psthomas · September 16, 2020 22:17
diff --git a/microcovid.ipynb b/microcovid.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import urllib\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New York"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "contents = urllib.request.urlopen(\n",
    "    \"https://data.covidactnow.org/latest/us/states/NY.OBSERVED_INTERVENTION.timeseries.json\"\n",
    ").read().decode('utf8')\n",
    "nydata = json.loads(contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.microcovid.org/paper/7-basic-method#step-two-underreporting-factor\n",
    "\n",
    "We use these multipliers:\n",
    "* If the percentage of positive tests is 5% or lower, we suggest a 6x underreporting factor.[4]\n",
    "* If the percentage of positive tests is between 5% and 15%, we suggest a 8x factor.\n",
    "* If the percentage of positive tests is greater than 15%, we suggest at least a 10x factor. This indicates dangerously little testing in your area compared to the number of infected people.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_multiplier(el):\n",
    "    # Assume a 10x multiplier if no test positivity ratio\n",
    "    # is available. Mainly because missing data is early\n",
    "    # during rapid growth phase for NY, Spain.\n",
    "    if pd.isna(el):\n",
    "        return 10 #6\n",
    "    elif el <= 0.05:\n",
    "        return 6\n",
    "    elif el <= 0.15:\n",
    "        return 8\n",
    "    elif el > 0.15:\n",
    "        return 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>testPositivityRatio</th>\n",
       "      <th>multiplier</th>\n",
       "      <th>cumulativeConfirmedCases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>estimated_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>10</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>0.380282</td>\n",
       "      <td>10</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>220.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>2020-09-10</td>\n",
       "      <td>0.008971</td>\n",
       "      <td>6</td>\n",
       "      <td>446637.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>4536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>0.009063</td>\n",
       "      <td>6</td>\n",
       "      <td>447498.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>5166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2020-09-12</td>\n",
       "      <td>0.009099</td>\n",
       "      <td>6</td>\n",
       "      <td>448347.0</td>\n",
       "      <td>849.0</td>\n",
       "      <td>5094.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2020-09-13</td>\n",
       "      <td>0.009317</td>\n",
       "      <td>6</td>\n",
       "      <td>449072.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>4350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2020-09-14</td>\n",
       "      <td>0.009357</td>\n",
       "      <td>6</td>\n",
       "      <td>449658.0</td>\n",
       "      <td>586.0</td>\n",
       "      <td>3516.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  testPositivityRatio  multiplier  cumulativeConfirmedCases  \\\n",
       "0   2020-03-02                  NaN          10                       1.0   \n",
       "1   2020-03-03                  NaN          10                       2.0   \n",
       "2   2020-03-04                  NaN          10                      11.0   \n",
       "3   2020-03-05             0.363636          10                      22.0   \n",
       "4   2020-03-06             0.380282          10                      44.0   \n",
       "..         ...                  ...         ...                       ...   \n",
       "192 2020-09-10             0.008971           6                  446637.0   \n",
       "193 2020-09-11             0.009063           6                  447498.0   \n",
       "194 2020-09-12             0.009099           6                  448347.0   \n",
       "195 2020-09-13             0.009317           6                  449072.0   \n",
       "196 2020-09-14             0.009357           6                  449658.0   \n",
       "\n",
       "     new_cases  estimated_cases  \n",
       "0          0.0              0.0  \n",
       "1          1.0             10.0  \n",
       "2          9.0             90.0  \n",
       "3         11.0            110.0  \n",
       "4         22.0            220.0  \n",
       "..         ...              ...  \n",
       "192      756.0           4536.0  \n",
       "193      861.0           5166.0  \n",
       "194      849.0           5094.0  \n",
       "195      725.0           4350.0  \n",
       "196      586.0           3516.0  \n",
       "\n",
       "[197 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = pd.DataFrame(nydata['metricsTimeseries'])\n",
    "metrics = metrics[['date', 'testPositivityRatio']]\n",
    "metrics['date'] = pd.to_datetime(metrics['date'])\n",
    "metrics['multiplier'] = metrics['testPositivityRatio'].apply(get_multiplier)\n",
    "\n",
    "cases = pd.DataFrame(nydata['actualsTimeseries'])\n",
    "cases =  cases[['date', 'cumulativeConfirmedCases']]\n",
    "cases['date'] = pd.to_datetime(cases['date'])\n",
    "cases.sort_values(by='date', ascending=True)\n",
    "cases.set_index('date', inplace=True)\n",
    "cases['new_cases'] = cases['cumulativeConfirmedCases'].diff()\n",
    "cases = cases.dropna()\n",
    "cases.reset_index(drop=False, inplace=True)\n",
    "\n",
    "est_cases = metrics.merge(cases, on='date')\n",
    "est_cases['estimated_cases'] = est_cases['new_cases'] * est_cases['multiplier']\n",
    "est_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>testPositivityRatio</th>\n",
       "      <th>multiplier</th>\n",
       "      <th>cumulativeConfirmedCases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>estimated_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>194.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.109436</td>\n",
       "      <td>7.370558</td>\n",
       "      <td>312442.619289</td>\n",
       "      <td>2282.522843</td>\n",
       "      <td>20623.624365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.147683</td>\n",
       "      <td>1.752475</td>\n",
       "      <td>148239.338938</td>\n",
       "      <td>2906.914878</td>\n",
       "      <td>29992.285098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.007526</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.010094</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>251608.000000</td>\n",
       "      <td>626.000000</td>\n",
       "      <td>3768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.014273</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>383591.000000</td>\n",
       "      <td>779.000000</td>\n",
       "      <td>4674.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.173796</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>417056.000000</td>\n",
       "      <td>2715.000000</td>\n",
       "      <td>21720.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.507358</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>449658.000000</td>\n",
       "      <td>12274.000000</td>\n",
       "      <td>122740.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       testPositivityRatio  multiplier  cumulativeConfirmedCases  \\\n",
       "count           194.000000  197.000000                197.000000   \n",
       "mean              0.109436    7.370558             312442.619289   \n",
       "std               0.147683    1.752475             148239.338938   \n",
       "min               0.007526    6.000000                  1.000000   \n",
       "25%               0.010094    6.000000             251608.000000   \n",
       "50%               0.014273    6.000000             383591.000000   \n",
       "75%               0.173796   10.000000             417056.000000   \n",
       "max               0.507358   10.000000             449658.000000   \n",
       "\n",
       "          new_cases  estimated_cases  \n",
       "count    197.000000       197.000000  \n",
       "mean    2282.522843     20623.624365  \n",
       "std     2906.914878     29992.285098  \n",
       "min        0.000000         0.000000  \n",
       "25%      626.000000      3768.000000  \n",
       "50%      779.000000      4674.000000  \n",
       "75%     2715.000000     21720.000000  \n",
       "max    12274.000000    122740.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_cases.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116c1e880>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "est_cases.plot(x='date', y=['new_cases', 'estimated_cases', 'multiplier'],\n",
    "    kind='line', grid=True, logy=True, figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed infection rate: 0.013446895403880041\n",
      "Estimated infection rate: 0.13439822148757238\n"
     ]
    }
   ],
   "source": [
    "# Just assume April 23 was the end date, even though that's when it was reported\n",
    "total_observed_cases = est_cases[est_cases['date'] < '2020-04-23']['new_cases'].sum()\n",
    "total_estimated_cases = est_cases[est_cases['date'] < '2020-04-23']['estimated_cases'].sum()\n",
    "pop = nydata['population']\n",
    "print('Observed infection rate:', total_observed_cases/pop)\n",
    "print('Estimated infection rate:', total_estimated_cases/pop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New York state did a preliminary [seroprevalence study](https://twitter.com/NYGovCuomo/status/1253352837255438338) completed on April 23rd that estimated a New York City infection rate of `21.2%` and a statewide rate of `13.9%`. The multiplier approach does really well, estimating `13.4%` seroprevalence at that date. This was early in pandemic, how well does this approach work now that some locations have built up more testing capacity?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "spainsource = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>new_cases</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>multiplier</th>\n",
       "      <th>estimated_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36918</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36927</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36942</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36943</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36944</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37139</th>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>8866.0</td>\n",
       "      <td>0.101</td>\n",
       "      <td>8</td>\n",
       "      <td>70928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37140</th>\n",
       "      <td>2020-09-10</td>\n",
       "      <td>10764.0</td>\n",
       "      <td>0.104</td>\n",
       "      <td>8</td>\n",
       "      <td>86112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37141</th>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>12183.0</td>\n",
       "      <td>0.107</td>\n",
       "      <td>8</td>\n",
       "      <td>97464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37144</th>\n",
       "      <td>2020-09-14</td>\n",
       "      <td>27404.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>274040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37145</th>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>9437.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>94370.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  new_cases  positive_rate  multiplier  estimated_cases\n",
       "36918 2020-02-01        1.0            NaN          10             10.0\n",
       "36927 2020-02-10        1.0            NaN          10             10.0\n",
       "36942 2020-02-25        1.0            NaN          10             10.0\n",
       "36943 2020-02-26        6.0            NaN          10             60.0\n",
       "36944 2020-02-27        8.0            NaN          10             80.0\n",
       "...          ...        ...            ...         ...              ...\n",
       "37139 2020-09-09     8866.0          0.101           8          70928.0\n",
       "37140 2020-09-10    10764.0          0.104           8          86112.0\n",
       "37141 2020-09-11    12183.0          0.107           8          97464.0\n",
       "37144 2020-09-14    27404.0            NaN          10         274040.0\n",
       "37145 2020-09-15     9437.0            NaN          10          94370.0\n",
       "\n",
       "[184 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spaindata = spainsource.copy()\n",
    "spaindata = spaindata[spaindata.iso_code == 'ESP']\n",
    "spaindata = spaindata[['date', 'new_cases', 'positive_rate']]\n",
    "spaindata['date'] = pd.to_datetime(spaindata['date'])\n",
    "# Get rid of zero days, for plot\n",
    "spaindata = spaindata[spaindata['new_cases'] != 0]\n",
    "spaindata['multiplier'] = spaindata['positive_rate'].apply(get_multiplier)\n",
    "spaindata['estimated_cases'] = spaindata['new_cases']*spaindata['multiplier']\n",
    "spaindata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>new_cases</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>multiplier</th>\n",
       "      <th>estimated_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>184.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3278.081522</td>\n",
       "      <td>0.046296</td>\n",
       "      <td>7.760870</td>\n",
       "      <td>28052.423913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4477.298175</td>\n",
       "      <td>0.050818</td>\n",
       "      <td>1.782548</td>\n",
       "      <td>39448.516695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-713.000000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-7130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>395.500000</td>\n",
       "      <td>0.013000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2394.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1359.500000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8361.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4701.000000</td>\n",
       "      <td>0.072000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>45402.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27404.000000</td>\n",
       "      <td>0.278000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>274040.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          new_cases  positive_rate  multiplier  estimated_cases\n",
       "count    184.000000     125.000000  184.000000       184.000000\n",
       "mean    3278.081522       0.046296    7.760870     28052.423913\n",
       "std     4477.298175       0.050818    1.782548     39448.516695\n",
       "min     -713.000000       0.008000    6.000000     -7130.000000\n",
       "25%      395.500000       0.013000    6.000000      2394.000000\n",
       "50%     1359.500000       0.025000    8.000000      8361.000000\n",
       "75%     4701.000000       0.072000   10.000000     45402.500000\n",
       "max    27404.000000       0.278000   10.000000    274040.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spaindata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11797d3a0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAFmCAYAAAC4IzkXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOydd5wV1fn/37O990JZYBdYellkaSIIKE0EC5aoMXZjLDHFJCQxXzWW6E+iRjEqJmo0saDRKIrSl96W3nbZ3tjee7l3fn+cO7fs3gZ7t3LerxevuXfumZkzy979zPOcpyiqqiKRSCQSiaR34NbTE5BIJBKJRGJCCrNEIpFIJL0IKcwSiUQikfQipDBLJBKJRNKLkMIskUgkEkkvwqOnJwAQERGhxsbGuux89fX1+Pv7u+x8PU1/u5/29Mf764/3ZE5/vL/+eE/m9Of764v3dvjw4TJVVSOtfdajwqwoynJg+ciRI0lOTnbZeZOSkpg3b57LztfT9Lf7aU9/vL/+eE/m9Mf764/3ZE5/vr++eG+KouTY+qxHXdmqqq5XVfXB4ODgnpyGRCKRSCS9BrnGLJFIJBJJL0IKs0QikUgkvYheEfwlkfQWWltbyc/Pp6mpqVPnCQ4O5uzZsy6aVe+jL96fj48PMTExeHp69vRUJBK79JrgL4mkN5Cfn09gYCCxsbEoinLR56mtrSUwMNCFM+td9LX7U1WV8vJy8vPziYuL6+npSCR2kcFfEokZTU1NhIeHd0qUJb0PRVEIDw/vtCdEIukO5BqzRNIOKcr9E/n/KukrSGGWSCQSiaQXIYVZIpFIJJJehBRmiUQikUh6ET0qzIqiLFcUZW11dXVPTkMikUgkkl5Dj6ZLqaq6HlifmJj4QE/OQyKxxjPrT3PmfM1FHavT6XB3d++wf9ygIJ5aPt7usdnZ2SxdupQrrriCvXv3MnjwYL7++mvOnz/PI488QmlpKX5+frz77rvEx8cTHx9PRkYG1dXVhIWFkZSUxNy5c5kzZw7vv/8+1tIR6+rqeOyxx0hOTkZRFJ566ilWrlzJz372Mw4dOkRjYyM33XQTzzzzDACrVq3im2++wcPDg0WLFvHUU09RWlrKQw89RG5uLgCvvfYas2fPZseOHTz++OOACLjauXNnn0qtkkiM5OyDDU/AvRvBO6DbLisLjEgkvZC0tDQ++eQT3n33XW655Rb++9//8v777/P2228THx/PgQMHePjhh9m2bRujRo3izJkzZGVlMXXqVHbt2sWMGTPIz8+3KsoAzz77LMHBwZw8eRKAyspKAJ5//nnCwsLQ6XRcddVVnDhxgpiYGL766itSUlJQFIWqqioAHn/8cX75y19yxRVXkJuby+LFizl79iyrV6/mzTffZPbs2dTV1eHj49M9PzSJxNWc+gKKT0FVLkSP67bLSmGWSGzgyLK1R2cLcMTFxZGQkADA1KlTyc7OZu/evdx8883GMc3NzQDMmTOHnTt3kpWVxe9//3veffddrrzySqZNm2bz/Fu2bOHTTz81vg8NDQVg3bp1rF27lra2NgoLCzlz5gzjxo3Dx8eH+++/n2XLlnHttdfS3NzMli1bOHPmjPEcNTU11NbWMnv2bH71q19xxx13cOONNxITE3PRPweJpEfJ2Su2DeXdelkZ/CWR9EK8vb2Nr93d3amoqCAkJIRjx44Z/2klMefMmcOuXbs4ePAg11xzDVVVVUZ3ti1UVe2Q15uVlcXq1avZunUrJ06cYNmyZTQ1NeHh4cHBgwdZuXIl//vf/1iyZAkAer2effv2GedTUFBAYGAgq1at4h//+AeNjY3MnDmTlJSULvgJSSRdTEMFlBgePKUwSySS9gQFBREXF8fnn38OCGE9fvw4ADNmzGDv3r24ubnh4+NDQkIC77zzDnPmzLF5vkWLFrFmzRrj+8rKSmpqavD39yc4OJji4mK+//57QKxHV1dXc8011/Daa69x7Ngxq+fQ9mdkZDBx4kR+97vfkZiYKIVZ0jfJ3Wd6LYVZIpFY4z//+Q///Oc/mTx5MuPHj+frr78GhHU9ZMgQZs6cCQgLura2lokTJ9o815NPPkllZSUTJkxg8uTJbN++ncmTJzNlyhTGjx/Pvffey+zZswHhlr/22muZNGkSV155Ja+++ioAr7/+OsnJyUyaNIlx48bx9ttvAyIITDuvr68vS5cu7cofi0TiPKk/QGmqc2Oz94C7wXPVzcIs15glkl5GbGwsp06dMr5/4oknjK9/+OEHq8fs2rXL+Pr222/n9ttvt3uNgIAA/vWvf3XY/8EHH1gdf/DgQYv3tbW1RERE8Nlnn3UY+8Ybb9i9tkTSI7Q2wrqfwMSb4Pq/Ox6fswdipkHRSWkxSyQSiUTicnL2gq4Z6sscj22qgaITMOxy8Au7tIRZFhiRSLqW999/n4SEBIt/jzzySE9PSyLpPOlbYM10YQk7Q+Z2sXVGZPMOgKo3CHP4peXKlgVGJJKu5Z577uGee+7p6WlIJK7n6H+gLBWqC5wbn5Ekto0Vjsfm7AE3DxgyXQhzXdFFT/NikK5siUQikfQtdG2QsU28dsaarSuB4pPg5unc+Jy9MGgKePkbLGYnxNyFSGGWSCQSSd+i4DA0iQp0TgltZpLYjrwamqpB12p7bEsDFBwRbmy49NaYJRKJRCIBYMNvIPV758ambza9dkY0M7aDbyiMmC/eN1baHpt/CPStMOwK8d4/AlobhGB3E1KYJRKJRNKzVObAwbVwdr1z49M2wwBDnr4jYVZVEfgVd6UQWbDvms7ZCygwdIZ47xfu3HVciBRmiaQP88EHH3D+/Hnj+/vvv9+ifvXFkp2dzccff3zBx91999188cUXnb6+5BJDs4DrSx2PrSuBwmMw7jrw8HEsmKWpUFsIIxY4J7I5e4To+wSL91KYJRLJhdBemP/xj38wblznu+BcrDBLJEZU1fmxaZowO5FjnL5FbEcudC4wS0uTGjHfsci2NQtXduwVpn09IMyy8pdEYovvV4mqPxeBr64N3K18vQZMhKUvOjz+3//+N6+//jotLS3MmDGDv//979x3333G/sn33nsvQ4YMITk5mTvuuANfX1/27dvH0qVLWb16NYmJiQQEBPDII4+wZcsWQkNDeeGFF/jtb39Lbm4ur732GitWrCA7O5s777yT+vp6ANasWcPll1/OqlWrOHv2LAkJCdx11138/Oc/Z9WqVSQlJdHc3Mx9993H448/jqqqPPbYY2zbto24uDhUB3+MDx06xOOPP059fT3e3t5s3bqV8vJyq3MoLCzk1ltvpaamhra2Nt566y3mzJnDpk2beOqpp2hubmbEiBG8//77BAQEdOgZvXr16gv/j5O4hpNfwA+r4BenwNNB28/WRsjcIV43OCHMaZvBPwoGTDIFZoXYGZ+xHcJGQMhQU2qVLZE9fxTamkyBX2AmzN0XmS2FWSLpZZw9e5bPPvuMPXv24OnpycMPP8xzzz1HQUGBsVRnVVUVISEhrFmzxijE7amvr2fevHm89NJL3HDDDTz55JNs3ryZM2fOcNddd7FixQqioqLYvHkzPj4+pKWlcdttt5GcnMyLL77I6tWr+fbbbwFYu3YtwcHBHDp0iObmZmbNmsWKFSs4evQoqampnDx5kuLiYsaNG8e9995r9b5aWlq49dZb+eyzz5g2bRo1NTX4+vranMPHH3/M4sWL+eMf/4hOp6OhoYGysjKee+45tmzZgr+/Py+99BKvvPIKjz76qNWe0ZIeIuU74ZauLxGCaI/sPdDWCJFjoSrH/lgtTWrMMnBzc1z8o60FsndDwm3ivV+Y2NrKZc7ZI7ZDrQmztJglkp7HCcvWFo2d6Me8detWDh8+bOyn3NjYyJIlS8jMzOSxxx5j2bJlLFq0yOF5vLy8jC0aJ06ciLe3N56enkycOJHs7GwAWltbefTRRzl27Bju7u6cO3fO6rk2bdrEiRMnjOvHVVVVpKWlsXPnTm677Tbc3d0ZNGgQCxYssDmf1NRUBg4caLyvoKAgQDxAWJvDtGnTuPfee2ltbeX6668nISGBHTt2cObMGWODjZaWFmbNmkVQUFCHntGSHiTvgNg2VDgW5rRN4OEr1ox3vAgt9SJ/2BpamtTIq8V7vwioOmL73PkHobUehhuisT19wdPftvWbs1c8IPiHm/b5BIPi1reFWVGUecCzwGngU1VVk1x9DYmkP6OqKnfddRd/+ctfLPY///zzbNy4kTfffJN169bx3nvv2T2Pp6enseeym5ubscezm5sbbW1tALz66qtER0dz/Phx9Ho9Pj7W3Y6qqvLGG2+wePFiQDSxCAwMZMOGDR36Otu7L2tjbc1h7ty57Ny5k++++44777yT3/zmN4SGhrJw4UI++eSTDuc5ePAgW7du5dNPP2XNmjVs27bNqXlJXExVHtQYXMb20pJArEOnbYThV0JwjNhXX2ZbmNM3C5HU0p4cWcwZ20FxhzizFqi28pJ1bZC7HybdarnfzV2kWvW24C9FUd5TFKVEUZRT7fYvURQlVVGUdEVRVhl2q0Ad4APku3a6Ekn/56qrruKLL76gpKQEgIqKCnJyctDr9axcuZJnn32WI0eElRAYGEhtbe1FX6u6upqBAwfi5ubGRx99hE6ns3rexYsX89Zbb9HaKgozpKWlUV9fz9y5c/n000/R6XQUFhayfft2m9caM2YM58+f59ChQ4AQ97a2NptzyMnJISoqigceeID77ruPI0eOMHPmTPbs2UN6ejoADQ0NnDt3zmbPaEkPoFnL4Lj8ZXk6VGZD/EKzVCY768xpmyFmuhBKEMLcVI2ib7M+PnM7xCSaIqzBIMxW5lV0AlrqLNeXjceEO7f+7SKctZg/ANYAH2o7FEVxB94EFiIE+JCiKN8Au1RV3aEoSjTwCnCHS2cskfRzxo0bx3PPPceiRYvQ6/V4enryyiuvcMMNN6DX6wGM1vTdd9/NQw89ZAz+ulAefvhhVq5cyeeff878+fPx9xeWyqRJk/Dw8GDy5MncfffdPP7442RnZ3PZZZehqiphYWGsX7+eG264gW3btjFx4kRGjRrFlVdeafNaXl5efPbZZzz22GM0Njbi6+vLli1bbM4hKSmJl19+GU9PTwICAvjwww+JjIzkgw8+4LbbbqO5uRmA5557jsDAQK677jqamppQVdXYM1rSA+TuF1atqndsMadtEtv4RSINCqDehmVaWyzSpBY8adpnWDP2aKvrOL6hQgRzzf2t5X5bVnbOXrEdNrvjZ91cllNxFEVpHKgoscC3qqpOMLyfBTytqupiw/vfA6iq+hfDey/gY1VVb7JxvgeBBwGio6Onfvrpp526EXPq6uoICAhw2fl6mv52P+3pTfcXHBzMyJEjO30enU6Hu7u7C2bUO+mr95eeno6tbna96fewK+iu+5ua/At07r6EVJ8hK/YOcmJvsTl28rE/4dVSxaHpb+DTWMTMAz8lZfTPKRp4VYex0UXbGJvyN5Knvkpd4HAAIkt2M/7MyySNfwkix1iMjyzZw/gz/48jU16kJniscf/YM38lqOYcB2a+Yxqsqkw+/n94N5dycMbbHa49/tQL+DYWkTzt9Qv9cdhk/vz5h1VV7Ri1SefWmAcDeWbv84EZiqLcCCxGBLCvsXWwqqprgbUAiYmJ6rx58zoxFUuSkpJw5fl6mj53P811InIyZw8k3geRo+wO7033d/bs2YsO2jKnthPBX32Bvnp/Pj4+TJkyxepnven3sCvolvtrqoEdOTD3N7Avl7gBIcTZumZzLew8CzN/JubVXAsHYMzQCMbMtnLM5/+CgGgSr70HtFiFTAXOvEywZxtT2l9n/VfgFchl194P7p6m/Q0b4Phxy5/Fic+h6gQs/DPzrF275ks4l91tvx+dEWZrER+qqqpfAl924rySvkprkyirt2u1KBQPwlX1YJLlGo+k33PDDTeQlZVlse+ll14yBo9J+in5h4QLe8gMOPax/TXmzB2iJnW8IcPAK0BU8rJW/cs8Tco8gNCQyuTZWtPxmIztIujLXJS1Y5oNjSzcPaGmEDY8ATHTYNaj1uc646cw8WY7N+5aOiPM+cAQs/cxwHkbY62iKMpyYLkrXIeSHkbXCv9eCTm7RUWe2T8HFPjoevjqZ/Cj/1h+oXRtItrRyYheSd/iq6++6ukpSHqCvANifTlmmgjQsrfGnLYJvINg6EzxXlFE+pO1Neb2aVIatoS5PEPkRF/+WMdzGXOZKyEgCrY/L4qKXP+2+Jtkjejxtu+jC+hMSc5DQLyiKHGG9eQfAd9cyAlUVV2vquqDwcHSmurzbP4/Icor1sCPv4C4ueJp9epnIPU7U9s1ECkSb06DLx8EQzCTRCLpB+TuFyLmE2RIMbJhMauqiLAeMd/SovWP6Ggxt7XA2W8s06Q0DCLr1dJOmPe+Lnovj1rS8dqaMDeUi4eAk5/D5NsgovcYiM6mS30C7ANGK4qSryjKfaqqtgGPAhuBs8A6VVVPX8jFFUVZrijKWlvBGJI+Qtpm2P93mPEQXHan5WfT7hcuqtNmFlRpClRkwsl18MMqoop3ws7VUC2z6ySSPouuDfKTYYjBAvYLs20xF5+C2vMmN7aGf4RlWtLBd+H5AbBvDQydZUqT0vDwBu8gS4u5LA2OfASJ90LIEDpgXsnr8PvCWp7x0IXdaxfjlCtbVdXbbOzfAGy42IurqroeWJ+YmPjAxZ5D0gtI3yKq6Sx6ruNnnj4w+hrRzm3ZX8XTcfZu8dm46+DgO4wD8WiX9KJYy1n4Z9suJYlE0jspPiWqbGmuad9Q22vMWprUyIWW+/0jodSs+tyRf0FEPFzxK+GFs4ZfmEmY9XrY8rSo8DX3NzbGG4Q5dz8c+icMnwdRY6yP7SFkdylJ5ylNEZHX7YMsNMbfIL6gWTvF++zdEBQDN70PN/6Dw5f9P3j8OEy+VTwZf/uLC+tMI7FAS4lp3yEqOTmZn//853aPzc7OZsKECU6Pl/RBWpug5OyFHXPma+HlsodWWGSIoY+xr8FitrZcdW4TDEyAwGjL/X7hJld2db5oIjP5NvG3IWig9ev6hQthPrcR3r4CUr6F2b+AgEjr44MGg7sXbHtWWO0zfmb/vnqAHhVm6cruJ5SkiPqythixQAR5nP5KCG72btFWzc0dJt1MbdBoCI2F696EOU/AkQ9h+wvdNv3+SnthTkxM5PXXnc/DvNDxWplPSS9n7+vw9hzHxT80KnNg3U9gf8f8Xgty9wvR09zHvqEiQru53fpvQ4WoYd3ejQ3Cld3WKOpln9so9o1eav+6fuGEVJ2Ej28BXTOs/CfM+bWd8WGi69VPvoHbPoNRvS9ToEeFWQZ/9QMaK6GuCCJH2x6jubPPfAMZW8UaUqyV6jogqvpMWAl73+jWSju9iezsbMaMGcP999/PhAkTuOOOO9iyZQuzZ88mPj6egwcP8vTTT1u0NZwwYYKxMYXGqlWr2LVrFwkJCbz66qskJSUZmzs8/fTT3HnnnSxYsID4+HjefffdDvMwH19fX8+9997LtGnTmDJlCt999x0g+kHffPPNLF++3KnGGpJeQGaSSFMqsNP8wZxj/xHb2kLbY1RVCLPmxgbbnZyO/lsI9mgrgVn+Biu3vhTO/SAe2CPs10EgNBZQYd7v4eH9MPEm0XnKHoHRoj736CW9MjNEdpeSdI7SVLGNsmMxg3iCTd0AnxoqtJo3IjdHUcTa0Kn/QvI/ba8TdQMvHXyJlIqUizrWVmWsMWFj+N303zk8Pj09nc8//5y1a9cybdo0Pv74Y3bv3s0333zDCy+8QEJCgsNztG/dmJSUZPH5iRMn2L9/P/X19UyZMoVly5bZPNfzzz/PggULeO+996iqqiIxMZHly5cDsG/fPk6cOEFYWJjDOUl6mNZGkWsMIgVpZMcKWxbodXDUIMx1xZafZe0SJS9n/xyq84RbeIiZMGuBWuaWeV0p7HxZrC0Pntrxen6GetlVeSLPOfFex8J51f+x330Wl8+70f64PoR0ZUs6h7ZWFekgeCJylFhT1rUId1donO2xUWNFvuKBtWI9zBxVFX8Mtv5ZFBDop8TFxTFx4kTc3NwYP348V111FYqiWLRs7CzXXXcdvr6+REREMH/+fA4ePGhz7KZNm3jxxRdJSEhg3rx5NDc3k5ubC8DChQulKPcV8pPFdxBFCLMjMrZDTb4Q2doiy8+OfAhbnoLGKsg1rC8PnWH63FdLSzIT5u3PQWsDLLaxVKVZzN88KtzSzriZvQNp8e5fv389ajHLqOw+SM4+8A6AARPF+9JU8PSDYCtpCe2Jvxpu/kC8dvQUfPlj8OF1wnKeYrCyGyvhi/uEOxzg7LfwyIEuc0U5Y9naorMlK7UWjWC9ZaOHh4exoQVAU1NTh3M4on0LRnvtG1VV5b///S+jR4slC+3+Dhw4YGw6IekDZO8W+cCjrxGuZ1W1//05+qEQ2Ik3iwhp8/F1RcIlnb0b8vaLtMgos0Ic5hZzY5UQ8cMfiGArW2V6w+JEPIqHr1jWirPdFKU/I6OyJc7TUAH/uQk+WAZVwlqi9KxYX3a0pqMx7jrxzxFxV0LAAMjaId5X58O7V4nI7kXPwVVPQVmqaNV2CRIbG2ts/XjkyJEO5S/BcUvIr7/+mqamJsrLy0lKSmLatGk2xy5evJg33ngDrenN8ePHO3kHkh4hZ494qB4xX8R6aN9ja9SXQcoGERUdMkTk+zaZeTe1blBZO4TFHJMI7ma2nvka80fXCwt71qNw9dO2r+kXBqty4ZH9YhnL2b8r/YxL864lF8fBd0W/Ul0bfHGvKMNZmmo/IvtiURQYfJlwWwMkvy/6tt71jbCmp94Nbh5wYp3rr90HWLlyJRUVFSQkJPDWW28xalRHC8S8daO1NojTp09n2bJlzJw5kz/96U8MGjTI5vX+9Kc/0drayqRJk5gwYQLPPWclZ13Sq3HTtUDeQYidA4MNTY0Kkm0fcPxTESR22Z3iIRlMYgwm13bqD1By2nJ9GcAnRGxz94vv8aLnYfHzIhjUHr0wGKu76VFXtqyV3YdoroMDb8GopTDpFvjiHnhviYjUtBeR3RkGTYHU70XHmpy9MHCyqYm5X5gIIDn1335XkCQ2NpZTp04Z33/wwQdWP9u0aZPV4+vqRG9aT09Ptm7davGZeXecUaNGsXbtWpvXnjdvnnG8r68v77xjapOnWeJ33303d999t/M3J+kxAmvTxLrtsNmibKaHD+QfFlkQ7VFVOPqREPCosabc4roi4YZubRK1q/3CodpgdQ9tJ8zuHuAdLPKKAcZf33U318+Q6VIS5zj0D7FWNOfXMOFGuP4tEYkJED2ha645aAqgiijSgsMmUdaYdLN4MPjsTvj6Udt5mbIet0RCSNUpQIFhs0QxoIGTbQeA5SeLwkGX/US81yzmWkNkthahPd4QCa24CVd2e3xDRLDZ4KkQZNsjI7FEpktJHFOWJspljloCQwzrkAm3w9gVwpIdsaBrrjvQkBKU/J540h86y/LzUUuFeBedFA8JPsHCVWZOXSm8MRWWrRaWvgQQecySS4uQqlMwYIIpKCtytHBDW+Poh6LM7gSD8AZEiW2dwX2tubRHXg2nvhDBn95Wgh39wkSXpzHXuu5GLgHkGrPEPro2+OqnYl1o+d8sP/MOgFGLui5AIyBSfOFTRDGLDsLs5Sd6Pf/ypIjcPrhWVCkyJ3un6L268Y+iEbsTqLIcaL/kkv5/bWsmqCYFhpnVDwgZCvUl0NJgOba5Dk59KUrpamLrEyxc39q6sibQQQPhmtUiGNMa2kOAFOYLQgqzxDaqCj+sEu6ua1+FwAHdP4dBCYAKEaPBP9z2uHl/EO60be2CknL2ibq49SWw66+m/Tb+SPv4+FBeXn5p/xHvh6iqSnl5OT4+DgKP+isFR3DXt1gW9gmJFVttSUrj9FciyFNzY4MIyAqINlnKmkAHDBCVtuLb9UnWiBwj3Ni20qMkVpHBXxLb7HgJDr0roqDH39Azcxg0RXSmGjbL/rjgwTD9QVHKc+GfTQXvc/eJYJeAaNjzN5FHqTfU7028RzxwmBETE0N+fj6lpaUdr3EBNDU19WsR6Iv35+PjQ0xMTE9Pw7WoqqgRfdldMNaOVZpj6OhmHqcRMlRsq3ItAziPfiTKYA6ZbnmOwAFmruxi8SDsH2F/fotfAL2soX6hyAIjEuuUnIWkv8Dk22Hhsz03D61s3zAbtbXNmfJjUaD/7DeifWRjFRSfhvl/EH2hgwYJS0BxE3W7y9I6nMLT05O4ODtVyZwkKSmJKVOmdPo8vZX+fn99htJU0UIxbIR9Yc7eTZ1/LAF+ZhWyjMJstvxTeEJ0iVr4bMe0pYAoU0vGumJRpctRNoSi2O46J7GJDP6SWCdnr9jO+13P5hXGXSmqhTmzRhU5GqLGwen/CWHOOwCoYm3aLwyuNlsHK7w0C5NI+hmaJdxUZXuMrhXyDlIVNZ8A8/0B0WKZRysyUl8G6+4UgptwR8fzBAwwtW6tLRbHS7oEucYssU5+sigoHzKsZ+ehKMKN7uxT97jrhfu6tkhUOXLztJ7GIZH0B7QHaHstHM8fhdYGqkImWu53cxPBlVW5olnFp7eL781tn1qP5wiMFpW/WhuFS7snYk4uEaQwS6xTkAwx0/peFZ7x1wMq7Fsj+rkOvgw8fXt6VhKJ61FVM2G2YzFn7wKgOnh8x89ChopMhvxk4WFa8qLtB1lj9a9ig8Uc1YnJS+whhVnSkcZKKDvXNy1NzZ299w1xD1PudP5Y8+bs2vu2ZtfPUSJxBZVZhh7Jin1XdvYeiBxLq1dQx89ChgqLWXNR26tjr1nINYWiEliAtJi7Ctn2UdIRrYF6XxRmgOvWwIo34NfnRJ1fZ9n/lohwLTgsrJH3lsC3v+y6eUoknUGzlodMt20x61pFrWpb/c9Dh4lmFue+F80t/Oy0Tww0ZDpkJoGqk67sLkSW5JR0JD8ZUGDQZT09k4tj8FSRgxkQeWHHadbyqS+h8JjoXKU9pEgk3UHq9/Dvm2zm2VuQs1fUqh4y3bbFXHgcWush1kZWgxZDUnDYcYvFqHEQM91UD6APBn/VNrXS0uZ8id5TBdXc/Rf3KlkAACAASURBVP5Bmlp1XTirjkhXtqQjBcmiMICPFddXf6W+XNTkVtyEMB/7ROyvyBCBMRJJd3BiHaRvFssojsjZIzIOfENFS8bWxo5jDOvLFhW/zNFSpgDi5tq/npubyPtXDcLWx4RZVVWufWM3r2w+5/QxH+3LISm1lLyKBseDXYgUZoklqios5r7qxr5Y0rcAqmjiXnte1Of28BUF+KtyHB4ukbgEramEvShrgOoC0QZ12GxTe0Vr7uzsPaJqni3vkSbMinvHkrfWGDABZj0CKKJHcx8itbiWnPIGssuceOgBdHqVrSmiWUdFfUtXTq0DUpglllRkisbml5own/sB/KNg3iohyPpWmG6oe2OlEIlE4nLqy0wPgY6EOXef2A67XHRwgo7ubL1e9F+2VzXPPwrcvUX2grMesqufhod293i3qIKqRt7YmuZ0+dxd58oAqGhwTmSP5VVSVifGVjp5jKuQwiyxJN/QOD1mWs/OozvRtUHGVohfJP44jVkmcrhnPSI+l8Is6Q7MWzDai7IG4cb2ChQBW1qjiPYWc3m6aOAy2M5DtpubeACd8ZDz83RzF5ZzD/Pe7iz+uvkcBVVWXPhW2JUuhLnSSet30+li4+uK+tYLn2AnkJW/JKYyfNMfEOvLXgFijflSoeycKJww3BD8cu0r4n3gAPANg3IpzJJuwFyYHVnM2Xtg6Ewhkj42LGbtfI68X+1bpfYRklJFQ41KJ0SzqVXHwaxyMb7B8XhVVdl0ppjpcWEczKqQFrOkB9j6Z9jwBJRniACoQVMc18DtT2hF9j39xNYn2LT2FjHKZDHXl3XPfKpyYedq5yJzJf2HgsPiQRDsC3NdKZSlmhpS+NpYYy5IFlZ1RP/r7JRX0UBGqVgrdsY1fSSnkqZWPWMGBFLZ0OLQ/Z1RWkdWWT3LJw3E38tdrjFLupn6csjcLl4f/gCKTl5668v2iBgphDltC7w80uTq70pO/w+2PSuKOEguDVRVCPOIBeK9vUpexvVlQwqUMfirnZjnJ8PgvvOQvS+j3Gm3dNI503fDGdf0rvQyPNwUlk4YiE6vUtNkv+PVpjPCjX31uGjCArwuLWGWBUZ6AWf+JyzG0Fg48I54fSmtLzsiPF70ct70JGBWArEraa4V29buTdGQ9CAVmUJYY68QjSXsWcw5e8HDR3i2QHh4wNKV3doIxafsry/3Iuqb27jrvYO8nZTh1PiklBLC/L0A5yKmd6WVctnQUGJCRXneKgdW9uYzxUwcHMzAYF/C/C4xYZYFRnoBp74Urq55vwedofxkH/kydwuaG7D0rNgWHuv6axqF2TnrQdIPMK+25xvqQJj3iIdnDyFMuLmDd7CllV14wvCQ3Te+y3vSy2jR6SmtdVwCt6lVx96McpZOGICb4jhiuqK+hdPna7giPsIpMS+paeJobhWLxok87VB/L7nGLOlGas6LL/mEm2DscvD0h+ChoouMRBARL7bBQyB+MZzvTmGWFvMlQ8FhEeMQOVa4pm1FZTdVi+Wm9v3JfdsdU2BYctH6mfdytqcK17Qzlumh7AoaW3UsGBNFqBPW7J70MlQVroiPINQgzPaEdstZEVS2cLz4O3jJWcySHqStBb56SDxtT7wJvPxFfuIVv+jpmfUuQmNFadKFzwgrpSJD/HHsSpprxLa1qWuvI+k9FCTDwARw97BvMecaeoy3L7HpG2JpMecdEA+TPVTPOrWolmfWn0avdxzAqKqqMcK6rN6xxZyUWoqXhxuzRoQ7Zc3uTisjyMeDSYODCfUT7WPtpT9tPlPE0DA/RkcHAgaLWQqzpMtRVfjmUcjaASvWQPgIsX/GgzDtvp6dW2/D3RMe3A4TVsKgBLGv6GTXXlO6si8t2lqE63mwoTZ9e2FuqoY3Z0D6VlOP8fbLTT4hpmP0etEtylGJzS7k4wM5vL8nm7I6x0KbUlRLYXUTQT4eTlmmSaklzIgLw8/Lw6E1q6oqu9PLuHxEBB7ubkaL2dYac11zG3vSy1k4LhrF0PI2zN+L+hZdt9bLlsJ8KXL+KJz4DK78HSTc1tOz6TsMnCy2Xe3Olq7sS4uS0yK+Q3M7+4ZAo5lXpvgMlKaIlMbMJCHgXn6W5zB3ZRedECI9fF43TN46yTniIaGq0XHO8HaDtXzt5EFUNbTSqrPdZEJLk5o3WvSCDvO3L8yZZfUUVDVyRXwEAIHeHni4KTaP2XmulBad3ri+DBDqp4l59xUZkcJ8KXJuI6DA9J/29Ez6FgFREDio6wPApMV8aWGstmewgttbzFqZzopM8bun5S+b42Pmys5MEltH3aK6iLrmNs4WiuUYZ1zA21NKmDA4iLEDhOvYnmtac3nPGy1qf4f6e9l1S+9OE7UH5hiEWVEUQvxsu783nS4i1M+TqcNCjfvC/IX7u9wJN7urkMJ8KXLuB9Eqzj+8p2fS9xiUIIqwnPlauB+7AmkxX1oUHAH/SLEmDEKYW2pFL2UQBWcAYueIbfvALzBZzKoqhDlybI8FcR7LrUJbWnZkMVc3tHI4p5L5o6MID/AG7AeAJaWWMiTMl+ER/oAQTXsFQ3allTEkzJdh4f7GfWH+nlarhbXq9GxLKWHBmGg83E3SqFnMzlQYcxVSmC81aovEU3f8op6eSd9k8FTR1WfdT+Dd+ZCywfXXkBbzpUXBYfF7ZVjTNJXYNLizK3MgYACseB2m3i1yndvjGyo6oTVWQu7+HnZjVxhfO8oX3plWil6FeaOjjKlM5XXWj9HSpOaPjjKu/4b6eaHTqzRYqRfSqtOzP7OcOfGWnbVC/bysVgs7lFVBTVMbi8ZbPtCEBxhSrLoxZUoK86VG2iaxHbWkZ+fRV5nxENz2Kdy/FQZMEgKtuQ5dgV4vrCWQFnNfR1VhyzNQeNz2mKZqUavdPK3J2JTC4M6uyoHQYRA2HJb/DTx9O55HE/NzG6GtsUeF+XBOJUPDxBq4o3XZ7aklhPp5kjAkhAiDAJbbsJi1NCnNjQ0YxbyupaPFfDyvirrmNuaMjLDYH2Yjynrj6SK8PdyMbm8Nk8UshVnSVZzbCEExED2+p2fSN/EOgNFLxXrgnV+JP6KH/+W687fUmV5Li7lvU3IGdr8Cp7+yPeb8UUBtJ8ztSmxW5Zhqt9tCO2bTk+J3sn06VTfRptNzJKeSuaMi8HRX7DaM0OtVdqSWcuWoSNzdFML8hSu73EYktzFNarhJOLUo69rWjsK8K60MNwUuH2EptNbWmDNK6/j0UB6Lxg/Az8uyt1OwryeK0r09mbtEmBVF8VcU5bCiKNd2xfklF0ltEaRvEcKiuc0kF49vCISPhLoS151Ty2EGKcx9nbTNYttQYXuM1gFKK68Jlm0cdW1QXQAhw+xfy1gvuwJueg+8Ay9uzp0kpaiW+hYd02LDCPHzorrRtpidKKimvL6F+WNEhHWIrydudgRwuyFNytfLVPs7zM+2xbwrrZSJMSEEG3KXjcf4e1LZ0Gpcl9bpVZ74/Dg+nu78adnYDufxcHcj2NezW6t/OSXMiqK8pyhKiaIop9rtX6IoSqqiKOmKoqwy++h3wDpXTlTiAvb8TQSUzHq4p2fSfwiIgroi151PW18G17uyv/k5ZGxz7TklttGEudGeMB+BsBHgF2baZ+7KrskHVSdc2fbQLOqrnzY1wugBDhvSpKYOCyXE13qQlcb2lBLcFJhrWAN2c1MI8/eizMoac15FA5ml9cw3pElpaK7s2nbCXNPUyvH86g5ubDCtS2uNLD7cl83R3Cr+fN14ooJ8rM61u6t/OWsxfwBYLEoqiuIOvAksBcYBtymKMk5RlKuBM0Bx+5NIepDaIkh+DybfJtaqJK4hcICLLWZzYXahxaxrhSP/MomFpGtpqoa8/eJ1g40qXqoqUqXa17M27xZVaUiVcuTKDh8BT6TD7Mcvfs4uIDmnkgFBPgwO8SXUz4sqOxbz9tQSpgwNNbqjQctL7ujKbp8mpWHLlf31sfPo9GqH8WC5Ztym0/OPXVnMiAtjxeRBNuca6iBf2tV4OB4CqqruVBQltt3u6UC6qqqZAIqifApcBwQA/gixblQUZYOqqh0yxhVFeRB4ECA6OpqkpKSLvIWO1NXVufR8PY0r7mdE+nvEtLVwwPsKmnrZz6Yn/r8SqqtQFXeOJyURUJtJInDq1CnKigMu6DxDS+oZ3lzDzq0b0bt7G/df7D2FVhzBUMaEksJczrjo5+LZUsNsoDA7hVQXnLO/fcfAtfcUUbqXCfo2WjxDaCnLI9nKeb2byphVV0RaQxAFZp8reh1XAlkpx2jOymcMsD+1iKbczs2tO/7P9qQ2MDLEjR07dtDa0ERpg97qNaubVU7kN3BjvKfF5+6tjWSdbzDuy6zW8UNWK6mVeiJ9FXJOHSLXbBlOVVU83KCirsV4THObyupdjYwKdaM26zhJ2ZbLdvmlwlLesns/lU0qBVXN3BinZ8eOHTbvS9/YRF6j2m2/804Jsw0GA3lm7/OBGaqqPgqgKMrdQJk1UQZQVXUtsBYgMTFRnTdvXiemYklSUhKuPF9P0+n70bXCgXtg3ApmLu36Sl96vcrJgmoOZJWzYvJgBgRbdw9p9Mj/V2YIuLmL6xaGwWGYMGECjL3AeRwtgKyPmDtlFITFGXdf9D2droQTgIcPUSEBRLnq51KeAXthYIgfA11wzv72HQMX39PXX4B3MF5jrsErY5v18575BvZD/LwfEd/eaj4QTFx0MHj7wDk3Zi5aKcrDdoKu/j87X9VIxQ/bWDptNPNmx7Gh7DiF58qsXvOLw/nAce5dMoMJg03dBT8/f4Sz52uMx3z4wSFOVZQzPS6CWxOHMH/iwA7niti3lWbajMes2ZZGdfM53rt3lkWhEI2QvCpePbyH4WMm8PaOTGJC3fjFzfNxd7Mdd7Oh7Djnz5V22+98Z4TZ2l0Y/Qmqqn7QiXNLXEn6VrHONblrRVmvV/n2ZCGrN6aSWyHWR789Uci6n87Cx7NvNGu/YAIMOY91xRbCfNForuyAKNe6srVyjV3dgEMiXNTpW2DEfAiIFN89Ve0YcFmQLOpeD5jY8Ry+wcKV3VABQYM7LcqdpaapFT9Pd4vCG+3RynAmDhPr5fYqbG1PKSEq0Jvxg4Is9of7exnTpRpbdOxJL+O26UN5eoXtLJJQfy/qWlupb27jg73ZvLk9g4Xjoq2KsnYNgF+tO05VQyt/vGasXVEGuGnqEGZbWa/uKjoTlZ0PDDF7HwOcv5ATKIqyXFGUtdXV8o9Fl3LiM/ANg5FXddklVFXlsU+P8vNPjuLn5c4rt0zm1VsncyK/mmfWn+6y6/Y4gWbCrFFXSkjlRVYFMwpztGuDvzRBttVOUOI6ik9BbSHELwS/cFH4o6Xe9LmqiqCvtM1ClD28O57DN1REZVflOo7IvkA2ni7iujW77dakNqexRceC1Um8szPT7rjk7Ar8vNwZO1BEhIf4edLcpqexxbL5Q6tOz860UotCIRrh/t5UN4p62XvSy2hu03P1WPsVzML8Pcmp0bPgr0m8vDGV2SPDefa6CTbHx4T68ux141k8bgArJg/i1ulDbI7VmB4XxnUJgx2OcxWdsZgPAfGKosQBBcCPgNsv5ASqqq4H1icmJj7QiXlI7NFUA6kbYMqdXfrU/daODL47Ucgvrx7FowtGGp9A04rr+HtSBjdNjWGq4UlaVVVueWcflw0LZdWSMV02p25Bs5hrDcLc2gQf3UBC8UkYPwZGXWCFNU2Y/aOgMst182yUFnO3oRXxGXm1ZWS2tyF+IekvsOMlcPOAa1ZbP4dvmOE8KiTc4dLpfX2sgOP51aQU1jIxJtjh+A0nCymrayGtuNbuuOTsSqYMDTFa1SG+huYPjS34epmKouxJL6O2qY35YzoGZoUFmAKztqYUE+DtwfS4sA7jzAn186KiSWV0sBd/v2OqTUtZQ1EU7pwVa3dMT+NsutQnwD5gtKIo+Yqi3KeqahvwKLAROAusU1X1gkwjaTF3AynfQVsTTLq1yy5xKLuC1RtTWT55ED+/aqSFW+jh+SPx8nBj/fFC476c8gYOZVfyzo5M3t6RSVqlji+P5FPb1H21aF2GXzgo7iaL+YdVUHySJu8I+PrhC4/Ybq4FT3/xR7xLLGb5Xbto9HrQO9H6L22LqAoXOMCUBtVQbvo8ZYPo7f1EGiTeY/0c8/8geqPP/gVc/ljn525AVVUOZIr0rWP5znlPPksWoUSl7Qp/VNS3kF4ixLquuY2Uohrjwzdg7H1snjKl06u89EMqg0N8jR2izIkwuJlL65rZeraEuaMi8PKwL1MPzBnOHWO9+Oax2Q5Fua/glDCrqnqbqqoDVVX1VFU1RlXVfxr2b1BVdZSqqiNUVX3+Qi+uqup6VVUfDA52/NQmuUjOHwGvwI4pGS5k0+kiPNzdeGnlxA6uqQBvD+aPjmTDyUJj0/T9meKP1PTYMF76IYXnDzTxq3XHmfP/tvPP3Vk2C9L3StzcRQOCuiLI2QuH34fZv+DEpKeEyH7/uws7X3ONKA7h6dtFa8w1QmAkF87+N+H1BPtjGqsg74CpFr2vJsyGXOa2FtHCcdhsy9zl9gyZLnKSFz4DUR2LXlws6SV1xjXcY7mOhTmztI6DWRUoCpTUWArza1vOsfKtfbTp9BzNrUSvQqKZMGqFPcxTptYl53G2sIZVS8dYjTvR8pL/uTuLktpmrhrjuBHH5CEhLBzmibdH/4ljkSU5+zvlGSLHsQsrfZ0rrmNEZECHUnYayyYNoqS22RgcciCrgogALz68bzqrlo7hkQRv1v10FpNiQnj22zO8tiWty+baJQRGC8s4cwcobjD3CRr8h8LUe4THwnx90Rp6HXx2pwgYaq41CLOfi4VZs5RVUy1ue9SXOx5zqZGyQaz5tjbZHpO5XRQE0YRZE1+tvGbpWdC3wsBJXTtXG+zPEg8Io6IDOJZnI7/ajHXJ+bi7KSweN4CSWkthzq9spLqxlRMF1SRnV+KmwJShIcbP2/cx3ptRxssbU5kWG8q1kzpGVwNEGwp8fHmkgPioAIfry/2VHhVm6cruBioMwtyFpJfUMSradv7vVWOi8PZw47sT51FVlf2Z5cyIC8fH052HrhzBtAFiHemDu6dx89QY/rY1jQ/2uHB9tasJiBYFXPIPQdQ4UznE0UtB1wwZ2+0fn70bzn4Dp740CbOHT9e4stu/tkbRSXh5hKGOswQQYlxg6Jts7+eXtlkUCDH2Vm5nMRedFNsBkzse2w3szyxnYLAP104aREZpPTV2lo9adXr+eySf+aOjGD8oiOrGVppaTa78klrxgLInrYzDOZWMHhBEoI8pjiVEs5gbWnl5Ywq3v3uAAG8PXriho2dNIzbCn3/elcj3j89h0y/ndiineanQo8IsXdldTFuLeMIP6zphrmtuo6CqkVHRtmvz+nt7sGBMFN+eKOREfjWF1U3MHN7RjefmpvDiyknMHRXJK5vPUddspZdbb8QozO2qOA27HLyD4dz3pn16HZSlWx5/6guxLTppaTHr20w9eTtLo5nb0pEwl6QAqhRmcwoOi+hqsB3ZrtcLr8fIq8QSB5iV1zQIc+EJEUPgwup7f9uSxsl8x8aNtr48Iy6MhCHCsj2RZ/u47SkllNY2c+u0IUQFicjxUjOrWXu9K62Mo7mVTIu1XN/VLOac8nre2ZHJsokD2fiLucTb+VsBcNXYaMYODLIp3pcC0pXdn6nKAVXfpRZzeonohjQyyn7FrJ9eOYLapjZ+8t5BAGYOD7c6zt1N4ZdXx1PT1Ma6Q3lWx/Q6AqKhvgSaq0VQj4a7J8RfLTp6aeu6Z76GNVNFz1wQD09nvhGvS1OEZaWtMYPrrOYLsZhrDVmP7R8gLmVy9ppe2/r5FZ0QQYAjF5r2uXuIhzNzi3nABHBzzZ/egqpGXt1yjq+PFTgcW1ivUlbXzMzh4Uw2CPNxOwFg65LziAz0Zv7oSKIChYtZCwDT6VXK6lrwcFM4mF1BfYuuQ+CVj6c73h5u/O9YAW16lQfmDrdoQCGxjXRl92fKDX9Yu9BiPmdIobBnMQMkDAnhpZsmUt3YSri/l10hnzI0lGmxoby3J4s2K7mWJTVN/Ht/DqcKesnvTeAA02tzYQYYtRTqS01dhCoyxHb7C2KbsU1YYBNuEhZZeTp4B5kJs4vWmZuqRQS59toeNYYI+rJzrrl2fyBntygGApbeB3PSDalRI6+23O8XKixmvd4gzK5bXz5kWDOubnTsWUmtEG7oGcPDCfb1ZHikP0dtBIAV1zSxPbWUm6bG4OHuRmSgsJi1ALCK+hZ0epW5o0wpT4mxHb1goX5eFNc0MyDIh0mDpWfUWaQruz9TbhABF1rMGaV1nK8yiUV6SR1eHm7Gxuj2uGFKDE8vH8cvFo5y6Ka6f85w8isb2XTGVLijqVXH7744wYy/bOXJ/53i91+evPgbcSUBhrQPn2AIj7f8LP5qkU6l5bZqope1A85+C/v/LtydxpQYFXyChCsbXCjMVaZCFQ4tZsMcy/tYEF5XoWuFvIMQN0e8t+XKTtss2jcGtMvP9Q0TFnNllgi8s1bp6yI5cAHCnFKhIzrIm9hw8bs1flAwqcU1Vsd+cTgfnV7llkRRfMPkyhbrytr68rKJA/FwUxgYLBpXtEdbZ144Lho3B9W1JCakK7s/U5EhAlHspWVcAA0tbdz89j5u/PteY6eVc8W1jIgMcFjSTuPu2XHcOdNxJaOrx0YT5u9l7CpTWd/Cbe/u57PkPO65PI77r4jjZEE1GaV1F39DriLAYDEPTuzoovQNFZ2BNO9FbaFYXwyIhs/ugOxdMOfXED0e3A1ddixc2S60mLUORdaEuakGWhpMcwQRn9BmvWn9JUXhcbGkMPoa8d7az6+hQgT/xVspKOMXJixmLfDLhRHZB7NE9LwjYVZVldRKPTPiwo0PxUNCfSmsakKnVzuM/Tw5j+lxYcRF+AOiIpebgjEyW1tfjo3w49pJA212ZtKEefH4AVY/l1hHCnN/pty1EdmfHMyjor6FsrpmfrXuGHq9Slqx/Yjsi8XdTWHi4GBOGIJaPtqfw7G8Kv5+x2X83/JxPDB3OIoi2rv1OFpZzvZubI2QIVBtWC+vLYTQOFHxaeIt8NAeYS27e0LkaDFGC/6CjsJcngFvJEJltvPzU1Xhfg0xlB60Jiyf3g7f/Vq8rikED18Rn1BhvwzjJUHOHrEdvVRsrbmys3aIn5f5+rKGX7gQ7uxd4v810jV5yaW1zWSUilQ8R8KcXd5AVbNqEdsRE+pHm16luMYy/etAVgXZ5Q38aJqpVKW7m0J4gLfRla0JdFSgD6/9aAq/v8b6PYUHeBPs68kMK8GeEtvINeb+TEWmy9aXm9t0rN2ZwYy4MJ5aPo6k1FJ++u/DFFQ1Eu8g8OtimRQTTFpJHY0tOg5mVTA6OpBrDN1looN8uHxEOF8fK+j5giQhw2DR87arOAUPhSqDMNcUQtBAGLcCVr4L0eNM46INLk57wV+5+4WL+dSXzs+vtVHkzvqFi2Iz1oS5vkykA+n14uFh6EyxX64zi8Cv8HgIjhHCas2Vff6o8HgMslKARHNlp34PIxaAp/1ua85yKFu4sUdE+lPjQJi1oj7mAhkTKn7H8iosf8c+O5RHoLcHSydY5hpHBXobXdiaxaytPdvi1wtH8d7d0/C00/xC0hG5xtxfaW2C6nyXWcxfHM6nuKaZRxeM5Mczh/HEolHsTisDHAd+XSwTBwejM7SQPJJb2aFm7nUJg8kpb+Cpb07z6uZzHYrldxuKApc/ahkEZk5wjKgM1tIgorcDbTRkH2AovO9tZ425Kldsz/3g/Pw0IfYJFv9srTFXZIp56ltN66lll/g6s14HOfsgdrZ47xNiXZiLTkLkGOv16P3CxNpyTYHJHW6DUwXVvLbFuYehg1kV+Hq6c8XICKocCPOBzHKCvRWGG1zTAEMMcSH5labfserGVjacLGRFwqAOEdRRgd7GqOySmiYCfTwcdo0bHhnQb8pkdifyMaa/UpkFqC6xmIuqm3jp+xQSh4VyxcgIFEXh0QXxJP1mHs/fMIH5YzrWvHUFk2JESsenB3NpaNExrV3U55IJA4gK9Obf+3P429Y01lrpflPd2MrCV3awPfUCa1a7Es2FfP6ocHcGWa96xECDteUXZrKq2lvMVTlim3cQ6kqdu74mJD4h9oVZ32ZKCwofKR4gyi/xlKni0yINbpgmzMHWXdlFp2xHW2u5zIobjFps93If7cvhtS1pFoU8bHEgq4LLhoUQHuBNQ4vOZrcoUdSngtGhbhZBl4NCxO+YJsyqqvLU16dobtNz2/ShHc4TFehjdGWX1jUT5cBallw8Upj7K1p6Tifr7Kqqym++OE6rTuXlmydbfLGjg3y4Y8awLnNTRQd5ExnozfoTYh25vcUc5OPJgT9cReZflrFk/ADW7sygrF2h/f2Z5aSV1PF/X5+iua2HLOpggzDnHRDbQBvCPOxyuPMriJtnO/irMsdQTUo1RXo7wlmLGURJSRCiHDFSWszag8qwy8XWN6Tjz6+2WHhCBthoNagFXw6ZAf72e/pqecWO1oyrG1pJKaphemy4McDK1jG5FQ0U1TQxJszSuvX2cCc6yJv8SvHw99qWNP537DxPLBrFBCupTZGB3pTVNaPTq5TUNBtzmyWuRwpzfyVlgxCEaNsNxp1hzbZ0dqWV8YdlY40Rmt2FoihMjgmmVacyLNzPWEe3/RiA3ywZTVObnte3WgrJwawK3BTIq2jkvd3Z3THtjmgWc/4hsbUlzIoi1iDd3Mxc2e0t5lwR+Rs4yLKiWFm6qR5ze4zCrFnMdpoXZO4Q26CBYl215Ax8fjcceMf2Mf2ZnD0imj04Rry39vMzltm0kQalleV04Maub24z1gXQ6kvbIjmnAlUVD6vBvvaFWVtfbi/MIALA8isbyato4G9b07jxssE8Mn+k1fNEBXmjV6G8/Ob86QAAIABJREFUvpmS2mZjCpXE9cjgr/5IS4MoXDF6aaeaV3x8IJe/bj7H9QmD+PGMjq6t7mDiYOHObu/Gbs+IyABunTaEjw/kGlO5QATIJMaGcfXYKF7bco4lr+1k0as7OJZXRW6FC2tR2yMoBlBMFnOQjTVmc6xZzG0tYp0ydBiMXgLp28T/dUs9vDsfkl60fi7N9errwJUNhuhxRaRzDZ8nimqc2wj733I85/6GqgqLWXNjg2GNud3Pr9ggzNE2LOaYRLjsJzD5NruXO1lQjZa55MhiPphVgZe7G1OGhhDkQJgPZIqmMQP9O/4tiAn1Ja+ygd3pIl7k4XkjbdYYiDIrMlJS20RkgBTmrkIGf/VHMpOgrdHhE7o9csrrefJ/J1kwJqqDC7s7mWRo5D7dgTAD3D59KG16lU2niwBhgZw+X8P02DCeuW4C10wcyJAwP+Ii/NGrKrVN3VSL28NLBIY1lIObB/jZd2cC1oO/avIBVUSBj78RWush5VsR7dtcY4r8bs+FuLJBtLF09xSR47/PhSk/tm2N92fK0qChzFKYfUOgsd3Pr+ikiLz3DcEq3oGw4o2OhUfacTzPZIlXNbTYGSnWlycPCcbH091kMVuxss2bxlj7DseE+lJY3cTutDKiAr0ZEWnbKxZpcF2nldTS1KqXFnMXIl3Z/ZHU70R93tgrLvoUBzIr0Kvwh2vG9miqwxXxETy5bCzLbRQwMGf8oCCGhvnx3UlRIONIbiU6vcq0uDAGh/jy6q0JvPuTRN65M9FhNKnL0VyhAQMc1kmua25jV2aVEHFzV3alIfArZKgQi5BhcPTfcPJzsb/eRoCbMfhLE2YHPZnbB6f5hgox1/fQGr3GD3+Arx66+ONb6p0PmANRhhNM68sgfn7N7X5+RadcUs3rWF4VPp7id8NelHV9cxunCqqNMRf2XNn5lY2cr26ymUccE+qHTq+y5Wwxl4+wLt4ao6IDCPXz5JXNImpcrjF3HVKY+xt6PaT+APELraduOMnRvCqCfDws0it6Ak93N+6f41zxe0VRWDZpIHszyqmsb+GQYX25V6RraAFgtiKyzfjySD4/ee8gqocvtJkVf9BSpUKHCXFPuAOydoqORiB6QlujqVp0NHL3FMLiqCdz+3Qu31BxjCNLu6s594PjFpr2+P638P5S58fn7BUPUuadoHxCAFVEaoPwaJSn2Q78ugCO51Uxy1AAxF5e8tHcKtr0KtPjxFh7wqytL9tqGjMkVHhmmtv0XD7Cvicn0MeT318zlrwK4cWRUdldhxTm/kbJaeF+s1Ya8AI4mltJwtDQPlffdtnEgej0Kp8l57HjXCnjBwUT4O3R09MyBYDZCvwyo6K+BVUFvYevpcVclSPqbmvCOflHgCrSnOKuFM0yzKkrFcFcTVUGQca0tSay/gZXqzWLGXrWnd3aKFIA64ourkypXi9c/uXpIsffEaoK2XuEtWxuRWruam3dvvCESIHrZGOKkpomzlc3MXtkBO5uit3grwNZ5RYPnPaFuYIwfy+bRYC0IiMAs0ZYF29zbp4aY7TUHRUXkVw8Upj7G+3TOy4CLTpU69nal9Dc2S9+n8Lx/GquneRYCDXa9KpFxyqXVhQLdl6Y6wxr30KYzUSoKle4xN0NDxqhw0QnowGTYMR8IeLNZrXDv34EPlwh2kp2EGYrzQsix4hc2+AhlvuNwmwnmvtiKTgC/3vEcd/psnNCAMHkObgQCo+KNX5U546vyhHtL9t/j9o/2OQavm9apbSL5Lih9GzCkBCCfT2parS9xnwgq4IJg00PnJ7ubvh7uXcQ5vSSWjaeLrLroh4Y4oOiwJAwX2PBEXsoisJfb57Mw/NGMDyyayr+SaBHTQlFUZYDy0eOtB6eL7kIcvaIQJSQIY7H2uBEvogOnTK07wmzoij8+brxHM2tYsmEAYwZ4HxVsqTUEu4/uJtNv5zLqOhA7vjHAYZH+vPc9S7oBqQ1kHDClV3XLIRZ5+6NZ2sDrP+FsGYrc0zn0bj5X8JiTvlOvK8vAe8AKEmBtI0QMUqImq8hbU4Tlk1PgqqDO/4rgtNACPBd6ztGF3eVxayqoj73+SMi0Mxe8Y2Ss6bXlTmmuuLOkr7V7PhsiBxlf7zxAXe25X4fw3dCW7fP2QsRox3mJzviWF4l7m4KEwYHE+LrSXWj9cDEplYdx/Kq+Em7RjDBvp4Wwlzd0MoDHx7Gx9ONP9ioYw0ilzk+KsChG9ucIWF+/HbJGKfHSy6cHhVmVVXXA+sTExMf6Ml59BtUVZQPHLGgU6c5mif+ACfE9D1hBpg3Oop5oy+8GtnxvGpgAOuPn2fF5EHszSgntaiWZ6+b0Pmo9HDDw2donMOhtQZhbnP3gfPHTA0wUGDKHZaDvQ1Wi9Z6sq5UrInufUM0orjnB7E2qxW50MqGaoVEGsotHxasBQx2lTCnbhCiDCKAzWlhzr7wa6VvEQ+s1bn2j9frhbs8M0nkH0e2EyBzi1mvE7XLJ6y88Pm043heNWMGBOLj6U6Qr6fNqOwT+dW0tOk7Ftvx9bRwf6/elEp+ZQMfPzCTQVbaMZrz1cOzZS3rXob83+hPlGcIi2nYrE6d5lhuFXER/oT6e7loYn2DtBIRELX++Hm+OloAQHl9C6nFdgKlnCV8BNy/DcYudzhUc2W3ufkIUXb3Nhynmnoqt0dbH64vhdoiOPGZSHPyDxdirnVGihwN926Eq59xfu5dIcx6PWx7XjxETPmxsPhb6m2PLzkLUePEw4ZWltRZGitFcZfJt4o0NFvCrKrwyY/glbHi5zfscittPM3WmItPiwjtTiwbAej1Ksfzq5hsWDoK8bO0fpvbdPz2i+Okl9Qa2zy2z+sP9vU0Boypqsq2lBKuGhPtMP8fwN/bAy8PKQW9Cfm/0Z/Q2tO1d79dAKqqcjSvqk+uL3cWvapy42WDyS5v4F97sxk7MAiAPenl5Fc2cOc/D3RokXdBxEwFN8fR5Zoru9XNkI4y4UZY+R7MfxIm3WL9IM1iri8RpTr1rTDtPutjh840WX7OoLlvXSnM2btEoOKVv4PJt4v18ZQNtseXnhXlZUOGXpjF3FgFW542tWQMjTXUke9IdPEO4f6f/iAsfsH6w4vRlV3tkngOgMyyemqb2ozfufZu6ZTCWtYl5/PHr05xwNBlrf1Ds/kxOeUNFFQ1Mju+c+51Sc8hhbk/kbtPWE7hF79mf766idLa5j65vtxZ/Lw8+MM1Y/FwU6hv0fHQlcOJDfdjb3oZr29NY1daGUnd0AxDs5iNwpx4n1gHvvI3QlisoVnMdaVQmgoePmJ92RW4e4iOV9aEuSQFDv3jws+ZsVVUFRtzLQydJaqjnVxnfWxzrQjYihprEFYnLeaqXHhjKhz+F0y9W/TLDo21LuwNFYzIeA8GJ8KSl2DWI6JWeHu8/EVkfFOVCPwKNivXeZFohUU0YQ5p55bWmkwcyKpgd3pZBzc2WFrZezJEFa/ZTkRZS3onUpj7C3q9WBcbOqtTZTiP5oo/vlOG9ILc325Cb4i+Hj8oiIgAb64cFYm/lzsLx0Vz+cgI9maU8+UR4do+VWAlmtnFaBZzcdBEGLVUlHR0hLuncDnXlwhhDo93yjp3Gt8Q68K882URwJW958LOl7FdNHXwDhDu4km3iHXg6vyOY0tTxTZqnIhEr8oRbmdH7HldWLYPbIXlfxPX0YS5/fEH3saztRaWv2a/AIyiiJ9FVS5k7er0shGIxhX+Xu6MMEQ5B/t5UdPUit5QnzPP0GQiNtwPVcVqsRBzi3lPehmDgn26vba9xHVIYe4v5O4VDe7HXXfBh+ZVNPDDKVHG8lhuFd4ebowZ2DU9lnsj+VXCIhk3SLiun7/h/7d33+FxnWXi97+PZjTq1ZJlq9iSS2zHJXbcUuxEIQkEQkijJIFANpAAvwV2WVgWlt42y8tSQ4ANC4SwQAgB0jbgVKU4jlviuBfZkiXZVu8ajaY97x9nRhpJM9KMNNIZHd2f69Ilaeac0XPrSHOfp6/mobsvJt1h59LFBfR7fCQpxZK5mRw4M/WLbPS4jDfYfWW3w20PRX+jlTHXWGSk5Vjso5bHk5Y3OjH7fUbNF6Dqnuhfq7cFGvcbU7yC1t9hJMu9vxl9fHDgV+Fyo499oNvYO/r3t8DxCDts9bUaq6KteR+UrB96PK/caDYPnfOtNRz6K525K6NbwSs1xxis5uqCTR8d//hx7KvvZE1pLrbAmgE5aclozeCSsfXtTnLTk/nPm9ewYn42l4YZQZ2Tlky/x4fL4+PVk21cEtieVcxMkpit4sAjxsCWZTGsbBTw7f87wsd/t5dzXf28Ud/J6pKcWTVK0+M15semO4xJCvNyUlkdWKP7okX5JNsUt24q4/LzCjlyrhtvhH1v40FrPVhj7o9iT95hMucatcmuuulJzGf2Go8tvNToM655KbrXOlVlfA6dPZC30FgU5/XfjJ7T3HzEGPSVV24cB8YqXsf/Bg/dZiwcMtLO/zbWi7/0U8MfD3YFhDZntxyF1uO0FEY5NiPYz3zFvxvjBsaw7VAjzWOMS3B5fBw51z048AuMpmxgcC5zfUc/pXlpXLRoDn/7p61hB2UGFxnZcbKNTqeHLUukf3kmmz3vvlbm88Dhx4yk7Iit+arH5eH5Y81oDX/a08DBM12zcuBXJHMyU3jyk1v592tXsLokhwGvnxPNveOfOEH9Ht/gDkMud4yJOaNwaAvCePUvB4VLzCeeMRYkufmXxsIpj/4/OPQovPID+MkmY6pXOCefN6Yizb9g+OMbPwy9TcbGHKGaDxs3Gkm2ocRa/ayRyOethj/ePrwJ3Oc1+r2XXTv6BiU4XS00MR96FFC0FkS5SEjpBjjvGtjy6TEPO9vZz0d/u5ff7Yy8oMmRc914fJq1ZUOD8YL7Kwf7mRs6nINLZ0YS3GHqu9uOYU9SXLJE+pdnMtn20QpOVUF/O6x6d8ynPn2oCbfXT0FmCve/dIoBr591C2ZP/3I0ls3LIsVuY1WJ0dR9cAqbs0N3vJpQjTm4Ola8a8ypYfqYTzxtDKjKng/v/a1xU/inDxmjoFuPwcE/j34dv99IzIsqR/eBL7nKSPCHHxv+eMtRo38Zhk8Xu+rrxq5Nfg/UvjL0eMNu4//hgltG//zgAi3tISOzDz8GCy/FnRLl3/07vgu3/XHcPvyqY0ZzeVvfQMRjggO/QmvMoUts+v2aho7+cVflCp5z+Fw3X7p2hWwwMcPJto9WcDSwm9SSK2M+9Yn9ZynJTePTVy8dbEJdOwtHZEejoiCTdIctcRNzcGS2skH+4lFP/35nHdXNE5yTHawxBwZNJbs74dw+YwoSQNlG+Ngr8O5fwYefgYVbwjdt1+80FvA475rRzyXZjFXHWquHHnO2G2Mn5gYW+kjNNkZwr7wJis43RmqnZBsLfQRVP2P8DhZdPvpnJKcaa40Hp0y1HDemYk1gbMZ4XgiM4O8YY93rffWdFGWnMD9naBGQwRpzv4eW3gHcXv+wNa3DKQjsjXzThSV86JLySZZcmE2asq2goxYKloI9tkXl2/vcvHKilesuKOadq4tx2JOYm5VCcY7cbYdjS1KsLM42NrT36/iupR0QvDkCcHli7MsOzmXOrxhaZjPA79d88dED/HF3hD2bx5OWZyzhOWAk9ryOQDN16M2gLdlYBatsE1RcBufeNBJrqP0PGWMhll8b/ufMWWwM7Ar+bluOGp+DNWaAjzwLN/zU+DrJZtTa63cOPV/9rDHiO9Jc7bnLoemg8XVwresJ3NSOZcDr49VqY9rSWHsrv9nQxQUjVtjLSTOuXZfTTUNgRPZ4Tdkri7P55Yc2cM9Nq2XQlwVIYraCnsahpRaj5PdrvvLYQbx+zQ3rislJT+burYu4bfMC+ccew6qSHPbVd7LiK3/nHT9+heaeSSw4EkbvpGrMgcRcMLoZu8flRWvojrAG87hGrP6V23nIaKUZ2U8cVHEZoIcWvQHwDsChvxqrmKVE2AAhfzF4+oy/aRgakT03ZL3n7PmQHFKDXHCRcVx/pzEq/dybYyfa4nXQdNjYIOTsG0YCD93aMQ721HbQ5/aRlmyjoy98jbnT6aamtW9YMzYMb8oObrFYlj92jVkpxZUrikixT/M+42JKSGK2gp5zkF08/nEh/vPvR3ly/zk+//blLJ9n9J1+9m3L+Oer4jxoyGLes76Ma9cUc+umBdS29nHbL3bS0hO5DzFWvQPGm7g9ScU++Csz0JQdpn+5OzAFK/g5ZqMS80EjIUbqZy1Zb+wBHdqcfXybMcUo0uplYNSYAdpPGp+bjxhN1dklkc8p2wxoo2/55PPGY0uuinx88YVG7b/xoJGYi9dNau5/OFXHmnHYkrhieWHEGnNwR6l1IxKzw55EusNGp9NDfbtRYy4dp8YsrEUS80zn6TdWIYqhxnyiqYf7XzrFBy5awEcvi29NwerOL87m3lvX8bV3reTX/7CR+nYn33/meNxeP9jHXJCZEnuNOa/CaCYOs0RkcPGJuCTmnkbS+89C+RjTi+wOY/GN0MR84GGjVl9RGfm8YGJuC0nMhcvHTpwl640+5dOvGtMGMwrH3h+5eJ3xuW6Hsd518Ps4euFYC5sX5VOckxaxj/nN+k6UglWlo5vcja0fPdR3OCnITCE1WWrCs4kk5pmu55zxOYp9foOePWIMSvnEFUul2XoSLlo0h7VluRwfscmFz6/pdk+s/znYx1yYNYHEnJ4P/3Yall496qngBgdxacoeXJM9zE5UoSouM/qIe5qMPuPaV4wdpGxjbGqXUwY2h1Fj1tqYKjU38raFgNEsPm8VbP+hMfBr00fHXr0ruxgyi+CN3xpbZhZfOPbrx6i+3Ul1cy+Vy+aSl+EYXPgjlMvjY8fJNhYXZpKdmjzqNYIreRkjssduxhbWI4l5pusOJuboa8zPH21iZXE282SQ16RVFGRwum1oVyStNZ/4/et89kXnqIQdjd7BGrOD/libsmHUoK+guDZl1243tqSM1L8ctCBQc69/zRgF3d8xfBWucIJzldtOGqtz9bcPH/gVyeIrIcluTJ+6/F/HPlYpo5bcGmjpiHONueq4MU2qclkheenG9Qhd+/rRN86w/pvPsONUG1cuD789aW56Mq+dauONus5xB34J65HEPNMN1pij62Pu6HOz93RHxDcEEZuFczJo7XUPLqP56+21/O1gI34/fOoPb4yqKY2nd8BLij2J7LTkmM8dy2BTdv9EE3PIDlOnX6U7e8XYNV8wErc91ZjKdCaw7/J4iRmMAWDtp4bOCU6VGkvlF+Azx+DCD45/LAzVkjMKJ70JxUgvHmtmQX46iwoyyAtMfeoI6Wf+7WunmZudyu8+spnPXRM+tvesL2NlcTZrSnO4cd0Y/evCksb5zxIJLzh6Ncoa84vHW/BruEISc1xUFBi1mdNtTlKTk7jnb0e4asVcVqV18cPXe/jBM8f5wjvGaYoN0TPgJSvVTqrdFntT9hiCTdjdLi9aa2LuwEhOM5bFfP030FlHZ8XtjLvTr91hJOK61wBlnD9eszQY/cynXjBW78qYa2zMEs3Pso+/9/CgYC05hoFffz94jsffPMtP3x/55sLl8bG9uo33bChFKUVuoMYcTMw+v+bIuW7eu6GMS8dYNvPm9aXcvD6+Nwxi5pAa80zXc854w4tyf93njzYzJ8Mxau6kmJiFc4wlUGta+3j+aDMen+bbN65m7Vw7111QzEO768ddW1trzX+/eJLjTT30urxkpthJc9gm1pQdQbAJ2+fXOCf6urllRn/xhjs5UxJhHvJIZZuNDStqXzZq0LbR/amj5C8Cr8voL974kZjn50eleJ2xnGjpxqhPeXhPA08daMQzxvXcXdtOv8fHFcuMG9+8jECNOTBlqratD6fbN7hhihDhxD0xK6VWKKV+rpR6RCn18Xi/vhih55wxrzOKu/4up4fnjzbzluVzSUqSQV/xsHBOsMbcxxt1nSzIT6co2+i7v3b1PLr6Pew5HWa7xBBHzvVwz9+O8r+vnaZ3wEtmqp3UZFvsC4yMoSukCXu8fub6dieV332Bujbn8Cc++Dj8y2F45w/w2aMckLTgImOAVeN+KIlykFVwZLYtBTbcGd05scoshDu3wUXRvUX5/JrdNcZiKWN1B7xwtAWHPYmLFhlrVeeNqDEfPmtsG3r+fEnMIrKoErNS6ldKqWal1MERj1+jlDqmlKpWSn0eQGt9RGv9MeC9QBQbyYpJ6WmMekT2L7fX0Dvg5c4tFVNcqNkj3WGnKDuFmlYnb9R1si5kOdOtSwtx2JJ47kjTsHNGrhj2xP6zgLGhwWCNOdmG2+fH54/P6mKhyWS8kdkHznRR2+ZkV+2IVbuy50NGjLsWhdZIo+lfBmMvaYA17xmamz0VyjZBSnTbmx45103PwFB3QCRVx5u5eNEc0hzG9KahDSmMxHzobDfJNsV5RbNnW1URu2hrzA8Awxa3VUrZgPuAtwPnA7cqpc4PPPcu4BXgubiVVITXfTaq/uUup4dfv1LD21fNY4XcrcfVwjkZvHaqjcZu17DFIjJS7Fy0eM7g9DSAV6tbufCbz1Dbaozk1lrzxJvBxNxDt8tDZkoyaQ7jXzNeA8BiqTE3dhmrmZ1qicMuWun5xjxkiL7GnFMCN/63sUlFgthVM3ST0hWhxny6rY9TLX1ULhu6mUix20h32AbnMh8+183SuVk47NKLKCKL6q9Da/0SMOL2mU1Atdb6lNbaDTwEXB84/nGt9SXA++NZWDGC1lHXmH+1vYaeAS+funLpNBRsdqmYk8GZTmPpxJE7c121Yi41rX2cDCS5A2e66HB6+PHzJwB4va6Tho5+Npbn0Tvg5VRLH1mpRo0ZJrAsZwTdLi9ZKcZYz/FGZjd1BxNz35jHRW3RFcasgbwYWmouuCX22vkUCk3MkX5/wd2kgv3LQXnpDjqcbrTWHD7bJf3LYlyTGZVdAoSuiN8AbFZKVQI3ASnAU5FOVkrdDdwNUFRURFVV1SSKMlxvb29cX89skeKxe3rZ4u2nutlJwxjx+rXmwVf6WVNoo+nY6zQdm7qyToQZ12uOz4sPTVVVFd2N1SwG6upO0zWBcvi6jWbK5CRoOfEGVSfVYEzp/UY/8f1P7uDtFcnsOWIs3/nX18+wMb2dv9V6sCfB5QX97K4Ft89Pd1sTtR7jTf6Fl7ZTmD752tW5Nie5DkXPAOx84wClHGcZ8OqOV3GnzGFjXx9O3cKhqirePGEk5gOnmyJel1iuWZLjCmyrL8Lz4ouTjmMqRYpJa832404qcpKo6fKzY++b+M+Ofuv8814XRemK2oO7qQ153O4f4GR9I49ta6e1101KX7Mp709We18MZbXYJpOYw40e0lrrKqBqvJO11vcD9wNs2LBBV1ZWTqIow1VVVRHP1zNbxHiaj8B2WLL2UpasDvN8wKvVrXQM7OSbV6+hck1sa2pPBzOu1+HtdpKUjcrKSk7ud8BRWLBgIesmUI7+Oed45PjrXFCWx1VvMRbVCI3pe/uew5Mxh8rKtTx8Zi+FWR30uDx8a5ebPreP96wv5c53reR7e7ehNSxbtNCoVR18g7XrN7I0Dv2R3u3PsrIsl/rDTZSUL2GZox6OwyUXX2L0HR/KIKOgkMrKSn5+fAfQTku/Yutll2MLM1DQav9jEDmmE0099Gx7iY9uXsJ/PX2ckoqlVF60cNgxLo+PY88+za2bFlJZuXLYc2XVO+lze8kpXwrs5l2XXcjmwOCw6WTFaxZktdgmcyveAJSFfF8KnI3lBZRS1yml7u/qmrr9bS0tuLjIOBtY/PWNM2Sm2LlqRdE0FGr2CU6ZWhdhH+uS3DTOdBhN3Y1dLpbOzeQTVyyhMCuF7757Df958xrSHXYqAq+TOUZTdlvvAP/y8D66xtjjN5xul4eSXGMkdU+YPubGbhfHm43m9qbuAWxJCrfPP7jt4Gy2M9CMffX5xliOcH30O2vaGfD6w64PkJueTKfTw97THSQppClbjGsyiXk3sFQpVaGUcgC3AI/H8gJa6ye01nfn5EQ3B1eM0BJokx5j8JfL4+NvBxt5+6p5shD+FFkyN5NrV8/nhggrNJXkpQ32QTd1D1CUncon3rKUqn+9gvdsKBuskQYH5WWlhCTmEXOOX6lu5S+vn+HJA9HfAw94fbg8fgoyHaQl28KOKnZ5fDR3u9Ba09jlYnWJ8T8Zt37mGWxXTTtF2SmcV5SJw5YUdvDXG3UdKAUby/NGPZefYfQxP3O4iY3l+WSFWRtbiFDRTpf6A7ADWKaUalBKfVhr7QU+AWwDjgAPa60PxfLDpcY8CQf/Ak9/2Vi0IWdBxMOeO9JM74BXlvWbQg57Eve9/0JWFoe/wSzJTaOxy4XX56e5xzU4z3mkFfONJuvMVDspEWrMDYGa97OHh0/BGktwelROWjLZafaIg5f63F66+j30e3xcsthoaj0Zj5HZM5jWml017WyqmINSiuy05LDTzQ6f7aaiIIN0x+jewdx0B51OD8eaerj6fGm1EuOLqo9Za31rhMefYowBXlG87hPAExs2bLhroq8xKzUfgUfuNJYqvPUPY65ZvPd0B2nJNlP6tIShJC8Nr19ztLEHj08zLzv8SlbBGnNmSvJgjXnkdKlg0/L2k2043d6wiWCkYNNrdloy2anJEadL+fzwep2xGMry+dnkpidzcpbXmOvb+2nsdrGpwljuMyfCjc2Rxm7WRFhNL7heNiCJWURFJtPNREefBDS89zdDmwtEUNvWx8I56WEH8IjpURzo2w0mvUi7em1ZWsBnrj6PS5cMLVAxssZc396Pw56E2+vnlROtUf38YNNrdmpyxBpf0PbqNgCKslJYXJgZn7nMM9hrNcbvY3MgMWcHtmMM1e3yUN/eH3E1r+DqX+cVZQ6ORxBiLKYmZmnKnqATzxhr/WaOvxFFbWsfiwrlzcBMpYHEvDewNOdXd0UcAAAgAElEQVTcCE3ZKXYbn7xyKemO0D7m4ctyNnQ4uWJZIVmpdp4NWVHM7fWPWlEsKFjDM2rM9jEXGNlebST7eTmpLCrI4HhTD//z8ilerY7uJsBqdtW0k5/hYOncTMDoDhj5+zt6ztjeM9KgruDqX1JbFtEyNTHL4K8JcLZDw25YcvW4h3p9furanZTLXbqpSvJG1JgjJOZQ4UZl+/yaM539VBRkcvl5hTx/tBmfX+Pza972w5f40XMnwr5WcLBXTpo9UGOOnJiPNhpJpig7lVUlOXQ4PXzr/47w5ccORjzHynbVtLOxPA8VWIs+O3V0jfnwWaNiEanGfP78bJbPy+LGdbJblIiObPs405x8HrQflr513EPPdPbj9WvKCyQxmyndYScvPZn69n6UgsKs8XdLSg2zJGdzjwuPT1OWn8aa0hye3H+O7dWtKGXsbnW8qSfsaw1ryk5NHnOtZzBqeKnJNj5w0UK2LC3gvheqeTGwqtVscq6rn7p2Jx+6pHzwsZwwNzZHzvUwJ8PB3AjXdW52Kn//58umsqjCYkxNzEqp64DrlixZYmYxZpbqZyEtP6p1h2sC6zFXSGI2XUleGh1OD3MyUki2jd9Q5bAlkaSGJ+b6dmNEdmleOhctyic3PZk/7W3AEXi91h532Nca1pQdGLykCb9CEEBRllGjtyUpFhdmMj8nlc5+j7GPc5R7F0+FP+yqo8fl4e7LFk/LzwsuwxnsX4ZgU7Z32O/i8Lluzi/ONvV3I6xFmrJnCk8/7P0NHH0KllwJSePPSQ4mZmnKNl9wcY95OdHtLayUIi15+J7MwRHZZXlppNhtXH9BMdsONbLtUCMArb0DYV+r2+XBYU8iNdlGdmoyXr/GPcaewkUjBqflpjnw+TW9A2PXtKfagztO8+CO0xM+/8fPneD2X+6M+vhdNe1kpdiHbfqSnWbH59f0Ba6L1+fnWFOPbAwj4kpGZc8Uf7kbnvgU5C6ALZ+O6pTa1j4yU+wUZDqmuHBiPMGR2dH0LwelOWzD+piDNebga717fRlur5/eAS9L52bSMiIxe31+OvrcdPd7yA4sapGdZnx2uUdvjmEPjNwfOZ0rZ3DrwthWG4snr8/PyeZeznb24xnjpiISrTV/2lvPjpNteKM8f2dNOxvK84bNaMgJ/P6C3QMnmntxe/2yv7KIK0nMM0X9Llh1M3zsZShaOf7xQE2bk/KCdGliSwDBGnOkEdnhpNiHJ+aGDidF2SmDK7itKjEGFRVlp/DONcX0uLzDmr6//sRhLrrnObZXt5GTZvRaBRO0yzs6MWcGdp8aefOQOyIZxVNjl4vf7TwdcUR50Ol2J26fH7+Gc52umH9ObZuT+nZjzMW5rvHPb+0doLq5l00Vw+f/DybmwE3KnsCe1esXjl7xS4iJkulSM0CSbwB6G6FgGcSQZGtb+6QZO0GU5k2sxuzy+PjDrjqePtRIfYeT0rz0weeVUtz3/gv51R0bmRuo5bb1Gf3MzT0u/ri7niSlqGt3DtaUswMJeneNMUJcM5QQU5NtfPEdK7h5/fDRw7mBebhTUWP+2uOH+OJfD7KvvnPM4443Dg1sq5/A+t0vHhvaE7uuffzzgwl3U0j/Mgzd2ASnTO2saWd+Turg9RUiHqSPeQZIdQVGxOYtHPvAEG6vsQGBDPxKDMGEGmlxkXDSkm3UtDr50qMH+fjvXmd/QxdlIxLA4sJMVhbnUJBpJObWHqM5+8FXT+Px+3n0Hy/l/ZsX8LaVxnrqwRrf9pPGvOTW3uEDxu66bNGoRTCC83A7+8MPLpuoAw1d/D3QP/7YvrHX/j4WMuI8msQ60ovHW8hOtUd9/s6adlKTkwbXDA/KDmk90Fqzu7adTRX50iol4kqasmeAVFfgbj83+sRc3+HEr2XgV6I4f342X3/XSt6+KvKGIyOlJds4cq4bv9YsLszA6fYNqzGHCo4jaO0doG/Ay29fO83bzp/HsnlZfPvG1XzscmMk88riHL507YphI43HE2zKjneN+XvPHCM3PZmtSwt4cv/ZMft+TzT1UpqXhj1JUR9jYnZ5fOw41cYN60qwJ6kxE/MPnjnORf/xHH/YVceFC/Jw2Ie/RQZvbLr7PdS1O2nqHmBjefS/SyGiIYl5Bkh1BVZ4yo28WcVIB88Y3QNLAisWCXMlJSk+dEl5TDsLpQaW5bxsaSG/v+sirlw+N+y2gsBgjbmt181zR5vp6vdw55aKUcfZkhQf2bqI+TnRN71mT0Ef8+Gz3VQda+Gjly3m/ZsX0Nrr5tWTbRGPD458Ls5Noz6wkUc0tNY8vKcel8fYkrE0Ly1iYj7e4eNHz51gQX46V64oGryZCRX6uwg3nUqIeJAFRmaAVFcz2ByQNT/qc5490kxBpoNVJdJNMFOlJRv3zR+4aCEFmSn88o6NEY8NLlrS0jtAv9uHLUmxtmzsddSjlZpsIy3ZRqdzdFN2p9PN6TYnF8T4s14I9Pm+Z0MpmSl2slLtPLrvDJedVzjq2AGvj5rWPq5ZOQ+n2xt1jbnH5eHDv9nDrpp2VpVkc/GiOZTlp4c93+3185tDA5TkpvHAnRsjbg6SlWJHKWM1tWONPeSlJ8vNr4g7Gfw1A6S6miCnDJKiu1xur5+qo81cubxINq+YwebnpFE+J523RKglh0pNtpGZYqe1d4ATzT0snJM+qhl2MnLTk8M2ZX//mePc9LNXY97s4uUTLZw/P5uCTGOU+bWr5/O3A41h1/Guae3D59csLcpkQYTEGs6DO06zq6adb16/kkf/36WkJttYOCed022jz//j7jrO9Gq+cf3KMXfsSkpSZKXY6ehzs+NUGxvLpX9ZxJ8M/poBUl1NMTVj76xpo2fAK4vmz3BfeMdyHv/klqhvrgoyHbT2uqlu7mVJYXxrcTlpyXSOaMrWWvPcEWO97nufr476tfoGvOw93cHW8woGH7tt8wL6PT7++vqZUccfC4zIXjYvi9K8dNr63HQ5Pfzk+RODi+iM5PL4+PX2Gi47r5DbLy7HHlgdbUF+Ol39nsHpTkGP7TtLWVYSV64Y/38mJz2ZR/Y20NDRzw2yz7mYAtLHPAOkuppjGpH9zOEm0pJtbFlaMP7BImGl2G2D03OiUZCZQmNXP6fbnHFvXs1NTx6VzE629HKms5/SvDQe23eG6uboas07a9rw+DSXLR1qtl5Tmsua0hz+97XRc5qPN/VgT1IsKsikLN8Y/Pb9Z47xX08f573/vSPsz31kbwOtvW4+dvmiYY8vCJwfOuWqscvFntMdbJw3/mp6YEyZ6vf4uGldCe9YHX33khDRksSc6AZ6cXi6ox6RrbXm2cNNbF1aMLgQhZgdCjJT2N/Qhdev45+Y0xyjpku9cNSYxvez968nNdnGF/6yn/p2JztOtvHZP73J2c7wg7ReOt5KanLSqEU5PrB5ISeaewcHVQUdb+qloiADhz1pMLE++Nppls/LQmu49RevDRuYprXmf14+xQVluVy8aPgCIcHEHjoA7G8HzwGwsSi6ITcluUYXwzduWBXV8ULEShJzousMrA0cZVP2yZZezna5ouqXFNZSkOVgwGtMOZqKGvPIPuaq480snZvJ6tIcvnH9Kg6e6abyv6q49Rev8cjeBv68tyHsa718ooXNFXNG3Thed0ExWal2/jKiOft4Uw/nFWUBDM7j1hr+/R0r+PEta2npGWBHyIjuY0091LY5uW1T2aj+3wXhEvOBRpYVZTE/M7q3w++99wIe/+SWwZXShIg3ScyJrrPO+JxXHtXhuwIrOm0eUVMQ1hecMgXGwiPxlJOePLjDFIDLq9lV007lMqM5+t3rS3n+s5dzxyXlfOWd57N8XlbY6U/VzT2cbOkbPC9UmsPGquKcYYuJ9Lt91LU7BxNzfoaDrBQ7Fy7IZevSAtaX5+GwJbH39FAtO7hF5eXnjb45zUpNJj/DMTgArLnbxe7T7TE1SWcFts8UYqrIto+JriNYY46uKXtXTRuFWSmUzwm/EIWwrmBiLs5JJSPOtbncNAdurx+Xx0+aw8aRdh8en6Zy2VDym5+TxpffeT5g7GX8mx2ncXl8w2rGf33jDEkKrl0TPhGWF2QMNi0DVDf3ojUsm2fcaCil+O/b11OWb6wBn2K3sbo0h72nOwbPefF4C8vnZUVcZW1RQQYnAsl/x6k2tIYrV8yl9cTogWdCmEFGZSe6jhp8SSmQEd1Arl01skTgbBVMzEsCtct4Cq54FexnPtruw2Ef3U8cdMniAtxe/7CE6fdrHn3jLFuXFjI3K3zSrChIp9PpGZwzHaw9Lw2J6ZIlBYN9xQAbFuZx8Ew3Lo+PvgEvu2vbuTzMfOig1aU5HDrbjdfnZ39DFyn2JJbNi//vTIiJkqbsRHf2DXozK6LavKKhw8nZLhebZInAWakwy1iWM95TpSBkvexAP/Oxdj9ry3IjDjDcWJGPLUnxamBNboDdte2c6eznxjGmGFUUGGUPToM63tSDw57EwvzILUDrF+bh9vk5cKaLHSeNEd9jJeY1pTn0e3xUt/RyoKGLlcXZJNvkrVAkDvlrTGQ+D5x7k+7sZVEdHhzNOnJHHDE7FOemoRScXxz/vYFD18vucXk43e3nojH+zjJT7FxQmjOsn/nRfWdId9h468rIc4UrCowEXNs2lJiXFGYOzkMOJ1hr31PbwXNHm0h32FhfHnkbxjWlxipl++o6OXi2a/B7IRKFDCtMZE0HweuiO/u8qA7fVdNOdqqdZVPQlCkS3/ycNB7/xy2smD8FTdnpwTWi3ew53YGGUXsVj3TpkgJ+WnWSHpeHrNRkXjzWwhXL5o65slZZfjpJCmpajcFZxxt7xh3IOCczhUUFGfzPy6do63Nz07oSUuyRpwpWzMkgK8XOX984g9PtG7WDlBBmkxpzImvYAxBVYtZa8+pJY4nAJFmGc9ZaXZozZu1yokL3ZN55qh2bggsXjl3T3FwxB59f83pdJ809Ls52uVi3YOxzUuw2inPTqG3to9vl4WyXi6VF4zfNbyzPp63Pza2bFnDPzavHPDYpSbGqJIedgRamC8okMYvEIjXmRNawBzKLGEiJ3F8WtL+hi7p2J/94xegdcYSYrMGm7H4PO2vaqMhJGrPmC7B2QS5JCvbWtuMJzK+OZmONioIMatv6OHousBRnFC1An33bMq5fW8wlS6IbJLmmNIcdp9rIcNgG+7WFSBSSmBPZmT1QsiGqgV+Pv3mWZJvimpWyRKCIv3SHjWSb4rkjTRxo6OJtC8d/68hMsbNifvZg07ctSbGyePzaafmcDB7dd4ZH9taHXSEsnMKslMEdtqIR7FdeVZIjG72IhCNN2YnK2Q5t1VC6YdxDfX7Nk/vPcvl5cwf7AoWIJ6UU+RkOdtd2sGxeFpVl0d3Tb1iYx776Tvae7uC8oizSHOMvE1tekEGPy8tfXj/DzReWDjajx9Oa0pxhn4VIJLLtYyLSGl5/0Pg6isS8q6adpu4B3rW2eIoLJmazH75vHb/7yGae/OQWCtOje+tYX56P0+1jx6k21kbZlxscme31a+7cUjHh8o6lNC+Nr153Ph+8uHxKXl+IyZAFRhKNzwu/ezc8+1Uo3wplm8c9ZduhRtKSbVy1QtbHFlPn4sVzuHRJQUyL12wMTFvSmqinJZXPyQDgimWFcV9aNEgpxT9cWjFsoRIhEoU0ZSeapoNQ/Sxc9q/wwcfBPn6/WU1rH0uLMscdjCPEdJufk0ZJrrHxxAVRJuaFczL44MUL+dw1y6eyaEIkLHknTzQ9jcbn894OSdHdNzV1uyjNkzt/kZg2lufRcdjNeVFMewJjkNg3rpctFcXsJYk50fQEFvDPmhf1Kc09A2wYY6UjIcz0uWuW8/6LFk7J/GohrEgSc6LpOQcoyIy8bGGoAa+P9j43RRE2BRDCbMW5aRQHmrOFEOOTW9hE03MOMueCLbp7pubuAQCKsiUxx06bXQAhhBhFEnOi6T4HWdEvEtLU7QKgKMLes0IIIWYWScyJpqcxxsQcrDFHv+qRAJDVnoQQiUkSc6LpOQvZE6gxSx+zEEJYgiTmROIdAGdbzE3ZDnvS4Eb2QgghZrYpScxKqRuUUr9QSj2mlHrrVPwMSwrOYY4xMRdlp8S0GpMQQojEFXViVkr9SinVrJQ6OOLxa5RSx5RS1UqpzwNorR/VWt8F3AG8L64ltrIJJObGbpc0Y0+ajM4WQiSOWGrMDwDXhD6glLIB9wFvB84HblVKnR9yyJcCz4to9Jw1PsfQx9zcPSBTpeJE0rMQIhFEnZi11i8B7SMe3gRUa61Paa3dwEPA9crwHeBvWuvX41dci5twU7YkZiGEsIrJrvxVAtSHfN8AbAY+CVwF5Cillmitfz7yRKXU3cDdAEVFRVRVVU2yKEN6e3vj+nrTZdHJnZSqZF7a+SaE9BlHiqffq+lz++htbaCqqnkaSxpfZlyvfJ8XjaKqqoqexhMsBurq6uiOUzkS/W+wp7kJgD179pCWXcsCv5+BgYGoy5zo8U2EFWMKZeX4rBbbZBNzuBFHWmv9Y+DHY52otb4fuB9gw4YNurKycpJFGVJVVUU8X2/atP0OeoqpvOKKYQ9Hiqe6uReefZFL1q6kcl3JNBUy/sy4Xoe329FKUVlZycn9yXAUFixYwIVxKkei/w3ubNkLLbBhwwYKi8upfSmJlJQULo6yzIke30RYMaZQVo7ParFNdlR2A1AW8n0pcDbak5VS1yml7u/q6ppkMSyiJ7ZVv5qDc5ilKVsIISxjsol5N7BUKVWhlHIAtwCPR3uy1voJrfXdOTk5kyyGRfSci2ngV127E5BVv4QQwkpimS71B2AHsEwp1aCU+rDW2gt8AtgGHAEe1lofiuE1pcYc5HZCZz3klI1/LPDqyVa+9X9HKMtPoyRPdu4RQgiriLqPWWt9a4THnwKemsgP11o/ATyxYcOGuyZyvqXUvgy+AVhUOe6hdW1O7vj1bhbmp/PghzeRYrdNefGEEEJMD1mSM1GceBqS02HhpeMeuu1QI26vn1/dsZH5OVJbFkIIKzE1MUtTdoDWcPxpo7acPP5ArqrjzSwryqIsP33KiyaEEGJ6mZqYZfBXQMtR6KqDpeMvK9474GVXTTuVywqnoWBCCCGmmzRlJ4ITTxufl1497qGvVrfi8Wkql82d4kIJIYQwgyTmRFC7HQqXQ07puIe+cKyFzBQ7G8rzpqFgQgghppv0MScCVydkzRvzEL9fU3WsmWcON7JlSQHJNrmnEkIIK5I+5kTg7gNH5piHfOEvB7jj17tRSvHhrRXTVDAhhBDTbbJrZYt4cPeCI2PMQ1460cJbls/l5x9Yj8MutWURb7LppRCJQpqyE4G7b8zE7PVrGrtdrCrJkaQshBAWJ03ZiWCcxNzWr9EaSmXpTSGEsDypfpnN7wePE5IjJ+aWfqOZsSxPFhQRQgirk8RsNo+xQ9RYNebWfj8AZflSY46bcDuJCyFEApDEbDZ3n/F5zMSssSUp5sm+y2IKyfAvIRKDDP4ym7vX+DzGdKnWfj/FuanYZe6yiDct6ViIRCODv8wWRY25xamlf1nEl5K2fCESlVTBzBZNYu7XMiJbCCFmCUnMZhtMzOGbsl0eH91uqTELIcRsIYnZbIN9zOFrzA0dxqht2XtZCCFmB0nMZhunKbu+vR+QqVJCCDFbyKhss43TlB2sMZdKU7YQQswKMirbbOM0Zde1O7EnQWFmyjQWSgghhFmkKdts7j5QNrCHT7wHznRRlplEUpJMbxFCiNlAErPZgnsxh5lX6vNrDjR0sShXLpMQQswW8o5vtjH2Yj7R3EOf28fiXNs0F0oIIYRZJDGbbYwtH9+o6wRgUY5cJiGEmC3kHd9sYyTmfXWd5KYnU5Qu/ctCCDFbSGI2W7CPOYx99Z2sLctFybrGQggxa0hiNluEPuYel4fjzT2sK8szoVBCCCHMIguMmC1CU/a2Q01oDWsX5JpQKCGEEGaRBUbMNiIxa635t0f289k/vcl5RZlsLJcasxBCzCZ2swsw643oYz7a2MMf99Rz2+YFfOWd55OaLFOlhBBiNpE+ZjNpPaqPubHbBcDNF5ZKUhZCiFlIErOZvAOgfcMSc1OXkZjn5aSaVSohhBAmksRspjA7SzV1DwCyaYUQQsxWkpjNFGZnqcZuF3MyHDjscmmEEGI2knd/Mw3WmIf2Wm7udlGULc3YQggxW0liNlO4puweF0XZ0owthBCzlSRmM4Vryu4akIFfQggxi0liNtNgjdlIzB6fn7a+AeZmSWIWQojZKu6JWSm1SCn1S6XUI/F+bcsZ0ZTd0jOA1jJVSgghZrOoErNS6ldKqWal1MERj1+jlDqmlKpWSn0eQGt9Smv94akorOWMaMpuCiwuIn3MQggxe0W7JOcDwE+AB4MPKKVswH3A1UADsFsp9bjW+nC8CxkLv8+L3+/B53WbWYzouAKbd9hTwe/jXFcf4KcwKxmf3zd4mF/7h31vNWbE50ejAZ/fh0/78UUohy1JVl8TYjbzaz9aa2D63g+iSsxa65eUUuUjHt4EVGutTwEopR4CrgdMTcwPP/Npvt1UBb8zsxQxqFgAD28d/DZrBdz2bJjjfjt9RTLFdMdXEvy5a43PFQug8bvw2+8OO+x9y97Hly760vSWTQhhOq01P9//c+5/83682suc1DlUva9qWn72ZDaxKAHqQ75vADYrpeYA3wbWKaW+oLW+J9zJSqm7gbsBioqKqKqqmkRRhtidRbzPW4LNNjNqOp7kbJwZCwE42OrjRKePG5Yko1CDxwy4B0hxWLd524z40k4/jUbhWng1A71tLGv9O0eyt5KWv2DwmOOu4/zl+F9Y71xPWlJaTK/f29sbt7/pqdDd1AjAnr17SDtxmoV+P+6BgajLnOjxTYQVYwpl5fjiHZtP+/hj+x/Z0buDC9IvoDS5FEeSY/p+f1rrqD6AcuBgyPfvAf4n5PvbgXujfb3Qj/Xr1+t4euGFF+L6etPl0398Q19yz3OjHp+p8UTLjPgOfftSffDbW7TWWle/+YrWX83We7f9dtgx+5v361UPrNKPHHsk5tdP9Gv22p++p/VXs3VzwymttdY1X1+p9/x/74z6/ESPbyKsGFMoK8cXz9j63H364898XK96YJW+9/V7td/vj9trhwL26Ag5cTKjshuAspDvS4GzsbyAUuo6pdT9XV1dkyiGdTR1u5grA78SxqqCVSzKWcRjJx8zuyhCiGnQ1t/GndvuZPvZ7Xzl4q/wiXWfQCk1/olxNpmm7N3AUqVUBXAGuAW4LZYX0Fo/ATyxYcOGuyZRDsto6h5g6dzM8Q8U00IpxfVLrucHe3/A07VPk5eaF/a4FfkryHTIdROJp6mvibqeOgBOuE6Q0ZgxzhkzUzxic3ld3LPrHlqcLfzoih9RWVYZn8JNQFSJWSn1B6ASKFBKNQBf1Vr/Uin1CWAbYAN+pbU+FMsPV0pdB1y3ZMmS2EptQW6vnzMd/Vy2tNDsoogQ71z0Tu59414+8+JnIh5z3aLr+I+t/zGNpRJifFprPvL0R6jtrh16cJtpxZl6cYgtLyWPX73tV6wuXD35F5uEaEdl3xrh8aeApyb6w6XGPGRPbTv9Hh8XLco3uygixNz0ufz5XX+mrb8t7PMPHn6QFxtexOv3Yk+aTAOUEPFV011DbXctd666ky0lW9i3bx9r1641u1hTIl6xLc5dTH6q+e/B8k6SIJ4/2ozDlsSlSwrMLooYYVHOIhblLAr7XLurnar6Kva37OfCogunuWRCRPZyw8sA3LLsFuZnzqcvtY+N8zaaXKqpYbXYTF0rWwZ/DXnhWDObF+WTkSL3SjPJJcWXYFd2Xj7zstlFEWKYlxpeYknuEuZnzje7KCJGpiZmrfUTWuu7c3JyzCyG6eranJxs6eOKZXPNLoqIUZYji3VF63ip4SWziyLEoB53D683vc5lpZeZXRQxAVI9SwDPH20C4C3LJTHPRFtLtvL9vd/nUNsh8lOM/ql2bzvnes+ZXLLI2vy9nLPZaHO14O1NpdkGnUneqMuc6PFNhJVievXsq3i1l60lW8c/WCQcScwJ4NWTbZTPSae8wJpTGazu8tLL+f7e73PLk7cMf+LP5pQnagtK4JXAfjOlCmiAP781+vMTPb6JsFBMWY4s1s615mAvqzM1Mct0KUOH001xbmxLPorEsSh3Efdded+wkdtHjx1l+bLlJpZqbKf2bGPRmcfpuexrZOUV0vr4l+l1FFB+zT9FdX6ixzcRVotpSe4SmSkwQ5l61WS6lKFvwEdxrsPsYohJGNmXV3WmisqlleYUJgo736xhc28fLSVXU1hSQW3fl2nzZ7F+6Y1RnZ/o8U2EFWMSM5Opg7+Ewen2kpEyMzbdEEIIMbUkMSeAPrePdIc0OQkhhJB5zAnBOeAlwyE1ZiGEEDKP2XR+v8bp8ZEuC4sIIYRAmrJN5/L60BqpMQshhAAkMZuub8AHIDVmIYQQgPQxm87p9gJSYxZCCGGQPmaTDdaYZVS2EEIIpCnbdIM1ZpnHLIQQAknMputzS41ZCCHEEEnMJnMOSI1ZCCHEEEnMJgvWmDOkxiyEEAIZlW26YB9zuozKFkIIgYzKNl1wVHaGzGMWQgiBNGWbzun2kqQgxS6XQgghhCRm0/UN+Mhw2FFKmV0UIYQQCUASs8mcbi/pMiJbCCFEgCRmk/W5fTIiWwghxCBJzCZzDkiNWQghxBBJzCbrc3tl1S8hhBCDJDGbzOn2yRxmIYQQg2SBEZP1DXilj1kIIcQgWWDEZFJjFkIIEUqask3WN+CVVb+EEEIMksRsIq211JiFEEIMI4nZRG6fH69fS41ZCCHEIEnMJnIGNrCQGrMQQoggScwm6gts+SijsoUQQgRJYjaR0x2oMcvKX0IIIQIkMZsomJilxiyEECJIErOJnANGU7b0MQshhAiSxGyivmCNWUZlCyGECJDEbHNXMpwAAA9HSURBVCKnW2rMQgghhot7VU0plQH8FHADVVrr38X7Z1hF34DUmIUQQgwXVY1ZKfUrpVSzUurgiMevUUodU0pVK6U+H3j4JuARrfVdwLviXF5LkRqzEEKIkaKtqj0A/AR4MPiAUsoG3AdcDTQAu5VSjwOlwIHAYb64lTRKf9pTz7decOLY/ux0/+iYDQ3+khqzEEIIQ1QZQWv9klKqfMTDm4BqrfUpAKXUQ8D1GEm6FNjHGDVypdTdwN0ARUVFVFVVxVj08FrbfKzK85Nsn/Z7gglQzM908PJLL455VG9vb9x+P4nIjPjyvV40iqqqKnoaT7AYqKuroztO5Uj0a9bd1AjAnr17SDtxmoV+P+6BgajLnOjxTYQVYwpl5fisFttkqmolQH3I9w3AZuDHwE+UUtcCT0Q6WWt9P3A/wIYNG3RlZeUkijKkElhRVUW8Xi8RVFksnpHMiO/wq3Y0isrKSk7uT4ajsGDBAi6MUzkS/ZrtbH0dWmDD+g0UllRQ+1ISjpQULo6yzIke30RYMaZQVo7ParFNJjGrMI9prXUf8A9RvYBS1wHXLVmyZBLFEEIIIaxjMtOlGoCykO9LgbOxvIDW+gmt9d05OTmTKIYQQghhHZNJzLuBpUqpCqWUA7gFeDw+xRJCCCFmp2inS/0B2AEsU0o1KKU+rLX2Ap8AtgFHgIe11odi+eFKqeuUUvd3dXXFWm4hhBDCkqIdlX1rhMefAp6a6A/XWj8BPLFhw4a7JvoaQgghhJWYuiSn1JiFEEKI4UxNzDL4SwghhBhONrEQQgghEogkZiGEECKBSB+zEEIIkUCkj1nMYtrsAphOa/kdCJFoVCL8YyqlWoDTcXzJAqA1jq9nNqvFM5IV47NiTKGsGJ8VYwpl5fhmYmwLtdaF4Z5IiMQcb0qpPVrrDWaXI16sFs9IVozPijGFsmJ8VowplJXjs1psMvhLCCGESCCSmIUQQogEYtXEfL/ZBYgzq8UzkhXjs2JMoawYnxVjCmXl+CwVmyX7mIUQQoiZyqo1ZiGEEGJGksQshBBCJBBJzEIIIRKeUkqZXYbpMmMTs1IqK+TrGX/BlFL5IV/P+HhCKaVWmF2GqaCUqlRKhV0gYKZTSt2ulFptdjniTSn1GaXUWwNfW+3/rFwplRr4esa+t4/BUu/5Y5lxF08p9Xal1AvAfUqpLwLoGTyCTSl1jVLqJeCHSqnvwcyOZySl1L3AU0qpcpOLEjch1+z9wIDZ5YknpdQFSqk3gZuZge8PkSil3qqU2gb8G/BBsM7/mVLqKqXUTuBHwF8BtNZ+c0sVP0qpq5VSrwD/pZT6HFjn2kUyI/7xlMGmlPoY8A3gu8B9wMVKqTvNLV3sQuK5G/ga8D3gS8B6pdTbTS3cJIW5k80HOoCrlFIpJhQpLgLXLEkpdSvwMHCv1vourXW32WWLs3cA92mtb9Bav2l2YSYjcM0cSqlvAV/GSFx3A6eVUslWqHUppcow3hO/o7W+HshSSt1kcrHiRilVivEe+R3gM8DlSqnvBJ6b8dcvkoRPzEoppQ0+oA64TWv9lNZ6J/AskGtuCWMzIp5XgC1a68cAF9AMHAo2Q820P7xgbIGvbYGHXwN+hlG7XGpW2SYj5Jr5gbPAg0B14Ln3KKVKlVLJwWNNLGrMwpR3OdAYeO7TgdaBGbfLTMg1cwOPaa23aq2fwrhJvEVr7Zmpta4R12wR8CbGeyHAOeBE8O9xJhoR33LgQGDDox6MCtmnlVJLtdZ6pv2/RSuhE7NS6hPAX5RS/6KUKgj8Y51SStkDh6xgBm0RFBLPp5VS87XWh7XWXqXUhcCjQDlGU9v3g6eYVNSYhcT2z0qpYq21TynlAK7BaF57AbhFKXXTTOqXHfk3iHEztR/4mVLqKPBe4F7gp8FTzClp7EZcs5LAw2eBuUqpvwLnAXcAD8zQaxb8P9sdeDxZa/0ixnvIjGyZGhFbLnAEyMPo2qvBqKh8Cfi9icWcsBHxZQPHgS1KqYsDh8wFDmHEaFkJm5iVUjcCHwJ+DKwGvqaUWhuoaQalADtHnJeQb4wj4lkDfFEptTbwdPAufhPwOeAOpdSGmdJPNCK2C4B/V0qtD9RW9mitW4ETwKeAbzNDkleYv8GvA0uAJzFuNG7VWr8H+DBwQyDmmXzNFmDUvm4DqrXWHw98nQ1sCZyX0Ncuwv/ZBYGnvcoYZHka8EV4iYQVJrb/ALK11rdgvA/+Umt9NfABjG6xywPnJfQ1CwoT33cwxnD8APioUmo78FbgJmCtUqp8prZ6jCdhEzOwGfiZ1voFjD6GGow3dgK1zBSgDHg90JT4kcBziXqhwsXzTwBa6xqtdV3g6z6MPsxsk8o5EeFi+3jguWuVUi9jtAQ8itG0PVP6ZUfGVQv8q9b6LPB1rfUbAFrrdozYMk0q50SMjO008AWt9UMYzaHJSqmiwI3GDmAhJPT/V9BY/2c6cK3SgCtgxo1eHhnbKeCLgeeyMWqSaK09GDePFYHvE/2aBYW7dl/XWv8SuAv4tNb6NowuzV3MnPeRmJn+Rznybi7k+1MYd+torU8D/wdkKKWuDzy/HJiDkawfD3xt+t1hjPGkK6XeNeL4LwErgcNTX9rYxBhbbqD56UfAq1rrtVrrDwLzMLogEkYMcT2BMbjmXVprV8jxX8a4Zkenp8TRiyG2x4FCpdQW4L8AD/CFQGzvBl6ctkJHYQLvG6H/Z/8LbFJKpSZiC0cMsT0JZCqlLgk896+BMQFfBK7EuKFKODH+TeYppW4MjAnYFTjum0AG0DNNRZ52pidmYNgghZC7u0cAZ0giPgdUASsCF3IRxht8BXCt1vo7I843S6zxnA+D08BewejXe7fWunF6ihuTWGJ7HrgM+J3W+t9CTrsxWNNMIBO9ZluVMXXvPOBmrXXT9BQ3JrFes0sC1+cejBuNdOAqK1yzkASQBjxE4jZnxxrbJYGWjv8FbgUWA2/VWh+bnuLGLNb4lgEopZYqpR4DVmHUnj3TU9zpZ1piVkpdrJT6E/BdpdT5KjCKVw0N7OrAGDT0caWU0lp3YTQVpgUuZDXGiOaPa63PmRFDqMnEE3j+CPAxrfUHEyGeUBOMLQNI1Vr7lTE1LAkgtKZptjhcs1rgH7XWt1vommWA0Tyvtf651voLWusGM2IIZxLXLCUkATymtf5For2xTzC2dCAHQGv9U+BurfWdWuszJoQwpklcu9TA840Y/2/vStCb4LgxJTErpeYCPwGeAtow+oDuBKP/OHBYGrAN467pfqVUMbAOo4kNrfWB4GhLs00yHnfguFqt9cFpLvq4JhmbN3CcL9GaDON0zeq11onY5TDp2BJRPP4WA8cmXE15krEN3uxqrRNywZs4vef3JNJN4pTSWk/7B3A18IfA1xnA2zD6S5YHHvsWxgVah7FAxbcwmjR+CtjMKPNsimc2xGbVuKwcm1XjsnpssyG+uP++pumi3AD8O0ZfMEAhxvSZxYHv84GvYgyPT8eYg7d4xGukm/3Lsmo8syE2q8Zl5disGpfVY5sN8U31x5Q2ZSulCpVSjwL/ArQDv1ZKvVtr3QL8Gfhk4NBO4LnAxUrVWt+mtT6pQqYyaK2dU1nWaFgtnlBWjc2qcYF1Y7NqXGDt2MD68U2Xqe5jXgxs11pfprX+OcZap/8SeO4PwHKl1FXa6H9sA4oIbAqglErSCdYvifXiCWXV2KwaF1g3NqvGBdaODawf37Swj39IbJRSH2RoAvhejEniwbWTDxOYBA8cwJiy8EOl1A0Y8+4UgaH0iXKBrBZPKKvGZtW4wLqxWTUusHZsYP34zBCXxKyUUhgLR/we8AMnMVZq+SetdZNSyqaNtZNXMDS034+xBu9c4PMYC4bcpbXujEeZJsNq8YSyamxWjQusG5tV4wJrxwbWj890k+2kJjBiDmORhf8NfG3HWNj/LyOOeRB4b+DreSGv4ZhsOeL1YbV4ZkNsVo3LyrFZNS6rxzYb4kuEjwnXmJUxKfwbgE0p9RTGWq0+GFzL+lPAWaXU5drY0QWgF6hRSn0DuEkpdY3WukEbmx2YymrxhLJqbFaNC6wbm1XjAmvHBtaPL5FMaPCXMnYt2Yux3Vg1xtqlHuAKpdQmGFxm7RsYi5EH+xvuxFh2LRu4QifIZHGrxRPKqrFZNS6wbmxWjQusHRtYP76EM5FqNrAVuD3k+59i7CZ0B7A38FgSRh/Ewxg70ywGfghcaHYzgdXjmQ2xWTUuK8dm1bisHttsiC/RPiZ6kdIx9kIO9iO8H7gn8PU+4JOBrzcAD5kd5GyLZzbEZtW4rBybVeOyemyzIb5E+5hQU7bW2qm1HtBDa85eDbQEvv4HjB2gnsSYt7YXzN+OcSxWiyeUVWOzalxg3disGhdYOzawfnyJZlLTpQJ9CBpjkvjjgYd7MJZiWwXU6MAuJzpwO5XIrBZPKKvGZtW4wLqxWTUusHZsYP34EsVkV/7yY0wObwXWBO6Yvgz4tdav6ATcemwcVosnlFVjs2pcYN3YrBoXWDs2sH58CUFN9qZGKXUR8Grg49da61/Go2BmsVo8oawam1XjAuvGZtW4wNqxgfXjSwTxSMylwO3A93WC7gUaC6vFE8qqsVk1LrBubFaNC6wdG1g/vkQw6cQshBBCiPiZ6t2lhBBCCBEDScxCCCFEApHELIQQQiQQScxCCCFEApHELIQQQiQQScxCWJxS6mtKqc+O8fwNSqnzp7NMQojIJDELIW4AJDELkSBkHrMQFqSU+iLwQaAeY7OBvUAXcDfgwNhT93ZgLfBk4Lku4ObAS9wHFAJO4C6t9dHpLL8Qs5kkZiEsRim1HngA2IyxUc3rwM8xlk9sCxzzLaBJa32vUuoB4Emt9SOB554DPqa1PqGU2oyxvd9bpj8SIWanSe0uJYRISFuBv2qtnQBKqeAuQKsCCTkXyAS2jTxRKZUJXAL8KWTXvpQpL7EQYpAkZiGsKVxT2APADVrrN5VSdwCVYY5JAjq11munrmhCiLHI4C8hrOcl4EalVJpSKgu4LvB4FnBOKZUMvD/k+J7Ac2itu4EapdR7wNjsXil1wfQVXQghfcxCWFDI4K/TQANwGOgDPhd47ACQpbW+Qyl1KfALYAB4N8aeuz8D5mPsvfuQ1vob0x6EELOUJGYhhBAigUhTthBCCJFAJDELIYQQCUQSsxBCCJFAJDELIYQQCUQSsxBCCJFAJDELIYQQCUQSsxBCCJFA/n+aXYj8VT6v9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A bit of a mess because not smoothed, but multiplier appears to be working.\n",
    "spaindata.plot(x='date', y=['new_cases', 'estimated_cases', 'multiplier'],\n",
    "    kind='line', grid=True, logy=True, figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed infection rate: 0.00486500339404247\n",
      "Estimated infection rate: 0.04692102835710837\n"
     ]
    }
   ],
   "source": [
    "# https://en.wikipedia.org/wiki/Demographics_of_Spain\n",
    "spainpop = 47007367\n",
    "spaincases = spaindata[spaindata['date'] <= '2020-05-13']['new_cases'].sum()\n",
    "spainestimates = spaindata[spaindata['date'] <= '2020-05-13']['estimated_cases'].sum()\n",
    "print('Observed infection rate:', spaincases/spainpop)\n",
    "print('Estimated infection rate:', spainestimates/spainpop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the total reported count, `0.48%` of the nation has been infected as of 5/13/2020, while [seroprevalence](https://www.reuters.com/article/us-health-coronavirus-spain-study/spanish-antibody-study-points-to-5-of-population-affected-by-coronavirus-idUSKBN22P2RP) [says](https://www.vox.com/2020/5/16/21259492/covid-antibodies-spain-serology-study-coronavirus-immunity) `5%`. So there's a 10x undercount of official cases. The model predicts around `4.7%` of Spain has been infected (`3.05%` if you fill in missing early values with 6x multiplier instead of 10x). Overall this seems to perform really well, but I still wonder if it will undercount in places with really good testing capacity? Or will places with good testing capacity not tend to have large outbreaks?  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
 }