DATA604/FINAL/FINAL_SUBMISSION.ipynb

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   "source": [
    "# DATA 604 Final Project:  Simulation of COVID-19 Vaccine Administration\n",
    "## *Deepika Dilip*"
   ]
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    "## Introduction\n",
    "In December 2020, hospitals released the first doses of the COVID-19 vaccine. Populations were tiered per demographic criteria, including employment at a major hospital, being an at-risk population, etc. The process became chaotic and was openly mocked, to the point that unused doses were rushed to local prisons to avoid waste. **Here, I simulate a simple COVID-19 vaccine site, inspired by my own experience when getting the vaccine from my employer (a hospital cancer center).**"
   ]
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   "source": [
    "## Methods: Simulation Workflow"
   ]
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   "source": [
    "import simpy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import itertools \n",
    "import matplotlib.pyplot as plt\n"
   ]
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    "The first step here is defining a vaccine clinic that contains all the functions I'll be using. Below is the **VaccineClinic** that contains a parameter for COVID-19 prevalence, along with nurse/staff counts. This is followed by the following processes:\n",
    "\n",
    "* `def_check_in`: Checking in at the front desk\n",
    "* `covid_shot_healthy`: Receiving the shot + waiting afterwards\n",
    "* `covid_shot_risk`: Receiving the shot + waiting afterwards for at-risk populations"
   ]
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   "source": [
    "class VaccineClinic(object):\n",
    "    def __init__(self, env, num_staff, num_nurses, covid_prob):\n",
    "        self.env = env\n",
    "        self.staff = simpy.Resource(env, num_staff)\n",
    "        self.nurse = simpy.Resource(env, num_nurses)\n",
    "        self.covid_prob = covid_prob\n",
    "    def check_in(self, patient):\n",
    "        wait_time = random.randrange(5, 20) # Check-in time\n",
    "        wait_time_total['Patient'].append(patient)\n",
    "        wait_time_total['Total Wait Time'].append(wait_time)  # Storing the front desk wait time\n",
    "        yield self.env.timeout(wait_time)\n",
    "    def covid_shot_risk(self, patient):\n",
    " #       at_risk = random.random() < random.uniform(0, 0.1)\n",
    "        vaccine_time = random.randrange(10, 20)\n",
    "        post_vaccine_time = random.randrange(40, 60)\n",
    "#        print(patient, 'is at risk and is waiting post-vax')\n",
    "        total_time =  vaccine_time + post_vaccine_time # Metrics\n",
    "        yield self.env.timeout(total_time)\n",
    "        time_total_d['Patient'].append(patient)\n",
    "        time_total_d['Total Vax Time'].append(total_time)\n",
    "\n",
    "\n",
    "        print(patient, \"got vaccine in\", total_time, \" minutes\")\n",
    "    def covid_shot_healthy(self, patient):\n",
    "        vaccine_time = random.randrange(10, 20)\n",
    "        post_vaccine_time = random.randrange(10, 20)\n",
    "#        print(patient, 'is not at risk and waiting post-vax')\n",
    "        total_time =  vaccine_time + post_vaccine_time # Metrics\n",
    "        yield self.env.timeout(total_time)\n",
    "        time_total_d['Patient'].append(patient)\n",
    "        time_total_d['Total Vax Time'].append(total_time)\n",
    "        \n",
    "        print(patient, \"got vaccine in a total of\", total_time, \" minutes\")\n",
    "\n"
   ]
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   "source": [
    "Here we define the `patient` who goes through the schema listed later. "
   ]
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    "def patient(env, name, vc):\n",
    "#    covid = random.random() < random.uniform(0, 0.05) \n",
    "    covid = random.random() < random.uniform(0, vc.covid_prob )\n",
    "#    print(str(covid))\n",
    "    \"\"\"Boolean for whether patient  has COVID\"\"\"\n",
    "    at_risk= random.random() < random.uniform(0, 0.22)\n",
    "    \"\"\"Boolean for whether patient is at risk\"\"\"\n",
    "    \n",
    "    with vc.staff.request() as request: # First request at the front desk\n",
    "        yield request\n",
    "#        print(name, 'arrives for check in')\n",
    "        yield env.process(vc.check_in(name))\n",
    "    if covid is True: # COVID patients can't get vaccine\n",
    "        print(name, 'has COVID and rescheduled')\n",
    "        covid_count.append(name)\n",
    "    else:\n",
    "        with vc.nurse.request() as request: # Second request fo a nurse/vaccine\n",
    "            yield request\n",
    "            if at_risk is True:\n",
    "#                print(name, '(at risk) gets shot')\n",
    "                yield env.process(vc.covid_shot_risk(name))  \n",
    "#                print(name, 'leaves')\n",
    "            else:\n",
    "#                print(name, '(not at risk) gets shot')\n",
    "                yield env.process(vc.covid_shot_healthy(name)) \n",
    "#                print(name, 'leaves')"
   ]
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   "source": [
    "Lastly, we set up the environment, starting with two patients and then add two per time interval. "
   ]
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    "def setup(env, num_staff, num_nurses, t_inter, covid_prob):\n",
    "    \"\"\"Initiates two patients to check in and then creates more patients \n",
    "    entering the clinic\"\"\"\n",
    "    # Create the vaccine clinic\n",
    "    vc = VaccineClinic(env, num_staff, num_nurses, covid_prob)\n",
    "\n",
    "    # 2 initial patients\n",
    "    for i in range(2):\n",
    "        env.process(patient(env,  'Patient %d' % i,  vc))\n",
    "\n",
    "    # More patients\n",
    "    while True:\n",
    "        yield env.timeout(random.randint(t_inter - 2, t_inter + 2))\n",
    "        i += 1\n",
    "        env.process(patient(env,  'Patient %d' % i,  vc))\n"
   ]
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   "source": [
    "### Flow Chart\n",
    "In total, the model looks like this:\n",
    "![flowchart](flow_diagram.png)"
   ]
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    "Some points I took into consideration:\n",
    "1. Individuals with COVID-19 **cannot** get the vaccine. Their appointment needs to be rescheduled. This is also where the COVID-19 prevalence of the region kicks in. \n",
    "2. COVID-19 prevalence also affects staff and nurse availability.\n",
    "3. Individuals with co-morbidities will require extra monitoring at the end of their shot.\n"
   ]
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   "source": [
    "## Methods: Verification\n",
    "Let's see if the model works! Here, I'll incorporate the COVID-19 prevalence in the staffing/nurse counts (people will be taking time off, hopefully). Just to see if this is working, I'll use a COVID-19 prevalence of 5%. I'll run this for 480 interations (or an 8 hour workday, because I want to visualize this on a daily basis). I'll also set the time interval for 15 minutes, because appointments are usually scheduled every quarter of the hour.  "
   ]
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     "text": [
      "Patient 1 got vaccine in a total of 34  minutes\n",
      "Patient 0 got vaccine in a total of 31  minutes\n",
      "Patient 2 got vaccine in a total of 30  minutes\n",
      "Patient 4 got vaccine in a total of 36  minutes\n",
      "Patient 6 got vaccine in a total of 29  minutes\n",
      "Patient 3 got vaccine in 69  minutes\n",
      "Patient 7 got vaccine in a total of 24  minutes\n",
      "Patient 8 got vaccine in a total of 25  minutes\n",
      "Patient 5 got vaccine in 61  minutes\n",
      "Patient 9 got vaccine in a total of 28  minutes\n",
      "Patient 10 got vaccine in a total of 28  minutes\n",
      "Patient 11 got vaccine in a total of 27  minutes\n",
      "Patient 12 got vaccine in a total of 24  minutes\n",
      "Patient 13 got vaccine in a total of 31  minutes\n",
      "Patient 15 got vaccine in a total of 26  minutes\n",
      "Patient 16 got vaccine in a total of 27  minutes\n",
      "Patient 14 got vaccine in 68  minutes\n",
      "Patient 17 got vaccine in a total of 28  minutes\n",
      "Patient 18 got vaccine in a total of 22  minutes\n",
      "Patient 19 got vaccine in a total of 34  minutes\n",
      "Patient 20 got vaccine in a total of 26  minutes\n",
      "Patient 21 got vaccine in a total of 24  minutes\n",
      "Patient 23 got vaccine in a total of 22  minutes\n",
      "Patient 22 got vaccine in 56  minutes\n",
      "Patient 24 got vaccine in a total of 33  minutes\n",
      "Patient 25 got vaccine in a total of 33  minutes\n",
      "Patient 26 got vaccine in a total of 32  minutes\n",
      "Patient 27 got vaccine in a total of 27  minutes\n",
      "Patient 28 got vaccine in a total of 30  minutes\n",
      "Patient 29 got vaccine in a total of 21  minutes\n",
      "Patient 30 got vaccine in a total of 28  minutes\n"
     ]
    }
   ],
   "source": [
    "# Parameters\n",
    "STAFF_COUNT = 3  * (1- COVID_PROB) # 3 staffers are usually at the front desk\n",
    "TIME_INTERVAL = 15 # Appointments are usually scheduled every 15 minutes \n",
    "COVID_PROB = 0.05 # COVID-19 prevalence \n",
    "NURSE_COUNT = 15 * (1- COVID_PROB) # Ideally there will be 15 nurses. This number will change per COVID-19 prevalence\n",
    "\n",
    "# Objects we'll use to capture results\n",
    "covid_count = [] # Patients with COVID\n",
    "wait_time_total = {'Patient':[], 'Total Wait Time': []} # Wait Time per patient\n",
    "time_total_d = {'Patient':[], 'Total Vax Time': []} # Vax Time per patient\n",
    "\n",
    "env = simpy.Environment()\n",
    "env.process(setup(env, num_staff = STAFF_COUNT, num_nurses = NURSE_COUNT, t_inter = TIME_INTERVAL, covid_prob = COVID_PROB))\n",
    "env.run(until = 480) \n",
    "# We'll do this for 480 iterations (minutes) since we're looking at a 9-5 workday\n"
   ]
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   "source": [
    "## Methods: COVID-19 at 5%\n",
    "Here, I'll run the simulation, assuming COVID-19 prevalence is at 5%. I'll do this for 30 days, and we can visualize the results"
   ]
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  {
   "cell_type": "code",
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    "%%capture\n",
    "# Next Step, doing this for a month.\n",
    "# Parameters\n",
    "STAFF_COUNT = 3 * (1- COVID_PROB)  # 3 staffers are usually at the front desk\n",
    "TIME_INTERVAL = 15 # Appointments are usually scheduled every 15 minutes \n",
    "COVID_PROB = 0.05 # COVID-19 prevalence \n",
    "NURSE_COUNT = 10 * (1- COVID_PROB) # Ideally there will be 10 nurses. This number will change per COVID-19 prevalence\n",
    "\n",
    "\n",
    "seed_no = range(30)\n",
    "month_total_patients_5 = [] # Total number of patients who arrived at check in per day\n",
    "month_covid_patients_5 = [] # Total number of COVID patients per day\n",
    "month_total_vax_patients_5 = [] # Patients who got vaccinated per day\n",
    "month_mean_vax_time_5 = [] # Mean time it took to get vaccinated per day\n",
    "\n",
    "for i in range(30):\n",
    "    random.seed(seed_no[i])\n",
    "    \n",
    "    # METRICS\n",
    "    covid_count = [] # Patients with COVID\n",
    "    wait_time_total = {'Patient':[], 'Total Wait Time': []} # Wait Time per patient\n",
    "    time_total_d = {'Patient':[], 'Total Vax Time': []} # Vax Time per patient\n",
    "    \n",
    "    # RUN SIMULATION\n",
    "    env = simpy.Environment()\n",
    "    env.process(setup(env, num_staff = STAFF_COUNT, num_nurses = NURSE_COUNT, t_inter = TIME_INTERVAL, covid_prob = COVID_PROB))\n",
    "    env.run(until = 480) \n",
    "    \n",
    "    # STORE DATA\n",
    "    month_total_patients_5.append(len(wait_time_total['Patient']))\n",
    "    month_covid_patients_5.append(len(covid_count))\n",
    "    month_total_vax_patients_5.append(len(time_total_d['Patient']))\n",
    "    month_mean_vax_time_5.append(np.mean(time_total_d['Total Vax Time']))\n",
    "                                \n",
    "    "
   ]
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   "source": [
    "### Patients Vaccinated \n",
    "Here, I'll depict a histogram of patients vaccinated daily at the clinic. The distribution is (somewhat), normal, with a median of 29 patients getting the jab."
   ]
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(month_total_vax_patients_5, density=False, facecolor='r', alpha=0.75)\n",
    "plt.xlabel('Number of Patients')\n",
    "plt.ylabel('Total')\n",
    "plt.title('Histogram of Patients Vaccinated Daily')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "8767ddec-98d8-42a7-ac55-99d152ae4d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.hist(month_mean_vax_time_5, density=False, facecolor='g', alpha=0.75)\n",
    "plt.xlabel('Mean Time per Day')\n",
    "plt.ylabel('Total')\n",
    "plt.title('Histogram of Mean Time for Vaccine Administration')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eac69ad-d976-493b-8028-83f109cfc44a",
   "metadata": {},
   "source": [
    "### Vaccine Administration\n",
    "Here, I'll visualize the average time it took for an individual to get the vaccine (getting the shot and waiting afterwards). Data is skewed right, with most individuals taking ~32 minutes (a couple take longer and are most likely individuals at risk for an adverse reaction later)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ba6419-07e4-4cca-8e2f-b1a6eb669ed0",
   "metadata": {},
   "source": [
    "And to check, here's the number of COVID-19 patients who arrived at the clinic per day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c27999-b62a-4bd0-8725-72c2179ac092",
   "metadata": {},
   "source": [
    "Here is the number of patients who got their shot per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "c96b8718-99bb-438c-af99-2f63be2b950a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(30), month_total_vax_patients_5, color ='blue',\n",
    "        width = 0.4)\n",
    "plt.xlabel(\"Day of the Month\")\n",
    "plt.ylabel(\"No. of Vaccinated Patients\")\n",
    "plt.title(\"Vaccinated Patients per Day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "bbdd7c1b-8cfe-45ce-955a-4b7a787b3e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(30), month_covid_patients_5, color ='maroon',\n",
    "        width = 0.4)\n",
    "plt.xlabel(\"Day of the Month\")\n",
    "plt.ylabel(\"No. of COVID-19 Patients\")\n",
    "plt.title(\"COVID-19 Patients per Day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a20854ab-57a0-4dcc-b241-2558e9928c93",
   "metadata": {},
   "source": [
    "## Methods: COVID-19 at 25%\n",
    "Here, I'll run the simulation, assuming COVID-19 prevalence is at 25%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "4e8c4fcb-3bb5-466c-847d-e394e3303cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# Next Step, doing this for a month.\n",
    "# Parameters\n",
    "STAFF_COUNT = 3 * (1- COVID_PROB) # 3 staffers are usually at the front desk\n",
    "TIME_INTERVAL = 15 # Appointments are usually scheduled every 15 minutes \n",
    "COVID_PROB = 0.25 # COVID-19 prevalence \n",
    "NURSE_COUNT = 10 * (1- COVID_PROB) # Ideally there will be 10 nurses. This number will change per COVID-19 prevalence\n",
    "\n",
    "\n",
    "seed_no = range(60, 90, 1)\n",
    "month_total_patients_25 = [] # Total number of patients who arrived at check in per day\n",
    "month_covid_patients_25 = [] # Total number of COVID patients per day\n",
    "month_total_vax_patients_25 = [] # Patients who got vaccinated per day\n",
    "month_mean_vax_time_25 = [] # Mean time it took to get vaccinated per day\n",
    "\n",
    "for i in range(30):\n",
    "    random.seed(seed_no[i])\n",
    "    \n",
    "    # METRICS\n",
    "    covid_count = [] # Patients with COVID\n",
    "    wait_time_total = {'Patient':[], 'Total Wait Time': []} # Wait Time per patient\n",
    "    time_total_d = {'Patient':[], 'Total Vax Time': []} # Vax Time per patient\n",
    "    \n",
    "    # RUN SIMULATION\n",
    "    env = simpy.Environment()\n",
    "    env.process(setup(env, num_staff = STAFF_COUNT, num_nurses = NURSE_COUNT, t_inter = TIME_INTERVAL, covid_prob = COVID_PROB))\n",
    "    env.run(until = 480) \n",
    "    \n",
    "    # STORE DATA\n",
    "    month_total_patients_25.append(len(wait_time_total['Patient']))\n",
    "    month_covid_patients_25.append(len(covid_count))\n",
    "    month_total_vax_patients_25.append(len(time_total_d['Patient']))\n",
    "    month_mean_vax_time_25.append(np.mean(time_total_d['Total Vax Time']))\n",
    "                                \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de0b141-f1c6-49e7-bed4-60f3b9f847c7",
   "metadata": {},
   "source": [
    "### Plots\n",
    "Here, I replicate the plots. The results are in accordance with the following:\n",
    "* Fewer patients are vaccinated when COVID-19 prevalence is higher\n",
    "* It takes longer to get vaccinated if COVID-19 prevalence is higher (most likely because of staffing shortages)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "30bc0cd2-05bf-4ae1-ab9a-c90468d441f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(month_mean_vax_time_25, density=False, facecolor='g', alpha=0.75)\n",
    "plt.xlabel('Mean Time per Day')\n",
    "plt.ylabel('Total')\n",
    "plt.title('Histogram of Mean Time for Vaccine Administration')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ea9dcf57-b32a-4dd4-b21e-3e7ce9e28e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(month_total_vax_patients_25, density=False, facecolor='r', alpha=0.75)\n",
    "plt.xlabel('Number of Patients')\n",
    "plt.ylabel('Total')\n",
    "plt.title('Histogram of Patients Vaccinated Daily')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "9b440319-ca58-4911-a090-57fc57721ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(30), month_covid_patients_25, color ='maroon',\n",
    "        width = 0.4)\n",
    "plt.xlabel(\"Day of the Month\")\n",
    "plt.ylabel(\"No. of COVID-19 Patients\")\n",
    "plt.title(\"COVID-19 Patients per Day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "56c2b55d-9e4b-46d1-81ba-af31a96e4f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(30), month_total_vax_patients_25, color ='blue',\n",
    "        width = 0.4)\n",
    "plt.xlabel(\"Day of the Month\")\n",
    "plt.ylabel(\"No. of Vaccinated Patients\")\n",
    "plt.title(\"Vaccinated Patients per Day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3cf85b-fa6f-4dd7-bd6b-ab337a9a187c",
   "metadata": {},
   "source": [
    "## Limitations\n",
    "The model has the following limitations\n",
    "\n",
    "* Does not account for adverse responses to the vaccine: these were quite rare, so I excluded it for now. \n",
    "* Vaccine supply is unlimited here. I think this is fair, given the federal expansion of vaccine availability, but this might not be the case in a medically-underserved region.\n",
    "* COVID-19 surveillance: this model takes in COVID-19 prevalence and hits an estimate. As we know, surveillance is flawed, due to municipalities not reporting rapid tests and lag in data reporting.\n",
    "\n",
    "## Discussion\n",
    "* If there’s a high incidence of COVID-19 in the area, more folks are going to be ineligible for the vaccine. This in turn could influence staffing decisions\n",
    "* Real life example of resource scarcity: during the COVID-29 surge in December 2021, hospital staff were redeployed across the cancer center. Nurses were calling in sick. Individuals who were not trained were still sent to serve the front desk of a vaccination site. If vaccine sites administer fewer vaccines since more patients are getting COVID-19, employees should (theoretically) be deployed somewhere else for efficiency."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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