Covid-19-Data-Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "from functools import reduce\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set of external covariates\n",
    "population = pd.read_csv('datasets/popolazione2019.csv') \n",
    "\n",
    "population['SchoolClo'] = population['SchoolClo'].apply(lambda x: datetime.strptime(x, '%d/%m/%Y').strftime('%Y-%m-%d'))\n",
    "population['Lockdown'] = population['Lockdown'].apply(lambda x: datetime.strptime(x, '%d/%m/%Y').strftime('%Y-%m-%d'))\n",
    "population['Manufactur'] = population['Manufactur'].apply(lambda x: datetime.strptime(x, '%d/%m/%Y').strftime('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>stato</th>\n",
       "      <th>codice_regione</th>\n",
       "      <th>denominazione_regione</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>ricoverati_con_sintomi</th>\n",
       "      <th>terapia_intensiva</th>\n",
       "      <th>totale_ospedalizzati</th>\n",
       "      <th>isolamento_domiciliare</th>\n",
       "      <th>...</th>\n",
       "      <th>note</th>\n",
       "      <th>ingressi_terapia_intensiva</th>\n",
       "      <th>note_test</th>\n",
       "      <th>note_casi</th>\n",
       "      <th>totale_positivi_test_molecolare</th>\n",
       "      <th>totale_positivi_test_antigenico_rapido</th>\n",
       "      <th>tamponi_test_molecolare</th>\n",
       "      <th>tamponi_test_antigenico_rapido</th>\n",
       "      <th>codice_nuts_1</th>\n",
       "      <th>codice_nuts_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>13</td>\n",
       "      <td>Abruzzo</td>\n",
       "      <td>42.351222</td>\n",
       "      <td>13.398438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>17</td>\n",
       "      <td>Basilicata</td>\n",
       "      <td>40.639471</td>\n",
       "      <td>15.805148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>18</td>\n",
       "      <td>Calabria</td>\n",
       "      <td>38.905976</td>\n",
       "      <td>16.594402</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>15</td>\n",
       "      <td>Campania</td>\n",
       "      <td>40.839566</td>\n",
       "      <td>14.250850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>8</td>\n",
       "      <td>Emilia-Romagna</td>\n",
       "      <td>44.494367</td>\n",
       "      <td>11.341721</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  data stato  codice_regione denominazione_regione        lat  \\\n",
       "0  2020-02-24T18:00:00   ITA              13               Abruzzo  42.351222   \n",
       "1  2020-02-24T18:00:00   ITA              17            Basilicata  40.639471   \n",
       "2  2020-02-24T18:00:00   ITA              18              Calabria  38.905976   \n",
       "3  2020-02-24T18:00:00   ITA              15              Campania  40.839566   \n",
       "4  2020-02-24T18:00:00   ITA               8        Emilia-Romagna  44.494367   \n",
       "\n",
       "        long  ricoverati_con_sintomi  terapia_intensiva  totale_ospedalizzati  \\\n",
       "0  13.398438                       0                  0                     0   \n",
       "1  15.805148                       0                  0                     0   \n",
       "2  16.594402                       0                  0                     0   \n",
       "3  14.250850                       0                  0                     0   \n",
       "4  11.341721                      10                  2                    12   \n",
       "\n",
       "   isolamento_domiciliare  ...  note  ingressi_terapia_intensiva  note_test  \\\n",
       "0                       0  ...  None                         NaN       None   \n",
       "1                       0  ...  None                         NaN       None   \n",
       "2                       0  ...  None                         NaN       None   \n",
       "3                       0  ...  None                         NaN       None   \n",
       "4                       6  ...  None                         NaN       None   \n",
       "\n",
       "   note_casi  totale_positivi_test_molecolare  \\\n",
       "0       None                              NaN   \n",
       "1       None                              NaN   \n",
       "2       None                              NaN   \n",
       "3       None                              NaN   \n",
       "4       None                              NaN   \n",
       "\n",
       "   totale_positivi_test_antigenico_rapido  tamponi_test_molecolare  \\\n",
       "0                                     NaN                      NaN   \n",
       "1                                     NaN                      NaN   \n",
       "2                                     NaN                      NaN   \n",
       "3                                     NaN                      NaN   \n",
       "4                                     NaN                      NaN   \n",
       "\n",
       "   tamponi_test_antigenico_rapido  codice_nuts_1  codice_nuts_2  \n",
       "0                             NaN           None           None  \n",
       "1                             NaN           None           None  \n",
       "2                             NaN           None           None  \n",
       "3                             NaN           None           None  \n",
       "4                             NaN           None           None  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load updated regional covid data from json\n",
    "# Source: Dipartimento della Protezione Civile\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-json/dpc-covid19-ita-regioni.json\"\n",
    "json_data = pd.read_json(url)\n",
    "json_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_json = len(json_data)\n",
    "covid = {\n",
    "    'date': json_data.data,\n",
    "    'region': json_data.denominazione_regione,\n",
    "    'icu': json_data.terapia_intensiva,\n",
    "    'positives': json_data.totale_positivi,\n",
    "    'deaths': json_data.deceduti,\n",
    "    'cases': json_data.totale_casi,\n",
    "    'swabs': json_data.tamponi,\n",
    "}\n",
    "\n",
    "# Remove time from dates\n",
    "FMT = '%Y-%m-%dT%H:%M:%S' #datetime format\n",
    "covid['date'] = covid['date'].apply(lambda x: datetime.strptime(x, FMT).strftime('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most recent date is 2021-06-24\n"
     ]
    }
   ],
   "source": [
    "datetoday = covid['date'].max()\n",
    "print(\"Most recent date is\", datetoday)\n",
    "\n",
    "dates = covid['date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           0\n",
       "1           0\n",
       "2           0\n",
       "3           0\n",
       "4          18\n",
       "         ... \n",
       "10222    4753\n",
       "10223    2555\n",
       "10224     836\n",
       "10225      35\n",
       "10226    4827\n",
       "Name: totale_positivi, Length: 10227, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define time variable\n",
    "x = np.arange(0, len(dates))\n",
    "m = len(x)\n",
    "\n",
    "# look at regions in covid data\n",
    "regions = covid['region'].unique()\n",
    "n_reg  = len(regions)\n",
    "\n",
    "# create matrices with CUMULATIVE counts of deaths, cases and swabs\n",
    "# and current counts for ICU and positives\n",
    "covid['positives']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEAcum = pd.DataFrame(0, index=regions, columns=dates, dtype='int')\n",
    "CAScum = pd.DataFrame(0, index=regions, columns=dates, dtype='int')\n",
    "SWABcum = pd.DataFrame(0, index=regions, columns=dates, dtype='int')\n",
    "ICUt = pd.DataFrame(0, index=regions, columns=dates, dtype='int')\n",
    "POSt = pd.DataFrame(0, index=regions, columns=dates, dtype='int')\n",
    "\n",
    "for i in range(0, n_reg):\n",
    "    deaths = covid['deaths'].loc[json_data['denominazione_regione'] == regions[i]]\n",
    "    cases = covid['cases'].loc[json_data['denominazione_regione'] == regions[i]]\n",
    "    swabs = covid['swabs'].loc[json_data['denominazione_regione'] == regions[i]]\n",
    "    icut = covid['icu'].loc[json_data['denominazione_regione'] == regions[i]]\n",
    "    post = covid['positives'].loc[json_data['denominazione_regione'] == regions[i]]\n",
    "    \n",
    "    DEAcum.loc[regions[i]] = deaths.values\n",
    "    CAScum.loc[regions[i]] = cases.values\n",
    "    SWABcum.loc[regions[i]] = swabs.values\n",
    "    ICUt.loc[regions[i]] = icut.values\n",
    "    POSt.loc[regions[i]] = post.values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## wide format\n",
    "regions_series = pd.Series(regions, index=regions, name='Territorio')\n",
    "\n",
    "DEAcum_wide = pd.concat([regions_series, DEAcum], axis=1)\n",
    "CAScum_wide = pd.concat([regions_series, CAScum], axis=1)\n",
    "SWABcum_wide = pd.concat([regions_series, SWABcum], axis=1)\n",
    "ICUt_wide = pd.concat([regions_series, ICUt], axis=1)\n",
    "POSt_wide = pd.concat([regions_series, POSt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# long format\n",
    "def pivot_longer(df, id_vars, value_vars, value_name, rename_col, sort_by):\n",
    "    return pd.melt(df, id_vars=id_vars, value_vars=value_vars, value_name=value_name)\\\n",
    "                        .rename(columns=rename_col)\\\n",
    "                        .sort_values(by=sort_by)\\\n",
    "                        .reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEAcum_long = pivot_longer(DEAcum_wide, id_vars='Territorio', value_vars=dates, value_name='deathsCUM', rename_col={'variable': 'Date'}, sort_by=['Territorio', 'Date'])\n",
    "CAScum_long = pivot_longer(CAScum_wide, id_vars='Territorio', value_vars=dates, value_name='casesCUM', rename_col={'variable': 'Date'}, sort_by=['Territorio', 'Date'])\n",
    "SWABcum_long = pivot_longer(SWABcum_wide, id_vars='Territorio', value_vars=dates, value_name='swabsCUM', rename_col={'variable': 'Date'}, sort_by=['Territorio', 'Date'])\n",
    "ICUt_long = pivot_longer(ICUt_wide, id_vars='Territorio', value_vars=dates, value_name='icu', rename_col={'variable': 'Date'}, sort_by=['Territorio', 'Date'])\n",
    "POSt_long = pivot_longer(POSt_wide, id_vars='Territorio', value_vars=dates, value_name='positives', rename_col={'variable': 'Date'}, sort_by=['Territorio', 'Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# we need daily number of counts, deaths and swabs\n",
    "DEAt_long = DEAcum_long.copy()\n",
    "CASt_long = CAScum_long.copy()\n",
    "SWABt_long = SWABcum_long.copy()\n",
    "\n",
    "DEAt_long['deaths'] = DEAcum_long['deathsCUM'] - DEAcum_long.groupby(['Territorio'])['deathsCUM'].shift(1)\n",
    "CASt_long['cases'] = CAScum_long['casesCUM'] - CAScum_long.groupby(['Territorio'])['casesCUM'].shift(1)\n",
    "SWABt_long['swabs'] = SWABcum_long['swabsCUM'] - SWABcum_long.groupby(['Territorio'])['swabsCUM'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Territorio</th>\n",
       "      <th>Date</th>\n",
       "      <th>Pop2019</th>\n",
       "      <th>ICU2019</th>\n",
       "      <th>Pop65</th>\n",
       "      <th>SchoolClo</th>\n",
       "      <th>Lockdown</th>\n",
       "      <th>Manufactur</th>\n",
       "      <th>GDP</th>\n",
       "      <th>GDPcap</th>\n",
       "      <th>Density2019</th>\n",
       "      <th>RSA</th>\n",
       "      <th>RSAp</th>\n",
       "      <th>DepRatio</th>\n",
       "      <th>DepRatioOld</th>\n",
       "      <th>House2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>125666</td>\n",
       "      <td>10</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>4902.0</td>\n",
       "      <td>39008.164500</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083368</td>\n",
       "      <td>58.7</td>\n",
       "      <td>37.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>125666</td>\n",
       "      <td>10</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>4902.0</td>\n",
       "      <td>39008.164500</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083368</td>\n",
       "      <td>58.7</td>\n",
       "      <td>37.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>125666</td>\n",
       "      <td>10</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>4902.0</td>\n",
       "      <td>39008.164500</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083368</td>\n",
       "      <td>58.7</td>\n",
       "      <td>37.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>125666</td>\n",
       "      <td>10</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>4902.0</td>\n",
       "      <td>39008.164500</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083368</td>\n",
       "      <td>58.7</td>\n",
       "      <td>37.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>125666</td>\n",
       "      <td>10</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>4902.0</td>\n",
       "      <td>39008.164500</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083368</td>\n",
       "      <td>58.7</td>\n",
       "      <td>37.8</td>\n",
       "      <td>2.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10222</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1639591</td>\n",
       "      <td>120</td>\n",
       "      <td>23.2</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>34541.7</td>\n",
       "      <td>21067.266166</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>0.666945</td>\n",
       "      <td>53.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10223</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>1639591</td>\n",
       "      <td>120</td>\n",
       "      <td>23.2</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>34541.7</td>\n",
       "      <td>21067.266166</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>0.666945</td>\n",
       "      <td>53.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10224</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>1639591</td>\n",
       "      <td>120</td>\n",
       "      <td>23.2</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>34541.7</td>\n",
       "      <td>21067.266166</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>0.666945</td>\n",
       "      <td>53.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10225</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>1639591</td>\n",
       "      <td>120</td>\n",
       "      <td>23.2</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>34541.7</td>\n",
       "      <td>21067.266166</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>0.666945</td>\n",
       "      <td>53.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10226</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>1639591</td>\n",
       "      <td>120</td>\n",
       "      <td>23.2</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>34541.7</td>\n",
       "      <td>21067.266166</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>0.666945</td>\n",
       "      <td>53.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10227 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Territorio        Date  Pop2019  ICU2019  Pop65   SchoolClo  \\\n",
       "0      Valle d'Aosta  2020-02-24   125666       10   23.5  2020-03-04   \n",
       "1      Valle d'Aosta  2020-02-25   125666       10   23.5  2020-03-04   \n",
       "2      Valle d'Aosta  2020-02-26   125666       10   23.5  2020-03-04   \n",
       "3      Valle d'Aosta  2020-02-27   125666       10   23.5  2020-03-04   \n",
       "4      Valle d'Aosta  2020-02-28   125666       10   23.5  2020-03-04   \n",
       "...              ...         ...      ...      ...    ...         ...   \n",
       "10222       Sardegna  2021-06-20  1639591      120   23.2  2020-03-04   \n",
       "10223       Sardegna  2021-06-21  1639591      120   23.2  2020-03-04   \n",
       "10224       Sardegna  2021-06-22  1639591      120   23.2  2020-03-04   \n",
       "10225       Sardegna  2021-06-23  1639591      120   23.2  2020-03-04   \n",
       "10226       Sardegna  2021-06-24  1639591      120   23.2  2020-03-04   \n",
       "\n",
       "         Lockdown  Manufactur      GDP        GDPcap  Density2019  RSA  \\\n",
       "0      2020-03-09  2020-03-22   4902.0  39008.164500    38.537236    2   \n",
       "1      2020-03-09  2020-03-22   4902.0  39008.164500    38.537236    2   \n",
       "2      2020-03-09  2020-03-22   4902.0  39008.164500    38.537236    2   \n",
       "3      2020-03-09  2020-03-22   4902.0  39008.164500    38.537236    2   \n",
       "4      2020-03-09  2020-03-22   4902.0  39008.164500    38.537236    2   \n",
       "...           ...         ...      ...           ...          ...  ...   \n",
       "10222  2020-03-09  2020-03-22  34541.7  21067.266166    68.032763   16   \n",
       "10223  2020-03-09  2020-03-22  34541.7  21067.266166    68.032763   16   \n",
       "10224  2020-03-09  2020-03-22  34541.7  21067.266166    68.032763   16   \n",
       "10225  2020-03-09  2020-03-22  34541.7  21067.266166    68.032763   16   \n",
       "10226  2020-03-09  2020-03-22  34541.7  21067.266166    68.032763   16   \n",
       "\n",
       "           RSAp  DepRatio  DepRatioOld  House2019  \n",
       "0      0.083368      58.7         37.8        2.0  \n",
       "1      0.083368      58.7         37.8        2.0  \n",
       "2      0.083368      58.7         37.8        2.0  \n",
       "3      0.083368      58.7         37.8        2.0  \n",
       "4      0.083368      58.7         37.8        2.0  \n",
       "...         ...       ...          ...        ...  \n",
       "10222  0.666945      53.8         36.5        2.2  \n",
       "10223  0.666945      53.8         36.5        2.2  \n",
       "10224  0.666945      53.8         36.5        2.2  \n",
       "10225  0.666945      53.8         36.5        2.2  \n",
       "10226  0.666945      53.8         36.5        2.2  \n",
       "\n",
       "[10227 rows x 16 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# expand dataset\n",
    "t = np.repeat(population['Territorio'], len(dates))\n",
    "d = pd.Series(np.tile(dates, n_reg))\n",
    "\n",
    "my_data = pd.DataFrame({'Territorio': t.values, 'Date': d.values })\n",
    "\n",
    "# attach time-unvarying covariates\n",
    "my_data2 = pd.merge(my_data, population, how=\"left\", on=[\"Territorio\"])\n",
    "my_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dummies for time-varing covariates\n",
    "# (school closure, start lockdown)\n",
    "my_data2['SchoolD'] = np.where(my_data2['SchoolClo'] <= my_data2['Date'], 1, 0)\n",
    "my_data2['LockD'] = np.where(my_data2['Lockdown'] <= my_data2['Date'], 1, 0)\n",
    "my_data2['ManufactD'] = np.where(my_data2['Manufactur'] <= my_data2['Date'], 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# attach COVID cases, deaths, swabs and current ICU use\n",
    "data_frames_to_merge = [my_data2, DEAt_long, CASt_long, SWABt_long, ICUt_long, POSt_long]\n",
    "my_data3 = reduce(lambda left, right: pd.merge(left, right, on=[\"Territorio\", \"Date\"]), data_frames_to_merge)\n",
    "my_data3['deaths'] = np.where(my_data3['deaths'] < 0, 0, my_data3['deaths'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define start of epidemic as day when cumulative cases\n",
    "# surpass total population / 10^6\n",
    "population['thresh_epi'] = np.round(population['Pop2019'] * 1e-6, 0).astype('int')\n",
    "population['thresh_epi'] = np.where(population['thresh_epi'] == 0, 1, population['thresh_epi'])\n",
    "\n",
    "df_startepi = population[['Territorio', 'thresh_epi']]\n",
    "# attach start epidemic and delay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "my_data4 = pd.merge(my_data3, df_startepi, how=\"left\", on=[\"Territorio\"])\n",
    "\n",
    "def mutate1(data):\n",
    "    data['startEpiD'] = np.where(data.casesCUM >= data.thresh_epi, 1, 0)\n",
    "    data['startEpi'] = data['Date'][data['startEpiD'] == 1].min()\n",
    "\n",
    "    return data\n",
    "\n",
    "my_data5 = my_data4.groupby(['Territorio']).apply(mutate1)\n",
    "my_data5['startEpi'] = pd.to_datetime(my_data5['startEpi'])\n",
    "my_data5['Date'] = pd.to_datetime(my_data5['Date'])\n",
    "my_data5['DelayEpi'] = (my_data5['startEpi'] - my_data5['Date'].min()).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_covid = my_data5.copy().dropna()\n",
    "# transform variables\n",
    "df_covid['swabsCUM'] = df_covid['swabsCUM'] / 1000\n",
    "df_covid['time'] = np.tile(np.arange(1, len(dates)), n_reg)\n",
    "df_covid['lGDPcap'] = np.round(np.log(df_covid.GDPcap), 2)\n",
    "\n",
    "def mutate2(data):\n",
    "    data['icupch'] = np.round((data.icu / data.ICU2019) * 100, 2)\n",
    "    data['sqrtICU'] = np.round(np.sqrt(data.icupch), 2)\n",
    "    data['sqrtPOS'] = np.round(np.sqrt(data.positives), 2)\n",
    "\n",
    "    return data\n",
    "\n",
    "df_covid = df_covid.groupby(['Territorio']).apply(mutate2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         0.0\n",
       "2         0.0\n",
       "3         0.0\n",
       "4         0.0\n",
       "5         0.0\n",
       "         ... \n",
       "10222     9.0\n",
       "10223     4.0\n",
       "10224     6.0\n",
       "10225    18.0\n",
       "10226    16.0\n",
       "Name: cases, Length: 10206, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid['cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep variables of interest and rename\n",
    "columns_rename = {\n",
    "    \"Territorio\": \"region\", \n",
    "    \"Date\": \"date\",\n",
    "    \"Pop2019\": \"population\",\n",
    "    \"Pop65\": \"pop65\",\n",
    "    \"Density2019\": \"density\",\n",
    "    \"RSA\": \"nursehome\",\n",
    "    \"swabsCUM\": \"swabs\",\n",
    "    \"DelayEpi\": \"delayepi\",\n",
    "    \"lGDPcap\": \"gdpcap\",\n",
    "    \"sqrtICU\": \"icupch\",\n",
    "    \"sqrtPOS\": \"positives\",\n",
    "    \"House2019\": \"households\"\n",
    "}\n",
    "\n",
    "var_of_interest = ['Territorio', 'Date', 'Pop2019', 'Pop65', 'Density2019', 'RSA','deaths','cases', 'swabsCUM', 'DelayEpi', 'time', 'lGDPcap','icu', 'sqrtICU','sqrtPOS', 'House2019']\n",
    "df_covid = df_covid[var_of_interest].rename(columns=columns_rename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_covid.to_csv(\"datasets/dataCOVID_all.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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>region</th>\n",
       "      <th>date</th>\n",
       "      <th>population</th>\n",
       "      <th>pop65</th>\n",
       "      <th>density</th>\n",
       "      <th>nursehome</th>\n",
       "      <th>deaths</th>\n",
       "      <th>cases</th>\n",
       "      <th>swabs</th>\n",
       "      <th>delayepi</th>\n",
       "      <th>time</th>\n",
       "      <th>gdpcap</th>\n",
       "      <th>icu</th>\n",
       "      <th>icupch</th>\n",
       "      <th>positives</th>\n",
       "      <th>households</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>125666</td>\n",
       "      <td>23.5</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>10</td>\n",
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       "      <td>10.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>125666</td>\n",
       "      <td>23.5</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>125666</td>\n",
       "      <td>23.5</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>10.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>125666</td>\n",
       "      <td>23.5</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>10.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>125666</td>\n",
       "      <td>23.5</td>\n",
       "      <td>38.537236</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10222</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1639591</td>\n",
       "      <td>23.2</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1354.772</td>\n",
       "      <td>9</td>\n",
       "      <td>482</td>\n",
       "      <td>9.96</td>\n",
       "      <td>5</td>\n",
       "      <td>2.04</td>\n",
       "      <td>107.60</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10223</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>1639591</td>\n",
       "      <td>23.2</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1355.601</td>\n",
       "      <td>9</td>\n",
       "      <td>483</td>\n",
       "      <td>9.96</td>\n",
       "      <td>5</td>\n",
       "      <td>2.04</td>\n",
       "      <td>49.92</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10224</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>1639591</td>\n",
       "      <td>23.2</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1360.214</td>\n",
       "      <td>9</td>\n",
       "      <td>484</td>\n",
       "      <td>9.96</td>\n",
       "      <td>4</td>\n",
       "      <td>1.82</td>\n",
       "      <td>49.41</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10225</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>1639591</td>\n",
       "      <td>23.2</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1364.083</td>\n",
       "      <td>9</td>\n",
       "      <td>485</td>\n",
       "      <td>9.96</td>\n",
       "      <td>4</td>\n",
       "      <td>1.82</td>\n",
       "      <td>49.05</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10226</th>\n",
       "      <td>Sardegna</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>1639591</td>\n",
       "      <td>23.2</td>\n",
       "      <td>68.032763</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1367.138</td>\n",
       "      <td>9</td>\n",
       "      <td>486</td>\n",
       "      <td>9.96</td>\n",
       "      <td>5</td>\n",
       "      <td>2.04</td>\n",
       "      <td>48.75</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10206 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              region       date  population  pop65    density  nursehome  \\\n",
       "1      Valle d'Aosta 2020-02-25      125666   23.5  38.537236          2   \n",
       "2      Valle d'Aosta 2020-02-26      125666   23.5  38.537236          2   \n",
       "3      Valle d'Aosta 2020-02-27      125666   23.5  38.537236          2   \n",
       "4      Valle d'Aosta 2020-02-28      125666   23.5  38.537236          2   \n",
       "5      Valle d'Aosta 2020-02-29      125666   23.5  38.537236          2   \n",
       "...              ...        ...         ...    ...        ...        ...   \n",
       "10222       Sardegna 2021-06-20     1639591   23.2  68.032763         16   \n",
       "10223       Sardegna 2021-06-21     1639591   23.2  68.032763         16   \n",
       "10224       Sardegna 2021-06-22     1639591   23.2  68.032763         16   \n",
       "10225       Sardegna 2021-06-23     1639591   23.2  68.032763         16   \n",
       "10226       Sardegna 2021-06-24     1639591   23.2  68.032763         16   \n",
       "\n",
       "       deaths  cases     swabs  delayepi  time  gdpcap  icu  icupch  \\\n",
       "1         0.0    0.0     0.007        10     1   10.57    0    0.00   \n",
       "2         0.0    0.0     0.007        10     2   10.57    0    0.00   \n",
       "3         0.0    0.0     0.009        10     3   10.57    0    0.00   \n",
       "4         0.0    0.0     0.009        10     4   10.57    0    0.00   \n",
       "5         0.0    0.0     0.009        10     5   10.57    0    0.00   \n",
       "...       ...    ...       ...       ...   ...     ...  ...     ...   \n",
       "10222     0.0    9.0  1354.772         9   482    9.96    5    2.04   \n",
       "10223     0.0    4.0  1355.601         9   483    9.96    5    2.04   \n",
       "10224     3.0    6.0  1360.214         9   484    9.96    4    1.82   \n",
       "10225     1.0   18.0  1364.083         9   485    9.96    4    1.82   \n",
       "10226     0.0   16.0  1367.138         9   486    9.96    5    2.04   \n",
       "\n",
       "       positives  households  \n",
       "1           0.00         2.0  \n",
       "2           0.00         2.0  \n",
       "3           0.00         2.0  \n",
       "4           0.00         2.0  \n",
       "5           0.00         2.0  \n",
       "...          ...         ...  \n",
       "10222     107.60         2.2  \n",
       "10223      49.92         2.2  \n",
       "10224      49.41         2.2  \n",
       "10225      49.05         2.2  \n",
       "10226      48.75         2.2  \n",
       "\n",
       "[10206 rows x 16 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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