{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence de la varicelle depuis Sentinelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek\n",
    "import requests\n",
    "import os\n",
    "import glob\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1990 et toujours en update regulier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"http://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"\n",
    "local_file_pattern = \"-sentinelle-varicelle.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected 1 file(s) with pattern -sentinelle-varicelle.csv\n",
      "Using latest : 20251210--sentinelle-varicelle.csv\n",
      "! Consider checking latest data for update, keep original file\n"
     ]
    },
    {
     "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>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202548</td>\n",
       "      <td>7</td>\n",
       "      <td>5654</td>\n",
       "      <td>3398</td>\n",
       "      <td>7910</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202547</td>\n",
       "      <td>7</td>\n",
       "      <td>5184</td>\n",
       "      <td>3279</td>\n",
       "      <td>7089</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202546</td>\n",
       "      <td>7</td>\n",
       "      <td>3136</td>\n",
       "      <td>1451</td>\n",
       "      <td>4821</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202545</td>\n",
       "      <td>7</td>\n",
       "      <td>4733</td>\n",
       "      <td>2070</td>\n",
       "      <td>7396</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202544</td>\n",
       "      <td>7</td>\n",
       "      <td>3461</td>\n",
       "      <td>1763</td>\n",
       "      <td>5159</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week  indicator   inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
       "0  202548          7  5654     3398    7910       8           5         11   \n",
       "1  202547          7  5184     3279    7089       8           5         11   \n",
       "2  202546          7  3136     1451    4821       5           2          8   \n",
       "3  202545          7  4733     2070    7396       7           3         11   \n",
       "4  202544          7  3461     1763    5159       5           2          8   \n",
       "\n",
       "  geo_insee geo_name  \n",
       "0        FR   France  \n",
       "1        FR   France  \n",
       "2        FR   France  \n",
       "3        FR   France  \n",
       "4        FR   France  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# detect latest version of local data \n",
    "files = glob.glob(f'*{local_file_pattern}')\n",
    "\n",
    "if files == [] : \n",
    "    print(f'No local file detected using pattern {local_file_pattern}, ... Downloading up to date file')\n",
    "    \n",
    "    # checking if URL is still active \n",
    "    url_file = requests.get(data_url, timeout = 50)\n",
    "    assert url_file.raise_for_status() != 200, f'Provided URL is unreachable or returned {url_file.status_code}'\n",
    "\n",
    "    # loading file from URL\n",
    "    url_content = url_file.content\n",
    "\n",
    "\n",
    "    with open(f'{datetime.today().strftime(\"%Y%m%d\")}{local_file_pattern}', 'wb') as f:\n",
    "        f.write(url_content)\n",
    "    raw_data = f'{datetime.today().strftime(\"%Y%m%d\")}{local_file_pattern}'\n",
    "else : \n",
    "    print(f'Detected {len(files)} file(s) with pattern {local_file_pattern}')\n",
    "    latest = max(files) \n",
    "    print(f'Using latest : {latest}')\n",
    "    print('! Consider checking latest data for update, keep original file')\n",
    "    raw_data = latest\n",
    "        \n",
    "raw_data = pd.read_csv(raw_data,  encoding = 'iso-8859-1', skiprows=1)     \n",
    "\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]\n",
    "data = raw_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pas de donnée manquante dans ce jeux de donnée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_week(year_and_week_int):\n",
    "    year_and_week_str = str(year_and_week_int)\n",
    "    year = int(year_and_week_str[:4])\n",
    "    week = int(year_and_week_str[4:])\n",
    "    w = isoweek.Week(year, week)\n",
    "    return pd.Period(w.day(0), 'W')\n",
    "\n",
    "data['period'] = [convert_week(yw) for yw in data['week']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il restent deux petites modifications à faire.\n",
    "\n",
    "Premièrement, nous définissons les périodes d'observation\n",
    "comme nouvel index de notre jeux de données. Ceci en fait\n",
    "une suite chronologique, ce qui sera pratique par la suite.\n",
    "\n",
    "Deuxièmement, nous trions les points par période, dans\n",
    "le sens chronologique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data = data.set_index('period').sort_index()\n",
    "periods = sorted_data.index\n",
    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
    "    delta = p2.to_timestamp() - p1.end_time\n",
    "    if delta > pd.Timedelta('1s'):\n",
    "        print(p1, p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il ne semble pas y avoir d'incoherence numerique dans le reformattage des periodes du jeux de donnée, effectuons maintenant une inspection visuelle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reformater `inc` en integer car il a ete apellé en tant que object "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1143"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data['inc']= pd.to_numeric(sorted_data['inc'])\n",
    "sorted_data['inc'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f550a7fa828>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f550b048860>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'][-500:-400].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il semble y avoir une periodicité avec un pic en avril - juillet (printemps) et un creux en septembre octobre (automne) qui peux paraitre etre une variation hivernale ou une circulation du virus venu d'ailleur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_august_week = [pd.Period(pd.Timestamp(y, 10, 1), 'W')\n",
    "                     for y in range(sorted_data.index[0].year,\n",
    "                                    sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_august_week[:-1],\n",
    "                        first_august_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    print(abs(len(one_year)-52))\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque que certaines année ne sont pas completes particulierement l'année 1990, certaines année possendent une semaine en moins, ce qui peut avoir un impact si certaines année sont proches.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f550a67e128>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAD8CAYAAAC2PJlnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHJBJREFUeJzt3X+QVWV+5/H3B5oBnYAD2Bh+qO2WxAo6Ozh0gdnJZscxAu6khKnVSUdHOxuqSByza5I/FDJssav8oVtba0K5ulIxI8r4g2XHko3DOC2uVdYuAZrRCaKSblenZWTsdpsIZoseG777x316uN02t+893O57bvfnVXXrnvvt85z7nAPd3/v8OM9VRGBmZpbFpFpXwMzM6peTiJmZZeYkYmZmmTmJmJlZZk4iZmaWmZOImZll5iRiZmaZOYmYmVlmTiJmZpZZQ60rUG0XXnhhNDU11boaZmZ15cCBAx9FRGOl5cZdEmlqaqK9vb3W1TAzqyuSfpqlnLuzzMwsMycRMzPLzEnEzMwycxIxM7PMnETMzCwzJxGznOs+fpJvPrqH7hMna10Vs89wEqmQf6FtrG3e3cH+93rZ/FJHrati9hlOIhXyL7SNlSs27KJp3Qts29tFBGzb20XTuhe4YsOuz+zrDzdWK04iZarkF9qsGl69+1puXDyPaVMKv6bTpkxi1eJ5vHrPtZ/Z1x9urFbG3R3ro+XVu69l0w/e4keHfs7JT08zbcokVlz5q3zn679e66rZODVnxjSmT22gr/80Uxsm0dd/mulTG5gzfdov97liwy76+k//8vW2vV1s29vF1IZJHN50Qy2qbROMWyJlKucX2qzaPvqkj1uXXcpz3/4Kty67lJ5P+gb9vJLWitlocEukAgO/0LcsvYSn9nXR4/5nG2WP3tb8y+1Nq6/6zM/94cZqzUmkAiP9QpvVgj/cWC0pImpdh6pqbm4Or+JrZlYZSQcionnkPQfzmIiZmWXmJGJmZpmVlUQk/amkQ5LekPS0pGmSZklqk9SRnmcW7b9eUqekw5JWFMWXSDqYfrZZklJ8qqRnU3yvpKaiMq3pPToktVbv1M3M7FyNmEQkzQf+LdAcEVcBk4EWYB2wOyIWArvTayQtSj+/ElgJPCxpcjrcI8BaYGF6rEzxNcCxiLgceBB4IB1rFrARWAYsBTYWJyszM6utcruzGoDzJDUA5wMfAKuArennW4HVaXsV8ExE9EXEu0AnsFTSXGBGROyJwmj+E0PKDBxrB3BdaqWsANoiojcijgFtnEk8ZmZWYyMmkYj4GfCfgC7gKPBxRPwIuCgijqZ9jgJzUpH5wPtFhziSYvPT9tD4oDIR0Q98DMwucSwzM8uBcrqzZlJoKVwGzAM+L+lbpYoME4sS8axliuu4VlK7pPaenp4SVTMzs2oqpzvrt4F3I6InIj4Fvg/8M+DD1EVFeu5O+x8BLi4qv4BC99eRtD00PqhM6jK7AOgtcaxBImJLRDRHRHNjY2MZp2QTkVe6Nau+cpJIF3CNpPPTOMV1wFvATmBgtlQr8Hza3gm0pBlXl1EYQN+XurxOSLomHef2IWUGjnUT8HIaN3kRWC5pZmoRLU8xs4p5pVuz6htx2ZOI2CtpB/BjoB94DdgC/AqwXdIaConm5rT/IUnbgTfT/ndGxKl0uDuAx4HzgF3pAfAY8KSkTgotkJZ0rF5J9wH70373RkTvOZ2xTThe6dZs9HjZExv3uo+fPOsy/l6o0KzAy56YnYVXuq1vHsvKNycRmxBG+l4Oyy+PZeWbu7PMLJeGjmUN8FjW6HB3lpmNK/7WxvrgJGJmueSxrPrgbzY0s9zytzbmn8dEzMzMYyI2cXkKqFntOIlY3avXKaBOfjYeOIlYRfL0h++KDbtoWvcC2/Z2EVFYzqRp3QtcsWHXyIVzoF6Tn1kxJxGrSJ7+8NXrFNB6T35mxTw7y8qSx0UM63UK6Kt3X3vWtbzM6o1bIlaWvH7qr8flTOo1+ZkNxy0RK0te//A9etuZGYmbVl9Vw5pUxvc/2HjhJGJl8x++8nUfP8kfP/0aD91y9bCJtl6Tn9lQvtnQbBRseO4g39vXxa1LL2HTN75Y6+qYjSjrzYZuiZhVUR4nIJiNJg+sm1VRXicgmI0WJxGzKsrrBASz0eLuLLMq8wQEm0g8sG5mZl7F18zMxp6TiJmZZTZiEpF0haTXix7HJf2JpFmS2iR1pOeZRWXWS+qUdFjSiqL4EkkH0882S1KKT5X0bIrvldRUVKY1vUeHpNbqnr6ZZZGn1ZyttkZMIhFxOCIWR8RiYAnw/4DngHXA7ohYCOxOr5G0CGgBrgRWAg9LmpwO9wiwFliYHitTfA1wLCIuBx4EHkjHmgVsBJYBS4GNxcnKzGojT6s5W21VOjvrOuCdiPippFXAV1N8K/AKcA+wCngmIvqAdyV1AkslvQfMiIg9AJKeAFYDu1KZf5+OtQN4KLVSVgBtEdGbyrRRSDxPV3ymZnbOfDOlDVXpmEgLZ/6AXxQRRwHS85wUnw+8X1TmSIrNT9tD44PKREQ/8DEwu8SxzKwGfDOlDVV2EpH0OeBG4L+NtOswsSgRz1qmuG5rJbVLau/p6RmhemaWlW+mtKEqaYncAPw4Ij5Mrz+UNBcgPXen+BHg4qJyC4APUnzBMPFBZSQ1ABcAvSWONUhEbImI5ohobmxsrOCUzKxS9fgdLjZ6Kkkiv8fgsYidwMBsqVbg+aJ4S5pxdRmFAfR9qcvrhKRr0njH7UPKDBzrJuDlKNwF+SKwXNLMNKC+PMVsFHjGjZXj0dua2bT6KhbNm8Gm1VcNWtbeJp6ykoik84Hrge8Xhe8HrpfUkX52P0BEHAK2A28CPwTujIhTqcwdwF8BncA7FAbVAR4DZqdB+D8jzfRKA+r3AfvT496BQXarPs+4MbNKedmTCaLUlyQNnXEzwDNuzCYOL3tiJZVqZXjGjZll5VV8x7ly5vV7xo2NhpG+ItjGB7dExrlyWxmecWPV5jG2icEtkRoZq09p5bYyimfYbFp91ajVx8Y/39U+sbglUiNj+SnNrQwbSx5jm1jcEhljtfiU5laGjSWPsU0sbomMMX9Ks4nArd+Jwy2RMeZPaTYRuPU7cbglUgOVfErzUiRmlme+Yz3nNjx3kO/t6+LWpZew6RtfrHV1rI75vg0rJesd6+7OyilPk7RqK54R6A8kVi1uiYyCanzi6z5+kk0/eIsfHfo5Jz89zbQpk1hx5a/yna//uj9FWkW8NpqVw2tn5Ug17gHxALxVi2cE5lu9j3u6O6uKqt0FNTAAf8vSS3hqXxc9dfqfzGrLH0jyrd67Gd2dVUXugrK8+sMn22mcPm3QBxJ/mVRt5a2b0d1ZOeBPfGfUexO9lHo8t3K/jbAez61ejZduRieRKvOdugXjeQVXn5tVw3j50OnuLKuqvDXRKzVRvwFyPJ9bnuWpmzFrd5aTiFVVvY8Llbq5s97PrZTxfG5WHt9saLlQr030if4NkOP53Gx0eUzEqq4ex4X8DZC1OTcP5Nc/d2eZJd957iBP7evic5Mn8YtTp71e2Rjw2nD54e4sqzt5WxDQN3eOHa8NN36U1Z0l6QuSdkh6W9Jbkn5D0ixJbZI60vPMov3XS+qUdFjSiqL4EkkH0882S1KKT5X0bIrvldRUVKY1vUeHpNbqnbrVWt6mk5Z7L4Wdu/Fyj4SV3xL5S+CHEXGTpM8B5wN/DuyOiPslrQPWAfdIWgS0AFcC84CXJP1aRJwCHgHWAn8L/ABYCewC1gDHIuJySS3AA8DvSpoFbASagQAOSNoZEceqcvZWE/4Uah7IHz9GbIlImgH8FvAYQET8IiL+AVgFbE27bQVWp+1VwDMR0RcR7wKdwFJJc4EZEbEnCgMxTwwpM3CsHcB1qZWyAmiLiN6UONooJB6rY/4UajC+JylMJOW0RP4J0AN8V9KXgAPAXcBFEXEUICKOSpqT9p9PoaUx4EiKfZq2h8YHyryfjtUv6WNgdnF8mDJWp/wp1MBfoTtelDMm0gB8GXgkIq4G/pFC19XZaJhYlIhnLXPmDaW1ktoltff09JSomuWFP4WajQ/ltESOAEciYm96vYNCEvlQ0tzUCpkLdBftf3FR+QXABym+YJh4cZkjkhqAC4DeFP/qkDKvDK1gRGwBtkBhim8Z52Q15k+hlld5mzWYdyO2RCLi58D7kq5IoeuAN4GdwMBsqVbg+bS9E2hJM64uAxYC+1LX1wlJ16TxjtuHlBk41k3Ay2nc5EVguaSZafbX8hQzMxsVeZs1mHflzs76N8D30sys/wP8awoJaLukNUAXcDNARByStJ1CoukH7kwzswDuAB4HzqMwK2tXij8GPCmpk0ILpCUdq1fSfcD+tN+9EdGb8VzNzM7Kswaz8R3rZmZ4EUp/KdUE5vWHzM6dZw1m4yQyDrgP16w6PGuwcu7OqmP+IiEzqxZ3Z1VBvXUL+c5vM6s1J5Ei9dYt5D5cM6s1LwVPfU/t8/LlZlZLHhPBU/vMzDwmcg7cLWRW3+ptPHM8cRJJPLXPrH7V23jmeOLuLDOrW57mXj3uzjKzCcfT3GvPScTM6lal45keO6k+JxEzq2uVjGd67KT6PCZiZuOex05G5jERM7Oz8NjJ6HESsVxzH7ZVg+8FGz1OIpZr7sO2avG9YKPDYyKWS+7DNhtbHhOxccV92Gb1wUnEcsl92Gb1wUvBW255mXuz/POYiJmZeUzEzMzGXllJRNJ7kg5Kel1Se4rNktQmqSM9zyzaf72kTkmHJa0oii9Jx+mUtFmSUnyqpGdTfK+kpqIyrek9OiS1VuvEzczs3FXSErk2IhYXNXfWAbsjYiGwO71G0iKgBbgSWAk8LGlyKvMIsBZYmB4rU3wNcCwiLgceBB5Ix5oFbASWAUuBjcXJyszMautcurNWAVvT9lZgdVH8mYjoi4h3gU5gqaS5wIyI2BOFgZgnhpQZONYO4LrUSlkBtEVEb0QcA9o4k3jMzKzGyk0iAfxI0gFJa1Psoog4CpCe56T4fOD9orJHUmx+2h4aH1QmIvqBj4HZJY5lZmY5UO4U369ExAeS5gBtkt4usa+GiUWJeNYyZ96wkNjWAlxyySUlqmZmZtVUVkskIj5Iz93AcxTGJz5MXVSk5+60+xHg4qLiC4APUnzBMPFBZSQ1ABcAvSWONbR+WyKiOSKaGxsbyzklMzOrghGTiKTPS5o+sA0sB94AdgIDs6VagefT9k6gJc24uozCAPq+1OV1QtI1abzj9iFlBo51E/ByGjd5EVguaWYaUF+eYmZmVqRWK16X0511EfBcmo3bADwVET+UtB/YLmkN0AXcDBARhyRtB94E+oE7I+JUOtYdwOPAecCu9AB4DHhSUieFFkhLOlavpPuA/Wm/eyOi9xzO18xsXCpe8XrTN744Zu/rO9bNzOpYtVa89h3rZmYTUK1XvHYSMTOrY7Ve8dqr+JqZ1blarnjtMREzM/OYiJmZjT0nETMzy8xJxMzMMnMSMTOzzJxEzMwsMycRMzPLzEnEzMwycxIxM7PMnETMzCwzJxEzM8vMScTMzDJzEjEzs8ycRMzMLDMnETMzy8xJxMzMMnMSMTOzzJxEzMwsMycRMzPLzEnEzMwyKzuJSJos6TVJf5Nez5LUJqkjPc8s2ne9pE5JhyWtKIovkXQw/WyzJKX4VEnPpvheSU1FZVrTe3RIaq3GSZuZWXVU0hK5C3ir6PU6YHdELAR2p9dIWgS0AFcCK4GHJU1OZR4B1gIL02Nliq8BjkXE5cCDwAPpWLOAjcAyYCmwsThZmZlZbZWVRCQtAL4O/FVReBWwNW1vBVYXxZ+JiL6IeBfoBJZKmgvMiIg9ERHAE0PKDBxrB3BdaqWsANoiojcijgFtnEk8ZmZWY+W2RP4CuBs4XRS7KCKOAqTnOSk+H3i/aL8jKTY/bQ+NDyoTEf3Ax8DsEscyM7McGDGJSPodoDsiDpR5TA0TixLxrGWK67hWUruk9p6enjKraWZm56qclshXgBslvQc8A3xN0jbgw9RFRXruTvsfAS4uKr8A+CDFFwwTH1RGUgNwAdBb4liDRMSWiGiOiObGxsYyTsnMzKphxCQSEesjYkFENFEYMH85Ir4F7AQGZku1As+n7Z1AS5pxdRmFAfR9qcvrhKRr0njH7UPKDBzrpvQeAbwILJc0Mw2oL08xM7Nc6z5+km8+uofuEydrXZVRdS73idwPXC+pA7g+vSYiDgHbgTeBHwJ3RsSpVOYOCoPzncA7wK4UfwyYLakT+DPSTK+I6AXuA/anx70pZmaWa5t3d7D/vV42v9RR66qMKhU+8I8fzc3N0d7eXutqmNkEdcWGXfT1n/5MfGrDJA5vuqEGNSqPpAMR0VxpOd+xbmZWRa/efS03Lp7HtCmFP6/Tpkxi1eJ5vHrPtTWu2ehwEjEzq6I5M6YxfWoDff2nmdowib7+00yf2sCc6dNqXbVR0VDrCpiZjTcffdLHrcsu5Zall/DUvi56xvHgusdEzMxyrvv4Sf746dd46JarR61F4zERM7NxKs8zvdydZWaWU0Nnem3b28W2vV25munlloiZWU7Vw0wvJxEzs5yqh5le7s4yM8uxvM/08uwsMzPz7CwzMxt7TiJmZpaZk4iZmWXmJGJmZpk5iZiZWWZOImZmlpmTiJmZZeYkYmZmmTmJmJlZZk4iZmaWmZOImZll5iRiZmaZOYmYmVlmIyYRSdMk7ZP0E0mHJP2HFJ8lqU1SR3qeWVRmvaROSYclrSiKL5F0MP1ssySl+FRJz6b4XklNRWVa03t0SGqt5smbmdm5Kacl0gd8LSK+BCwGVkq6BlgH7I6IhcDu9BpJi4AW4EpgJfCwpMnpWI8Aa4GF6bEyxdcAxyLicuBB4IF0rFnARmAZsBTYWJyszMystkZMIlHwSXo5JT0CWAVsTfGtwOq0vQp4JiL6IuJdoBNYKmkuMCMi9kThS0yeGFJm4Fg7gOtSK2UF0BYRvRFxDGjjTOIxM7MaK2tMRNJkSa8D3RT+qO8FLoqIowDpeU7afT7wflHxIyk2P20PjQ8qExH9wMfA7BLHMjOzHCgriUTEqYhYDCyg0Kq4qsTuGu4QJeJZy5x5Q2mtpHZJ7T09PSWqZmZm1VTR7KyI+AfgFQpdSh+mLirSc3fa7QhwcVGxBcAHKb5gmPigMpIagAuA3hLHGlqvLRHRHBHNjY2NlZySmZmdg3JmZzVK+kLaPg/4beBtYCcwMFuqFXg+be8EWtKMq8soDKDvS11eJyRdk8Y7bh9SZuBYNwEvp3GTF4HlkmamAfXlKWZmZjnQUMY+c4GtaYbVJGB7RPyNpD3AdklrgC7gZoCIOCRpO/Am0A/cGRGn0rHuAB4HzgN2pQfAY8CTkjoptEBa0rF6Jd0H7E/73RsRvedywmZmVj0qfOAfP5qbm6O9vb3W1TAzqyuSDkREc6XlfMe6mZll5iRiZmaZOYmYmVlmTiJmZpaZk4iZmWXmJGJmZpk5iZiZWWZOImZmlpmTiJmZZeYkYmZmmTmJmJlZZk4iZmaWmZOImZll5iRiZmaZOYmYmVlmTiJmZpaZk4iZmWXmJGJmZpk5iZiZVaj7+Em++egeuk+crHVVas5JxMysQpt3d7D/vV42v9RR66rUXEOtK2BmVi+u2LCLvv7Tv3y9bW8X2/Z2MbVhEoc33VDDmtWOWyJmZmV69e5ruXHxPKZNKfzpnDZlEqsWz+PVe66tcc1qx0nEzKxMc2ZMY/rUBvr6TzO1YRJ9/aeZPrWBOdOn1bpqNTNiEpF0saT/KektSYck3ZXisyS1SepIzzOLyqyX1CnpsKQVRfElkg6mn22WpBSfKunZFN8rqamoTGt6jw5JrdU8eTOzSn30SR+3LruU5779FW5ddik9n/TVuko1pYgovYM0F5gbET+WNB04AKwGfh/ojYj7Ja0DZkbEPZIWAU8DS4F5wEvAr0XEKUn7gLuAvwV+AGyOiF2Svg3804j4I0ktwDci4nclzQLagWYg0nsviYhjZ6tvc3NztLe3Z78iZmYTkKQDEdFcabkRWyIRcTQifpy2TwBvAfOBVcDWtNtWComFFH8mIvoi4l2gE1iaktGMiNgThcz1xJAyA8faAVyXWikrgLaI6E2Jow1YWelJmpnZ6KhoTCR1M10N7AUuioijUEg0wJy023zg/aJiR1JsftoeGh9UJiL6gY+B2SWOZWZmOVB2EpH0K8B/B/4kIo6X2nWYWJSIZy1TXLe1ktoltff09JSompmZVVNZSUTSFAoJ5HsR8f0U/jB1UQ2Mm3Sn+BHg4qLiC4APUnzBMPFBZSQ1ABcAvSWONUhEbImI5ohobmxsLOeUzMysCsqZnSXgMeCtiPjPRT/aCQzMlmoFni+Kt6QZV5cBC4F9qcvrhKRr0jFvH1Jm4Fg3AS+ncZMXgeWSZqbZX8tTzMzMcqCcO9a/AtwGHJT0eor9OXA/sF3SGqALuBkgIg5J2g68CfQDd0bEqVTuDuBx4DxgV3pAIUk9KamTQgukJR2rV9J9wP60370R0ZvxXM3MrMpGnOJbbyT1AD8d5kcXAh+NcXWqpV7r7nqPLdd7bI23el8aERWPB4y7JHI2ktqzzIHOg3qtu+s9tlzvseV6F3jZEzMzy8xJxMzMMptISWRLrStwDuq17q732HK9x5brzQQaEzEzs+qbSC0RMzOrsrpOIpL+WlK3pDeKYl+StCctOf8/JM1I8c9J+m6K/0TSV4vKDLtEfR3U+5W03P7r6TFnmLerZr1H/WsB6qDeY3bNK623pNlp/08kPTTkWLm93iPUO8/X+3pJB9J1PSDpa0XHyvP1LlXvyq93RNTtA/gt4MvAG0Wx/cC/SNt/ANyXtu8Evpu251BYVn5Ser0P+A0Ka3XtAm6ok3q/AjSP4fWeC3w5bU8H/h5YBPxHYF2KrwMeSNuLgJ8AU4HLgHeAyWN9zatc7zG75hnq/XngN4E/Ah4acqw8X+9S9c7z9b4amJe2rwJ+VifXu1S9K77eo/4PMwb/8E0M/mN8nDNjPRcDb6bt/wJ8q2i/3RS+82Qu8HZR/PeAR/Ne76z/4FU+h+eB64HDFL5zZuA/9OG0vR5YX7T/i+kXqybX/FzrXetrPlK9i/b7fYr+GOf9ep+t3vVyvVNcwP+l8MGjLq730Hpnvd513Z11Fm8AN6btmzmzgONPgFWSGlRY02tJ+lmpJerHUqX1HvDd1Oz8d6PZZB5Ko/e1AKPqHOs9YMyveZn1Ppu8X++R1MP1/lfAaxHRR31d7+J6D6joeo/HJPIHwJ2SDlBo2v0ixf+awj9mO/AXwP+msLZXWcvNj4FK6w1wa0R8Efjn6XHbWFRUo/u1AKOmCvWGGlzzCup91kMME8vT9S4l99db0pXAA8AfDoSG2S1313uYekOG6z3ukkhEvB0RyyNiCYWv6X0nxfsj4k8jYnFErAK+AHRQeon6PNebiPhZej4BPEWhe25UafS/FiDP9R7za15hvc8m79f7rPJ+vSUtAJ4Dbo+Id1I499f7LPXOdL3HXRIZmE0gaRKwAfiv6fX5kj6ftq8H+iPizSi9RH1u6526ty5M8SnA71DoEhvNOo7F1wLktt5jfc0z1HtYdXC9z3acXF9vSV8AXqAwfva/BnbO+/U+W70zX++xGuwZjQeFT+xHgU8pZP81wF0UZif8PYXl6gcGq5soDDS9BbxEYcXKgeM0p4v1DvDQQJk815vCjJYDwN8Bh4C/JM0gGsV6/yaFZvnfAa+nx7+k8FXGuym0kHYDs4rKfCdd18MUzVAZy2terXqP9TXPWO/3KHydwifp/9aiOrnen6l33q83hQ97/1i07+vAnLxf77PVO+v19h3rZmaW2bjrzjIzs7HjJGJmZpk5iZiZWWZOImZmlpmTiJmZZeYkYmZmmTmJmJlZZk4iZmaW2f8HtX//45mbkmEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.plot(style='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    217605\n",
       "2023    364553\n",
       "2021    382779\n",
       "2024    483209\n",
       "2002    526035\n",
       "2018    540799\n",
       "2017    552105\n",
       "1991    565956\n",
       "1996    574093\n",
       "2019    585143\n",
       "2001    606520\n",
       "2015    611634\n",
       "2005    620796\n",
       "2006    626180\n",
       "2012    627384\n",
       "2000    627405\n",
       "2022    635251\n",
       "1993    638384\n",
       "2011    644660\n",
       "1995    650679\n",
       "1994    664684\n",
       "2014    672401\n",
       "1997    677145\n",
       "1998    682638\n",
       "2013    703305\n",
       "2007    729321\n",
       "1999    746617\n",
       "2008    750410\n",
       "2003    752007\n",
       "2016    775321\n",
       "2004    786328\n",
       "2010    830938\n",
       "1992    834566\n",
       "2009    836245\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  }
 ],
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