range(0, MC)):\n",
    "    vaccineBrand_ageGroup_transition_whichDose = [[np.random.triangular(0.4, 0.5, 0.6), np.random.triangular(0.68, 0.78, 0.85), np.random.triangular(0.82, 0.89, 1)], \n",
    "                                                [np.random.triangular(0.7, 0.8, 0.9), np.random.triangular(0.84, 0.92, 1), np.random.triangular(0.89, 0.97, 1)]]\n",
    "\n",
    "    this_pfizer_70_79, this_non_70_79 = calculate_expectation(3, willingness_vaccineBrand_whichDose, different_outcome_ageGroup, QALY_loss_ageGroup, vaccineBrand_ageGroup_transition_whichDose)\n",
    "    pfizer_70_79.append(this_pfizer_70_79)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pfizer_70_79)\n",
    "plt.xlabel('Utility of QUALY for 70-79 (age), Pfizer Vaccine')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of E(U(QUALY)) for 70-79 (age), Pfizer Vaccine')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8510346643938776\n",
      "0.9452831210628625\n",
      "16930.017896634632\n"
     ]
    }
   ],
   "source": [
    "print(this_non_70_79)\n",
    "print(np.mean(pfizer_70_79))\n",
    "QALY_gained_70_79 = QALY * (np.mean(pfizer_70_79) ** 2 - this_non_70_79 ** 2)\n",
    "print(QALY_gained_70_79)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:00<00:00, 87895.71it/s]\n"
     ]
    }
   ],
   "source": [
    "# Pfizer 80+\n",
    "\n",
    "willingness_vaccineBrand_whichDose = [willingness_no_dose, willingness_first_dose, willingness_second_dose]\n",
    "different_outcome_ageGroup = [0.75, 0.23, 0.02]\n",
    "QALY_loss_ageGroup = [0.7, 0.4, 0]\n",
    "\n",
    "MC = 10000\n",
    "pfizer_80 = []\n",
    "\n",
    "for i in tqdm(range(0, MC)):\n",
    "    vaccineBrand_ageGroup_transition_whichDose = [[np.random.triangular(0.4, 0.5, 0.6), np.random.triangular(0.68, 0.78, 0.85), np.random.triangular(0.82, 0.89, 1)], \n",
    "                                                [np.random.triangular(0.6, 0.7, 0.8), np.random.triangular(0.84, 0.92, 1), np.random.triangular(0.86, 0.94, 1)]]\n",
    "\n",
    "    this_pfizer_80, this_non_80 = calculate_expectation(4, willingness_vaccineBrand_whichDose, different_outcome_ageGroup, QALY_loss_ageGroup, vaccineBrand_ageGroup_transition_whichDose)\n",
    "    pfizer_80.append(this_pfizer_80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pfizer_80)\n",
    "plt.xlabel('Utility of QUALY for 80 (age), Pfizer Vaccine')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of E(U(QUALY)) for 80 (age), Pfizer Vaccine')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7854934754662192\n",
      "0.885755264393034\n",
      "16756.23883999736\n"
     ]
    }
   ],
   "source": [
    "print(this_non_80)\n",
    "print(np.mean(pfizer_80))\n",
    "QALY_gained_80 = QALY * (np.mean(pfizer_80) ** 2 - this_non_80 ** 2)\n",
    "print(QALY_gained_80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the analysis above, we know that the absolute gain for the elderly is higher than other age groups. Now, we want to calculate the implementation costs for the vaccine and see which one has the highest net expected return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4941.357488747043\n"
     ]
    }
   ],
   "source": [
    "# The weighted unit gain of QALY in dollars\n",
    "\n",
    "# if we consider the side effects here\n",
    "\n",
    "side_effects = []\n",
    "QALY_gain = [0, QALY_gained_20_59, QALY_gained_60_69, QALY_gained_70_79, QALY_gained_80]\n",
    "weighted_QALY_gain = 0\n",
    "for i in range(len(population_A)):\n",
    "    weighted_QALY_gain += QALY_gain[i] * population_A[i]\n",
    "print(weighted_QALY_gain)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.5",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}