Covid 19 dashboard.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19 Dashboard - Canada\n",
    "\n",
    "### This is a dashboard for visualizing Canadian COVID statistics using the OpenCOVID API.\n",
    "\n",
    "By default, daily new infections and daily deaths will be visualized if data regarding only a specific health region is queried. For data queried at a provincial, territorial, or national level, visualizations concerning hospitalizations and ICU occupancies will also be included. \n",
    "\n",
    "While this is currently the only data visualized, the OpenCOVID API also includes support for additional data at the provincial, territorial, or national level, regarding COVID testing, vaccine administration, and vaccine coverage, with data being separable for each vaccine dose up to and including the fourth. Additional support for the extended data as well as additional data representations such as tables are being considered for this dashboard for future updates. \n",
    "\n",
    "***\n",
    "\n",
    "Requested data can be customized via editing the following four parameters, as follows: \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PARAMS: change these to see different data\n",
    "# All dates must follow the format YYYY-MM-DD\n",
    "\n",
    "geographic_data_level = 'pt'\n",
    "# This can be 'pt' for provincial or territorial, 'hr' for health region, or 'can' for the entirety of Canada. \n",
    "\n",
    "start_date = '2023-11-01'\n",
    "# This is the first day that you want to see data for. Nothing before this date will be shown.\n",
    "\n",
    "end_date = '2023-12-10'\n",
    "# This is the final day that you want to see data for. Nothing after this date will be shown."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final parameter is our location ID. This is either a two letter province or territory abbreviation, or a three or four digit health region code. Please note that hospitalization data is only provided for data at or above the provincial level. In the event that data is only requested for a specific health region, only case and death data are available. This is a limit of the OpenCOVID API, which this notebook uses.   \n",
    "\n",
    "[The lookup table for individual health regions and provincial abbreviations can be found here:](https://github.com/ccodwg/CovidTimelineCanada/blob/main/geo/hr.csv)\n",
    "\n",
    "https://github.com/ccodwg/CovidTimelineCanada/blob/main/geo/hr.csv\n",
    "\n",
    "For health region codes, refer to values under the column `hruid`, and for provincial abbreviations, please reference the values under the column labelled `region`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PARAM: change this to see different data\n",
    "# IMPORTANT: different geographic data levels have different data available and different update frequencies; for example, BC only updates some data weekly, and only for the province as a whole, not for health regions.\n",
    "\n",
    "location_ID = 'ON'\n",
    "\n",
    "# This is either a two letter province or territory abbreviation, or a three or four digit health region code.\n",
    "# Examples would be 'BC' for British Columbia, 'AB' for Alberta, '593' for Vancouver Coastal Health, or '4601' for Winnipeg Regional Health Authority. \n",
    "# If you want to see data for the entirety of Canada, change this to an empty string - ''."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code runs using our provided parameters from above. Let's begin by importing the modules we'll need for our dashboard. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime, json, requests\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's import plotly, as one of the purposes of this notebook is to learn how to use the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "import pandas as pd\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters() # ensures pandas datatypes will work with matplotlib without having to cast them"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can define a few functions that'll be used multiple times for processing data. We'll starting with a `rolling_avg` function that returns a list of rolling averages based on a specified window size, a `format_date_axis` function that'll be used to format the dates on the X-axis of our plots, and a `subquery_data` function that helps us process specific kinds of information returned from our API call. We'll also include `final_value_label` to help us save on repetition when we're annotating our final values for each line plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rolling_avg(data, window_size):\n",
    "    '''returns a list of rolling averages for a given list of data, using a given window size'''\n",
    "    return [sum(data[i-window_size:i])/window_size for i in range(window_size, len(data))] # list comprehension that calculates the rolling average for each day.\n",
    "    # we'll be using this almost exclusively for 7-day rolling averages, but it can be used for any window size.\n",
    "\n",
    "def format_date_axis(ax):\n",
    "    '''format the dates shown on the x-axis of given axes, ax'''\n",
    "    locator = mdates.AutoDateLocator(minticks=10, maxticks=20) # set the number of ticks on the x-axis\n",
    "    formatter = mdates.ConciseDateFormatter(locator) # format the dates shown on the x-axis \n",
    "    ax.xaxis.set_major_locator(locator)\n",
    "    ax.xaxis.set_major_formatter(formatter) \n",
    "\n",
    "def subquery_data(query_type, result_data, value_type):\n",
    "    '''used to gather information for the subqueries for pt and above, such as hospitalizations, ICU counts, and tests.'''\n",
    "    query_data = result_data[query_type] # get the data for the subquery\n",
    "    query_dates = [daily['date'] for daily in query_data] # extract the date strings\n",
    "    n_query = [daily[value_type] for daily in query_data] # extract the data values - whether we want daily or cumulative values\n",
    "    query_datetime_objects = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in query_dates] # convert the date strings to datetime objects\n",
    "    query_dates = [dt.date() for dt in query_datetime_objects] # convert the datetime objects to date objects\n",
    "    # query 7-day rolling averages\n",
    "    if query_type != 'deaths':\n",
    "        # not all provinces and health regions report deaths on a daily basis, so the rolling average would be misleading.\n",
    "        # we'll only generate rolling averages for the other subqueries.\n",
    "        query_rolling = rolling_avg(n_query, 7) # calculate the 7-day rolling average\n",
    "        query_dates_rolling = query_dates[7:] \n",
    "        return query_dates, n_query, query_dates_rolling, query_rolling\n",
    "    else:\n",
    "        # in the event that we ARE looking at deaths, we don't need to return the rolling averages.\n",
    "        return query_dates, n_query\n",
    "\n",
    "def final_value_label(ax, value, color):\n",
    "    '''add a label to the right side of the graph showing the final value'''\n",
    "    ax.annotate(f'{int(value[-1])}', xy=(1, value[-1]), xycoords=('axes fraction', 'data'), ha = 'left', va = 'center', color = color)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our tools ready, we're ready to make our API call using the parameters declared at the start of this notebook. We'll be using basic string concatenation for this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# string concatenation to create the query URL\n",
    "query = 'https://api.opencovid.ca/timeseries?geo=' + geographic_data_level + '&loc=' + location_ID + '&after=' + start_date + '&before=' + end_date\n",
    "# api call uses the default value for stat, returning all available data.\n",
    "# this data isn't as comprehensive when it's requested for a specific HR instead of PT or above, so \n",
    "# the additional data provided for provincial queries and above will be handled behind an if statement.\n",
    "response = requests.request(\"GET\", query, headers={}, data={}) # make the request\n",
    "result = json.loads(response.text) # convert the response to a json object\n",
    "result_data = result['data'] # get the data from the json object\n",
    "\n",
    "# if location_ID is empty, we're looking at Canada as a whole, so now that we're done with our API call, we can set location_ID to 'CAN'\n",
    "if location_ID == '':\n",
    "    location_ID = 'CANADA'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we're ready to go through the raw data with our `subquery_data` function to pick out what exactly we're looking for and plot it with matplotlib. \n",
    "\n",
    "We'll also clarify how many plots we'll be making here: if we're only looking at a health region (`hr`), we'll only have access to data regarding daily infections and daily deaths (and even the deaths might not necessarily be updated daily!) so we'll only need 2 plots. However, if we're looking at data at a provincial level or higher (`pt` or `can`), we'll also have hospitalization and ICU patient numbers - so we'll have a total of 4 plots instead. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if geographic_data_level != 'hr':\n",
    "    # fig, [[ax1, ax2], [ax3, ax4], [ax5, ax6]] = plt.subplots(nrows=3, ncols=2, figsize = [20,30])\n",
    "    # two of the charts have been removed as vaccine coverage is only updated once per month, as is testing data, and it doesn't plot well.\n",
    "    fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(nrows=2, ncols=2, figsize = [20,20]) # create the figure - a 2 x 2 plot of subplots\n",
    "else:\n",
    "    fig, [ax1, ax2] = plt.subplots(nrows=2, figsize = [20,20]) # create the figure and axes - 2 plots in 2 rows. \n",
    "\n",
    "# case data\n",
    "case_dates, n_cases, cases_dates_rolling, cases_rolling = subquery_data('cases', result_data, 'value_daily')\n",
    "format_date_axis(ax1)\n",
    "ax1.plot(case_dates, n_cases, c ='b', label='Daily Cases')  # Plot some data on the axes.\n",
    "ax1.plot(cases_dates_rolling, cases_rolling, c='r', label='7-day Rolling Average of Cases')\n",
    "ax1.set_xlabel('Date')  # Add an x-label to the axes.\n",
    "ax1.set_ylabel('Daily Newly Confirmed Cases')  # Add a y-label to the axes.\n",
    "ax1.set_title(f'Daily COVID-19 Infections for {location_ID}')  # Add a title to the axes.\n",
    "final_value_label(ax1, n_cases, 'b')\n",
    "final_value_label(ax1, cases_rolling, 'r')\n",
    "ax1.legend() # make sure to add the legend to the plot. it won't show up otherwise, even if we've added labels to the lines.\n",
    "\n",
    "# death data\n",
    "death_dates, n_deaths = subquery_data('deaths', result_data, 'value_daily')\n",
    "'''plot deaths as a bar chart using matplotlib'''\n",
    "ax2.bar(death_dates, n_deaths, label='Daily Deaths')  # Plot some data on the axes.\n",
    "format_date_axis(ax2) \n",
    "# the date axis formatting function doesn't actually work particularly well with the bar chart, particularly when it comes to health regions\n",
    "# or provinces that might not update this value daily. it works well for line charts, though, and works well enough for provinces that DO update\n",
    "# daily, so we'll keep it for now. it's likely to get an update in the future.\n",
    "ax2.set_xlabel('Date')  # Add an x-label to the axes.\n",
    "ax2.set_ylabel('Newly Confirmed Deaths')  # Add a y-label to the axes.\n",
    "ax2.set_title(f'COVID-19 Deaths per Day for {location_ID}') \n",
    "ax2.legend()\n",
    "\n",
    "# if we're looking at the provincial level or higher, we can include graphs for additional data as well.\n",
    "# otherwise, if we're only looking at a specific health region, we only need two graphs - one for cases and one for deaths, so this is skipped.\n",
    "if geographic_data_level != 'hr':\n",
    "    # hospitalization data\n",
    "    hosp_dates, n_hosp, hosp_dates_rolling, hosp_rolling = subquery_data('hospitalizations', result_data, 'value')\n",
    "    # plot hospitalizations as a line graph using matplotlib\n",
    "    ax3.plot(hosp_dates, n_hosp, c ='b', label='Actively Hospitalized Patients')  # Plot some data on the axes.\n",
    "    ax3.plot(hosp_dates_rolling, hosp_rolling, c='r', label='7-day Rolling Average of Active Hospitalizations')\n",
    "    format_date_axis(ax3)\n",
    "    ax3.set_xlabel('Date')  # Add an x-label to the axes.\n",
    "    ax3.set_ylabel('Number of Hospitalized Patients')  # Add a y-label to the axes.\n",
    "    ax3.set_title(f'COVID-19 Hospitalizations for {location_ID}')  # Add a title to the axes.\n",
    "    final_value_label(ax3, n_hosp, 'b')\n",
    "    final_value_label(ax3, hosp_rolling, 'r')\n",
    "    ax3.legend()\n",
    "\n",
    "    # ICU data\n",
    "    icu_dates, n_icu, icu_dates_rolling, icu_rolling = subquery_data('icu', result_data, 'value')\n",
    "    # plot ICU as a line graph using matplotlib\n",
    "    ax4.plot(icu_dates, n_icu, c ='b', label='Number of Patients in ICU')  # Plot some data on the axes.\n",
    "    ax4.plot(icu_dates_rolling, icu_rolling, c='r', label='7-day Rolling Average of ICU Occupancies')\n",
    "    format_date_axis(ax4)\n",
    "    ax4.set_xlabel('Date')  # Add an x-label to the axes.\n",
    "    ax4.set_ylabel('Number of Patients in ICU')  # Add a y-label to the axes.\n",
    "    ax4.set_title(f'COVID-19 ICU Occupancies for {location_ID}')  # Add a title to the axes.\n",
    "    final_value_label(ax4, n_icu, 'b')\n",
    "    final_value_label(ax4, icu_rolling, 'r')\n",
    "    ax4.legend()\n",
    "\n",
    "    # vaccine coverage data\n",
    "    # this code works perfectly fine, but it's been removed for now due to the fact that vaccine coverage is only updated once per month.\n",
    "    '''colors = ['b', 'y', 'g', 'r']\n",
    "    for i in range(1,5):\n",
    "        vax_dates, n_vax, vax_dates_rolling, vax_rolling = subquery_data(f'vaccine_coverage_dose_{i}', result_data, 'value')\n",
    "        # plot vaccine coverage as line graphs using matplotlib\n",
    "        ax5.plot(vax_dates, n_vax, c = colors[i-1], label=f'Dose {i}')\n",
    "        ax5.annotate(f'{n_vax[-1]:.2f}', xy=(1,n_vax[-1]), xycoords=('axes fraction', 'data'), \n",
    "                     ha='left', va='center', color=colors[i-1])\n",
    "    ax5.set_xlabel('Date')  # Add an x-label to the axes.\n",
    "    ax5.set_ylabel('Percentage of Population With Dose Administered')  # Add a y-label to the axes.\n",
    "    ax5.set_title(f'COVID-19 Vaccine Coverage for {location_ID}')  # Add a title to the axes.\n",
    "    ax5.legend()'''\n",
    "\n",
    "    # testing data\n",
    "    # same issue as vaccine coverage data - thus not included in the final implementation. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your `geographic_data_level` was `hr`, there should be two graphs presented above. If it was `pt` or `can`, you should be seeing four graphs above. Additional graphs pertaining to vaccination rates and the number of tests being performed have been temporarily disabled, as those values are only updated once per month, and don't graph well. \n",
    "\n",
    "Final values for each line shown on a lineplot are displayed as annotations in their corresponding graphs, and are color coded for ease of use. Additionally, the line graphs are color-coded: the lines representing daily data are shown in `blue`, while the lines representing seven-day rolling averages are displayed in `red`. Hopefully, the added contrast aids in ease of use. Please feel free to contact me with concerns regarding accessibility issues, should any arise, and I'll do my best to hopefully implement a solution as fast as possible. \n",
    "\n",
    "The date axis labels could be improved upon slightly, particularly when displaying data that might not be updated daily (specifically, the death counts - not every province and health region updates this information on a daily basis, and some organizations only provide data once per week. While this isn't as bad as the once-a-month updates such as those for vaccination and testing, it still make the axis label slightly messier than it needs to be. )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anyhow, `matplotlib` is of limited used in our case; we can't see specific values on any given day, and it'd be nice to see finer details and provide some degree of interactivity, so let's take a look at the same data, but visualize it this time with `plotly`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Daily Cases: %{y}<extra></extra>",
         "marker": {
          "color": "blue"
         },
         "mode": "lines+markers",
         "name": "Daily Cases",
         "type": "scatter",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x",
         "y": [
          631,
          523,
          426,
          466,
          317,
          429,
          628,
          593,
          581,
          542,
          365,
          316,
          458,
          652,
          790,
          695,
          700,
          511,
          350,
          719,
          780,
          770,
          651,
          743,
          517,
          452,
          552,
          751,
          769,
          608,
          605,
          492,
          432,
          452,
          681,
          665,
          644,
          736,
          452,
          456
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "7-day Rolling Average of Cases: %{y}<extra></extra>",
         "line": {
          "color": "red"
         },
         "mode": "lines",
         "name": "7-day Rolling Average of Cases",
         "type": "scatter",
         "x": [
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x",
         "y": [
          488.57142857142856,
          483.14285714285717,
          491.42857142857144,
          508,
          493.57142857142856,
          493.42857142857144,
          497.57142857142856,
          501,
          529.1428571428571,
          545.4285714285714,
          568,
          588.8571428571429,
          593.7142857142857,
          631,
          649.2857142857143,
          646.4285714285714,
          640.1428571428571,
          646.2857142857143,
          647.1428571428571,
          661.7142857142857,
          637.8571428571429,
          633.7142857142857,
          633.5714285714286,
          627.4285714285714,
          607.7142857142857,
          604.1428571428571,
          601.2857142857143,
          587,
          577,
          562.1428571428571,
          567.2857142857143,
          586,
          580.2857142857143
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "Daily Deaths: %{y}<extra></extra>",
         "name": "Daily Deaths",
         "type": "bar",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x2",
         "y": [
          11,
          5,
          7,
          4,
          11,
          7,
          7,
          3,
          8,
          5,
          10,
          6,
          7,
          12,
          13,
          8,
          10,
          8,
          9,
          6,
          6,
          9,
          10,
          5,
          6,
          9,
          4,
          7,
          9,
          3,
          6,
          6,
          12,
          2,
          6,
          2,
          10,
          4,
          4,
          3
         ],
         "yaxis": "y2"
        },
        {
         "hovertemplate": "Actively Hospitalized Patients: %{y}<extra></extra>",
         "marker": {
          "color": "blue"
         },
         "mode": "lines+markers",
         "name": "Actively Hospitalized Patients",
         "type": "scatter",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x3",
         "y": [
          1091,
          1077,
          1060,
          1055,
          1127,
          1139,
          1138,
          1166,
          1169,
          1110,
          1176,
          1169,
          1286,
          1251,
          1337,
          1371,
          1402,
          1515,
          1600,
          1627,
          1652,
          1645,
          1731,
          1710,
          1688,
          1816,
          1798,
          1763,
          1786,
          1696,
          1623,
          1660,
          1706,
          1666,
          1619,
          1533,
          1439,
          1470,
          1521,
          1605
         ],
         "yaxis": "y3"
        },
        {
         "hovertemplate": "7-day Rolling Average of ACtive Hospitalizations: %{y}<extra></extra>",
         "line": {
          "color": "red"
         },
         "mode": "lines",
         "name": "7-day Rolling Average of Active Hospitalizations",
         "type": "scatter",
         "x": [
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x3",
         "y": [
          1098.142857142857,
          1108.857142857143,
          1122,
          1129.142857142857,
          1146.4285714285713,
          1152.4285714285713,
          1173.4285714285713,
          1189.5714285714287,
          1214,
          1242.857142857143,
          1284.5714285714287,
          1333,
          1394.5714285714287,
          1443.2857142857142,
          1500.5714285714287,
          1544.5714285714287,
          1596,
          1640,
          1664.7142857142858,
          1695.5714285714287,
          1720,
          1735.857142857143,
          1756,
          1751,
          1738.5714285714287,
          1734.5714285714287,
          1718.857142857143,
          1700,
          1679.4285714285713,
          1643.2857142857142,
          1606.5714285714287,
          1584.7142857142858,
          1564.857142857143
         ],
         "yaxis": "y3"
        },
        {
         "hovertemplate": "Number of Patients in ICU: %{y}<extra></extra>",
         "marker": {
          "color": "blue"
         },
         "mode": "lines+markers",
         "name": "Number of Patients in ICU",
         "type": "scatter",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x4",
         "y": [
          57,
          61,
          57,
          58,
          63,
          67,
          77,
          81,
          84,
          77,
          78,
          81,
          79,
          84,
          84,
          84,
          92,
          91,
          97,
          106,
          103,
          97,
          94,
          90,
          94,
          93,
          102,
          113,
          114,
          114,
          113,
          120,
          120,
          113,
          110,
          100,
          107,
          111,
          109,
          113
         ],
         "yaxis": "y4"
        },
        {
         "hovertemplate": "7-day Rolling Average of ICU Occupancies: %{y}<extra></extra>",
         "line": {
          "color": "red"
         },
         "mode": "lines",
         "name": "7-day Rolling Average of ICU Occupancies",
         "type": "scatter",
         "x": [
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x4",
         "y": [
          62.857142857142854,
          66.28571428571429,
          69.57142857142857,
          72.42857142857143,
          75.28571428571429,
          77.85714285714286,
          79.57142857142857,
          80.57142857142857,
          81,
          81,
          83.14285714285714,
          85,
          87.28571428571429,
          91.14285714285714,
          93.85714285714286,
          95.71428571428571,
          97.14285714285714,
          96.85714285714286,
          97.28571428571429,
          96.71428571428571,
          96.14285714285714,
          97.57142857142857,
          100,
          102.85714285714286,
          106.14285714285714,
          109.85714285714286,
          113.71428571428571,
          115.28571428571429,
          114.85714285714286,
          112.85714285714286,
          111.85714285714286,
          111.57142857142857,
          110
         ],
         "yaxis": "y4"
        }
       ],
       "layout": {
        "annotations": [
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Daily COVID-19 Infections for ON",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 1,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "COVID-19 Deaths per Day for ON",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 1,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "COVID-19 Hospitalizations for ON",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "COVID-19 ICU Occupancies for ON",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         }
        ],
        "height": 1600,
        "showlegend": true,
        "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
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "COVID-19 Data for ON"
        },
        "width": 1600,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          0.45
         ]
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0.55,
          1
         ]
        },
        "xaxis3": {
         "anchor": "y3",
         "domain": [
          0,
          0.45
         ]
        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
          0.55,
          1
         ]
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.625,
          1
         ]
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0.625,
          1
         ]
        },
        "yaxis3": {
         "anchor": "x3",
         "domain": [
          0,
          0.375
         ]
        },
        "yaxis4": {
         "anchor": "x4",
         "domain": [
          0,
          0.375
         ]
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if geographic_data_level != 'hr':\n",
    "    # Create a 2x2 subplot layout for higher geographic levels\n",
    "    fig = make_subplots(rows=2, cols=2, subplot_titles=(f'Daily COVID-19 Infections for {location_ID}',\n",
    "                                                        f'COVID-19 Deaths per Day for {location_ID}',\n",
    "                                                        f'COVID-19 Hospitalizations for {location_ID}',\n",
    "                                                        f'COVID-19 ICU Occupancies for {location_ID}'))\n",
    "else:\n",
    "    # Create a 2x1 subplot layout for health regions\n",
    "    fig = make_subplots(rows=2, cols=1, subplot_titles=(f'Daily COVID-19 Infections for {location_ID}',\n",
    "                                                        f'COVID-19 Deaths per Day for {location_ID}'))\n",
    "    \n",
    "case_dates, n_cases, cases_dates_rolling, cases_rolling = subquery_data('cases', result_data, 'value_daily')\n",
    "\n",
    "# Creating the case data plot\n",
    "fig.add_trace(go.Scatter(x=case_dates, y=n_cases, mode='lines+markers', name='Daily Cases', marker=dict(color='blue'), \n",
    "                         hovertemplate='Daily Cases: %{y}<extra></extra>'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=cases_dates_rolling, y=cases_rolling, mode='lines', name='7-day Rolling Average of Cases', \n",
    "                         line=dict(color='red'), hovertemplate='7-day Rolling Average of Cases: %{y}<extra></extra>'), row=1, col=1)\n",
    "\n",
    "death_dates, n_deaths = subquery_data('deaths', result_data, 'value_daily')\n",
    "\n",
    "# Creating the death data plot\n",
    "fig.add_trace(go.Bar(x=death_dates, y=n_deaths, name='Daily Deaths', hovertemplate='Daily Deaths: %{y}<extra></extra>'), row=1, col=2)\n",
    "\n",
    "if geographic_data_level != 'hr':\n",
    "    hosp_dates, n_hosp, hosp_dates_rolling, hosp_rolling = subquery_data('hospitalizations', result_data, 'value')\n",
    "\n",
    "    # Creating the hospitalization plot\n",
    "    fig.add_trace(go.Scatter(x=hosp_dates, y=n_hosp, mode='lines+markers', name='Actively Hospitalized Patients', marker=dict(color='blue'), \n",
    "                             hovertemplate='Actively Hospitalized Patients: %{y}<extra></extra>'), row=2, col=1)\n",
    "    fig.add_trace(go.Scatter(x=hosp_dates_rolling, y=hosp_rolling, mode='lines', name='7-day Rolling Average of Active Hospitalizations', \n",
    "                             line=dict(color='red'), hovertemplate='7-day Rolling Average of ACtive Hospitalizations: %{y}<extra></extra>'), row=2, col=1)\n",
    "\n",
    "if geographic_data_level != 'hr':\n",
    "    icu_dates, n_icu, icu_dates_rolling, icu_rolling = subquery_data('icu', result_data, 'value')\n",
    "\n",
    "    # Creating the ICU plot\n",
    "    fig.add_trace(go.Scatter(x=icu_dates, y=n_icu, mode='lines+markers', name='Number of Patients in ICU', marker=dict(color='blue'), \n",
    "                             hovertemplate='Number of Patients in ICU: %{y}<extra></extra>'), row=2, col=2)\n",
    "    fig.add_trace(go.Scatter(x=icu_dates_rolling, y=icu_rolling, mode='lines', name='7-day Rolling Average of ICU Occupancies', \n",
    "                             line=dict(color='red'), hovertemplate='7-day Rolling Average of ICU Occupancies: %{y}<extra></extra>'), row=2, col=2)\n",
    "\n",
    "# Update layout if necessary, e.g., for setting a common x-axis title\n",
    "fig.update_layout(height=1600, width=1600, title_text=f\"COVID-19 Data for {location_ID}\", showlegend=True)\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Already, our visualization is looking a lot better, but we can get a *little* more information out of the data we now have. Let's create a forecast for the next 30 days for each of our plots, using ARIMA models for each plotted value. We'll start with just the subplot for daily new infections to begin with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Daily Cases: %{y}<extra></extra>",
         "marker": {
          "color": "blue"
         },
         "mode": "lines+markers",
         "name": "Daily Cases",
         "type": "scatter",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x",
         "y": [
          631,
          523,
          426,
          466,
          317,
          429,
          628,
          593,
          581,
          542,
          365,
          316,
          458,
          652,
          790,
          695,
          700,
          511,
          350,
          719,
          780,
          770,
          651,
          743,
          517,
          452,
          552,
          751,
          769,
          608,
          605,
          492,
          432,
          452,
          681,
          665,
          644,
          736,
          452,
          456
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "7-day Rolling Average of Cases: %{y}<extra></extra>",
         "line": {
          "color": "red"
         },
         "mode": "lines",
         "name": "7-day Rolling Average of Cases",
         "type": "scatter",
         "x": [
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x",
         "y": [
          488.57142857142856,
          483.14285714285717,
          491.42857142857144,
          508,
          493.57142857142856,
          493.42857142857144,
          497.57142857142856,
          501,
          529.1428571428571,
          545.4285714285714,
          568,
          588.8571428571429,
          593.7142857142857,
          631,
          649.2857142857143,
          646.4285714285714,
          640.1428571428571,
          646.2857142857143,
          647.1428571428571,
          661.7142857142857,
          637.8571428571429,
          633.7142857142857,
          633.5714285714286,
          627.4285714285714,
          607.7142857142857,
          604.1428571428571,
          601.2857142857143,
          587,
          577,
          562.1428571428571,
          567.2857142857143,
          586,
          580.2857142857143
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "Daily Deaths: %{y}<extra></extra>",
         "name": "Daily Deaths",
         "type": "bar",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x2",
         "y": [
          11,
          5,
          7,
          4,
          11,
          7,
          7,
          3,
          8,
          5,
          10,
          6,
          7,
          12,
          13,
          8,
          10,
          8,
          9,
          6,
          6,
          9,
          10,
          5,
          6,
          9,
          4,
          7,
          9,
          3,
          6,
          6,
          12,
          2,
          6,
          2,
          10,
          4,
          4,
          3
         ],
         "yaxis": "y2"
        },
        {
         "hovertemplate": "Actively Hospitalized Patients: %{y}<extra></extra>",
         "marker": {
          "color": "blue"
         },
         "mode": "lines+markers",
         "name": "Actively Hospitalized Patients",
         "type": "scatter",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x3",
         "y": [
          1091,
          1077,
          1060,
          1055,
          1127,
          1139,
          1138,
          1166,
          1169,
          1110,
          1176,
          1169,
          1286,
          1251,
          1337,
          1371,
          1402,
          1515,
          1600,
          1627,
          1652,
          1645,
          1731,
          1710,
          1688,
          1816,
          1798,
          1763,
          1786,
          1696,
          1623,
          1660,
          1706,
          1666,
          1619,
          1533,
          1439,
          1470,
          1521,
          1605
         ],
         "yaxis": "y3"
        },
        {
         "hovertemplate": "7-day Rolling Average of ACtive Hospitalizations: %{y}<extra></extra>",
         "line": {
          "color": "red"
         },
         "mode": "lines",
         "name": "7-day Rolling Average of Active Hospitalizations",
         "type": "scatter",
         "x": [
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x3",
         "y": [
          1098.142857142857,
          1108.857142857143,
          1122,
          1129.142857142857,
          1146.4285714285713,
          1152.4285714285713,
          1173.4285714285713,
          1189.5714285714287,
          1214,
          1242.857142857143,
          1284.5714285714287,
          1333,
          1394.5714285714287,
          1443.2857142857142,
          1500.5714285714287,
          1544.5714285714287,
          1596,
          1640,
          1664.7142857142858,
          1695.5714285714287,
          1720,
          1735.857142857143,
          1756,
          1751,
          1738.5714285714287,
          1734.5714285714287,
          1718.857142857143,
          1700,
          1679.4285714285713,
          1643.2857142857142,
          1606.5714285714287,
          1584.7142857142858,
          1564.857142857143
         ],
         "yaxis": "y3"
        },
        {
         "hovertemplate": "Number of Patients in ICU: %{y}<extra></extra>",
         "marker": {
          "color": "blue"
         },
         "mode": "lines+markers",
         "name": "Number of Patients in ICU",
         "type": "scatter",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x4",
         "y": [
          57,
          61,
          57,
          58,
          63,
          67,
          77,
          81,
          84,
          77,
          78,
          81,
          79,
          84,
          84,
          84,
          92,
          91,
          97,
          106,
          103,
          97,
          94,
          90,
          94,
          93,
          102,
          113,
          114,
          114,
          113,
          120,
          120,
          113,
          110,
          100,
          107,
          111,
          109,
          113
         ],
         "yaxis": "y4"
        },
        {
         "hovertemplate": "7-day Rolling Average of ICU Occupancies: %{y}<extra></extra>",
         "line": {
          "color": "red"
         },
         "mode": "lines",
         "name": "7-day Rolling Average of ICU Occupancies",
         "type": "scatter",
         "x": [
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x4",
         "y": [
          62.857142857142854,
          66.28571428571429,
          69.57142857142857,
          72.42857142857143,
          75.28571428571429,
          77.85714285714286,
          79.57142857142857,
          80.57142857142857,
          81,
          81,
          83.14285714285714,
          85,
          87.28571428571429,
          91.14285714285714,
          93.85714285714286,
          95.71428571428571,
          97.14285714285714,
          96.85714285714286,
          97.28571428571429,
          96.71428571428571,
          96.14285714285714,
          97.57142857142857,
          100,
          102.85714285714286,
          106.14285714285714,
          109.85714285714286,
          113.71428571428571,
          115.28571428571429,
          114.85714285714286,
          112.85714285714286,
          111.85714285714286,
          111.57142857142857,
          110
         ],
         "yaxis": "y4"
        },
        {
         "hovertemplate": "Forecasted Cases: %{y}<extra></extra>",
         "line": {
          "color": "green",
          "dash": "dash"
         },
         "mode": "lines",
         "name": "Forecasted Cases",
         "type": "scatter",
         "x": [
          "2023-12-11T00:00:00",
          "2023-12-12T00:00:00",
          "2023-12-13T00:00:00",
          "2023-12-14T00:00:00",
          "2023-12-15T00:00:00",
          "2023-12-16T00:00:00",
          "2023-12-17T00:00:00",
          "2023-12-18T00:00:00",
          "2023-12-19T00:00:00",
          "2023-12-20T00:00:00",
          "2023-12-21T00:00:00",
          "2023-12-22T00:00:00",
          "2023-12-23T00:00:00",
          "2023-12-24T00:00:00",
          "2023-12-25T00:00:00",
          "2023-12-26T00:00:00",
          "2023-12-27T00:00:00",
          "2023-12-28T00:00:00",
          "2023-12-29T00:00:00",
          "2023-12-30T00:00:00",
          "2023-12-31T00:00:00",
          "2024-01-01T00:00:00",
          "2024-01-02T00:00:00",
          "2024-01-03T00:00:00",
          "2024-01-04T00:00:00",
          "2024-01-05T00:00:00",
          "2024-01-06T00:00:00",
          "2024-01-07T00:00:00",
          "2024-01-08T00:00:00",
          "2024-01-09T00:00:00"
         ],
         "xaxis": "x",
         "y": [
          578.9851562943744,
          616.6709904891801,
          623.1214024629835,
          713.2074108726467,
          601.1821032776817,
          504.52329320779637,
          533.8368011826848,
          570.6136832950781,
          584.042936118394,
          659.6162899733513,
          652.4408553841397,
          577.0939226208225,
          549.2358842015977,
          555.974286835972,
          558.2219573598892,
          605.6385150297549,
          641.5236131841358,
          616.7751872788381,
          583.3311160898347,
          569.0548138452995,
          556.5478224385361,
          573.0330898585863,
          609.5102541301737,
          619.3798827312809,
          605.0341449015247,
          589.632940490816,
          571.3438139178884,
          565.7398675648877,
          584.3996540682114,
          603.6339070517208
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "annotations": [
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Daily COVID-19 Infections for ON",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 1,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "COVID-19 Deaths per Day for ON",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 1,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "COVID-19 Hospitalizations for ON",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "COVID-19 ICU Occupancies for ON",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         }
        ],
        "height": 1600,
        "showlegend": true,
        "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
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "COVID-19 Data for ON"
        },
        "width": 1600,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          0.45
         ]
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0.55,
          1
         ]
        },
        "xaxis3": {
         "anchor": "y3",
         "domain": [
          0,
          0.45
         ]
        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
          0.55,
          1
         ]
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.625,
          1
         ]
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0.625,
          1
         ]
        },
        "yaxis3": {
         "anchor": "x3",
         "domain": [
          0,
          0.375
         ]
        },
        "yaxis4": {
         "anchor": "x4",
         "domain": [
          0,
          0.375
         ]
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# begin with converting our case data to a pandas Series\n",
    "cases_series = pd.Series(n_cases, index=pd.DatetimeIndex(case_dates))\n",
    "\n",
    "# explicitly set the frequency of the data to daily\n",
    "cases_series = cases_series.asfreq('D')\n",
    "\n",
    "p, d, q, = 5, 1, 0 # order parameters for the ARIMA model\n",
    "\n",
    "# fit the model to the data\n",
    "model_cases = ARIMA(cases_series, order=(p, d, q)).fit()\n",
    "\n",
    "# generate the forecasted values for the next 30 days\n",
    "forecast_cases = model_cases.forecast(steps=30)\n",
    "# create the date range for the forecasted values\n",
    "forecast_dates = pd.date_range(start=cases_series.index[-1] + pd.Timedelta(days=1), periods=30)\n",
    "\n",
    "# plot the forecasted data as a dashed green line in our first subplot\n",
    "fig.add_trace(go.Scatter(x=forecast_dates, y=forecast_cases, mode='lines', name='Forecasted Cases', \n",
    "                         line=dict(color='green', dash='dash'), hovertemplate='Forecasted Cases: %{y}<extra></extra>'), row=1, col=1)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our forecast for the case count looks pretty good! Now let's try to generate forecasts for the remainder of our subplots, and see what the models might tell us. It's also important to consider the trends in the data prior to selecting the model: for example, I'll keep an ARIMA model for each of the four subplots, but for the final subplot, I'll also create a linear regression model, as we can see from the Nov-Dec data for Ontario, specifically its 7-day rolling average, that there's a continually-increasing trend in ICU occupancies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Daily Cases: %{y}<extra></extra>",
         "marker": {
          "color": "blue"
         },
         "mode": "lines+markers",
         "name": "Daily Cases",
         "type": "scatter",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x",
         "y": [
          631,
          523,
          426,
          466,
          317,
          429,
          628,
          593,
          581,
          542,
          365,
          316,
          458,
          652,
          790,
          695,
          700,
          511,
          350,
          719,
          780,
          770,
          651,
          743,
          517,
          452,
          552,
          751,
          769,
          608,
          605,
          492,
          432,
          452,
          681,
          665,
          644,
          736,
          452,
          456
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "7-day Rolling Average of Cases: %{y}<extra></extra>",
         "line": {
          "color": "red"
         },
         "mode": "lines",
         "name": "7-day Rolling Average of Cases",
         "type": "scatter",
         "x": [
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x",
         "y": [
          488.57142857142856,
          483.14285714285717,
          491.42857142857144,
          508,
          493.57142857142856,
          493.42857142857144,
          497.57142857142856,
          501,
          529.1428571428571,
          545.4285714285714,
          568,
          588.8571428571429,
          593.7142857142857,
          631,
          649.2857142857143,
          646.4285714285714,
          640.1428571428571,
          646.2857142857143,
          647.1428571428571,
          661.7142857142857,
          637.8571428571429,
          633.7142857142857,
          633.5714285714286,
          627.4285714285714,
          607.7142857142857,
          604.1428571428571,
          601.2857142857143,
          587,
          577,
          562.1428571428571,
          567.2857142857143,
          586,
          580.2857142857143
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "Daily Deaths: %{y}<extra></extra>",
         "name": "Daily Deaths",
         "type": "bar",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x2",
         "y": [
          11,
          5,
          7,
          4,
          11,
          7,
          7,
          3,
          8,
          5,
          10,
          6,
          7,
          12,
          13,
          8,
          10,
          8,
          9,
          6,
          6,
          9,
          10,
          5,
          6,
          9,
          4,
          7,
          9,
          3,
          6,
          6,
          12,
          2,
          6,
          2,
          10,
          4,
          4,
          3
         ],
         "yaxis": "y2"
        },
        {
         "hovertemplate": "Actively Hospitalized Patients: %{y}<extra></extra>",
         "marker": {
          "color": "blue"
         },
         "mode": "lines+markers",
         "name": "Actively Hospitalized Patients",
         "type": "scatter",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x3",
         "y": [
          1091,
          1077,
          1060,
          1055,
          1127,
          1139,
          1138,
          1166,
          1169,
          1110,
          1176,
          1169,
          1286,
          1251,
          1337,
          1371,
          1402,
          1515,
          1600,
          1627,
          1652,
          1645,
          1731,
          1710,
          1688,
          1816,
          1798,
          1763,
          1786,
          1696,
          1623,
          1660,
          1706,
          1666,
          1619,
          1533,
          1439,
          1470,
          1521,
          1605
         ],
         "yaxis": "y3"
        },
        {
         "hovertemplate": "7-day Rolling Average of ACtive Hospitalizations: %{y}<extra></extra>",
         "line": {
          "color": "red"
         },
         "mode": "lines",
         "name": "7-day Rolling Average of Active Hospitalizations",
         "type": "scatter",
         "x": [
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x3",
         "y": [
          1098.142857142857,
          1108.857142857143,
          1122,
          1129.142857142857,
          1146.4285714285713,
          1152.4285714285713,
          1173.4285714285713,
          1189.5714285714287,
          1214,
          1242.857142857143,
          1284.5714285714287,
          1333,
          1394.5714285714287,
          1443.2857142857142,
          1500.5714285714287,
          1544.5714285714287,
          1596,
          1640,
          1664.7142857142858,
          1695.5714285714287,
          1720,
          1735.857142857143,
          1756,
          1751,
          1738.5714285714287,
          1734.5714285714287,
          1718.857142857143,
          1700,
          1679.4285714285713,
          1643.2857142857142,
          1606.5714285714287,
          1584.7142857142858,
          1564.857142857143
         ],
         "yaxis": "y3"
        },
        {
         "hovertemplate": "Number of Patients in ICU: %{y}<extra></extra>",
         "marker": {
          "color": "blue"
         },
         "mode": "lines+markers",
         "name": "Number of Patients in ICU",
         "type": "scatter",
         "x": [
          "2023-11-01",
          "2023-11-02",
          "2023-11-03",
          "2023-11-04",
          "2023-11-05",
          "2023-11-06",
          "2023-11-07",
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x4",
         "y": [
          57,
          61,
          57,
          58,
          63,
          67,
          77,
          81,
          84,
          77,
          78,
          81,
          79,
          84,
          84,
          84,
          92,
          91,
          97,
          106,
          103,
          97,
          94,
          90,
          94,
          93,
          102,
          113,
          114,
          114,
          113,
          120,
          120,
          113,
          110,
          100,
          107,
          111,
          109,
          113
         ],
         "yaxis": "y4"
        },
        {
         "hovertemplate": "7-day Rolling Average of ICU Occupancies: %{y}<extra></extra>",
         "line": {
          "color": "red"
         },
         "mode": "lines",
         "name": "7-day Rolling Average of ICU Occupancies",
         "type": "scatter",
         "x": [
          "2023-11-08",
          "2023-11-09",
          "2023-11-10",
          "2023-11-11",
          "2023-11-12",
          "2023-11-13",
          "2023-11-14",
          "2023-11-15",
          "2023-11-16",
          "2023-11-17",
          "2023-11-18",
          "2023-11-19",
          "2023-11-20",
          "2023-11-21",
          "2023-11-22",
          "2023-11-23",
          "2023-11-24",
          "2023-11-25",
          "2023-11-26",
          "2023-11-27",
          "2023-11-28",
          "2023-11-29",
          "2023-11-30",
          "2023-12-01",
          "2023-12-02",
          "2023-12-03",
          "2023-12-04",
          "2023-12-05",
          "2023-12-06",
          "2023-12-07",
          "2023-12-08",
          "2023-12-09",
          "2023-12-10"
         ],
         "xaxis": "x4",
         "y": [
          62.857142857142854,
          66.28571428571429,
          69.57142857142857,
          72.42857142857143,
          75.28571428571429,
          77.85714285714286,
          79.57142857142857,
          80.57142857142857,
          81,
          81,
          83.14285714285714,
          85,
          87.28571428571429,
          91.14285714285714,
          93.85714285714286,
          95.71428571428571,
          97.14285714285714,
          96.85714285714286,
          97.28571428571429,
          96.71428571428571,
          96.14285714285714,
          97.57142857142857,
          100,
          102.85714285714286,
          106.14285714285714,
          109.85714285714286,
          113.71428571428571,
          115.28571428571429,
          114.85714285714286,
          112.85714285714286,
          111.85714285714286,
          111.57142857142857,
          110
         ],
         "yaxis": "y4"
        },
        {
         "hovertemplate": "Forecasted Cases: %{y}<extra></extra>",
         "line": {
          "color": "green",
          "dash": "dash"
         },
         "mode": "lines",
         "name": "Forecasted Cases",
         "type": "scatter",
         "x": [
          "2023-12-11T00:00:00",
          "2023-12-12T00:00:00",
          "2023-12-13T00:00:00",
          "2023-12-14T00:00:00",
          "2023-12-15T00:00:00",
          "2023-12-16T00:00:00",
          "2023-12-17T00:00:00",
          "2023-12-18T00:00:00",
          "2023-12-19T00:00:00",
          "2023-12-20T00:00:00",
          "2023-12-21T00:00:00",
          "2023-12-22T00:00:00",
          "2023-12-23T00:00:00",
          "2023-12-24T00:00:00",
          "2023-12-25T00:00:00",
          "2023-12-26T00:00:00",
          "2023-12-27T00:00:00",
          "2023-12-28T00:00:00",
          "2023-12-29T00:00:00",
          "2023-12-30T00:00:00",
          "2023-12-31T00:00:00",
          "2024-01-01T00:00:00",
          "2024-01-02T00:00:00",
          "2024-01-03T00:00:00",
          "2024-01-04T00:00:00",
          "2024-01-05T00:00:00",
          "2024-01-06T00:00:00",
          "2024-01-07T00:00:00",
          "2024-01-08T00:00:00",
          "2024-01-09T00:00:00"
         ],
         "xaxis": "x",
         "y": [
          578.9851562943744,
          616.6709904891801,
          623.1214024629835,
          713.2074108726467,
          601.1821032776817,
          504.52329320779637,
          533.8368011826848,
          570.6136832950781,
          584.042936118394,
          659.6162899733513,
          652.4408553841397,
          577.0939226208225,
          549.2358842015977,
          555.974286835972,
          558.2219573598892,
          605.6385150297549,
          641.5236131841358,
          616.7751872788381,
          583.3311160898347,
          569.0548138452995,
          556.5478224385361,
          573.0330898585863,
          609.5102541301737,
          619.3798827312809,
          605.0341449015247,
          589.632940490816,
          571.3438139178884,
          565.7398675648877,
          584.3996540682114,
          603.6339070517208
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "Forecasted Deaths: %{y}<extra></extra>",
         "marker": {
          "color": "black"
         },
         "name": "Forecasted Deaths",
         "type": "bar",
         "x": [
          "2023-12-10T00:00:00",
          "2023-12-11T00:00:00",
          "2023-12-12T00:00:00",
          "2023-12-13T00:00:00",
          "2023-12-14T00:00:00",
          "2023-12-15T00:00:00",
          "2023-12-16T00:00:00",
          "2023-12-17T00:00:00",
          "2023-12-18T00:00:00",
          "2023-12-19T00:00:00",
          "2023-12-20T00:00:00",
          "2023-12-21T00:00:00",
          "2023-12-22T00:00:00",
          "2023-12-23T00:00:00",
          "2023-12-24T00:00:00",
          "2023-12-25T00:00:00",
          "2023-12-26T00:00:00",
          "2023-12-27T00:00:00",
          "2023-12-28T00:00:00",
          "2023-12-29T00:00:00",
          "2023-12-30T00:00:00",
          "2023-12-31T00:00:00",
          "2024-01-01T00:00:00",
          "2024-01-02T00:00:00",
          "2024-01-03T00:00:00",
          "2024-01-04T00:00:00",
          "2024-01-05T00:00:00",
          "2024-01-06T00:00:00",
          "2024-01-07T00:00:00",
          "2024-01-08T00:00:00"
         ],
         "xaxis": "x2",
         "y": [
          7.421062968105771,
          4.211541145078195,
          5.088350554513203,
          3.6868734014128317,
          6.081985364627863,
          4.356966271221179,
          5.304562280786946,
          4.140047277014439,
          5.521968069754526,
          4.476377272052489,
          5.250732545165208,
          4.41695169286303,
          5.259406471066518,
          4.581264158318032,
          5.151419221127773,
          4.5830734307091054,
          5.1181013688963315,
          4.665898739094876,
          5.0659247244609675,
          4.684651501716871,
          5.033376486342628,
          4.729213259576905,
          5.003329066829092,
          4.748561448157293,
          4.979215530573849,
          4.774349604935402,
          4.960027897236127,
          4.789778588642539,
          4.943503822744685,
          4.8056318144309325
         ],
         "yaxis": "y2"
        },
        {
         "hovertemplate": "Forecasted Hospitalizations: %{y}<extra></extra>",
         "line": {
          "color": "orange",
          "dash": "dash"
         },
         "mode": "lines",
         "name": "Forecasted Hospitalizations",
         "type": "scatter",
         "x": [
          "2023-12-10T00:00:00",
          "2023-12-11T00:00:00",
          "2023-12-12T00:00:00",
          "2023-12-13T00:00:00",
          "2023-12-14T00:00:00",
          "2023-12-15T00:00:00",
          "2023-12-16T00:00:00",
          "2023-12-17T00:00:00",
          "2023-12-18T00:00:00",
          "2023-12-19T00:00:00",
          "2023-12-20T00:00:00",
          "2023-12-21T00:00:00",
          "2023-12-22T00:00:00",
          "2023-12-23T00:00:00",
          "2023-12-24T00:00:00",
          "2023-12-25T00:00:00",
          "2023-12-26T00:00:00",
          "2023-12-27T00:00:00",
          "2023-12-28T00:00:00",
          "2023-12-29T00:00:00",
          "2023-12-30T00:00:00",
          "2023-12-31T00:00:00",
          "2024-01-01T00:00:00",
          "2024-01-02T00:00:00",
          "2024-01-03T00:00:00",
          "2024-01-04T00:00:00",
          "2024-01-05T00:00:00",
          "2024-01-06T00:00:00",
          "2024-01-07T00:00:00",
          "2024-01-08T00:00:00"
         ],
         "xaxis": "x3",
         "y": [
          1631.5652861626727,
          1644.1797761883072,
          1653.8926968561839,
          1653.290318688631,
          1655.3779432709357,
          1656.2194741804217,
          1655.9606829339814,
          1656.5373893881774,
          1656.481985097044,
          1656.4859437491525,
          1656.5866360014077,
          1656.537845101689,
          1656.5615636593277,
          1656.5687994773677,
          1656.5583103966794,
          1656.5670013820754,
          1656.5645629853207,
          1656.5639761938517,
          1656.5656770933674,
          1656.5645602639372,
          1656.564955378901,
          1656.5650653749444,
          1656.5648378796477,
          1656.5650015261929,
          1656.5649491430233,
          1656.5649349752991,
          1656.5649678591676,
          1656.5649451658785,
          1656.5649526978768,
          1656.5649548090162
         ],
         "yaxis": "y3"
        },
        {
         "hovertemplate": "Forecasted ICU Occupancies (ARIMA): %{y}<extra></extra>",
         "line": {
          "color": "green",
          "dash": "dash"
         },
         "mode": "lines",
         "name": "Forecasted ICU Occupancies (ARIMA)",
         "type": "scatter",
         "x": [
          "2023-12-10T00:00:00",
          "2023-12-11T00:00:00",
          "2023-12-12T00:00:00",
          "2023-12-13T00:00:00",
          "2023-12-14T00:00:00",
          "2023-12-15T00:00:00",
          "2023-12-16T00:00:00",
          "2023-12-17T00:00:00",
          "2023-12-18T00:00:00",
          "2023-12-19T00:00:00",
          "2023-12-20T00:00:00",
          "2023-12-21T00:00:00",
          "2023-12-22T00:00:00",
          "2023-12-23T00:00:00",
          "2023-12-24T00:00:00",
          "2023-12-25T00:00:00",
          "2023-12-26T00:00:00",
          "2023-12-27T00:00:00",
          "2023-12-28T00:00:00",
          "2023-12-29T00:00:00",
          "2023-12-30T00:00:00",
          "2023-12-31T00:00:00",
          "2024-01-01T00:00:00",
          "2024-01-02T00:00:00",
          "2024-01-03T00:00:00",
          "2024-01-04T00:00:00",
          "2024-01-05T00:00:00",
          "2024-01-06T00:00:00",
          "2024-01-07T00:00:00",
          "2024-01-08T00:00:00"
         ],
         "xaxis": "x4",
         "y": [
          109.32475029965096,
          108.65680784716942,
          110.4599015693668,
          108.7699250375057,
          110.57948340269786,
          110.4551151160582,
          109.51807157425154,
          110.44662194643763,
          109.6182303577381,
          109.84745979283171,
          110.25959667380314,
          109.74865681921459,
          110.15733626217447,
          109.99906485002124,
          109.84046890867975,
          110.10918462197033,
          109.89526708417674,
          109.98893694532116,
          110.04536814196581,
          109.91207650113562,
          110.0269322477771,
          109.97306111962733,
          109.95472139144218,
          110.01822606566112,
          109.95708301183745,
          109.9881901706073,
          109.99300002644144,
          109.96348406070607,
          109.99538519108077,
          109.97748786226846
         ],
         "yaxis": "y4"
        },
        {
         "hovertemplate": "Forecasted ICU Occupancies (linreg): %{y}<extra></extra>",
         "line": {
          "color": "orange",
          "dash": "dashdot"
         },
         "mode": "lines",
         "name": "Forecasted ICU Occupancies (linreg)",
         "type": "scatter",
         "x": [
          "2023-12-11",
          "2023-12-12",
          "2023-12-13",
          "2023-12-14",
          "2023-12-15",
          "2023-12-16",
          "2023-12-17",
          "2023-12-18",
          "2023-12-19",
          "2023-12-20",
          "2023-12-21",
          "2023-12-22",
          "2023-12-23",
          "2023-12-24",
          "2023-12-25",
          "2023-12-26",
          "2023-12-27",
          "2023-12-28",
          "2023-12-29",
          "2023-12-30",
          "2023-12-31",
          "2024-01-01",
          "2024-01-02",
          "2024-01-03",
          "2024-01-04",
          "2024-01-05",
          "2024-01-06",
          "2024-01-07",
          "2024-01-08",
          "2024-01-09"
         ],
         "xaxis": "x4",
         "y": [
          122.13846153835766,
          123.59887429629453,
          125.05928705423139,
          126.51969981216826,
          127.98011257010512,
          129.44052532827482,
          130.90093808621168,
          132.36135084414855,
          133.8217636020854,
          135.28217636002228,
          136.74258911795914,
          138.20300187612884,
          139.6634146340657,
          141.12382739200257,
          142.58424014993943,
          144.0446529078763,
          145.50506566581316,
          146.96547842398286,
          148.42589118191972,
          149.8863039398566,
          151.34671669779345,
          152.80712945573032,
          154.26754221366718,
          155.72795497160405,
          157.18836772977374,
          158.6487804877106,
          160.10919324564748,
          161.56960600358434,
          163.0300187615212,
          164.49043151945807
         ],
         "yaxis": "y4"
        }
       ],
       "layout": {
        "annotations": [
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Daily COVID-19 Infections for ON",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 1,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "COVID-19 Deaths per Day for ON",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 1,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "COVID-19 Hospitalizations for ON",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "COVID-19 ICU Occupancies for ON",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         }
        ],
        "height": 1600,
        "showlegend": true,
        "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
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "COVID-19 Data for ON"
        },
        "width": 1600,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          0.45
         ]
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0.55,
          1
         ]
        },
        "xaxis3": {
         "anchor": "y3",
         "domain": [
          0,
          0.45
         ]
        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
          0.55,
          1
         ]
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.625,
          1
         ]
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0.625,
          1
         ]
        },
        "yaxis3": {
         "anchor": "x3",
         "domain": [
          0,
          0.375
         ]
        },
        "yaxis4": {
         "anchor": "x4",
         "domain": [
          0,
          0.375
         ]
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define each data series as a pandas series with a datetime index\n",
    "deaths_series = pd.Series(n_deaths, index=pd.DatetimeIndex(death_dates))\n",
    "hospitalizations_series = pd.Series(n_hosp, index=pd.DatetimeIndex(hosp_dates))\n",
    "ICU_series = pd.Series(n_icu, index=pd.DatetimeIndex(icu_dates))\n",
    "\n",
    "# explicitly set frequency for each series bc it throws a warning otherwise\n",
    "deaths_series = deaths_series.asfreq('D')\n",
    "hospitalizations_series = hospitalizations_series.asfreq('D')\n",
    "ICU_series = ICU_series.asfreq('D')\n",
    "\n",
    "# fit ARIMA model for each data series\n",
    "model_deaths = ARIMA(deaths_series, order=(p,d,q)).fit()\n",
    "model_hosp = ARIMA(hospitalizations_series, order=(p,d,q)).fit()\n",
    "model_ICU = ARIMA(ICU_series, order=(p,d,q)).fit()\n",
    "\n",
    "# calculate the 30-day forecast for each series\n",
    "forecast_deaths = model_deaths.forecast(steps=30)\n",
    "forecast_hosp = model_hosp.forecast(steps=30)\n",
    "forecast_ICU = model_ICU.forecast(steps=30)\n",
    "\n",
    "# date ranges for forrecasted values\n",
    "forecast_dates_deaths = pd.date_range(start=deaths_series.index[-1], periods=30, freq='D')\n",
    "forecast_dates_hosp = pd.date_range(start=hospitalizations_series.index[-1], periods=30, freq='D')\n",
    "forecast_dates_ICU = pd.date_range(start=ICU_series.index[-1], periods=30, freq='D')\n",
    "\n",
    "# new traces for forecast values\n",
    "fig.add_trace(go.Bar(x=forecast_dates_deaths, y=forecast_deaths, name='Forecasted Deaths', marker_color='black', hovertemplate='Forecasted Deaths: %{y}<extra></extra>'), row=1, col=2)\n",
    "fig.add_trace(go.Scatter(x=forecast_dates_hosp, y=forecast_hosp, mode='lines', name='Forecasted Hospitalizations', line=dict(color='orange', dash='dash'), \n",
    "                         hovertemplate='Forecasted Hospitalizations: %{y}<extra></extra>'), row=2, col=1)\n",
    "fig.add_trace(go.Scatter(x=forecast_dates_ICU, y=forecast_ICU, mode='lines', name='Forecasted ICU Occupancies (ARIMA)', line=dict(color='green', dash='dash'), \n",
    "                         hovertemplate='Forecasted ICU Occupancies (ARIMA): %{y}<extra></extra>'), row=2, col=2)\n",
    "\n",
    "# convert dates to ordinal numbers for the linear regression model\n",
    "icu_dates = pd.to_datetime(icu_dates)\n",
    "date_ordinal = np.array([date.toordinal() for date in ICU_series.index]).reshape(-1, 1)\n",
    "icu_values = ICU_series.values\n",
    "\n",
    "# create and fit the linear regression model\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(date_ordinal, icu_values)\n",
    "\n",
    "# lin reg model for the next 30 days of ICU occupancies\n",
    "last_day = ICU_series.index[-1].toordinal()\n",
    "next_30_days_ordinal = np.array([last_day + i for i in range(1, 31)]).reshape(-1, 1)\n",
    "lin_reg_forecast = lin_reg.predict(next_30_days_ordinal)\n",
    "\n",
    "# convert ordinal dates back to datetime to plot\n",
    "forecast_dates_ICU_lin_reg = [datetime.date.fromordinal(int(day)) for day in next_30_days_ordinal.flatten()]\n",
    "\n",
    "# plot the lin reg forecast as a dashed orange line in our final subplot\n",
    "fig.add_trace(go.Scatter(x=forecast_dates_ICU_lin_reg, y=lin_reg_forecast, mode='lines', name='Forecasted ICU Occupancies (linreg)',\n",
    "                        line=dict(color='orange', dash='dashdot'), hovertemplate='Forecasted ICU Occupancies (linreg): %{y}<extra></extra>'), row=2, col=2)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there we have it! While the remainder of our forecasts may not look as 'good' as our forecast for daily infections, it's still interesting to see what each model generates. The linear regression model aligns much more closely with the information we can discern using the last month's 7-day rolling average in terms of ICU occupancies, whereas the ARIMA model generated a roughly flat line for both ICU occupancy as well as hospitalization data - some rather unsettling information can be gleaned from the linear regression model, actually. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "8c2f246d3ce420063ed954338bc2bac546d72c83e6af55e115a8c50b3a9eb1f4"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}