krishna-anand/CoVid19 Comparison.ipynb

## CoVid19 Comparison.ipynb
{
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   "cell_type": "markdown",
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   "source": [
    "<h1>N-CoVid19 Spread Analysis (France, China, Italy)</h1>\n",
    "\n",
    "# Introduction\n",
    "\n",
    "## What is a coronavirus?\n",
    "Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19.\n",
    "\n",
    "## What is COVID-19?\n",
    "COVID-19 is the infectious disease caused by the most recently discovered coronavirus. This new virus and disease were unknown before the outbreak began in Wuhan, China, in December 2019. What are the symptoms of COVID-19? The most common symptoms of COVID-19 are fever, tiredness, and dry cough. Some patients may have aches and pains, nasal congestion, runny nose, sore throat or diarrhea. These symptoms are usually mild and begin gradually. Some people become infected but don’t develop any symptoms and don't feel unwell. Most people (about 80%) recover from the disease without needing special treatment. Around 1 out of every 6 people who gets COVID-19 becomes seriously ill and develops difficulty breathing. Older people, and those with underlying medical problems like high blood pressure, heart problems or diabetes, are more likely to develop serious illness. People with fever, cough and difficulty breathing should seek medical attention.\n",
    "\n",
    "The following illustrates the spread in France of 2019-nCoV. At this time, France is a neighbouring country to Italy, which has the second most confirmed cases of n-CoVid19 and the most deaths in the world (5746 deaths).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets and Create DataFrames Using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "raw_data = pd.read_csv( \"chiffres-cles.csv\", parse_dates=['date']) # raw dataframe\n",
    "df_china = pd.read_csv( \"china.csv\", parse_dates=['date'])\n",
    "df_italy = pd.read_csv( \"contagioitalia.csv\", parse_dates=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "    .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>date</th>\n",
       "      <th>granularite</th>\n",
       "      <th>maille_code</th>\n",
       "      <th>maille_nom</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>reanimation</th>\n",
       "      <th>source_nom</th>\n",
       "      <th>source_url</th>\n",
       "      <th>source_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-16</td>\n",
       "      <td>Charente</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARS Nouvelle-Aquitaine</td>\n",
       "      <td>https://www.nouvelle-aquitaine.ars.sante.fr/co...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-17</td>\n",
       "      <td>Charente-Maritime</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARS Nouvelle-Aquitaine</td>\n",
       "      <td>https://www.nouvelle-aquitaine.ars.sante.fr/co...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-19</td>\n",
       "      <td>Corrèze</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARS Nouvelle-Aquitaine</td>\n",
       "      <td>https://www.nouvelle-aquitaine.ars.sante.fr/co...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-23</td>\n",
       "      <td>Creuse</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARS Nouvelle-Aquitaine</td>\n",
       "      <td>https://www.nouvelle-aquitaine.ars.sante.fr/co...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-24</td>\n",
       "      <td>Dordogne</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARS Nouvelle-Aquitaine</td>\n",
       "      <td>https://www.nouvelle-aquitaine.ars.sante.fr/co...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  granularite maille_code         maille_nom  cases  deaths  \\\n",
       "0 2020-01-24  departement      DEP-16           Charente    0.0     NaN   \n",
       "1 2020-01-24  departement      DEP-17  Charente-Maritime    0.0     NaN   \n",
       "2 2020-01-24  departement      DEP-19            Corrèze    0.0     NaN   \n",
       "3 2020-01-24  departement      DEP-23             Creuse    0.0     NaN   \n",
       "4 2020-01-24  departement      DEP-24           Dordogne    0.0     NaN   \n",
       "\n",
       "   reanimation              source_nom  \\\n",
       "0          NaN  ARS Nouvelle-Aquitaine   \n",
       "1          NaN  ARS Nouvelle-Aquitaine   \n",
       "2          NaN  ARS Nouvelle-Aquitaine   \n",
       "3          NaN  ARS Nouvelle-Aquitaine   \n",
       "4          NaN  ARS Nouvelle-Aquitaine   \n",
       "\n",
       "                                          source_url               source_type  \n",
       "0  https://www.nouvelle-aquitaine.ars.sante.fr/co...  agences-regionales-sante  \n",
       "1  https://www.nouvelle-aquitaine.ars.sante.fr/co...  agences-regionales-sante  \n",
       "2  https://www.nouvelle-aquitaine.ars.sante.fr/co...  agences-regionales-sante  \n",
       "3  https://www.nouvelle-aquitaine.ars.sante.fr/co...  agences-regionales-sante  \n",
       "4  https://www.nouvelle-aquitaine.ars.sante.fr/co...  agences-regionales-sante  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.rename(columns={'cas_confirmes':'cases', 'deces':'deaths'},inplace=True) #important variable namess in English\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Identifying the Virus Spread at the National Level for France \n",
    "#### We can see the total number of cases has increased everyday and unfornately has the total number of deaths. As of 03/19/2020 there a total of 10,995 cases and 372 confirmed deaths. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date      datetime64[ns]\n",
      "cases            float64\n",
      "deaths           float64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>5423.0</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>6633.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>7730.0</td>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>9134.0</td>\n",
       "      <td>244.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>10995.0</td>\n",
       "      <td>372.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         date    cases  deaths\n",
       "32 2020-03-15   5423.0   127.0\n",
       "33 2020-03-16   6633.0   148.0\n",
       "34 2020-03-17   7730.0   175.0\n",
       "35 2020-03-18   9134.0   244.0\n",
       "36 2020-03-19  10995.0   372.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_national = raw_data[raw_data.maille_nom =='France']\n",
    "\n",
    "df_national.reset_index(inplace = True, drop=True)\n",
    "df_national = df_national[['date','cases','deaths']]\n",
    "\n",
    "print(df_national.dtypes)\n",
    "df_national.tail()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_national = df_national[df_national['date'] > '2020-03-01']\n",
    "df_national.date = pd.to_datetime(df_national.date)\n",
    "df_national.reset_index(inplace = True, drop=True)\n",
    "#df_google.set_index('datetime', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Calculate the Virus Spread using Differentiation and Plotting Virus Cases & Spread Rate Over Time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_national['cases'].values # transform the column to differentiate into a numpy array\n",
    "\n",
    "deriv_y = np.diff(y) # now we can get the derivative as a new numpy array\n",
    "\n",
    "output = np.transpose(deriv_y)\n",
    "#now add the numpy array to our dataframe\n",
    "df_national['ContagionRate'] = pd.Series(output)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (3,6))\n",
    "plt.subplot(211)\n",
    "plt.plot(df_national['date'],df_national['cases'], color = 'g') #trend cases\n",
    "plt.title('Cases over time')\n",
    "plt.ylabel('number of cases')\n",
    "plt.xticks(df_national['date'],\" \")\n",
    "plt.subplot(212)\n",
    "plt.plot(df_national['date'],df_national['ContagionRate'], color = 'r') #trend deaths\n",
    "plt.title('Spread rate over time')\n",
    "plt.ylabel('Rate (cases % increase)')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.suptitle('Virus spread over time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Virus \"Cases Over Time\" have grown exponentially over the course of 14 days in France, and so too the \"Spread Rate\" as well.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Virus Spread for both Italy and China, the two countries that have the most amount of cases and deaths. China having 81,426 confirmed cases and 3,153 deaths, and Italy currently having 59,138 confirmed cases and 5476 deaths\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain the Virus Spread Rate for China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "yc = df_china['Number of cases'].values # transform the column to differentiate into a numpy array\n",
    "\n",
    "deriv_yc = np.diff(yc) # now we can get the derivative as a new numpy array\n",
    "output_c = np.transpose(deriv_yc)\n",
    "\n",
    "df_china['ContagionRate'] = pd.Series(output_c) # \n",
    "\n",
    "df_china = df_china[df_china['ContagionRate'] < 4500] # clean the chinese data from the suspicious \"spike\" of 12/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Unnamed: 0</th>\n",
       "      <th>date</th>\n",
       "      <th>TotalPositiveCases</th>\n",
       "      <th>IntensiveCarePatients</th>\n",
       "      <th>TotalHospitalizedPatients</th>\n",
       "      <th>HomeConfinement</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>% hospitalized over time</th>\n",
       "      <th>% deaths over time</th>\n",
       "      <th>% intensive care over time</th>\n",
       "      <th>ContagionRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>24747</td>\n",
       "      <td>1672</td>\n",
       "      <td>11335</td>\n",
       "      <td>9268</td>\n",
       "      <td>1809</td>\n",
       "      <td>45.803532</td>\n",
       "      <td>7.309977</td>\n",
       "      <td>6.756375</td>\n",
       "      <td>3411.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>27980</td>\n",
       "      <td>1851</td>\n",
       "      <td>12876</td>\n",
       "      <td>10197</td>\n",
       "      <td>2158</td>\n",
       "      <td>46.018585</td>\n",
       "      <td>7.712652</td>\n",
       "      <td>6.615440</td>\n",
       "      <td>3379.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>31506</td>\n",
       "      <td>2060</td>\n",
       "      <td>14954</td>\n",
       "      <td>11108</td>\n",
       "      <td>2503</td>\n",
       "      <td>47.463975</td>\n",
       "      <td>7.944519</td>\n",
       "      <td>6.538437</td>\n",
       "      <td>3866.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>35713</td>\n",
       "      <td>2257</td>\n",
       "      <td>16620</td>\n",
       "      <td>12090</td>\n",
       "      <td>2978</td>\n",
       "      <td>46.537675</td>\n",
       "      <td>8.338700</td>\n",
       "      <td>6.319828</td>\n",
       "      <td>4764.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>41035</td>\n",
       "      <td>2498</td>\n",
       "      <td>18255</td>\n",
       "      <td>14935</td>\n",
       "      <td>3405</td>\n",
       "      <td>44.486414</td>\n",
       "      <td>8.297795</td>\n",
       "      <td>6.087486</td>\n",
       "      <td>5322.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0       date  TotalPositiveCases  IntensiveCarePatients  \\\n",
       "20          20 2020-03-15               24747                   1672   \n",
       "21          21 2020-03-16               27980                   1851   \n",
       "22          22 2020-03-17               31506                   2060   \n",
       "23          23 2020-03-18               35713                   2257   \n",
       "24          24 2020-03-19               41035                   2498   \n",
       "\n",
       "    TotalHospitalizedPatients  HomeConfinement  Deaths  \\\n",
       "20                      11335             9268    1809   \n",
       "21                      12876            10197    2158   \n",
       "22                      14954            11108    2503   \n",
       "23                      16620            12090    2978   \n",
       "24                      18255            14935    3405   \n",
       "\n",
       "    % hospitalized over time  % deaths over time  % intensive care over time  \\\n",
       "20                 45.803532            7.309977                    6.756375   \n",
       "21                 46.018585            7.712652                    6.615440   \n",
       "22                 47.463975            7.944519                    6.538437   \n",
       "23                 46.537675            8.338700                    6.319828   \n",
       "24                 44.486414            8.297795                    6.087486   \n",
       "\n",
       "    ContagionRate  \n",
       "20         3411.5  \n",
       "21         3379.5  \n",
       "22         3866.5  \n",
       "23         4764.5  \n",
       "24         5322.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_italy.tail() # check the last dates for Italy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56 56\n"
     ]
    }
   ],
   "source": [
    "X = df_china.index.values\n",
    "\n",
    "y = df_china['ContagionRate'].values\n",
    "\n",
    "print(len(X), len(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Create a Model of the Virus. Will be employing a Gaussian Model to Illustrate Spread."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### China currently has the most confirmed cases in the world of N-CoVid19 and thus contains as a country the most amount of data. As a result we can create a model using this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def gauss_function(X, a, x0, sigma):\n",
    "    return a*np.exp(-(X-x0)**2/(2*sigma**2))\n",
    "\n",
    "#estimate mean and standard deviation\n",
    "mean = 25 # select 25 as mean. For Italy 25 days correspond to 25/3/2020, for France to 31/3/2020\n",
    "sigma = 30\n",
    "#do the fit!\n",
    "popt, pcov = curve_fit(gauss_function, X, y, p0 = [1, mean, sigma])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1c75e4d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "plt.plot(df_national.index, df_national['cases'], label ='France')\n",
    "plt.plot(df_italy.index, df_italy['TotalPositiveCases'], label = 'Italy')\n",
    "plt.plot(df_china.index, df_china['Number of cases'], label = 'China')\n",
    "\n",
    "plt.xlim(0, 20)\n",
    "plt.ylim(0, 20000)\n",
    "plt.ylabel('Number of cases', fontsize=14)\n",
    "plt.xlabel('Days', fontsize = 14)\n",
    "plt.legend\n",
    "\n",
    "plt.title('International comparison of cases growth', fontsize = 20)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All Countries had an exponential increase in the number of cases with Italy having the most significant increase of the other countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Lets plot the rate of increase fitted by the gaussian model. We can use the model created on the Chinese dataset to compare the rates of other countries and how they are faring. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1c6d27d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (6,6))\n",
    "#plot the fit results\n",
    "plt.plot(X,gauss_function(X, *popt), '--', label = 'China-modelled gaussian')\n",
    "#confront with the given data\n",
    "plt.plot(df_national.index,df_national['ContagionRate'], label = 'France') #trend cases\n",
    "plt.plot(df_italy.index,df_italy['ContagionRate'], label = 'Italy') #trend cases\n",
    "plt.plot(df_china.index, df_china['ContagionRate'], label = 'China') #trend cases\n",
    "\n",
    "plt.title('Cases over time')\n",
    "plt.ylabel('Spread rate')\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlim(0, 50)\n",
    "plt.ylabel('Contagion rate (first derivative of cases count)', fontsize=14)\n",
    "plt.xlabel('Days', fontsize = 14)\n",
    "plt.legend\n",
    "\n",
    "plt.title('International comparison of spread rate', fontsize = 20)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the China Gausian Model we can see how the spread rate occurs in China and the expected days before the rates begins to decrease. The model accurately portrays the current rate in China over time with a small spike at around 30 days with a decreasing rate subsequently. However, what is concerning is that the rate in Italy has far exceeded the rate at around 20 days in both France and China. There seems to be no telling when the number of cases will begin to drop, indicating the need for further measures to be taken within the country to halt the exponential growth of the virus. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Lets Evaluate the Local Statistics of the Virus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### District Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>date</th>\n",
       "      <th>granularite</th>\n",
       "      <th>maille_code</th>\n",
       "      <th>district</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>reanimation</th>\n",
       "      <th>source_nom</th>\n",
       "      <th>source_url</th>\n",
       "      <th>source_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1902</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-74</td>\n",
       "      <td>Haute-Savoie</td>\n",
       "      <td>199.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARS Auvergne Rhône-Alpes</td>\n",
       "      <td>https://www.auvergne-rhone-alpes.ars.sante.fr/...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1903</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-79</td>\n",
       "      <td>Deux-Sèvres</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARS Nouvelle-Aquitaine</td>\n",
       "      <td>https://www.nouvelle-aquitaine.ars.sante.fr/sy...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1904</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-86</td>\n",
       "      <td>Vienne</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARS Nouvelle-Aquitaine</td>\n",
       "      <td>https://www.nouvelle-aquitaine.ars.sante.fr/sy...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1905</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-87</td>\n",
       "      <td>Haute-Vienne</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARS Nouvelle-Aquitaine</td>\n",
       "      <td>https://www.nouvelle-aquitaine.ars.sante.fr/sy...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1906</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>departement</td>\n",
       "      <td>DEP-971</td>\n",
       "      <td>Guadeloupe</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>ARS Guadeloupe</td>\n",
       "      <td>https://www.guadeloupe.ars.sante.fr/system/fil...</td>\n",
       "      <td>agences-regionales-sante</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  granularite maille_code      district  cases  deaths  \\\n",
       "1902 2020-03-19  departement      DEP-74  Haute-Savoie  199.0     7.0   \n",
       "1903 2020-03-19  departement      DEP-79   Deux-Sèvres    7.0     NaN   \n",
       "1904 2020-03-19  departement      DEP-86        Vienne   33.0     NaN   \n",
       "1905 2020-03-19  departement      DEP-87  Haute-Vienne    8.0     NaN   \n",
       "1906 2020-03-19  departement     DEP-971    Guadeloupe   45.0     NaN   \n",
       "\n",
       "      reanimation                source_nom  \\\n",
       "1902          NaN  ARS Auvergne Rhône-Alpes   \n",
       "1903          NaN    ARS Nouvelle-Aquitaine   \n",
       "1904          NaN    ARS Nouvelle-Aquitaine   \n",
       "1905          NaN    ARS Nouvelle-Aquitaine   \n",
       "1906          3.0            ARS Guadeloupe   \n",
       "\n",
       "                                             source_url  \\\n",
       "1902  https://www.auvergne-rhone-alpes.ars.sante.fr/...   \n",
       "1903  https://www.nouvelle-aquitaine.ars.sante.fr/sy...   \n",
       "1904  https://www.nouvelle-aquitaine.ars.sante.fr/sy...   \n",
       "1905  https://www.nouvelle-aquitaine.ars.sante.fr/sy...   \n",
       "1906  https://www.guadeloupe.ars.sante.fr/system/fil...   \n",
       "\n",
       "                   source_type  \n",
       "1902  agences-regionales-sante  \n",
       "1903  agences-regionales-sante  \n",
       "1904  agences-regionales-sante  \n",
       "1905  agences-regionales-sante  \n",
       "1906  agences-regionales-sante  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_departement =  raw_data[raw_data.granularite =='departement']\n",
    "df_departement.rename(columns={'maille_nom':'district'},inplace=True) \n",
    "df_departement.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Charente', 'Charente-Maritime', 'Corrèze', 'Creuse', 'Dordogne',\n",
       "       'Gironde', 'Landes', 'Lot-et-Garonne', 'Pyrénées-Atlantiques',\n",
       "       'Deux-Sèvres', 'Vienne', 'Haute-Vienne', 'Hérault', 'Haute-Savoie',\n",
       "       'Aisne', 'Doubs', 'Nord', 'Oise', 'Pas-de-Calais', 'Somme',\n",
       "       'Territoire de Belfort', \"Côte-d'Or\", 'Finistère',\n",
       "       'Loire-Atlantique', 'Bas-Rhin', 'Alpes-Maritimes',\n",
       "       'Maine-et-Loire', 'Mayenne', 'Seine-Maritime', 'Ille-et-Vilaine',\n",
       "       'Morbihan', 'Sarthe', 'Ain', 'Ardennes', 'Aube', 'Eure', 'Marne',\n",
       "       'Haute-Marne', 'Meurthe-et-Moselle', 'Meuse', 'Moselle',\n",
       "       'Haut-Rhin', 'Rhône', 'Vosges', 'Gard', 'Saône-et-Loire', 'Savoie',\n",
       "       'Drôme', 'Aveyron', 'Bouches-du-Rhône', \"Côtes-d'Armor\",\n",
       "       'Eure-et-Loir', 'Indre-et-Loire', 'Haute-Saône', 'Vaucluse',\n",
       "       'Guyane', 'Hautes-Alpes', 'Calvados', 'Cher', 'Corse du Sud',\n",
       "       'Corse-du-Sud', 'Haute-Corse', 'Haute-Garonne', 'Indre',\n",
       "       'Loir-et-Cher', 'Loiret', 'Manche', 'Puy-de-Dôme', 'Paris',\n",
       "       'Seine-et-Marne', 'Yvelines', 'Var', 'Essone', 'Hauts-de-Seine',\n",
       "       'Seine-Saint-Denis', 'Val-de-Marne', \"Val-d'Oise\", 'Martinique',\n",
       "       'Ardèche', 'Jura', 'Loire', 'Lot', 'Tarn', 'Tarn-et-Garonne',\n",
       "       'Vendee', 'Vendée', 'Yonne', 'Aude', 'Isère', 'Nièvre', 'Orne',\n",
       "       'Alpes-de-Haute-Provence', 'Gers', 'Haute-Loire', 'Haute-Pyrénées',\n",
       "       'Guadeloupe', 'La Réunion', 'Allier', 'Pyrénées-Orientales',\n",
       "       'Lozère', 'Cantal', 'Hautes-Pyrénées', 'Territoire-de-Belfort',\n",
       "       'Ariège'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_departement['district'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>district</th>\n",
       "      <th>TotalPositiveCases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>reanimation</th>\n",
       "      <th>mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Haut-Rhin</td>\n",
       "      <td>4106.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.243546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>Morbihan</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.476015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Bas-Rhin</td>\n",
       "      <td>1876.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.159915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>Rhône</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.202703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Haute-Savoie</td>\n",
       "      <td>1438.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.877608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        district  TotalPositiveCases  deaths  reanimation  mortality\n",
       "37     Haut-Rhin              4106.0    10.0          0.0   0.243546\n",
       "71      Morbihan              1897.0    28.0          0.0   1.476015\n",
       "11      Bas-Rhin              1876.0     3.0          0.0   0.159915\n",
       "82         Rhône              1480.0    77.0         12.0   5.202703\n",
       "43  Haute-Savoie              1438.0    27.0         10.0   1.877608"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_departement = df_departement.groupby(['district']).sum().reset_index() # get sum of cases by district\n",
    "gb_departement.rename(columns={'cases':'TotalPositiveCases'},inplace=True) \n",
    "gb_departement = gb_departement.sort_values(by=['TotalPositiveCases'], ascending=False) # sort descending\n",
    "gb_departement['mortality'] = (gb_departement['deaths']/gb_departement['TotalPositiveCases'])*100\n",
    "gb_departement.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It seems that Haut-Rhin has the most amount of cases, however Rhone has the highest number of deaths and the highest mortality rate unfornately. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.bar(gb_departement.district,gb_departement.TotalPositiveCases, color = 'tomato') #cases by region\n",
    "plt.title('Cases by district')\n",
    "plt.ylabel('Total Positive Cases ')\n",
    "plt.xticks(gb_departement['district'],\" \")\n",
    "plt.subplot(212)\n",
    "plt.bar(gb_departement.district,gb_departement['mortality'], color = 'k') # % deaths by region\n",
    "plt.title('Mortality by district')\n",
    "plt.ylabel('% deaths')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.title('Mortality by district')\n",
    "plt.ylabel('%deaths ')\n",
    "plt.xticks(rotation=90)\n",
    "plt.suptitle('Overall departemental stats')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above, we can see the cases and mortality by district side by side. Haut-Rhin indeed has the highest number of n-CoVid19 cases with Yonne having the highest % of deaths. That may be in part though due to a lower number of confirmed cases. Olse in my opinion should have further governmental aid and action instituted as a result of the higher number of positive cases and accompanying mortality percentage, which is alarming. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Region Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Nouvelle-Aquitaine', 'Hauts-de-France', 'Grand-Est',\n",
       "       'Auvergne Rhône-Alpes', 'Guadeloupe', 'Martinique', 'Guyane',\n",
       "       'La Réunion', 'Mayotte', 'Ile-de-France', 'Centre-Val de Loire',\n",
       "       'Bourgogne-Franche-Comté', 'Normandie', 'Grand Est',\n",
       "       'Pays de la Loire', 'Bretagne', 'Occitanie',\n",
       "       'Auvergne-Rhône-Alpes', 'Provence-Alpes-Côte d’Azur', 'Corse',\n",
       "       'Centre Val de Loire'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_region = raw_data[raw_data.granularite == 'region']\n",
    "df_region['maille_nom'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>TotalPositiveCases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>reanimation</th>\n",
       "      <th>mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Ile-de-France</td>\n",
       "      <td>20299.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>0.234192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Grand Est</td>\n",
       "      <td>14060.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Hauts-de-France</td>\n",
       "      <td>8657.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.159915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Grand-Est</td>\n",
       "      <td>8293.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Bourgogne-Franche-Comté</td>\n",
       "      <td>6719.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     region  TotalPositiveCases  deaths  reanimation  \\\n",
       "12            Ile-de-France             20299.0    16.0        310.0   \n",
       "7                 Grand Est             14060.0     3.0         45.0   \n",
       "11          Hauts-de-France              8657.0   247.0          0.0   \n",
       "8                 Grand-Est              8293.0   220.0          0.0   \n",
       "2   Bourgogne-Franche-Comté              6719.0    62.0        186.0   \n",
       "\n",
       "    mortality  \n",
       "12   0.234192  \n",
       "7    0.000000  \n",
       "11   0.159915  \n",
       "8    0.000000  \n",
       "2    0.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_region = df_region.groupby(['maille_nom']).sum().reset_index() # get sum of cases by district\n",
    "gb_region.rename(columns={'cases':'TotalPositiveCases', 'maille_nom':'region'},inplace=True) \n",
    "gb_region = gb_region.sort_values(by=['TotalPositiveCases'], ascending=False) # sort descending\n",
    "gb_region['mortality'] = (gb_departement['deaths']/gb_departement['TotalPositiveCases'])*100\n",
    "gb_region.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ille-de-France has the highest number of Total Postive Cases, but Hauts de France has the highest mortality rate with over 247 deaths.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.bar(gb_region.region,gb_region.TotalPositiveCases, color = 'tomato') #cases by region\n",
    "plt.title('Cases by region')\n",
    "plt.ylabel('Total Positive Cases ')\n",
    "plt.xticks(gb_region['region'],\" \")\n",
    "plt.subplot(212)\n",
    "plt.bar(gb_region.region,gb_region['mortality'], color = 'k') # % deaths by region\n",
    "\n",
    "plt.ylabel('% deaths')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.title('Mortality by region')\n",
    "plt.ylabel('%deaths ')\n",
    "plt.xticks(rotation=90)\n",
    "plt.suptitle('Overall regional stats', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following provides a glimpse side by side of cases and accompanying mortality by region in France. Ille-de-France having the highest number of confirmed cases, and Auvergne-Rhone-Alpes having the highest mortality. Appropriate resources should be dedicated to these regions accordingly to combat both the rate of the virus transmission and mortality from the virus itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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