SavarisCOVID_2020.ipynb

savariscovid_2020.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SavarisCOVID_2020.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "history_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/rsavaris66/eccfc6caf4c9578d676c134fac74d3fe/savariscovid_2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMjL08HHGYKX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpZbuJe0NGEy",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "86242a5e-c6c1-4b84-9152-9c66cc182457"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-4955137e-f4a1-4c73-bd88-323211afd014\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-4955137e-f4a1-4c73-bd88-323211afd014\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving CovidSavaris.xlsx to CovidSavaris.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txBIpR78NHhV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import math"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CAHQswDcgdc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "742eb7ec-99c5-4f59-8274-25cde69fe894"
      },
      "source": [
        "df = pd.read_excel('CovidSavaris.xlsx')\n",
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['location', 'epi-week', 'date', 'Stay-at-home',\n",
              "       'new_deaths_per_million'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JzleTkUcuCg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "location = df['location'][0]\n",
        "epiweek = df['epi-week'][0]\n",
        "primeirasemana = df['epi-week'][0]\n",
        "sum_of_death_million_epi_week = 0.0\n",
        "mean_stay_at_home_epi_week = 0.0\n",
        "\n",
        "smooth_death_M = 0.0\n",
        "smooth_stay_at_home = 0.0\n",
        "\n",
        "cont = 0\n",
        "\n",
        "for i in range(df.shape[0]):\n",
        "    \n",
        "    #When the week or the country changes, the totals are save as new columns in the last line of the epi-week\n",
        "    if (df['epi-week'][i] != epiweek or df['location'][i] != location):\n",
        "        if (df['location'][i] != location):              \n",
        "            primeirasemana = df['epi-week'][i]                   \n",
        "        \n",
        "        df.loc[i - 1, 'sum of death/million/epi-week'] = sum_of_death_million_epi_week\n",
        "        df.loc[i - 1, 'mean stay-at-home/epi-week'] = mean_stay_at_home_epi_week / cont\n",
        "        \n",
        "        #Smooth Death/M and Smooth Stay-at-Home cannot be calculated in the first week of the country \n",
        "        if (epiweek > (primeirasemana)):\n",
        "            df.loc[i - 1, 'Smooth Death/M', ] = sum_of_death_million_epi_week - smooth_death_M     \n",
        "            df.loc[i - 1, 'Smooth Stay-at-Home'] = (mean_stay_at_home_epi_week / cont) - smooth_stay_at_home \n",
        "        \n",
        "         \n",
        "        smooth_death_M = sum_of_death_million_epi_week\n",
        "        smooth_stay_at_home = mean_stay_at_home_epi_week / cont\n",
        "        \n",
        "        sum_of_death_million_epi_week = 0.0\n",
        "        mean_stay_at_home_epi_week = 0.0\n",
        "        cont = 0\n",
        "        location = df['location'][i]\n",
        "        epiweek = df['epi-week'][i]          \n",
        "    \n",
        "    #Total values per day and ignore when value is not given\n",
        "    if (math.isnan(float(df.loc[i, 'new_deaths_per_million'])) == False):\n",
        "        sum_of_death_million_epi_week += float(df.loc[i, 'new_deaths_per_million'])\n",
        "    if (math.isnan(float(df.loc[i, 'Stay-at-home'])) == False):\n",
        "        mean_stay_at_home_epi_week += float(df.loc[i, 'Stay-at-home'])        \n",
        "        cont += 1\n",
        "\n",
        "df.loc[i, 'sum of death/million/epi-week'] = sum_of_death_million_epi_week \n",
        "df.loc[i, 'mean stay-at-home/epi-week'] = mean_stay_at_home_epi_week / cont\n",
        "if (smooth_death_M > 0):\n",
        "    df.loc[i, 'Smooth Death/M', ] = sum_of_death_million_epi_week - smooth_death_M \n",
        "\n",
        "if (smooth_stay_at_home > 0):\n",
        "    df.loc[i, 'Smooth Stay-at-Home'] = (mean_stay_at_home_epi_week / cont) - smooth_stay_at_home     \n",
        "\n",
        "# Save results in the file\n",
        "df.to_excel(r'CovidSavaris_Python.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jkRMpW_DiOD8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "e4965c9d-6127-4aa2-a9f6-7ce8f2ff35cf"
      },
      "source": [
        "df = pd.read_excel('CovidSavaris_Python.xlsx')\n",
        "df['mean stay-at-home/epi-week'].notnull()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "16438    False\n",
              "16439    False\n",
              "16440    False\n",
              "16441    False\n",
              "16442     True\n",
              "Name: mean stay-at-home/epi-week, Length: 16443, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_iITlOUdADs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "48d6e433-e152-45c9-b2d2-5f1804dcd27b"
      },
      "source": [
        "#Read the data generated in the previous section\n",
        "df = pd.read_excel('CovidSavaris_Python.xlsx')\n",
        "# Remove blank lines in the column \"mean stay-at-home/epi-week\", leaving just the mean of the epidemiologic week  \n",
        "df_dadossemana = df[df['mean stay-at-home/epi-week'].notnull()]\n",
        "df_dadossemana.iloc[:, 2:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>epi-week</th>\n",
              "      <th>date</th>\n",
              "      <th>Stay-at-home</th>\n",
              "      <th>new_deaths_per_million</th>\n",
              "      <th>sum of death/million/epi-week</th>\n",
              "      <th>mean stay-at-home/epi-week</th>\n",
              "      <th>Smooth Death/M</th>\n",
              "      <th>Smooth Stay-at-Home</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>2020-02-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>9</td>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>10</td>\n",
              "      <td>2020-03-07</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.571429</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-2.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>11</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.044</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.044</td>\n",
              "      <td>1.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>12</td>\n",
              "      <td>2020-03-21</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.022</td>\n",
              "      <td>15.428571</td>\n",
              "      <td>-0.022</td>\n",
              "      <td>14.857143</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16414</th>\n",
              "      <td>30</td>\n",
              "      <td>2020-07-25</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.064</td>\n",
              "      <td>9.285714</td>\n",
              "      <td>0.040</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16421</th>\n",
              "      <td>31</td>\n",
              "      <td>2020-08-01</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.104</td>\n",
              "      <td>6.571429</td>\n",
              "      <td>0.040</td>\n",
              "      <td>-2.714286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16428</th>\n",
              "      <td>32</td>\n",
              "      <td>2020-08-08</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>0.261</td>\n",
              "      <td>5.857143</td>\n",
              "      <td>0.157</td>\n",
              "      <td>-0.714286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16435</th>\n",
              "      <td>33</td>\n",
              "      <td>2020-08-15</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.103</td>\n",
              "      <td>0.364</td>\n",
              "      <td>11.857143</td>\n",
              "      <td>0.103</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16442</th>\n",
              "      <td>34</td>\n",
              "      <td>2020-08-22</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.111</td>\n",
              "      <td>0.666</td>\n",
              "      <td>7.142857</td>\n",
              "      <td>0.302</td>\n",
              "      <td>-4.714286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2349 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       epi-week       date  ...  Smooth Death/M  Smooth Stay-at-Home\n",
              "6             8 2020-02-22  ...             NaN                  NaN\n",
              "13            9 2020-02-29  ...           0.000             1.857143\n",
              "20           10 2020-03-07  ...           0.000            -2.142857\n",
              "27           11 2020-03-14  ...           0.044             1.142857\n",
              "34           12 2020-03-21  ...          -0.022            14.857143\n",
              "...         ...        ...  ...             ...                  ...\n",
              "16414        30 2020-07-25  ...           0.040             3.000000\n",
              "16421        31 2020-08-01  ...           0.040            -2.714286\n",
              "16428        32 2020-08-08  ...           0.157            -0.714286\n",
              "16435        33 2020-08-15  ...           0.103             6.000000\n",
              "16442        34 2020-08-22  ...           0.302            -4.714286\n",
              "\n",
              "[2349 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tr8pkDZudQOa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "24a468a6-9bf6-426a-8e67-95454bb418eb"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "#Select all countries and regions from the dataframe\n",
        "locations = df_dadossemana['location'].unique()\n",
        "tabelacompleta = pd.DataFrame()\n",
        "linha = 0\n",
        "df.dropna()\n",
        "#Makes the interaction among regions\n",
        "for i in locations:\n",
        "    df_paisum = df_dadossemana[df_dadossemana['location'] == i].reset_index()\n",
        "    \n",
        "    #Se eu quero selecionar só um país, retirar \"#\" das linhas \n",
        "    #if (i != 'Canada'):\n",
        "    #    continue\n",
        "    \n",
        "    #For each country, compare to all others\n",
        "    for j in locations:        \n",
        "        \n",
        "        #if (j != 'Australia'):\n",
        "        #    continue\n",
        "        \n",
        "        #Do not compare a region to itself\n",
        "        if (i == j):\n",
        "            continue\n",
        "            \n",
        "        df_paisdois = df_dadossemana[df_dadossemana['location'] == j].reset_index()         \n",
        "        tabela = pd.DataFrame()\n",
        "\n",
        "        #Create a table with all epidemiological weeks\n",
        "        for k in range(df_paisdois.shape[0]):            \n",
        "            \n",
        "            #Subtract death from region A from region B\n",
        "            tabela.loc[k, '(' + df_paisum['location'][k] + ' - ' + df_paisdois['location'][k] + ') Death'] = float(df_paisum['Smooth Death/M'][k]) - float( df_paisdois['Smooth Death/M'][k])            \n",
        "            \n",
        "            #Subtrai o Stay at Home do pais A pelo pais B\n",
        "            tabela.loc[k, '(' + df_paisum['location'][k] + ' - ' + df_paisdois['location'][k] + ') Stay'] = float(df_paisum['Smooth Stay-at-Home'][k]) - float( df_paisdois['Smooth Stay-at-Home'][k])\n",
        "            \n",
        "            #tabela.loc[k, df_paisum['location'][k] + ' Death'] = df_paisum['Smooth Death/M'][k]            \n",
        "            #tabela.loc[k, df_paisdois['location'][k] + ' Death'] = df_paisdois['Smooth Death/M'][k]            \n",
        "            #tabela.loc[k, '(' + df_paisum['location'][k] + ' - ' + df_paisdois['location'][k] + ') Death'] = float(df_paisum['Smooth Death/M'][k]) - float( df_paisdois['Smooth Death/M'][k])            \n",
        "            #tabela.loc[k, '(' + df_paisum['location'][k] + ' - ' + df_paisdois['location'][k] + ') Stay'] = float(df_paisum['Smooth Stay-at-Home'][k]) - float( df_paisdois['Smooth Stay-at-Home'][k])            \n",
        "            #tabela.loc[k, df_paisum['location'][k] + ' Stay'] = df_paisum['Smooth Stay-at-Home'][k]            \n",
        "            #tabela.loc[k, df_paisdois['location'][k] + ' Stay'] = df_paisdois['Smooth Stay-at-Home'][k]            \n",
        "            #print('{0} {1} {2}'.format(float(df_paisum['Smooth Stay-at-Home'][k]) , float( df_paisdois['Smooth Stay-at-Home'][k]), float(df_paisum['Smooth Stay-at-Home'][k]) - float( df_paisdois['Smooth Stay-at-Home'][k])))\n",
        "            \n",
        "        #Ignore the 8th week    \n",
        "        tabela = tabela.dropna()\n",
        "        X = tabela.iloc[:, 1:2].values # Stay\n",
        "        x = sm.add_constant(X) #\n",
        "        y = tabela.iloc[:, 0:1] # (Argentina - Australia) Death \n",
        "        res = sm.OLS(y,x).fit() #Create and adjust the model\n",
        "        \n",
        "        #Generate the tables with statistical values obtaiend from the comparison\n",
        "        tabelacompleta.loc[linha, 'Location_A'] = df_paisum['location'][1]\n",
        "        tabelacompleta.loc[linha, 'Location_B'] = df_paisdois['location'][1]\n",
        "        tabelacompleta.loc[linha, 'A Death - B Death'] =  df_paisum['location'][1] + ' - ' + df_paisdois['location'][1]\n",
        "        tabelacompleta.loc[linha, 'p_const'] = res.pvalues[0]\n",
        "        tabelacompleta.loc[linha, 'p_x1'] = res.pvalues[1]\n",
        "        tabelacompleta.loc[linha, 'coef_const'] = res.params[0]\n",
        "        tabelacompleta.loc[linha, 'coef_x1'] = res.params[1]\n",
        "        tabelacompleta.loc[linha, 'rsquared'] = res.rsquared    \n",
        "        tabelacompleta.loc[linha, 'rsquared_adj'] = res.rsquared_adj \n",
        "        linha += 1\n",
        "tabelacompleta.to_excel(r'Regression.xlsx')        "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSPbcAGkQhQA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Read the data generated from previous step\n",
        "tabelacompleta = pd.read_excel('Regression.xlsx')\n",
        "#Ordena pelo menor valor de p\n",
        "tabOrdenada = tabelacompleta.sort_values(by=['p_x1']).reset_index()    \n",
        "for i in range(tabelacompleta.shape[0]):\n",
        "    #Calculate the FDR(False  DiscoveryRate) by Benjamini  &  Hochberg         \n",
        "    if (int(tabOrdenada.shape[0]) - (i + 1) > 0):\n",
        "        tabOrdenada.loc[i, 'FDR B-H'] = float(tabOrdenada['p_x1'][i]) * (int(tabOrdenada.shape[0]) /(int(tabOrdenada.shape[0]) - (i + 1)))\n",
        "\n",
        "#List from small to big    \n",
        "tabelacompleta = tabOrdenada.sort_values(by=['A Death - B Death']).reset_index()        \n",
        "# Week 9   \n",
        "# Get -0.0398\n",
        "# p 0.6\n",
        "#Save the result in Excel \n",
        "tabelacompleta.iloc[:,3:].to_excel(r'Regression_FDR.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBMWo6dgXADW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "outputId": "77c19195-b636-4895-a7fe-954a21cfb947"
      },
      "source": [
        "tabelacompleta.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>level_0</th>\n",
              "      <th>index</th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Location_A</th>\n",
              "      <th>Location_B</th>\n",
              "      <th>A Death - B Death</th>\n",
              "      <th>p_const</th>\n",
              "      <th>p_x1</th>\n",
              "      <th>coef_const</th>\n",
              "      <th>coef_x1</th>\n",
              "      <th>rsquared</th>\n",
              "      <th>rsquared_adj</th>\n",
              "      <th>FDR B-H</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4676</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Argentina - Australia</td>\n",
              "      <td>0.004260</td>\n",
              "      <td>0.641292</td>\n",
              "      <td>0.824354</td>\n",
              "      <td>-0.039775</td>\n",
              "      <td>0.009192</td>\n",
              "      <td>-0.032092</td>\n",
              "      <td>1.710570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>424</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Austria</td>\n",
              "      <td>Argentina - Austria</td>\n",
              "      <td>0.051814</td>\n",
              "      <td>0.055669</td>\n",
              "      <td>1.186947</td>\n",
              "      <td>-0.409690</td>\n",
              "      <td>0.144226</td>\n",
              "      <td>0.108568</td>\n",
              "      <td>0.059022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5196</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Bahrain</td>\n",
              "      <td>Argentina - Bahrain</td>\n",
              "      <td>0.302319</td>\n",
              "      <td>0.712108</td>\n",
              "      <td>0.710243</td>\n",
              "      <td>0.076579</td>\n",
              "      <td>0.005777</td>\n",
              "      <td>-0.035649</td>\n",
              "      <td>2.331725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>769</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Belarus</td>\n",
              "      <td>Argentina - Belarus</td>\n",
              "      <td>0.053085</td>\n",
              "      <td>0.108026</td>\n",
              "      <td>0.767722</td>\n",
              "      <td>0.123737</td>\n",
              "      <td>0.104043</td>\n",
              "      <td>0.066711</td>\n",
              "      <td>0.120419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4734</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>Argentina - Belgium</td>\n",
              "      <td>0.816565</td>\n",
              "      <td>0.648768</td>\n",
              "      <td>1.096698</td>\n",
              "      <td>-0.863872</td>\n",
              "      <td>0.008787</td>\n",
              "      <td>-0.032514</td>\n",
              "      <td>1.767047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6957</td>\n",
              "      <td>65</td>\n",
              "      <td>65</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>Argentina - Belo Horizonte</td>\n",
              "      <td>0.664527</td>\n",
              "      <td>0.926461</td>\n",
              "      <td>-0.542945</td>\n",
              "      <td>-0.063996</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>-0.041289</td>\n",
              "      <td>13.228592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4996</td>\n",
              "      <td>82</td>\n",
              "      <td>82</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>Argentina - Berlin</td>\n",
              "      <td>0.165501</td>\n",
              "      <td>0.684282</td>\n",
              "      <td>0.909379</td>\n",
              "      <td>0.109434</td>\n",
              "      <td>0.007010</td>\n",
              "      <td>-0.034365</td>\n",
              "      <td>2.060280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4107</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Bosnia and Herzegovina</td>\n",
              "      <td>Argentina - Bosnia and Herzegovina</td>\n",
              "      <td>0.442544</td>\n",
              "      <td>0.559532</td>\n",
              "      <td>0.415666</td>\n",
              "      <td>-0.165665</td>\n",
              "      <td>0.014382</td>\n",
              "      <td>-0.026685</td>\n",
              "      <td>1.240787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>6424</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Bulgaria</td>\n",
              "      <td>Argentina - Bulgaria</td>\n",
              "      <td>0.035542</td>\n",
              "      <td>0.853362</td>\n",
              "      <td>0.761718</td>\n",
              "      <td>-0.022294</td>\n",
              "      <td>0.001452</td>\n",
              "      <td>-0.040154</td>\n",
              "      <td>6.040540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4201</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Canada</td>\n",
              "      <td>Argentina - Canada</td>\n",
              "      <td>0.369790</td>\n",
              "      <td>0.576912</td>\n",
              "      <td>0.826420</td>\n",
              "      <td>0.241768</td>\n",
              "      <td>0.013154</td>\n",
              "      <td>-0.027964</td>\n",
              "      <td>1.315993</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   level_0  index  Unnamed: 0  ...  rsquared rsquared_adj    FDR B-H\n",
              "0     4676      0           0  ...  0.009192    -0.032092   1.710570\n",
              "1      424      1           1  ...  0.144226     0.108568   0.059022\n",
              "2     5196      2           2  ...  0.005777    -0.035649   2.331725\n",
              "3      769      3           3  ...  0.104043     0.066711   0.120419\n",
              "4     4734      4           4  ...  0.008787    -0.032514   1.767047\n",
              "5     6957     65          65  ...  0.000362    -0.041289  13.228592\n",
              "6     4996     82          82  ...  0.007010    -0.034365   2.060280\n",
              "7     4107      5           5  ...  0.014382    -0.026685   1.240787\n",
              "8     6424      6           6  ...  0.001452    -0.040154   6.040540\n",
              "9     4201      7           7  ...  0.013154    -0.027964   1.315993\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNAiF3CkVyPP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        },
        "outputId": "1253f855-f35d-4219-a94a-2f722227584d"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "from io import BytesIO\n",
        "\n",
        "#Height and lenght of the figure\n",
        "plt.figure(figsize=(25,25))\n",
        "\n",
        "tabelacompleta = pd.read_excel('Regression_FDR.xlsx')\n",
        "\n",
        "dados = tabelacompleta.pivot(\"Location_A\", \"Location_B\", \"p_x1\")\n",
        "\n",
        "fig = sns.heatmap(dados, robust=True, fmt=\"f\", cmap='RdBu_r', vmin=0, vmax=1)\n",
        "\n",
        "plt.savefig('heatmap_PDF.pdf', format='pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1800x1800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OINCX8JzUQoW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dados.to_excel('DadosPivot.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}

#Code for RStudio. Both files are available in the supplement#

setwd("~/Desktop/code_COVID_xlsx/conferencia.xlsx")
library("bstats")
library(lmtest)
library(readxl)

conferencia <- read_excel("conferencia.xlsx")
pares<- read_excel("pares_conf.xlsx")


i=1
A=as.character(pares[i,1])
B=as.character(pares[i,2])
xyA=as.matrix(conferencia[conferencia$location==A,2:3])
xyB=conferencia[conferencia$location==B,2:3]
deaths = (xyA - xyB)[,1]  
iso   = (xyA - xyB)[,2]
model = lm(deaths~iso)
ds = summary(model)



coefs = model$coefficients
pv=coef(ds)[,4]
# Residual analysis
resd = model$residuals
tests = c(shapiro.test(resd)$p.value, # H0: normality
          white.test(model)$p.value, # H0:  homoskedasticity
          bgtest(model)$p.value, # H0: uncorrelated errors
          resettest(model)$p.value) # Model is functionally well specified
coint = as.numeric(PP.test(lm(deaths~iso)$residuals)[3])

fr=data.frame("A" = A, "B" = B, "intercept" = as.numeric(coefs[1]), "coef_isol" = as.numeric(coefs[2]), 
              "p.value_iso" = pv[2], "Shapiro" = shapiro.test(resd)$p.value,
              "White" = white.test(model)$p.value, "LM-test" = bgtest(model)$p.value,
              "Reset" = resettest(model)$p.value, "Coint" = coint)

for(i in 2:dim(pares)[1]){
  A=as.character(pares[i,1])
  B=as.character(pares[i,2])
  xyA=as.matrix(conferencia[conferencia$location==A,2:3])
  xyB=conferencia[conferencia$location==B,2:3]
  deaths = (xyA - xyB)[,1]  
  iso   = (xyA - xyB)[,2]
  model = lm(deaths~iso)
  ds = summary(model)
  coefs = model$coefficients
  pv=coef(ds)[,4]
  # Residual analysis
  resd = model$residuals
  coint = as.numeric(PP.test(lm(deaths~iso)$residuals)[3])
  
  fr=rbind(fr,data.frame("A" = A, "B" = B, "intercept" = as.numeric(coefs[1]), "coef_isol" = as.numeric(coefs[2]), 
                         "p.value_iso" = pv[2], "Shapiro" = shapiro.test(resd)$p.value,
                         "White" = white.test(model)$p.value,  "LM-test" = bgtest(model)$p.value,
                         "Reset" = resettest(model)$p.value, "Coint" = coint))
}


write.table(fr, file = "Todos_conf.csv", sep = ",", row.names = F)