{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrvIX6l6lofx"
      },
      "source": [
        "# Dynamics of Disease Transmission and Human Behavior Project\n",
        "\n",
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1A_lBlsY0os2",
        "outputId": "7a10e28a-98ec-4084-f293-58e9cd070a57"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Vqhj-JCC01QL"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.impute import KNNImputer\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "A1f0gRL5xigQ"
      },
      "outputs": [],
      "source": [
        "california = pd.read_csv(\"./drive/MyDrive/CS109 Project/state_level_data/California.csv\",parse_dates=['date'],index_col=['date'])\n",
        "new_york = pd.read_csv(\"./drive/MyDrive/CS109 Project/state_level_data/New York.csv\",parse_dates=['date'],index_col=['date'])\n",
        "ma = pd.read_csv(\"./drive/MyDrive/CS109 Project/state_level_data/Massachusetts.csv\",parse_dates=['date'],index_col=['date'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHWS9uib1VUu"
      },
      "source": [
        "## Data Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdYKoNOc2Bt3"
      },
      "source": [
        "Remove the final 5 rows because the missing cases don't make sense."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emQJevYT1Q74",
        "outputId": "619076b1-f3b5-4733-a3b4-1dd01fe63a53"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "            JHU_cases  JHU_deaths  JHU_hospitalizations  up2date  \\\n",
            "date                                                               \n",
            "2022-01-04    76786.0        94.0                1679.0      NaN   \n",
            "2022-01-05    39356.0       126.0                1768.0      NaN   \n",
            "2022-01-06    86590.0        32.0                1950.0      NaN   \n",
            "2022-01-07   108391.0       234.0                1950.0      NaN   \n",
            "2022-01-08    34364.0        17.0                1973.0      NaN   \n",
            "2022-01-09        NaN         NaN                   NaN      NaN   \n",
            "2022-01-10        NaN         NaN                   NaN      NaN   \n",
            "2022-01-11        NaN         NaN                   NaN      NaN   \n",
            "2022-01-12        NaN         NaN                   NaN      NaN   \n",
            "2022-01-13        NaN         NaN                   NaN      NaN   \n",
            "\n",
            "            gt_after covid vaccine  gt_side effects of vaccine  \\\n",
            "date                                                             \n",
            "2022-01-04              606.538018                   76.071071   \n",
            "2022-01-05              893.467782                  149.409817   \n",
            "2022-01-06             1421.963705                  225.272227   \n",
            "2022-01-07             1297.019835                  153.101406   \n",
            "2022-01-08              572.753638                  246.287607   \n",
            "2022-01-09             1036.271881                   79.980083   \n",
            "2022-01-10              501.666380                  359.532743   \n",
            "2022-01-11              737.261085                  221.918779   \n",
            "2022-01-12              978.821343                  151.092032   \n",
            "2022-01-13              984.362561                   75.973690   \n",
            "\n",
            "            gt_effects of covid vaccine       gt_covid  \\\n",
            "date                                                     \n",
            "2022-01-04                   227.642128  265601.963427   \n",
            "2022-01-05                    74.517966  279350.860227   \n",
            "2022-01-06                    74.902834  255423.889506   \n",
            "2022-01-07                   229.077425  247917.601107   \n",
            "2022-01-08                   163.780862  224845.931548   \n",
            "2022-01-09                     0.000000  191709.747309   \n",
            "2022-01-10                   215.179826  222023.793840   \n",
            "2022-01-11                    73.787815  224041.563172   \n",
            "2022-01-12                   150.713937  204037.539213   \n",
            "2022-01-13                     0.000000  189624.711109   \n",
            "\n",
            "            gt_how long does covid last  gt_anosmia  ...  \\\n",
            "date                                                 ...   \n",
            "2022-01-04                  1218.650070   76.101725  ...   \n",
            "2022-01-05                  1346.359843   74.735013  ...   \n",
            "2022-01-06                   977.393073  150.242004  ...   \n",
            "2022-01-07                  1762.854735    0.000000  ...   \n",
            "2022-01-08                   904.177084   82.128952  ...   \n",
            "2022-01-09                   880.874506    0.000000  ...   \n",
            "2022-01-10                   719.959308    0.000000  ...   \n",
            "2022-01-11                  1185.038030    0.000000  ...   \n",
            "2022-01-12                   453.839532    0.000000  ...   \n",
            "2022-01-13                  1141.021922    0.000000  ...   \n",
            "\n",
            "            neighbor_South Dakota  neighbor_Tennessee  neighbor_Texas  \\\n",
            "date                                                                    \n",
            "2022-01-04                 3047.0              9230.0         53543.0   \n",
            "2022-01-05                 2254.0                 0.0         47266.0   \n",
            "2022-01-06                 2183.0                 0.0         45375.0   \n",
            "2022-01-07                 1944.0                 0.0         52571.0   \n",
            "2022-01-08                    0.0                 0.0         49690.0   \n",
            "2022-01-09                    NaN                 NaN             NaN   \n",
            "2022-01-10                    NaN                 NaN             NaN   \n",
            "2022-01-11                    NaN                 NaN             NaN   \n",
            "2022-01-12                    NaN                 NaN             NaN   \n",
            "2022-01-13                    NaN                 NaN             NaN   \n",
            "\n",
            "            neighbor_Utah  neighbor_Vermont  neighbor_Virginia  \\\n",
            "date                                                             \n",
            "2022-01-04         4661.0            1731.0            17395.0   \n",
            "2022-01-05         7247.0             805.0            10728.0   \n",
            "2022-01-06         8913.0            2180.0            15840.0   \n",
            "2022-01-07         9469.0            1860.0            18309.0   \n",
            "2022-01-08            0.0            2619.0                0.0   \n",
            "2022-01-09            NaN               NaN                NaN   \n",
            "2022-01-10            NaN               NaN                NaN   \n",
            "2022-01-11            NaN               NaN                NaN   \n",
            "2022-01-12            NaN               NaN                NaN   \n",
            "2022-01-13            NaN               NaN                NaN   \n",
            "\n",
            "            neighbor_Washington  neighbor_West Virginia  neighbor_Wisconsin  \\\n",
            "date                                                                          \n",
            "2022-01-04               6192.0                  2353.0              9934.0   \n",
            "2022-01-05              10700.0                  2928.0             13760.0   \n",
            "2022-01-06              14944.0                  4947.0             14454.0   \n",
            "2022-01-07              17091.0                  4134.0             15562.0   \n",
            "2022-01-08                  0.0                  4184.0                 0.0   \n",
            "2022-01-09                  NaN                     NaN                 NaN   \n",
            "2022-01-10                  NaN                     NaN                 NaN   \n",
            "2022-01-11                  NaN                     NaN                 NaN   \n",
            "2022-01-12                  NaN                     NaN                 NaN   \n",
            "2022-01-13                  NaN                     NaN                 NaN   \n",
            "\n",
            "            neighbor_Wyoming  \n",
            "date                          \n",
            "2022-01-04             746.0  \n",
            "2022-01-05             709.0  \n",
            "2022-01-06             995.0  \n",
            "2022-01-07             764.0  \n",
            "2022-01-08               0.0  \n",
            "2022-01-09               NaN  \n",
            "2022-01-10               NaN  \n",
            "2022-01-11               NaN  \n",
            "2022-01-12               NaN  \n",
            "2022-01-13               NaN  \n",
            "\n",
            "[10 rows x 498 columns]\n",
            "            JHU_cases  JHU_deaths  JHU_hospitalizations  up2date  \\\n",
            "date                                                               \n",
            "2022-01-04    52247.0       105.0                1789.0      NaN   \n",
            "2022-01-05    76000.0       158.0                1810.0      NaN   \n",
            "2022-01-06    78267.0       143.0                1822.0      NaN   \n",
            "2022-01-07    85079.0       190.0                1821.0      NaN   \n",
            "2022-01-08    81388.0        85.0                1681.0      NaN   \n",
            "2022-01-09        NaN         NaN                   NaN      NaN   \n",
            "2022-01-10        NaN         NaN                   NaN      NaN   \n",
            "2022-01-11        NaN         NaN                   NaN      NaN   \n",
            "2022-01-12        NaN         NaN                   NaN      NaN   \n",
            "2022-01-13        NaN         NaN                   NaN      NaN   \n",
            "\n",
            "            gt_after covid vaccine  gt_side effects of vaccine  \\\n",
            "date                                                             \n",
            "2022-01-04             1581.301166                  131.829535   \n",
            "2022-01-05              510.041393                    0.000000   \n",
            "2022-01-06              920.825937                  460.603169   \n",
            "2022-01-07              608.729289                    0.000000   \n",
            "2022-01-08             1172.583083                  293.266872   \n",
            "2022-01-09             1544.561417                    0.000000   \n",
            "2022-01-10             1175.166324                  117.565179   \n",
            "2022-01-11              766.103236                  127.736620   \n",
            "2022-01-12              790.487196                   98.851719   \n",
            "2022-01-13              845.471330                  241.663029   \n",
            "\n",
            "            gt_effects of covid vaccine       gt_covid  \\\n",
            "date                                                     \n",
            "2022-01-04                   131.775834  225601.935643   \n",
            "2022-01-05                   204.017698  169249.705743   \n",
            "2022-01-06                   230.207772  158841.078908   \n",
            "2022-01-07                   121.746539  138387.934333   \n",
            "2022-01-08                     0.000000  132848.514301   \n",
            "2022-01-09                     0.000000  136647.438751   \n",
            "2022-01-10                   117.517290  131237.074798   \n",
            "2022-01-11                   127.684587  138644.647324   \n",
            "2022-01-12                    98.811452   93499.706225   \n",
            "2022-01-13                   241.564588  112362.571589   \n",
            "\n",
            "            gt_how long does covid last  gt_anosmia  ...  \\\n",
            "date                                                 ...   \n",
            "2022-01-04                   657.236283    0.000000  ...   \n",
            "2022-01-05                  1322.808426  102.008051  ...   \n",
            "2022-01-06                  1377.802550  230.205971  ...   \n",
            "2022-01-07                   607.214844    0.000000  ...   \n",
            "2022-01-08                  1023.457608    0.000000  ...   \n",
            "2022-01-09                   840.392037    0.000000  ...   \n",
            "2022-01-10                   586.121326    0.000000  ...   \n",
            "2022-01-11                   254.732421    0.000000  ...   \n",
            "2022-01-12                   492.825349   98.810679  ...   \n",
            "2022-01-13                   963.849027    0.000000  ...   \n",
            "\n",
            "            neighbor_South Dakota  neighbor_Tennessee  neighbor_Texas  \\\n",
            "date                                                                    \n",
            "2022-01-04                 3047.0              9230.0         53543.0   \n",
            "2022-01-05                 2254.0                 0.0         47266.0   \n",
            "2022-01-06                 2183.0                 0.0         45375.0   \n",
            "2022-01-07                 1944.0                 0.0         52571.0   \n",
            "2022-01-08                    0.0                 0.0         49690.0   \n",
            "2022-01-09                    NaN                 NaN             NaN   \n",
            "2022-01-10                    NaN                 NaN             NaN   \n",
            "2022-01-11                    NaN                 NaN             NaN   \n",
            "2022-01-12                    NaN                 NaN             NaN   \n",
            "2022-01-13                    NaN                 NaN             NaN   \n",
            "\n",
            "            neighbor_Utah  neighbor_Vermont  neighbor_Virginia  \\\n",
            "date                                                             \n",
            "2022-01-04         4661.0            1731.0            17395.0   \n",
            "2022-01-05         7247.0             805.0            10728.0   \n",
            "2022-01-06         8913.0            2180.0            15840.0   \n",
            "2022-01-07         9469.0            1860.0            18309.0   \n",
            "2022-01-08            0.0            2619.0                0.0   \n",
            "2022-01-09            NaN               NaN                NaN   \n",
            "2022-01-10            NaN               NaN                NaN   \n",
            "2022-01-11            NaN               NaN                NaN   \n",
            "2022-01-12            NaN               NaN                NaN   \n",
            "2022-01-13            NaN               NaN                NaN   \n",
            "\n",
            "            neighbor_Washington  neighbor_West Virginia  neighbor_Wisconsin  \\\n",
            "date                                                                          \n",
            "2022-01-04               6192.0                  2353.0              9934.0   \n",
            "2022-01-05              10700.0                  2928.0             13760.0   \n",
            "2022-01-06              14944.0                  4947.0             14454.0   \n",
            "2022-01-07              17091.0                  4134.0             15562.0   \n",
            "2022-01-08                  0.0                  4184.0                 0.0   \n",
            "2022-01-09                  NaN                     NaN                 NaN   \n",
            "2022-01-10                  NaN                     NaN                 NaN   \n",
            "2022-01-11                  NaN                     NaN                 NaN   \n",
            "2022-01-12                  NaN                     NaN                 NaN   \n",
            "2022-01-13                  NaN                     NaN                 NaN   \n",
            "\n",
            "            neighbor_Wyoming  \n",
            "date                          \n",
            "2022-01-04             746.0  \n",
            "2022-01-05             709.0  \n",
            "2022-01-06             995.0  \n",
            "2022-01-07             764.0  \n",
            "2022-01-08               0.0  \n",
            "2022-01-09               NaN  \n",
            "2022-01-10               NaN  \n",
            "2022-01-11               NaN  \n",
            "2022-01-12               NaN  \n",
            "2022-01-13               NaN  \n",
            "\n",
            "[10 rows x 498 columns]\n",
            "            JHU_cases  JHU_deaths  JHU_hospitalizations  up2date  \\\n",
            "date                                                               \n",
            "2022-01-04    18845.0        95.0                 387.0      NaN   \n",
            "2022-01-05    30805.0        54.0                 404.0      NaN   \n",
            "2022-01-06    27411.0        46.0                 415.0      NaN   \n",
            "2022-01-07    29163.0        58.0                 415.0      NaN   \n",
            "2022-01-08        0.0         0.0                 374.0      NaN   \n",
            "2022-01-09        NaN         NaN                   NaN      NaN   \n",
            "2022-01-10        NaN         NaN                   NaN      NaN   \n",
            "2022-01-11        NaN         NaN                   NaN      NaN   \n",
            "2022-01-12        NaN         NaN                   NaN      NaN   \n",
            "2022-01-13        NaN         NaN                   NaN      NaN   \n",
            "\n",
            "            gt_after covid vaccine  gt_side effects of vaccine  \\\n",
            "date                                                             \n",
            "2022-01-04             1937.449656                    0.000000   \n",
            "2022-01-05              769.319435                    0.000000   \n",
            "2022-01-06             1539.434075                    0.000000   \n",
            "2022-01-07              392.985324                  392.823030   \n",
            "2022-01-08             1716.128865                    0.000000   \n",
            "2022-01-09             1219.735432                    0.000000   \n",
            "2022-01-10             1123.009909                  748.364087   \n",
            "2022-01-11              376.241450                    0.000000   \n",
            "2022-01-12             1167.021276                  388.846441   \n",
            "2022-01-13             1597.981741                    0.000000   \n",
            "\n",
            "            gt_effects of covid vaccine       gt_covid  \\\n",
            "date                                                     \n",
            "2022-01-04                     0.000000  316040.458308   \n",
            "2022-01-05                   387.071265  279639.120596   \n",
            "2022-01-06                     0.000000  255637.932912   \n",
            "2022-01-07                   395.449067  210159.476078   \n",
            "2022-01-08                     0.000000  201664.875490   \n",
            "2022-01-09                     0.000000  185037.155838   \n",
            "2022-01-10                   376.683465  204287.244747   \n",
            "2022-01-11                     0.000000  195959.664432   \n",
            "2022-01-12                   391.445894  199896.679345   \n",
            "2022-01-13                     0.000000  169082.675205   \n",
            "\n",
            "            gt_how long does covid last  gt_anosmia  ...  \\\n",
            "date                                                 ...   \n",
            "2022-01-04                  1162.954522    0.000000  ...   \n",
            "2022-01-05                   769.640227    0.000000  ...   \n",
            "2022-01-06                  2695.132985  384.060527  ...   \n",
            "2022-01-07                  1965.745960    0.000000  ...   \n",
            "2022-01-08                   858.422230    0.000000  ...   \n",
            "2022-01-09                   813.496026    0.000000  ...   \n",
            "2022-01-10                     0.000000    0.000000  ...   \n",
            "2022-01-11                  1129.195006    0.000000  ...   \n",
            "2022-01-12                  1945.846504    0.000000  ...   \n",
            "2022-01-13                  1598.648070    0.000000  ...   \n",
            "\n",
            "            neighbor_South Dakota  neighbor_Tennessee  neighbor_Texas  \\\n",
            "date                                                                    \n",
            "2022-01-04                 3047.0              9230.0         53543.0   \n",
            "2022-01-05                 2254.0                 0.0         47266.0   \n",
            "2022-01-06                 2183.0                 0.0         45375.0   \n",
            "2022-01-07                 1944.0                 0.0         52571.0   \n",
            "2022-01-08                    0.0                 0.0         49690.0   \n",
            "2022-01-09                    NaN                 NaN             NaN   \n",
            "2022-01-10                    NaN                 NaN             NaN   \n",
            "2022-01-11                    NaN                 NaN             NaN   \n",
            "2022-01-12                    NaN                 NaN             NaN   \n",
            "2022-01-13                    NaN                 NaN             NaN   \n",
            "\n",
            "            neighbor_Utah  neighbor_Vermont  neighbor_Virginia  \\\n",
            "date                                                             \n",
            "2022-01-04         4661.0            1731.0            17395.0   \n",
            "2022-01-05         7247.0             805.0            10728.0   \n",
            "2022-01-06         8913.0            2180.0            15840.0   \n",
            "2022-01-07         9469.0            1860.0            18309.0   \n",
            "2022-01-08            0.0            2619.0                0.0   \n",
            "2022-01-09            NaN               NaN                NaN   \n",
            "2022-01-10            NaN               NaN                NaN   \n",
            "2022-01-11            NaN               NaN                NaN   \n",
            "2022-01-12            NaN               NaN                NaN   \n",
            "2022-01-13            NaN               NaN                NaN   \n",
            "\n",
            "            neighbor_Washington  neighbor_West Virginia  neighbor_Wisconsin  \\\n",
            "date                                                                          \n",
            "2022-01-04               6192.0                  2353.0              9934.0   \n",
            "2022-01-05              10700.0                  2928.0             13760.0   \n",
            "2022-01-06              14944.0                  4947.0             14454.0   \n",
            "2022-01-07              17091.0                  4134.0             15562.0   \n",
            "2022-01-08                  0.0                  4184.0                 0.0   \n",
            "2022-01-09                  NaN                     NaN                 NaN   \n",
            "2022-01-10                  NaN                     NaN                 NaN   \n",
            "2022-01-11                  NaN                     NaN                 NaN   \n",
            "2022-01-12                  NaN                     NaN                 NaN   \n",
            "2022-01-13                  NaN                     NaN                 NaN   \n",
            "\n",
            "            neighbor_Wyoming  \n",
            "date                          \n",
            "2022-01-04             746.0  \n",
            "2022-01-05             709.0  \n",
            "2022-01-06             995.0  \n",
            "2022-01-07             764.0  \n",
            "2022-01-08               0.0  \n",
            "2022-01-09               NaN  \n",
            "2022-01-10               NaN  \n",
            "2022-01-11               NaN  \n",
            "2022-01-12               NaN  \n",
            "2022-01-13               NaN  \n",
            "\n",
            "[10 rows x 498 columns]\n"
          ]
        }
      ],
      "source": [
        "print(california.tail(10)) # remove the final 5 days since it's NA\n",
        "print(new_york.tail(10)) # remove the final 5 days since it's NA\n",
        "print(ma.tail(10)) # remove the final 5 days since it's NA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Y9V4_lhw1aG5"
      },
      "outputs": [],
      "source": [
        "california = california.iloc[:-5, :]\n",
        "new_york = new_york.iloc[:-5, :]\n",
        "ma = ma.iloc[:-5, :]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reO4lmA22iI1"
      },
      "source": [
        "Fill missing cases with 0 and flip the sign of negative cases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQ42R_uL2cGp",
        "outputId": "a95874a0-ba92-4087-b4fc-a9437412f81c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "date\n",
            "2020-03-29   -2019.0\n",
            "2021-06-29   -3935.0\n",
            "Name: JHU_cases, dtype: float64\n",
            "Series([], Name: JHU_cases, dtype: float64)\n",
            "date\n",
            "2020-09-03   -280.0\n",
            "Name: JHU_cases, dtype: float64\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "for state in [california, new_york, ma]:\n",
        "  state['JHU_cases']=state['JHU_cases'].fillna(0) # fill missing values with 0\n",
        "  print(state['JHU_cases'][state['JHU_cases'] < 0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "0VbHdTrV2g3I"
      },
      "outputs": [],
      "source": [
        "california['JHU_cases'][california['JHU_cases'] == -2019.0] = 2019.0\n",
        "california['JHU_cases'][california['JHU_cases'] == -3935.0] = 3935.0\n",
        "ma['JHU_cases'][ma['JHU_cases'] == -280.0] = 280"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 910
        },
        "id": "NESDPCIE2S3j",
        "outputId": "ef53c336-ee3f-4e05-9ada-80d12f48d1fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'COVID-19 cases over time in Massachusetts')"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig,axs = plt.subplots(3,1, figsize = (12,15))\n",
        "axs[0].plot(california.index.values, california['JHU_cases'])\n",
        "axs[1].plot(new_york.index.values, new_york['JHU_cases'])\n",
        "axs[2].plot(ma.index.values, ma['JHU_cases'])\n",
        "for i in range(3):\n",
        "  axs[i].set_xlabel('Time')\n",
        "axs[0].set_ylabel('Cases')\n",
        "axs[1].set_ylabel('Cases')\n",
        "axs[2].set_ylabel('Cases')\n",
        "axs[0].set_title('COVID-19 cases over time in California')\n",
        "axs[1].set_title('COVID-19 cases over time in New York')\n",
        "axs[2].set_title('COVID-19 cases over time in Massachusetts')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TiJ6JFzI3e0W"
      },
      "source": [
        "We observe a lot of noise so we take the rolling average with a window of 5 to smooth the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRQ1JxcP4WiR",
        "outputId": "510ed9d8-5e8c-4f8b-8735-d4917eb7c5ac"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((739, 495), (739,))"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#separate feature and outcome tables\n",
        "cali_features=california.iloc[:,3:]\n",
        "cali_outcomes=california.iloc[:,0]\n",
        "ny_features=new_york.iloc[:,3:]\n",
        "ny_outcomes=new_york.iloc[:,0]\n",
        "ma_features=ma.iloc[:,3:]\n",
        "ma_outcomes=ma.iloc[:,0]\n",
        "\n",
        "cali_features.shape, cali_outcomes.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdOKJzV84hUO",
        "outputId": "17a259c7-a439-4b65-c0d3-c49dc07913ae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((739, 495), (739,))"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Window == number of days in rolling window over which to calculate mean\n",
        "window = 5\n",
        "cali_outcomes = cali_outcomes.transform(lambda x: x.rolling(window).mean())\n",
        "ny_outcomes = ny_outcomes.transform(lambda x: x.rolling(window).mean())\n",
        "ma_outcomes = ma_outcomes.transform(lambda x: x.rolling(window).mean())\n",
        "\n",
        "cali_features.shape, cali_outcomes.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N1NpI9qf5Qs_"
      },
      "source": [
        "Fill the NA at the beginning with 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "LpJlKVdm4vjP"
      },
      "outputs": [],
      "source": [
        "cali_outcomes = cali_outcomes.fillna(0)\n",
        "ny_outcomes = ny_outcomes.fillna(0)\n",
        "ma_outcomes = ma_outcomes.fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 910
        },
        "id": "JtFp6yyy5Ejq",
        "outputId": "b86f226a-aefa-40cf-a78d-82af1799be8e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'COVID-19 cases over time in Massachusetts')"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig,axs = plt.subplots(3,1, figsize = (12,15))\n",
        "axs[0].plot(california.index.values, cali_outcomes)\n",
        "axs[1].plot(new_york.index.values, ny_outcomes)\n",
        "axs[2].plot(ma.index.values, ma_outcomes)\n",
        "for i in range(3):\n",
        "  axs[i].set_xlabel('Time')\n",
        "axs[0].set_ylabel('Cases')\n",
        "axs[1].set_ylabel('Cases')\n",
        "axs[2].set_ylabel('Cases')\n",
        "axs[0].set_title('COVID-19 cases over time in California')\n",
        "axs[1].set_title('COVID-19 cases over time in New York')\n",
        "axs[2].set_title('COVID-19 cases over time in Massachusetts')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHi7zebw5rj5"
      },
      "source": [
        "Drop Google Trend features with too many NAs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4WmIz756rYw",
        "outputId": "cd29cd7c-4588-46e8-f555-c2094735c117"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                           index         0\n",
            "204           gt2_Hepatotoxicity  0.504736\n",
            "386               gt2_Spasticity  0.504736\n",
            "30                   gt2_Ageusia  0.504736\n",
            "346                   gt2_Ptosis  0.504736\n",
            "32   gt2_Allergic conjunctivitis  0.504736\n",
            "..                           ...       ...\n",
            "20                 gt_nose bleed  0.000000\n",
            "21              gt_feeling tired  0.000000\n",
            "22              gt_joints aching  0.000000\n",
            "23                      gt_fever  0.000000\n",
            "18                gt_sars-cov-2   0.000000\n",
            "\n",
            "[495 rows x 2 columns]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(739, 428)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "missing_ratio = cali_features.isna().sum(axis=0) / cali_features.shape[0]\n",
        "print(missing_ratio.reset_index().sort_values(by = 0, ascending = False))\n",
        "cali_features_selected = cali_features[cali_features.columns[missing_ratio < 0.2]] \n",
        "cali_features_selected.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3AqcNNHp9ulV",
        "outputId": "b1d18a00-f77d-45f6-fadf-68d89a8e0aee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                     index         0\n",
            "328    gt2_Photodermatitis  0.519621\n",
            "433    gt2_Viral pneumonia  0.507442\n",
            "204     gt2_Hepatotoxicity  0.504736\n",
            "371  gt2_Shallow breathing  0.504736\n",
            "30             gt2_Ageusia  0.504736\n",
            "..                     ...       ...\n",
            "20           gt_nose bleed  0.000000\n",
            "21        gt_feeling tired  0.000000\n",
            "22        gt_joints aching  0.000000\n",
            "23                gt_fever  0.000000\n",
            "18          gt_sars-cov-2   0.000000\n",
            "\n",
            "[495 rows x 2 columns]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(739, 428)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "missing_ratio = ny_features.isna().sum(axis=0) / ny_features.shape[0]\n",
        "print(missing_ratio.reset_index().sort_values(by = 0, ascending = False))\n",
        "ny_features_selected = ny_features[ny_features.columns[missing_ratio < 0.2]] \n",
        "ny_features_selected.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q4cAf7Q090F6",
        "outputId": "18e03825-f165-4f47-ce75-a75c4d069356"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                           index         0\n",
            "328          gt2_Photodermatitis  0.695535\n",
            "433          gt2_Viral pneumonia  0.635995\n",
            "46                   gt2_Aphonia  0.519621\n",
            "32   gt2_Allergic conjunctivitis  0.515562\n",
            "117                 gt2_Crackles  0.514208\n",
            "..                           ...       ...\n",
            "20                 gt_nose bleed  0.000000\n",
            "21              gt_feeling tired  0.000000\n",
            "22              gt_joints aching  0.000000\n",
            "23                      gt_fever  0.000000\n",
            "18                gt_sars-cov-2   0.000000\n",
            "\n",
            "[495 rows x 2 columns]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(739, 428)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "missing_ratio = ma_features.isna().sum(axis=0) / ma_features.shape[0]\n",
        "print(missing_ratio.reset_index().sort_values(by = 0, ascending = False))\n",
        "ma_features_selected = ma_features[ma_features.columns[missing_ratio < 0.2]] \n",
        "ma_features_selected.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFHDwWnZO32X"
      },
      "source": [
        "Remove neighbor case features\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "lYTvSRy4O3Zn"
      },
      "outputs": [],
      "source": [
        "gt_features = [col for col in cali_features_selected.columns if ('gt' in col or 'gt2' in col) and 'neighbor' not in col]\n",
        "cali_features_selected = cali_features_selected[gt_features]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Fq6HiNrhPEf9"
      },
      "outputs": [],
      "source": [
        "gt_features = [col for col in ny_features_selected.columns if ('gt' in col or 'gt2' in col) and 'neighbor' not in col]\n",
        "ny_features_selected = ny_features_selected[gt_features]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "dd-OrWqiPE1Q"
      },
      "outputs": [],
      "source": [
        "gt_features = [col for col in ma_features_selected.columns if ('gt' in col or 'gt2' in col) and 'neighbor' not in col]\n",
        "ma_features_selected = ma_features_selected[gt_features]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3FXjxZqAIXe"
      },
      "source": [
        "Imput missing values with knn imputer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "zGX9w4zMAQcL"
      },
      "outputs": [],
      "source": [
        "#imputation using KNN\n",
        "col_names=cali_features_selected.columns\n",
        "imputer = KNNImputer(n_neighbors=5, weights='uniform', metric='nan_euclidean')\n",
        "imputer.fit(cali_features_selected)\n",
        "imputed_data = imputer.transform(cali_features_selected)\n",
        "imputed_cali_gt = pd.DataFrame(imputed_data,columns=col_names)\n",
        "\n",
        "col_names=ny_features_selected.columns\n",
        "imputer.fit(ny_features_selected)\n",
        "imputed_data = imputer.transform(ny_features_selected)\n",
        "imputed_ny_gt = pd.DataFrame(imputed_data,columns=col_names)\n",
        "\n",
        "col_names=ma_features_selected.columns\n",
        "imputer.fit(ma_features_selected)\n",
        "imputed_data = imputer.transform(ma_features_selected)\n",
        "imputed_ma_gt = pd.DataFrame(imputed_data,columns=col_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "molKxZHgAwaS"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amUFmdIUUJRb"
      },
      "source": [
        "### Pearson Correlation Coefficients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "GesO8_zMVYDU"
      },
      "outputs": [],
      "source": [
        "# Pearson's correlation requires that each dataset be normally distributed\n",
        "from scipy.stats.stats import pearsonr\n",
        "from scipy.ndimage.interpolation import shift"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XU7mkLkrJWj7",
        "outputId": "bb7b7605-252a-4c01-89bc-9c702e24521b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>features</th>\n",
              "      <th>corr</th>\n",
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              "      <th>p_value</th>\n",
              "      <th>relation</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>gt_how long does covid last</td>\n",
              "      <td>0.702711</td>\n",
              "      <td>0.702711</td>\n",
              "      <td>4.599268e-111</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>gt_covid</td>\n",
              "      <td>0.700754</td>\n",
              "      <td>0.700754</td>\n",
              "      <td>3.385993e-110</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>gt_covid symptoms</td>\n",
              "      <td>0.679652</td>\n",
              "      <td>0.679652</td>\n",
              "      <td>2.818283e-101</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>gt2_Hypoxemia</td>\n",
              "      <td>0.646365</td>\n",
              "      <td>0.646365</td>\n",
              "      <td>1.228470e-88</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>gt_loss of smell</td>\n",
              "      <td>0.571850</td>\n",
              "      <td>0.571850</td>\n",
              "      <td>2.138678e-65</td>\n",
              "      <td>positive</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                        features      corr  abs_corr        p_value  relation\n",
              "4    gt_how long does covid last  0.702711  0.702711  4.599268e-111  positive\n",
              "3                       gt_covid  0.700754  0.700754  3.385993e-110  positive\n",
              "11             gt_covid symptoms  0.679652  0.679652  2.818283e-101  positive\n",
              "197                gt2_Hypoxemia  0.646365  0.646365   1.228470e-88  positive\n",
              "9               gt_loss of smell  0.571850  0.571850   2.138678e-65  positive"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "corr_value = []\n",
        "p_value = []\n",
        "for i in range(len(imputed_cali_gt.columns)):\n",
        "  res = pearsonr(imputed_cali_gt.iloc[:,i], cali_outcomes)\n",
        "  corr_value.append(res[0])\n",
        "  p_value.append(res[1])\n",
        "\n",
        "res_tab = pd.DataFrame({\"features\" : imputed_cali_gt.columns, \n",
        "              \"corr\": corr_value,\n",
        "              'abs_corr': [abs(x) for x in corr_value],\n",
        "              \"p_value\": p_value})\n",
        "\n",
        "res_tab['relation'] = np.where(res_tab['corr'] > 0, 'positive', 'negative')\n",
        "res_tab = res_tab.sort_values(by='abs_corr', ascending = False)\n",
        "\n",
        "res_tab.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "1iQvkHmqVLXn"
      },
      "outputs": [],
      "source": [
        "def pcorrcoef(feature, outcome, shift_by):\n",
        "  corr_value = []\n",
        "  p_value = []\n",
        "\n",
        "  if shift_by != 0:\n",
        "    shifted_data = shift(outcome.reset_index()['JHU_cases'].values, shift_by, cval = 0)\n",
        "    outcome = shifted_data\n",
        "  \n",
        "  for i in range(len(feature.columns)):\n",
        "    res = pearsonr(feature.iloc[:,i], outcome)\n",
        "    corr_value.append(res[0])\n",
        "    p_value.append(res[1])\n",
        "\n",
        "  res_tab = pd.DataFrame({f\"features_{shift_by}\" : feature.columns, \n",
        "                f\"corr_{shift_by}\": corr_value,\n",
        "                f\"abs_corr_{shift_by}\": [abs(x) for x in corr_value],\n",
        "                f\"p_value_{shift_by}\": p_value})\n",
        "\n",
        "  res_tab[f\"relation_{shift_by}\"] = np.where(res_tab[f\"corr_{shift_by}\"] > 0, 'positive', 'negative')\n",
        "  # res_tab = res_tab.sort_values(by=f\"abs_corr_{shift_by}\", ascending = False)\n",
        "  return res_tab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rVah3KpRYtij",
        "outputId": "8d2a2d98-6cd6-415f-8c3b-f3df41f829fe"
      },
      "outputs": [
        {
          "data": {
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              "      <th>4</th>\n",
              "      <td>gt_how long does covid last</td>\n",
              "      <td>0.702711</td>\n",
              "      <td>0.702711</td>\n",
              "      <td>4.599268e-111</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>gt_covid</td>\n",
              "      <td>0.700754</td>\n",
              "      <td>0.700754</td>\n",
              "      <td>3.385993e-110</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>gt_covid symptoms</td>\n",
              "      <td>0.679652</td>\n",
              "      <td>0.679652</td>\n",
              "      <td>2.818283e-101</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>gt2_Hypoxemia</td>\n",
              "      <td>0.646365</td>\n",
              "      <td>0.646365</td>\n",
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              "      <th>9</th>\n",
              "      <td>gt_loss of smell</td>\n",
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              "      <td>2.138678e-65</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>gt2_Nodule</td>\n",
              "      <td>-0.000734</td>\n",
              "      <td>0.000734</td>\n",
              "      <td>9.841109e-01</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>gt2_Balance disorder</td>\n",
              "      <td>0.000410</td>\n",
              "      <td>0.000410</td>\n",
              "      <td>9.911241e-01</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>gt2_Pelvic inflammatory disease</td>\n",
              "      <td>-0.000380</td>\n",
              "      <td>0.000380</td>\n",
              "      <td>9.917671e-01</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>gt2_Canker sore</td>\n",
              "      <td>0.000376</td>\n",
              "      <td>0.000376</td>\n",
              "      <td>9.918601e-01</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>gt2_Abdominal pain</td>\n",
              "      <td>-0.000297</td>\n",
              "      <td>0.000297</td>\n",
              "      <td>9.935779e-01</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>379 rows × 5 columns</p>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                          features_0    corr_0  abs_corr_0      p_value_0  \\\n",
              "4        gt_how long does covid last  0.702711    0.702711  4.599268e-111   \n",
              "3                           gt_covid  0.700754    0.700754  3.385993e-110   \n",
              "11                 gt_covid symptoms  0.679652    0.679652  2.818283e-101   \n",
              "197                    gt2_Hypoxemia  0.646365    0.646365   1.228470e-88   \n",
              "9                   gt_loss of smell  0.571850    0.571850   2.138678e-65   \n",
              "..                               ...       ...         ...            ...   \n",
              "258                       gt2_Nodule -0.000734    0.000734   9.841109e-01   \n",
              "54              gt2_Balance disorder  0.000410    0.000410   9.911241e-01   \n",
              "275  gt2_Pelvic inflammatory disease -0.000380    0.000380   9.917671e-01   \n",
              "80                   gt2_Canker sore  0.000376    0.000376   9.918601e-01   \n",
              "24                gt2_Abdominal pain -0.000297    0.000297   9.935779e-01   \n",
              "\n",
              "    relation_0  \n",
              "4     positive  \n",
              "3     positive  \n",
              "11    positive  \n",
              "197   positive  \n",
              "9     positive  \n",
              "..         ...  \n",
              "258   negative  \n",
              "54    positive  \n",
              "275   negative  \n",
              "80    positive  \n",
              "24    negative  \n",
              "\n",
              "[379 rows x 5 columns]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "res_tab_0 = pcorrcoef(feature = imputed_cali_gt, outcome=cali_outcomes, shift_by=0)\n",
        "res_tab_0 = res_tab_0.sort_values(by=\"abs_corr_0\", ascending = False)\n",
        "res_tab_0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRp8RAweR7of"
      },
      "source": [
        "Features with correlation coefficent > 0.5 have strong correlation with cases"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
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        },
        "id": "tC9p12BgRckU",
        "outputId": "6076ff6d-2887-4a0e-df0d-aebcba70b273"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'features')"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# features with correlation coefficent > 0.5 have strong correlation with cases\n",
        "strong_cor = res_tab_0[res_tab_0['corr_0'] > 0.5]\n",
        "\n",
        "fig, ax = plt.subplots(1,1, figsize=(8,8))\n",
        "sns.barplot(y = 'features_0', x = 'corr_0', data = strong_cor)\n",
        "plt.title(\"Correlation coefficients of strong correlated features with COVID-19 cases\")\n",
        "plt.xlabel(\"correlation coefficients\")\n",
        "plt.ylabel(\"features\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "CeETGXP1QxLA",
        "outputId": "53624718-3be2-4f59-f741-2e13469352c7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'features')"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# features with correlation coefficent > 0.3 and < 0.5 have moderate correlation with cases\n",
        "moderate_cor = res_tab_0[(res_tab_0['corr_0'] >= 0.3) & (res_tab_0['corr_0'] < 0.5)]\n",
        "\n",
        "fig, ax = plt.subplots(1,1, figsize=(8,8))\n",
        "sns.barplot(y = 'features_0', x = 'corr_0', data = moderate_cor)\n",
        "plt.title(\"Correlation coefficients of moderate correlated features with COVID-19 cases\")\n",
        "plt.xlabel(\"correlation coefficients\")\n",
        "plt.ylabel(\"features\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9RULyV6Uu2t"
      },
      "source": [
        "### Lead and Lag Pearson Correlation Coefficients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "VZ2CLuSRZzHf"
      },
      "outputs": [],
      "source": [
        "shift_sample = np.linspace(-14, 14, 29).astype('int')\n",
        "for i in shift_sample:\n",
        "  if i == shift_sample[0]:\n",
        "    final_res_tab = pcorrcoef(feature = imputed_cali_gt, outcome=cali_outcomes, shift_by=i)\n",
        "  else:\n",
        "    tmp_res_tab = pcorrcoef(feature = imputed_cali_gt, outcome=cali_outcomes, shift_by=i)\n",
        "    final_res_tab = pd.concat((final_res_tab,tmp_res_tab), axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "0VZ7SJhAZzBf",
        "outputId": "7474e436-1528-4b95-9995-4c4c75575d50"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>features_-14</th>\n",
              "      <th>corr_-14</th>\n",
              "      <th>abs_corr_-14</th>\n",
              "      <th>p_value_-14</th>\n",
              "      <th>relation_-14</th>\n",
              "      <th>features_-13</th>\n",
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              "      <th>abs_corr_-13</th>\n",
              "      <th>p_value_-13</th>\n",
              "      <th>relation_-13</th>\n",
              "      <th>...</th>\n",
              "      <th>features_13</th>\n",
              "      <th>corr_13</th>\n",
              "      <th>abs_corr_13</th>\n",
              "      <th>p_value_13</th>\n",
              "      <th>relation_13</th>\n",
              "      <th>features_14</th>\n",
              "      <th>corr_14</th>\n",
              "      <th>abs_corr_14</th>\n",
              "      <th>p_value_14</th>\n",
              "      <th>relation_14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>gt_after covid vaccine</td>\n",
              "      <td>-0.074379</td>\n",
              "      <td>0.074379</td>\n",
              "      <td>4.324524e-02</td>\n",
              "      <td>negative</td>\n",
              "      <td>gt_after covid vaccine</td>\n",
              "      <td>-0.069475</td>\n",
              "      <td>0.069475</td>\n",
              "      <td>5.906251e-02</td>\n",
              "      <td>negative</td>\n",
              "      <td>...</td>\n",
              "      <td>gt_after covid vaccine</td>\n",
              "      <td>0.153289</td>\n",
              "      <td>0.153289</td>\n",
              "      <td>2.854271e-05</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt_after covid vaccine</td>\n",
              "      <td>0.159981</td>\n",
              "      <td>0.159981</td>\n",
              "      <td>1.243381e-05</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>gt_side effects of vaccine</td>\n",
              "      <td>0.026793</td>\n",
              "      <td>0.026793</td>\n",
              "      <td>4.670747e-01</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt_side effects of vaccine</td>\n",
              "      <td>0.034888</td>\n",
              "      <td>0.034888</td>\n",
              "      <td>3.435846e-01</td>\n",
              "      <td>positive</td>\n",
              "      <td>...</td>\n",
              "      <td>gt_side effects of vaccine</td>\n",
              "      <td>0.249163</td>\n",
              "      <td>0.249163</td>\n",
              "      <td>6.395263e-12</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt_side effects of vaccine</td>\n",
              "      <td>0.252555</td>\n",
              "      <td>0.252555</td>\n",
              "      <td>3.232514e-12</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>gt_effects of covid vaccine</td>\n",
              "      <td>0.127301</td>\n",
              "      <td>0.127301</td>\n",
              "      <td>5.226221e-04</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt_effects of covid vaccine</td>\n",
              "      <td>0.135786</td>\n",
              "      <td>0.135786</td>\n",
              "      <td>2.137402e-04</td>\n",
              "      <td>positive</td>\n",
              "      <td>...</td>\n",
              "      <td>gt_effects of covid vaccine</td>\n",
              "      <td>0.377314</td>\n",
              "      <td>0.377314</td>\n",
              "      <td>2.040208e-26</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt_effects of covid vaccine</td>\n",
              "      <td>0.376500</td>\n",
              "      <td>0.376500</td>\n",
              "      <td>2.661425e-26</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>gt_covid</td>\n",
              "      <td>0.478635</td>\n",
              "      <td>0.478635</td>\n",
              "      <td>1.401468e-43</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt_covid</td>\n",
              "      <td>0.488366</td>\n",
              "      <td>0.488366</td>\n",
              "      <td>1.487831e-45</td>\n",
              "      <td>positive</td>\n",
              "      <td>...</td>\n",
              "      <td>gt_covid</td>\n",
              "      <td>0.535658</td>\n",
              "      <td>0.535658</td>\n",
              "      <td>4.140852e-56</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt_covid</td>\n",
              "      <td>0.523245</td>\n",
              "      <td>0.523245</td>\n",
              "      <td>3.550618e-53</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>gt_how long does covid last</td>\n",
              "      <td>0.453184</td>\n",
              "      <td>0.453184</td>\n",
              "      <td>1.045040e-38</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt_how long does covid last</td>\n",
              "      <td>0.481980</td>\n",
              "      <td>0.481980</td>\n",
              "      <td>2.987161e-44</td>\n",
              "      <td>positive</td>\n",
              "      <td>...</td>\n",
              "      <td>gt_how long does covid last</td>\n",
              "      <td>0.511747</td>\n",
              "      <td>0.511747</td>\n",
              "      <td>1.449103e-50</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt_how long does covid last</td>\n",
              "      <td>0.503940</td>\n",
              "      <td>0.503940</td>\n",
              "      <td>7.546189e-49</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>374</th>\n",
              "      <td>gt2_Xeroderma</td>\n",
              "      <td>0.061131</td>\n",
              "      <td>0.061131</td>\n",
              "      <td>9.680151e-02</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt2_Xeroderma</td>\n",
              "      <td>0.058807</td>\n",
              "      <td>0.058807</td>\n",
              "      <td>1.101952e-01</td>\n",
              "      <td>positive</td>\n",
              "      <td>...</td>\n",
              "      <td>gt2_Xeroderma</td>\n",
              "      <td>-0.039836</td>\n",
              "      <td>0.039836</td>\n",
              "      <td>2.794606e-01</td>\n",
              "      <td>negative</td>\n",
              "      <td>gt2_Xeroderma</td>\n",
              "      <td>-0.045104</td>\n",
              "      <td>0.045104</td>\n",
              "      <td>2.206995e-01</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>375</th>\n",
              "      <td>gt2_Xerostomia</td>\n",
              "      <td>-0.106960</td>\n",
              "      <td>0.106960</td>\n",
              "      <td>3.601716e-03</td>\n",
              "      <td>negative</td>\n",
              "      <td>gt2_Xerostomia</td>\n",
              "      <td>-0.089814</td>\n",
              "      <td>0.089814</td>\n",
              "      <td>1.459200e-02</td>\n",
              "      <td>negative</td>\n",
              "      <td>...</td>\n",
              "      <td>gt2_Xerostomia</td>\n",
              "      <td>0.046457</td>\n",
              "      <td>0.046457</td>\n",
              "      <td>2.071463e-01</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt2_Xerostomia</td>\n",
              "      <td>0.047727</td>\n",
              "      <td>0.047727</td>\n",
              "      <td>1.949829e-01</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>376</th>\n",
              "      <td>gt2_Yawn</td>\n",
              "      <td>-0.081810</td>\n",
              "      <td>0.081810</td>\n",
              "      <td>2.615322e-02</td>\n",
              "      <td>negative</td>\n",
              "      <td>gt2_Yawn</td>\n",
              "      <td>-0.077098</td>\n",
              "      <td>0.077098</td>\n",
              "      <td>3.613151e-02</td>\n",
              "      <td>negative</td>\n",
              "      <td>...</td>\n",
              "      <td>gt2_Yawn</td>\n",
              "      <td>-0.093769</td>\n",
              "      <td>0.093769</td>\n",
              "      <td>1.076093e-02</td>\n",
              "      <td>negative</td>\n",
              "      <td>gt2_Yawn</td>\n",
              "      <td>-0.100712</td>\n",
              "      <td>0.100712</td>\n",
              "      <td>6.141052e-03</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>377</th>\n",
              "      <td>gt2_hyperhidrosis</td>\n",
              "      <td>-0.172094</td>\n",
              "      <td>0.172094</td>\n",
              "      <td>2.532216e-06</td>\n",
              "      <td>negative</td>\n",
              "      <td>gt2_hyperhidrosis</td>\n",
              "      <td>-0.162456</td>\n",
              "      <td>0.162456</td>\n",
              "      <td>9.065498e-06</td>\n",
              "      <td>negative</td>\n",
              "      <td>...</td>\n",
              "      <td>gt2_hyperhidrosis</td>\n",
              "      <td>-0.139982</td>\n",
              "      <td>0.139982</td>\n",
              "      <td>1.346970e-04</td>\n",
              "      <td>negative</td>\n",
              "      <td>gt2_hyperhidrosis</td>\n",
              "      <td>-0.134815</td>\n",
              "      <td>0.134815</td>\n",
              "      <td>2.374120e-04</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378</th>\n",
              "      <td>gt2_pancreatitis</td>\n",
              "      <td>0.032226</td>\n",
              "      <td>0.032226</td>\n",
              "      <td>3.816915e-01</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt2_pancreatitis</td>\n",
              "      <td>0.020163</td>\n",
              "      <td>0.020163</td>\n",
              "      <td>5.842074e-01</td>\n",
              "      <td>positive</td>\n",
              "      <td>...</td>\n",
              "      <td>gt2_pancreatitis</td>\n",
              "      <td>0.067936</td>\n",
              "      <td>0.067936</td>\n",
              "      <td>6.491954e-02</td>\n",
              "      <td>positive</td>\n",
              "      <td>gt2_pancreatitis</td>\n",
              "      <td>0.073793</td>\n",
              "      <td>0.073793</td>\n",
              "      <td>4.492367e-02</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>379 rows × 145 columns</p>\n",
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            "text/plain": [
              "                    features_-14  corr_-14  abs_corr_-14   p_value_-14  \\\n",
              "0         gt_after covid vaccine -0.074379      0.074379  4.324524e-02   \n",
              "1     gt_side effects of vaccine  0.026793      0.026793  4.670747e-01   \n",
              "2    gt_effects of covid vaccine  0.127301      0.127301  5.226221e-04   \n",
              "3                       gt_covid  0.478635      0.478635  1.401468e-43   \n",
              "4    gt_how long does covid last  0.453184      0.453184  1.045040e-38   \n",
              "..                           ...       ...           ...           ...   \n",
              "374                gt2_Xeroderma  0.061131      0.061131  9.680151e-02   \n",
              "375               gt2_Xerostomia -0.106960      0.106960  3.601716e-03   \n",
              "376                     gt2_Yawn -0.081810      0.081810  2.615322e-02   \n",
              "377            gt2_hyperhidrosis -0.172094      0.172094  2.532216e-06   \n",
              "378             gt2_pancreatitis  0.032226      0.032226  3.816915e-01   \n",
              "\n",
              "    relation_-14                 features_-13  corr_-13  abs_corr_-13  \\\n",
              "0       negative       gt_after covid vaccine -0.069475      0.069475   \n",
              "1       positive   gt_side effects of vaccine  0.034888      0.034888   \n",
              "2       positive  gt_effects of covid vaccine  0.135786      0.135786   \n",
              "3       positive                     gt_covid  0.488366      0.488366   \n",
              "4       positive  gt_how long does covid last  0.481980      0.481980   \n",
              "..           ...                          ...       ...           ...   \n",
              "374     positive                gt2_Xeroderma  0.058807      0.058807   \n",
              "375     negative               gt2_Xerostomia -0.089814      0.089814   \n",
              "376     negative                     gt2_Yawn -0.077098      0.077098   \n",
              "377     negative            gt2_hyperhidrosis -0.162456      0.162456   \n",
              "378     positive             gt2_pancreatitis  0.020163      0.020163   \n",
              "\n",
              "      p_value_-13 relation_-13  ...                  features_13   corr_13  \\\n",
              "0    5.906251e-02     negative  ...       gt_after covid vaccine  0.153289   \n",
              "1    3.435846e-01     positive  ...   gt_side effects of vaccine  0.249163   \n",
              "2    2.137402e-04     positive  ...  gt_effects of covid vaccine  0.377314   \n",
              "3    1.487831e-45     positive  ...                     gt_covid  0.535658   \n",
              "4    2.987161e-44     positive  ...  gt_how long does covid last  0.511747   \n",
              "..            ...          ...  ...                          ...       ...   \n",
              "374  1.101952e-01     positive  ...                gt2_Xeroderma -0.039836   \n",
              "375  1.459200e-02     negative  ...               gt2_Xerostomia  0.046457   \n",
              "376  3.613151e-02     negative  ...                     gt2_Yawn -0.093769   \n",
              "377  9.065498e-06     negative  ...            gt2_hyperhidrosis -0.139982   \n",
              "378  5.842074e-01     positive  ...             gt2_pancreatitis  0.067936   \n",
              "\n",
              "     abs_corr_13    p_value_13 relation_13                  features_14  \\\n",
              "0       0.153289  2.854271e-05    positive       gt_after covid vaccine   \n",
              "1       0.249163  6.395263e-12    positive   gt_side effects of vaccine   \n",
              "2       0.377314  2.040208e-26    positive  gt_effects of covid vaccine   \n",
              "3       0.535658  4.140852e-56    positive                     gt_covid   \n",
              "4       0.511747  1.449103e-50    positive  gt_how long does covid last   \n",
              "..           ...           ...         ...                          ...   \n",
              "374     0.039836  2.794606e-01    negative                gt2_Xeroderma   \n",
              "375     0.046457  2.071463e-01    positive               gt2_Xerostomia   \n",
              "376     0.093769  1.076093e-02    negative                     gt2_Yawn   \n",
              "377     0.139982  1.346970e-04    negative            gt2_hyperhidrosis   \n",
              "378     0.067936  6.491954e-02    positive             gt2_pancreatitis   \n",
              "\n",
              "      corr_14  abs_corr_14    p_value_14 relation_14  \n",
              "0    0.159981     0.159981  1.243381e-05    positive  \n",
              "1    0.252555     0.252555  3.232514e-12    positive  \n",
              "2    0.376500     0.376500  2.661425e-26    positive  \n",
              "3    0.523245     0.523245  3.550618e-53    positive  \n",
              "4    0.503940     0.503940  7.546189e-49    positive  \n",
              "..        ...          ...           ...         ...  \n",
              "374 -0.045104     0.045104  2.206995e-01    negative  \n",
              "375  0.047727     0.047727  1.949829e-01    positive  \n",
              "376 -0.100712     0.100712  6.141052e-03    negative  \n",
              "377 -0.134815     0.134815  2.374120e-04    negative  \n",
              "378  0.073793     0.073793  4.492367e-02    positive  \n",
              "\n",
              "[379 rows x 145 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_res_tab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zij6YFNfcZEe"
      },
      "source": [
        "We find that there the lag or lead correlation coefficients for a feature can vary between 0.01 and 0.30. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "O2sxCNqgbeoX",
        "outputId": "44a97416-cb71-4715-b653-d068f76fc01d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
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              "      <th></th>\n",
              "      <th>minmax_diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>379.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.109449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.058573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.010472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.061671</td>\n",
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              "      <td>0.099199</td>\n",
              "    </tr>\n",
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              "      <th>75%</th>\n",
              "      <td>0.147630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "          element.innerHTML = '';\n",
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              "  "
            ],
            "text/plain": [
              "       minmax_diff\n",
              "count   379.000000\n",
              "mean      0.109449\n",
              "std       0.058573\n",
              "min       0.010472\n",
              "25%       0.061671\n",
              "50%       0.099199\n",
              "75%       0.147630\n",
              "max       0.298565"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "select_abs_corr = [col for col in final_res_tab.columns if 'abs_corr' in col]\n",
        "select_tab = final_res_tab[select_abs_corr]\n",
        "select_tab['minmax_diff'] = select_tab.apply(lambda x: max(x)-min(x), axis=1)\n",
        "select_tab[['minmax_diff']].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PeicqM62dpga"
      },
      "source": [
        "Now we select features by the maximum abs lag or lead correlation coefficients. Those features are correlated with the number of cases or lag trend of the number of cases or lead trend of the number of cases. They are useful for building a predictive model for COVID-19 cases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "6AJZejM6eFHR",
        "outputId": "80bbe515-18ce-4918-ae39-2ede1d413784"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'features')"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "final_res_tab['max_abs_corr'] = select_tab.apply(lambda x: max(x), axis=1)\n",
        "final_res_tab = final_res_tab.sort_values(by = 'max_abs_corr', ascending = False)\n",
        "\n",
        "# features with correlation coefficent > 0.5 have strong correlation with cases\n",
        "strong_cor = final_res_tab[final_res_tab['max_abs_corr'] > 0.5]\n",
        "\n",
        "fig, ax = plt.subplots(1,1, figsize=(8,8))\n",
        "sns.barplot(y = 'features_0', x = 'max_abs_corr', data = strong_cor)\n",
        "plt.title(\"Correlation coefficients of strong correlated features with COVID-19 cases\")\n",
        "plt.xlabel(\"correlation coefficients\")\n",
        "plt.ylabel(\"features\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "GKiRCn3sf3PR",
        "outputId": "22273794-4894-48e8-de49-1341b8a9b60c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'features')"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# features with correlation coefficent > 0.3 and < 0.5 have moderate correlation with cases\n",
        "moderate_cor = final_res_tab[(final_res_tab['max_abs_corr'] < 0.5) & (final_res_tab['max_abs_corr'] >= 0.3)]\n",
        "\n",
        "fig, ax = plt.subplots(1,1, figsize=(8,8))\n",
        "sns.barplot(y = 'features_0', x = 'max_abs_corr', data = moderate_cor)\n",
        "plt.title(\"Correlation coefficients of moderate correlated features with COVID-19 cases\")\n",
        "plt.xlabel(\"correlation coefficients\")\n",
        "plt.ylabel(\"features\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "6ikhlKTbsg8e",
        "outputId": "d7dc5761-e514-4610-bbc5-e9a943b0f047"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'features')"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# features with correlation coefficent > 0.3 a\n",
        "strong_moderate_cor = final_res_tab[final_res_tab['max_abs_corr'] >= 0.3]\n",
        "\n",
        "fig, ax = plt.subplots(1,1, figsize=(8,8))\n",
        "sns.barplot(y = 'features_0', x = 'max_abs_corr', data = strong_moderate_cor)\n",
        "plt.title(\"Correlation coefficients of strongly and moderately correlated features with COVID-19 cases\")\n",
        "plt.xlabel(\"correlation coefficients\")\n",
        "plt.ylabel(\"features\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gEYJS40Ef3Kr",
        "outputId": "9c48e811-f865-42cc-a4a5-afb8bcc78454"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['gt2_Eye pain' 'gt2_Tachycardia' 'gt_effects of covid vaccine'\n",
            " 'gt2_Bradycardia' 'gt2_Abdominal obesity' 'gt2_Skin condition'\n",
            " 'gt2_Palpitations' 'gt2_Grandiosity' 'gt2_Skin rash'\n",
            " 'gt2_Erectile dysfunction' 'gt2_Conjunctivitis' 'gt2_Telangiectasia'\n",
            " 'gt2_Weight gain']\n",
            "['gt_how long does covid last' 'gt_covid' 'gt_covid symptoms'\n",
            " 'gt2_Hypoxemia' 'gt_loss taste' 'gt_loss of smell' 'gt_loss smell'\n",
            " 'gt2_Dysgeusia' \"gt2_Bell's palsy\" 'gt2_Facial nerve paralysis']\n",
            "['gt_how long does covid last', 'gt_covid', 'gt_covid symptoms', 'gt2_Hypoxemia', 'gt_loss taste', 'gt_loss of smell', 'gt_loss smell', 'gt2_Dysgeusia', \"gt2_Bell's palsy\", 'gt2_Facial nerve paralysis', 'gt2_Eye pain', 'gt2_Tachycardia', 'gt_effects of covid vaccine', 'gt2_Bradycardia', 'gt2_Abdominal obesity', 'gt2_Skin condition', 'gt2_Palpitations', 'gt2_Grandiosity', 'gt2_Skin rash', 'gt2_Erectile dysfunction', 'gt2_Conjunctivitis', 'gt2_Telangiectasia', 'gt2_Weight gain']\n"
          ]
        }
      ],
      "source": [
        "moderate_features = moderate_cor['features_0'].values\n",
        "print(moderate_features)\n",
        "strong_features = strong_cor['features_0'].values\n",
        "print(strong_features)\n",
        "\n",
        "full_selected_features = list(strong_features) + list(moderate_features)\n",
        "print(full_selected_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_H_w4VYeiw21"
      },
      "source": [
        "### Correlation among selected features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PT1kaXJBjbma"
      },
      "source": [
        "Display this heatmap to visualize the correlations between each pair of the google trend features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "fJTLRIz_iwZl",
        "outputId": "491b9fd9-3546-4910-cb23-1024cf0893ea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc3ad925fd0>"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.subplots(1,1, figsize = (15,8))\n",
        "corr_res = imputed_cali_gt[full_selected_features].corr()\n",
        "sns.heatmap(corr_res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkNJZIP6j94W"
      },
      "source": [
        "We observe two high correlated features: facial nerve paralysis and bell's palsy. We decide to delete Facial nerve paralysis. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCSO_JjSiwRb",
        "outputId": "4d7d853f-e2fb-4dec-85f8-d70df1960c1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['gt_how long does covid last',\n",
              " 'gt_covid',\n",
              " 'gt_covid symptoms',\n",
              " 'gt2_Hypoxemia',\n",
              " 'gt_loss taste',\n",
              " 'gt_loss of smell',\n",
              " 'gt_loss smell',\n",
              " 'gt2_Dysgeusia',\n",
              " \"gt2_Bell's palsy\",\n",
              " 'gt2_Eye pain',\n",
              " 'gt2_Tachycardia',\n",
              " 'gt_effects of covid vaccine',\n",
              " 'gt2_Bradycardia',\n",
              " 'gt2_Abdominal obesity',\n",
              " 'gt2_Skin condition',\n",
              " 'gt2_Palpitations',\n",
              " 'gt2_Grandiosity',\n",
              " 'gt2_Skin rash',\n",
              " 'gt2_Erectile dysfunction',\n",
              " 'gt2_Conjunctivitis',\n",
              " 'gt2_Telangiectasia',\n",
              " 'gt2_Weight gain']"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "full_selected_features.remove('gt2_Facial nerve paralysis')\n",
        "full_selected_features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Reference\n",
        "\n",
        "Kurian, Shyam J., et al. \"Correlations between COVID-19 cases and Google Trends data in the United States: a state-by-state analysis.\" Mayo clinic proceedings. Vol. 95. No. 11. Elsevier, 2020."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Exploratory_Data_Analysis.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}