fonnesbeck/Simple Measles Model.ipynb

## Simple Measles Model.ipynb
{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Disease Outbreak Response Decision-making Under Uncertainty: A retrospective analysis of measles in Sao Paulo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import pdb\n",
    "\n",
    "from IPython.core.display import HTML\n",
    "def css_styling():\n",
    "    styles = open(\"styles/custom.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
    "css_styling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = \"data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!rm -rf ~/.theano"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import outbreak data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = pd.read_csv(data_dir+\"measles.csv\", index_col=0)\n",
    "measles_data.NOTIFICATION = pd.to_datetime(measles_data.NOTIFICATION)\n",
    "measles_data.BIRTH = pd.to_datetime(measles_data.BIRTH)\n",
    "measles_data.ONSET = pd.to_datetime(measles_data.ONSET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = measles_data.replace({'DISTRICT': {'BRASILANDIA':'BRAZILANDIA'}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sao Paulo population by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sp_pop = pd.read_csv(data_dir+'sp_pop.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_names = sp_pop.index.values\n",
    "_names[_names=='BRASILANDIA'] = 'BRAZILANDIA'\n",
    "sp_pop.set_index(_names, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0 a 4 anos</th>\n",
       "      <th>5 a 9 anos</th>\n",
       "      <th>10 a 14 anos</th>\n",
       "      <th>15 a 19 anos</th>\n",
       "      <th>20 a 24 anos</th>\n",
       "      <th>25 a 29 anos</th>\n",
       "      <th>30 a 34 anos</th>\n",
       "      <th>35 a 39 anos</th>\n",
       "      <th>40 a 44 anos</th>\n",
       "      <th>45 a 49 anos</th>\n",
       "      <th>50 a 54 anos</th>\n",
       "      <th>55 a 59 anos</th>\n",
       "      <th>60 a 64 anos</th>\n",
       "      <th>65 a 69 anos</th>\n",
       "      <th>70 a 74 anos</th>\n",
       "      <th>75 anos e +</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AGUA RASA</th>\n",
       "      <td>5411</td>\n",
       "      <td>5750</td>\n",
       "      <td>6450</td>\n",
       "      <td>7122</td>\n",
       "      <td>7621</td>\n",
       "      <td>7340</td>\n",
       "      <td>6999</td>\n",
       "      <td>6984</td>\n",
       "      <td>6346</td>\n",
       "      <td>5608</td>\n",
       "      <td>4987</td>\n",
       "      <td>4212</td>\n",
       "      <td>4152</td>\n",
       "      <td>3595</td>\n",
       "      <td>2937</td>\n",
       "      <td>3637</td>\n",
       "      <td>89151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTO DE PINHEIROS</th>\n",
       "      <td>2070</td>\n",
       "      <td>2369</td>\n",
       "      <td>2953</td>\n",
       "      <td>3661</td>\n",
       "      <td>4612</td>\n",
       "      <td>4190</td>\n",
       "      <td>3539</td>\n",
       "      <td>3633</td>\n",
       "      <td>3448</td>\n",
       "      <td>3289</td>\n",
       "      <td>3040</td>\n",
       "      <td>2533</td>\n",
       "      <td>2298</td>\n",
       "      <td>1732</td>\n",
       "      <td>1305</td>\n",
       "      <td>1823</td>\n",
       "      <td>46495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANHANGUERA</th>\n",
       "      <td>3068</td>\n",
       "      <td>3006</td>\n",
       "      <td>2755</td>\n",
       "      <td>2431</td>\n",
       "      <td>2426</td>\n",
       "      <td>2636</td>\n",
       "      <td>2695</td>\n",
       "      <td>2308</td>\n",
       "      <td>1653</td>\n",
       "      <td>1107</td>\n",
       "      <td>753</td>\n",
       "      <td>509</td>\n",
       "      <td>352</td>\n",
       "      <td>217</td>\n",
       "      <td>162</td>\n",
       "      <td>171</td>\n",
       "      <td>26249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARICANDUVA</th>\n",
       "      <td>7732</td>\n",
       "      <td>7730</td>\n",
       "      <td>8373</td>\n",
       "      <td>8956</td>\n",
       "      <td>9182</td>\n",
       "      <td>8531</td>\n",
       "      <td>7813</td>\n",
       "      <td>7365</td>\n",
       "      <td>6551</td>\n",
       "      <td>5554</td>\n",
       "      <td>4887</td>\n",
       "      <td>3858</td>\n",
       "      <td>3320</td>\n",
       "      <td>2449</td>\n",
       "      <td>1611</td>\n",
       "      <td>1723</td>\n",
       "      <td>95635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARTUR ALVIM</th>\n",
       "      <td>9031</td>\n",
       "      <td>9078</td>\n",
       "      <td>10000</td>\n",
       "      <td>11058</td>\n",
       "      <td>11387</td>\n",
       "      <td>10347</td>\n",
       "      <td>9125</td>\n",
       "      <td>8658</td>\n",
       "      <td>7830</td>\n",
       "      <td>7055</td>\n",
       "      <td>5919</td>\n",
       "      <td>4612</td>\n",
       "      <td>3756</td>\n",
       "      <td>2633</td>\n",
       "      <td>1727</td>\n",
       "      <td>1724</td>\n",
       "      <td>113940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0 a 4 anos  5 a 9 anos  10 a 14 anos  15 a 19 anos  \\\n",
       "AGUA RASA                5411        5750          6450          7122   \n",
       "ALTO DE PINHEIROS        2070        2369          2953          3661   \n",
       "ANHANGUERA               3068        3006          2755          2431   \n",
       "ARICANDUVA               7732        7730          8373          8956   \n",
       "ARTUR ALVIM              9031        9078         10000         11058   \n",
       "\n",
       "                   20 a 24 anos  25 a 29 anos  30 a 34 anos  35 a 39 anos  \\\n",
       "AGUA RASA                  7621          7340          6999          6984   \n",
       "ALTO DE PINHEIROS          4612          4190          3539          3633   \n",
       "ANHANGUERA                 2426          2636          2695          2308   \n",
       "ARICANDUVA                 9182          8531          7813          7365   \n",
       "ARTUR ALVIM               11387         10347          9125          8658   \n",
       "\n",
       "                   40 a 44 anos  45 a 49 anos  50 a 54 anos  55 a 59 anos  \\\n",
       "AGUA RASA                  6346          5608          4987          4212   \n",
       "ALTO DE PINHEIROS          3448          3289          3040          2533   \n",
       "ANHANGUERA                 1653          1107           753           509   \n",
       "ARICANDUVA                 6551          5554          4887          3858   \n",
       "ARTUR ALVIM                7830          7055          5919          4612   \n",
       "\n",
       "                   60 a 64 anos  65 a 69 anos  70 a 74 anos  75 anos e +  \\\n",
       "AGUA RASA                  4152          3595          2937         3637   \n",
       "ALTO DE PINHEIROS          2298          1732          1305         1823   \n",
       "ANHANGUERA                  352           217           162          171   \n",
       "ARICANDUVA                 3320          2449          1611         1723   \n",
       "ARTUR ALVIM                3756          2633          1727         1724   \n",
       "\n",
       "                    Total  \n",
       "AGUA RASA           89151  \n",
       "ALTO DE PINHEIROS   46495  \n",
       "ANHANGUERA          26249  \n",
       "ARICANDUVA          95635  \n",
       "ARTUR ALVIM        113940  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annual vaccination data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BIRTHS</th>\n",
       "      <th>VAX</th>\n",
       "      <th>POP</th>\n",
       "      <th>SIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>3896442</td>\n",
       "      <td>0.57</td>\n",
       "      <td>121740438</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>3933136</td>\n",
       "      <td>0.73</td>\n",
       "      <td>124610790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3952137</td>\n",
       "      <td>0.66</td>\n",
       "      <td>127525420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>3952735</td>\n",
       "      <td>0.68</td>\n",
       "      <td>130455659</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>3935224</td>\n",
       "      <td>0.73</td>\n",
       "      <td>133364277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BIRTHS   VAX        POP  SIA\n",
       "YEAR                               \n",
       "1980  3896442  0.57  121740438    0\n",
       "1981  3933136  0.73  124610790    0\n",
       "1982  3952137  0.66  127525420    0\n",
       "1983  3952735  0.68  130455659    0\n",
       "1984  3935224  0.73  133364277    0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccination_data = pd.read_csv('data/BrazilVaxRecords.csv', index_col=0)\n",
    "vaccination_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YEAR\n",
       "1980    0.57\n",
       "1981    0.73\n",
       "1982    0.66\n",
       "1983    0.68\n",
       "1984    0.73\n",
       "1985    0.67\n",
       "1986    0.67\n",
       "1987    0.64\n",
       "1988    0.62\n",
       "1989    0.60\n",
       "1990    0.78\n",
       "1991    0.85\n",
       "1992    0.91\n",
       "1993    0.85\n",
       "1994    0.77\n",
       "1995    0.87\n",
       "1996    0.80\n",
       "1997    0.99\n",
       "Name: VAX, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccination_data.VAX[:18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vax_97 = np.r_[[0]*(1979-1921+1), vaccination_data.VAX[:17]]\n",
    "n = len(vax_97)\n",
    "FOI_mat = np.resize((1 - vax_97*0.9), (n,n)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Mean age of infection for those born prior to vaccination coverage, assuming R0=16\n",
    "A = 4.375"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  0.487,  0.343,  0.406,  0.388,  0.343,\n",
       "        0.397,  0.397,  0.424,  0.442,  0.46 ,  0.298,  0.235,  0.181,\n",
       "        0.235,  0.307,  0.217])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1 - vax_97*0.9)[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 64.84 ,  63.84 ,  62.84 ,  61.84 ,  60.84 ,  59.84 ,  58.84 ,\n",
       "        57.84 ,  56.84 ,  55.84 ,  54.84 ,  53.84 ,  52.84 ,  51.84 ,\n",
       "        50.84 ,  49.84 ,  48.84 ,  47.84 ,  46.84 ,  45.84 ,  44.84 ,\n",
       "        43.84 ,  42.84 ,  41.84 ,  40.84 ,  39.84 ,  38.84 ,  37.84 ,\n",
       "        36.84 ,  35.84 ,  34.84 ,  33.84 ,  32.84 ,  31.84 ,  30.84 ,\n",
       "        29.84 ,  28.84 ,  27.84 ,  26.84 ,  25.84 ,  24.84 ,  23.84 ,\n",
       "        22.84 ,  21.84 ,  20.84 ,  19.84 ,  18.84 ,  17.84 ,  16.84 ,\n",
       "        15.84 ,  14.84 ,  13.84 ,  12.84 ,  11.84 ,  10.84 ,   9.84 ,\n",
       "         8.84 ,   7.84 ,   6.84 ,   5.84 ,   5.353,   5.01 ,   4.604,\n",
       "         4.216,   3.873,   3.476,   3.079,   2.655,   2.213,   1.753,\n",
       "         1.455,   1.22 ,   1.039,   0.804,   0.497,   0.28 ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tril(FOI_mat).sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5  ,  0.217,  0.307,  0.235,  0.181,  0.235,  0.298,  0.46 ,\n",
       "        0.442,  0.424,  0.397,  0.397,  0.343,  0.388,  0.406,  0.343,\n",
       "        0.487,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,  1.   ,\n",
       "        1.   ,  1.   ,  1.   ,  1.   ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natural_susc = np.exp((-1/A) * np.tril(FOI_mat).sum(0))[::-1]\n",
    "vacc_susc = (1 - vax_97*0.9)[::-1]\n",
    "vacc_susc[0] = 0.5\n",
    "vacc_susc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sia_susc = np.ones(len(vax_97))\n",
    "birth_year = np.arange(1922, 1998)[::-1]\n",
    "by_mask = (birth_year > 1983) & (birth_year < 1992)\n",
    "sia_susc[by_mask] *= 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4.69002500e-01,   1.93697440e-01,   2.55462677e-01,\n",
       "         1.85322917e-01,   1.36953286e-01,   1.68513200e-01,\n",
       "         3.99236891e-02,   5.54765276e-02,   4.81834127e-02,\n",
       "         4.19519164e-02,   3.58729698e-02,   3.27610800e-02,\n",
       "         2.61705672e-02,   2.70916231e-02,   1.29180455e-01,\n",
       "         1.00905818e-01,   1.28176341e-01,   2.09416838e-01,\n",
       "         1.66626583e-01,   1.32579684e-01,   1.05489605e-01,\n",
       "         8.39348576e-02,   6.67844030e-02,   5.31383100e-02,\n",
       "         4.22805305e-02,   3.36413269e-02,   2.67673765e-02,\n",
       "         2.12979840e-02,   1.69461555e-02,   1.34835384e-02,\n",
       "         1.07284398e-02,   8.53629188e-03,   6.79206677e-03,\n",
       "         5.40424011e-03,   4.29998882e-03,   3.42136979e-03,\n",
       "         2.72227946e-03,   2.16603463e-03,   1.72344761e-03,\n",
       "         1.37129463e-03,   1.09109726e-03,   8.68152769e-04,\n",
       "         6.90762647e-04,   5.49618743e-04,   4.37314850e-04,\n",
       "         3.47958071e-04,   2.76859611e-04,   2.20288738e-04,\n",
       "         1.75277021e-04,   1.39462573e-04,   1.10966110e-04,\n",
       "         8.82923454e-05,   7.02515229e-05,   5.58969914e-05,\n",
       "         4.44755291e-05,   3.53878203e-05,   2.81570079e-05,\n",
       "         2.24036713e-05,   1.78259171e-05,   1.41835379e-05,\n",
       "         1.12854079e-05,   8.97945446e-06,   7.14467769e-06,\n",
       "         5.68480185e-06,   4.52322323e-06,   3.59899059e-06,\n",
       "         2.86360691e-06,   2.27848457e-06,   1.81292059e-06,\n",
       "         1.44248555e-06,   1.14774170e-06,   9.13223020e-07,\n",
       "         7.26623669e-07,   5.78152263e-07,   4.60018100e-07,\n",
       "         3.66022354e-07])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_susc = sia_susc * vacc_susc * natural_susc\n",
    "total_susc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10877ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(total_susc).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot of cumulative cases by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1077077b8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9HZoV4IUQAcBlUsoRAFJKB1AphLgZGFxbbQawCleQvwWYLaW0A5lCiHRgILD+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vgYPsWHe0IryR8ZeFZ6eTJ6Es9aPyd76lJ++OPfUlqWTXnMoLLFR1F1LoV1xZR4LPxLjtYug9ff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\nEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1087cd2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "measles_onset_dist = measles_data.groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0)\n",
    "measles_onset_dist.cumsum().plot(legend=False, grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_district_cases = measles_onset_dist.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 5 districts by number of cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DISTRICT\n",
       "GRAJAU             1074\n",
       "JARDIM ANGELA       944\n",
       "CAPAO REDONDO       849\n",
       "JARDIM SAO LUIZ     778\n",
       "CAMPO LIMPO         692\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals = measles_onset_dist.sum()\n",
    "totals.sort(ascending=False)\n",
    "totals[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age distribution of cases, by confirmation status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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37ty51PM+dOiQEhIS1KdPnxJ14Vxrj8ejhIQE/3Xr1auX5s+fX+rxisZdtWqV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\nRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107390f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_conclusion = measles_data.groupby([\"YEAR_AGE\", \"CONCLUSION\"])\n",
    "counts_by_cause = by_conclusion.size().unstack().fillna(0)\n",
    "ax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Disease Transmission Model\n",
    "\n",
    "As a baseline for comparison, we can fit a model to all the clinically-confirmed cases, regardless of lab confirmation status. For this, we will use a simple SIR disease model, which will be fit using MCMC.\n",
    "\n",
    "This model fits the series of 2-week infection totals in each district $i$ as a set of Poisson models:\n",
    "\n",
    "\\\\[Pr(I(t)_{i} | \\lambda(t)_i) = \\text{Poisson}(\\lambda(t)_i) \\\\]\n",
    "\n",
    "Where the outbreak intensity is modeled as:\n",
    "\n",
    "\\\\[\\lambda(t)_i = \\beta [I^{(w)}(t-1)_i]^{\\alpha} S(t-1)_i\\\\]\n",
    "\n",
    "\\\\[\\alpha \\sim \\text{Exp}(1)\\\\]\n",
    "\n",
    "We will assume here that the transmission rate is constant over time (and across districts):\n",
    "\n",
    "\\\\[\\beta \\sim \\text{Gamma}(1, 0.1)\\\\]\n",
    "\n",
    "To account for the influence of infected individuals from neighboring districts on new infections, the outbreak intensity was modeled using a spatial-weighted average of infecteds across districts, where populations were weighted as an exponential function of the distance between district centroids:\n",
    "\n",
    "\\\\[w_{d} = \\text{exp}(-\\theta d)\\\\]\n",
    "\n",
    "\\\\[\\theta \\sim \\text{Exp}(1)\\\\]\n",
    "\n",
    "### Confirmation Sub-model\n",
    "\n",
    "Rather than assume all clinical cases are true cases, we can adjust the model to account for lab confirmation probability. This is done by including a sub-model that estimates age group-specific probabilities of confirmation, and using these probabilities to estimate the number of lab-confirmed cases. These estimates are then plugged into the model in place of the clinically-confirmed cases.\n",
    "\n",
    "We specified a structured confirmation model to retrospectively determine the age group-specific probabilities of lab confirmation for measles, conditional on clinical diagnosis. Individual lab confirmation events $c_i$ were modeled as Bernoulli random variables, with the probability of confirmation being allowed to vary by age group:\n",
    "\n",
    "$$c_i \\sim \\text{Bernoulli}(p_{a(i)})$$\n",
    "\n",
    "where $a(i)$ denotes the appropriate age group for the individual indexed by i. There were 16 age groups, the first 15 of which were 5-year age intervals $[0,5), [5, 10), \\ldots , [70, 75)$, with the 16th interval including all individuals 75 years and older.\n",
    "\n",
    "Since the age interval choices were arbitrary, and the confirmation probabilities of adjacent groups likely correlated, we modeled the correlation structure directly, using a multivariate logit-normal model. Specifically, we allowed first-order autocorrelation among the age groups, whereby the variance-covariance matrix retained a tridiagonal structure. \n",
    "\n",
    "$$\\begin{aligned}\n",
    "\\Sigma = \\left[{\n",
    "\\begin{array}{c}\n",
    "  {\\sigma^2} & {\\sigma^2 \\rho} & 0& \\ldots & {0} & {0}  \\\\\n",
    "  {\\sigma^2 \\rho} & {\\sigma^2} &  \\sigma^2 \\rho & \\ldots & {0}  & {0} \\\\\n",
    "  {0} & \\sigma^2 \\rho & {\\sigma^2} & \\ldots & {0} & {0} \\\\\n",
    "  \\vdots & \\vdots & \\vdots &  & \\vdots & \\vdots\\\\\n",
    "  {0} & {0} & 0 & \\ldots &  {\\sigma^2} & \\sigma^2 \\rho  \\\\\n",
    "{0} & {0} & 0 & \\ldots & \\sigma^2 \\rho &  {\\sigma^2} \n",
    "\\end{array}\n",
    "}\\right]\n",
    "\\end{aligned}$$\n",
    "\n",
    "From this, the confirmation probabilities were specified as multivariate normal on the inverse-logit scale.\n",
    "\n",
    "$$ \\text{logit}(p_a) = \\{a\\} \\sim N(\\mu, \\Sigma)$$\n",
    "\n",
    "Priors for the confirmation sub-model were specified by:\n",
    "\n",
    "$$\\begin{aligned}\n",
    "\\mu_i &\\sim N(0, 100) \\\\\n",
    "\\sigma &\\sim \\text{HalfCauchy}(25) \\\\\n",
    "\\rho &\\sim U(-1, 1)\n",
    "\\end{aligned}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age classes are defined in 5-year intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age_classes = [0,5,10,15,20,25,30,35,40,100]\n",
    "measles_data.dropna(subset=['YEAR_AGE'], inplace=True)\n",
    "measles_data['YEAR_AGE'] = measles_data.YEAR_AGE.astype(int)\n",
    "measles_data['AGE_GROUP'] = pd.cut(measles_data.AGE, age_classes, right=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lab-checked observations are extracted for use in estimating lab confirmation probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CONFIRMED = measles_data.CONCLUSION == 'CONFIRMED'\n",
    "CLINICAL = measles_data.CONCLUSION == 'CLINICAL'\n",
    "DISCARDED = measles_data.CONCLUSION == 'DISCARDED'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract confirmed and clinical subset, with no missing county information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lab_subset = measles_data[(CONFIRMED | CLINICAL) & measles_data.COUNTY.notnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age = lab_subset.YEAR_AGE.values\n",
    "ages = lab_subset.YEAR_AGE.unique()\n",
    "counties = lab_subset.COUNTY.unique()\n",
    "y = (lab_subset.CONCLUSION=='CONFIRMED').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KlCn8/ve/Z/LkyaxZs4Y333yTq666ihtuuIHLL7+8zZgTJ07k73//O7Nnzy7q9gkdFV0X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108773cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_lab_subset = lab_subset.replace({\"CONCLUSION\": {\"CLINICAL\": \"UNCONFIRMED\"}})\n",
    "by_conclusion = _lab_subset.groupby([\"YEAR_AGE\", \"CONCLUSION\"])\n",
    "counts_by_cause = by_conclusion.size().unstack().fillna(0)\n",
    "ax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), figsize=(15,5), grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of lab-confirmed cases older than 20 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60257048468117846"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(measles_data[CONFIRMED].YEAR_AGE>20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['[0, 5)', '[5, 10)', '[10, 15)', '[15, 20)', '[20, 25)', '[25, 30)',\n",
       "       '[30, 35)', '[35, 40)', '[40, 100)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract cases by age and time.\n",
    "age_group = pd.cut(age, age_classes, right=False)\n",
    "age_index = np.array([age_group.categories.tolist().index(i) for i in age_group])\n",
    "age_groups = age_group.categories\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get index from full crosstabulation to use as index for each district\n",
    "dates_index = measles_data.groupby(\n",
    "        ['ONSET', 'AGE_GROUP']).size().unstack().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unique_districts = measles_data.DISTRICT.dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0 a 4 anos      844130\n",
       "5 a 9 anos      830880\n",
       "10 a 14 anos    858750\n",
       "15 a 19 anos    904972\n",
       "20 a 24 anos    945244\n",
       "25 a 29 anos    902086\n",
       "30 a 34 anos    835888\n",
       "35 a 39 anos    764605\n",
       "40 a 44 anos    662946\n",
       "45 a 49 anos    538872\n",
       "50 a 54 anos    437744\n",
       "55 a 59 anos    332195\n",
       "60 a 64 anos    282850\n",
       "65 a 69 anos    218202\n",
       "70 a 74 anos    164842\n",
       "75 anos e +     203482\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excludes = ['BOM RETIRO']\n",
    "\n",
    "N = sp_pop.drop(excludes).ix[unique_districts].sum().drop('Total')\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 5)        844130\n",
       "[5, 10)       830880\n",
       "[10, 15)      858750\n",
       "[15, 20)      904972\n",
       "[20, 25)      945244\n",
       "[25, 30)      902086\n",
       "[30, 35)      835888\n",
       "[35, 40)      764605\n",
       "[40, 100)    2841133\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_age = N.iloc[:8]\n",
    "N_age.index = age_groups[:-1]\n",
    "N_age[age_groups[-1]] = N.iloc[8:].sum()\n",
    "N_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calcualte average susceptibility over age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age_slice_endpoints = [g[1:-1].split(',') for g in age_groups.values]\n",
    "age_slices = [slice(int(i[0]), int(i[1])) for i in age_slice_endpoints]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p_susc_age = np.array([total_susc[s].mean() for s in age_slices])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile bi-weekly confirmed and unconfirmed data by Sao Paulo district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sp_counts_2w = lab_subset.groupby(\n",
    "    ['ONSET', 'AGE_GROUP']).size().unstack().reindex(dates_index).fillna(0).resample('2W', how='sum')\n",
    "\n",
    "# All confirmed cases, by district\n",
    "confirmed_data = lab_subset[lab_subset.CONCLUSION=='CONFIRMED']\n",
    "confirmed_counts = confirmed_data.groupby(\n",
    "    ['ONSET', 'AGE_GROUP']).size().unstack().reindex(dates_index).fillna(0).sum()\n",
    "\n",
    "all_confirmed_cases = confirmed_counts.reindex_axis(measles_data['AGE_GROUP'].unique()).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Ensure the age groups are ordered\n",
    "I_obs = sp_counts_2w.reindex_axis(measles_data['AGE_GROUP'].unique(), \n",
    "                            axis=1).fillna(0).values.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check shape of data frame\n",
    "\n",
    "- 28 bi-monthly intervals, 9 age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert I_obs.shape == (28, len(age_groups))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prior distribution on susceptible proportion:\n",
    "\n",
    "$$p_s \\sim \\text{Beta}(5, 100)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  386.,  2231.,  3046.,  2241.,  1240.,   541.,   212.,    79.,\n",
       "           18.,     6.]),\n",
       " array([ 0.00285801,  0.01787033,  0.03288265,  0.04789497,  0.0629073 ,\n",
       "         0.07791962,  0.09293194,  0.10794426,  0.12295659,  0.13796891,\n",
       "         0.15298123]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pqpPN8klgTbO8Fpjp6ztD7x2CJKllK4bs/8NV9XKSHwQeT3Kkf2VVVZKzvcx527qpqak3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\nMJAkAf8f4wDedUznVtcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10821af28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymc import rbeta\n",
    "plt.hist(rbeta(5, 100, 10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   1,    3,    0,    1,    0,    0,    0,    0,    1],\n",
       "       [   4,   13,    7,   18,    1,    2,    0,    1,    4],\n",
       "       [   3,   12,    2,   14,    0,    1,    1,    2,    5],\n",
       "       [   4,   10,    2,   17,    0,    2,    2,    2,    2],\n",
       "       [   6,   15,    7,   19,    1,    3,    1,    7,    6],\n",
       "       [  19,   27,   20,   34,    0,    7,    2,   13,    8],\n",
       "       [   9,   27,    6,   26,    1,    1,    1,    6,    8],\n",
       "       [  13,   27,   13,   20,    1,    4,    2,    5,    2],\n",
       "       [  28,   32,   16,   21,    2,    6,    1,    9,    9],\n",
       "       [  42,   39,   46,   31,    6,   17,    2,   13,   18],\n",
       "       [  93,   69,   72,   40,    4,   18,    6,   19,   26],\n",
       "       [ 157,   95,  153,   64,   12,   47,    5,   31,   42],\n",
       "       [ 359,  183,  315,  169,   26,   95,   18,   76,   68],\n",
       "       [ 807,  363,  622,  282,   65,  234,   34,  162,  136],\n",
       "       [1168,  660, 1035,  388,   87,  398,   63,  257,  166],\n",
       "       [1442,  913, 1193,  536,  137,  430,   48,  318,  292],\n",
       "       [1350, 1051, 1255,  643,  116,  476,   68,  366,  339],\n",
       "       [1314,  933, 1261,  525,  160,  474,   91,  448,  339],\n",
       "       [1218,  773, 1061,  444,  146,  458,   75,  424,  320],\n",
       "       [ 712,  485,  629,  292,   80,  262,   67,  267,  214],\n",
       "       [ 368,  295,  382,  187,   47,  163,   26,  122,   92],\n",
       "       [ 181,  162,  192,  130,   27,   97,   10,   43,   65],\n",
       "       [ 122,  151,   88,  102,   14,   43,   10,   27,   36],\n",
       "       [  72,   95,   63,   64,    6,   36,    2,   15,   18],\n",
       "       [  32,   46,   39,   52,    7,   15,    2,   20,   14],\n",
       "       [  20,   42,   30,   42,    2,    9,    2,    8,   17],\n",
       "       [   7,   23,    5,   15,    1,    4,    3,    3,    7],\n",
       "       [   1,    1,    2,    1,    0,    1,    0,    0,    0]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Extract observed data\n",
    "obs_date = '1997-06-15'\n",
    "obs_index = sp_counts_2w.index <= obs_date\n",
    "I_obs_t = I_obs[obs_index]\n",
    "\n",
    "# Specify confirmation model\n",
    "confirmation = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from theano.printing import pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymc3 import *\n",
    "import theano.tensor as tt\n",
    "from theano import shared, scan\n",
    "from theano.tensor.nlinalg import matrix_inverse as inv\n",
    "invlogit = tt.nnet.sigmoid\n",
    "\n",
    "with Model() as model:\n",
    "    \n",
    "    n_periods, n_groups = I_obs_t.shape\n",
    "    \n",
    "    if confirmation:\n",
    "    \n",
    "        mu = Normal('mu', mu=0, tau=0.0001, shape=n_groups)\n",
    "        sig = HalfCauchy('sig', 25, shape=n_groups, testval=np.ones(n_groups))\n",
    "\n",
    "        Tau = tt.diag(tt.pow(sig, -2)) \n",
    "\n",
    "        # Age-specific probabilities of confirmation as multivariate normal \n",
    "        # random variables\n",
    "        beta_age = MvNormal('beta_age', mu=mu, tau=Tau, \n",
    "                            shape=n_groups)\n",
    "\n",
    "        p_age = Deterministic('p_age', invlogit(beta_age))\n",
    "\n",
    "        p_confirm = invlogit(beta_age[age_index])\n",
    "\n",
    "        # Confirmation likelihood\n",
    "        lab_confirmed = Bernoulli('lab_confirmed', p=p_confirm, observed=y)\n",
    "\n",
    "        # Confirmed infecteds by age\n",
    "        I_conf = Binomial('I_conf', I_obs_t, p_age, shape=I_obs_t.shape)\n",
    "\n",
    "        I = I_conf\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        I = I_obs_t\n",
    "    \n",
    "    # Transmission parameter\n",
    "    beta = Uniform('beta', 1, 100, testval=10)   \n",
    "    \n",
    "    # Downsample annual series to observed age groups\n",
    "    downsample = lambda x: np.array([x[s].mean() for s in age_slices])\n",
    "    \n",
    "    A = Deterministic('A', 75./(beta - 1))\n",
    "    lt_sum = downsample(np.tril(FOI_mat).sum(0)[::-1])\n",
    "    natural_susc = tt.exp((-1/A) * lt_sum)\n",
    "    \n",
    "    total_susc = downsample(sia_susc) * downsample(vacc_susc) * natural_susc\n",
    "    \n",
    "    # Estimated total initial susceptibles\n",
    "    S_0 = Binomial('S_0', n=shared(N_age.values), p=total_susc, shape=N_age.shape)\n",
    "\n",
    "    # Susceptibles at each time step (subtract cumulative cases from initial S)\n",
    "    S = S_0 - I.cumsum(0)      \n",
    "\n",
    "    # Force of infection\n",
    "    lam = tt.transpose(beta * I.sum(1) * tt.transpose(S / N_age.values))\n",
    "    \n",
    "    # Likelihood of observed cases as function of FOI of prevous period\n",
    "    like = Potential('like', \n",
    "            dist_math.logpow(lam[:-1], I[1:]) - dist_math.factln(I[1:]) - lam[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [---------        24%                  ] 482 of 2000 complete in 9384.5 sec"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    step1 = NUTS(vars=model.cont_vars)\n",
    "    step2 = Metropolis(vars=model.disc_vars)\n",
    "    trace = sample(2000, step=(step1, step2))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "burn = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x119f600f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a320320>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a356b38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a39ffd0>]], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6Uk246+rad3H+4AOzoHWEdeuynxmxqyIrmjyksdFE5cqV5skwdqy5hye61wXrUEhGIWLv\nd+yI13wTsesSzl9Iq15ebqIsU5HVr58J4bffTt2PhobWQhDaWrKCBSsTd8HwO2hoyJ9rqtP7cZHl\nOEWCqrmCTJ5sbhGvvWaJLXKdsesLXzCxdfrpFhztOFngK8AUYDdwF7AD+FpBW5Qh6USWqj346EjB\n2TCx7OjkO7hSpfq+0E/1w/Gj7QgWlY4I0UAqkbJ5s7m9JU7WFy+G6mrLqJfK7S2R11+3fWaTrroL\nButPc7PF3a5YYe6DlZUwY0b83IZjRI8XTTxSUdFaZEFr0RN1F0wnslpa4uNQ//6WQCOVSye0jskK\nJFqygmBqaEj9EKKuDl5+Ob7f5maPm3Syg4usbkQxxm94n/PD6tUwezZ873vw+9/Db38bD1TOB7Nn\nwzXXVHPBBRZcXCwU4/2dD1S1VlWvUdUZsdd3VbVHVFMSibswJVJba9aS3bvNcpIpuRBZIXFBNkTW\ntm1WT+/++ztu2QmWtG3b4skTmposg9yyZTY57oiQSSWytm61JAolJeZSuHChrbt1q2W8GzfOit+G\nONN0ZJpwoSN01ZIV7pUPP7T2nXgiHHGEZQQsKbGYKIgfI3qfRtOqJxNZUfe9qCWrvDy1mI/GdlVU\n2PVMF0eVKiZr27b48cOrubl9K+XOnSbYR46Mt7PQDxec3oGLLMfpxTQ2WtbAGTMsHe+SJblN/ZuK\n6dMtFfAll8Df/16YNji9AxF5MsnrH4VuV6a0Z8167jkTYKedZhaTTCd6YSLcEZfBMFlvb/IbJqXZ\nmGy++65lIN1zT0v53REaG23y/MYbVg8JrJ9Tptgk/803YdOmzPcX4ouSTbjr601IBJH11lt2jN27\nzYozdap9n2ncVi5FVohL6yihPRs3Jo/HSxRZ0fZHLVkDB9rfqMgSiQufjrgLJlqywjFTWQ2TxWQF\nkbV0qVnDopasxL5EaW62mLLKShdZTnbJRzFiJ0OKMX7D+5w7nnoKrrzSJjcvvFD4Qoyh3/fdB5/8\npNW++XiHUgb0PIrx/s4T/1/kfT/gk0AnopIKQxBZiZPEHTsseUC/fvZUfcGCuLWhLMVo3RlLVhBk\n7QmzZG56nWXrVstg2thogqsj/4tCYdraWlveudPa3LcvVFXZOeqIy3P0XCWe0zBxLymJn5ddu0x8\n9e1r7dh7b/MMyKSOUkND11z7kqFqbX/xRXsf6lelY/VqE4uhPRs3xjMCRgkiK6wXBFBzc2uRNWCA\n/U08h336xK1QIfFFJjFZIbtgRUX887q69hOzJIvJCu6Cwd0vCKbmZvsu2TWHuCWtpMRFlpNd0lqy\nRGRqPhriOE522LABLr7Yklr8+MfmnldogRXl2GOtcPGnPmXiz3E6iqoujLyeUdWvA1WFblemJLNk\nBZemMJEdP95+y01NNkFORWcsWWEy2Z4lK+yrM5PNBx5obe3ZssWSIuy5p7lBBivSqlWp+7Z7tx0/\nTOj79TOxFSbvFRUwYkR6cbl8ebyfqQRposjq0yduUQziY8SIzFw5QzKScJyuJhAJ56ylJX5tVq3K\nfPsdO1qnZv/ww+QCJjEmq7nZ+n7kkXEhBHFLVuKDguj5S2bJSpYMJCp+gktiebmd+2QiVbV1ja5A\nSUk8hiy4CQZrVr9+7f8+gsgqLe3afe84iWTiLvhLEXlJRK4UkQzKkjqdpRjjN7zP2aOxEf7rv8yl\nZfRoS2xx3nn2BK87EO33CSdYXNjZZ1s7eyvFeH/nAxEZFnntISKnAimSkXcvkomsnTutzlxgzBg4\n9FDYZ5+4Fac9umLJak9kNTTYpLUzk826OnuA8uyzFjMVstH17WuT3ZCwYvNmK/6ajG3bLDHD7t1x\nkVVZ2dpCAukTidTVWezWtm22nEqQJoqsgQOtrWVlcYERREC685LoovbUU+3Xp8qEhQvNNTIqOjqS\nhCNYl5qbTexC6/st0Lev9Te4i9bU2DkeP771esncBcHEdGVl68QXISZr82aYPx/ee6/1NlFLVogL\nGz7cxPndd7cVqI2NdtzEsS2arr2uzo4b7pXy8tTugiHVfMBFlpMN0roLquoxInIA8AVgkYi8CPw2\nVlTYcZxuwLx5VpNq7FgLzD4waUWf7sUZZ8B//ieceqoVPd1770K3yOlBLAJCVE0TsBq4tGCt6SBl\nZW0n+TU18Ykr2ITvwAPNvS5dHFNXLFntbdPYaJaixAnu2rVmiRgxwtJ9J6NvX9vu3XdtOfrb3mOP\neDa7hgazCkVFU6C+3kSEKowaZeKsf/+4xS9MiEtLUydVWLOm9XIqQRpc0ILIGjzYxFmwYgX69bNJ\nfKK7WuK+oserqemaNWvbNhMpQXBWVHQsiUjUhW/MGJg5M76vKAMHmqvqpk12nZ5+Ovl60XTuUWbN\nMmGdaMlqbIxfi8TrFb2eYG2rrY1nGKytbX0Ndu5s/VsJhJislpb49Qn3cdQFNJEgsqJCz9O4O9kg\no5gsVX1TRL4HLAR+DkwXkRLgGlW9L9W2IlIa226tqp4lIsOAu4G9sYHxfFXd1oU+9BqKMX7D+9w1\nVq6Eb3zD3GFuusksQ93FcpVIsn5/7nP2tPLkk00cjhyZ/3blkmK8v/OBqo7P5v5E5CrgMkCAX6nq\nzQnfDwVuB/YF6oEvqOqrse++A3wOK4a8DPgnVU05nU5MEQ42iU5mWaioSG+xSJakIB3p3AUbG21y\nvWWLHX/FCks2sXy5LYtYofG99mq7bYjjCa5bo0fHvxs6NG5Vamiw/WzcaBP/KA0N9lK1bYYMiReK\nTRRZqcRGSIqRKK7SuQvW19tEfv36tnW5+ve379sTmaH94TgtLbZ+V6wjO3a0vmeC+1uy2KRkRFOT\nl5YmF05g99uxx8Kf/mSiMCQeSUTE4tKSiZ3wECGcTxE75oYNdp2TuQtGRdZee9n2w4aZp8OuXfY+\nkPhAItqmEJMVLFlg1yvquplI1F0Q4uLMcbpKJjFZ00Tkp8DrwAnAmao6CTge+GkGx7gKeI34U8er\ngXmqegDwRGzZcZwOsHMnXH21PfGbNQtefRXOOaf7CqxUfPWrcOGFZtHasaPQrXG6MyLySRE5r71X\nJ/d5ECawDgemAWeKSGIU4zXAIlWdBlwM3BzbdjxwOXCoqk4FSoEL0x0z0cWtqckemOyzT9t1g8j6\n8EMTOsnojCUrnbtgEFnjxsHf/mbZ/bZtM6vCGWfAtGlt3b6i+x440No1Zgx87GPx74JAARMIY8ZY\nnE4iweoTMr9VVdnkvb7ezl/4X5fOXXDLFhNDob9RsdXQ0DomLJm7YLLYn/79bRKfiqglK6zb2Yl7\nOBf9+8f3W15uQitTa1bUXTBdopAQ2xSO297606cn/y6IrKiFsk8fu38+9rG2bY5mF4zuY8QIuwcT\nHzIkutZG251oyQI7b8msx4HQztCGvn07V9zbcRLJJCbr58BiYJqqXqmqiwBUdR3wvVQbishewOnA\nr7EnhACzgTti7+8AzulEu3slxRi/4X3uGC0tcMcd5ka0YYPFGnznO62zPnVXUvX7Rz+yWi1nn93x\nOjrdmWK8v3PMWWlenWEi8IKq1qtqMzAfSBRsk4AnAVR1BTBeREZgRZAbgQoRKQMqgLRJyhMnfO+9\nZzEoyRIRBFHy+utmRYoWaQ3ipDMxWY2N7U8m6+rixV5nzrRsoHvvbUKvtDSeaS/ZbzXE1wR3sqOP\nbu3qFRUoDQ223/Xr2+4niIm+fU1QlZSYsKivb+1amEpk1dXZd4MHt86WF9q5bJmlzYd4MoXgNtbS\nYhasoUPbWn2Cu2AqoiIxmcjauTO5uEzGjh3Wh6hrZBBZoXZYIu+/39ry1RGRBa0thMksWakoK7P+\nl5TEtz3iCBPsgwa1vm+CtbO9Y1RUtD7XqhYnl0xkJVqygsgaOND6U1dnv6FEwjkJbejTx0WWkx0y\ncRc8A6iLDT7B/a9frBjknWm2/SmWajc6dIxS1Y2x9xuBUR1ss+MUJQsXwle+YgPSAw/Y5Ke3IAK3\n3AKf+Yy97rkndcpqpzhR1Tk52O1y4LqYK3s9Nua9mLDOUkx4PSMiMzF3971UdbGI3ASsAeqAx1Q1\nbRW4RGGwZk3bxAKBkhKbaK5fD6efDo89BhMn2vbz58MFF3TMkqVqcVWNjfEYpyh1dVbPrqIinqY8\nJK5Yv95iqSAushLjqcJy//4mBBIn9Ikia8QIex+ERCCIrGgB3PJy2zadyFq92jLvidj+o1njoiIr\nmiUwtDsIOrDlU09tew7TWbI2bbKEHhUVdpxgiYm6C65fb9c9auVLpKnJ2rJ9u52b7dvj/QgWt/nz\nzb0v0W3zpZesJmJwdQyumcni35IRXCahYynyw/qJ12nUKHtt395aZCWzYkUJ935g+XI7b8lS6IdY\nqpBdsLwcPvEJs2S++KI9qNixA/bdt3WqeLdk9W6amuIuq/kmk2nM34GTgPC8pAJ4DDgq1UYiciaw\nKTYIVSVbR1VVRNqpwQ1z5sxhfGzkqaysZPr06R/FOIQnxL1tOdBd2uPL2V+uqqrq0PpbtsDFF1fz\n7LNw001VXHIJPPVUNdXV3aM/HVkOtPf9735XxezZcMYZ1Xz723DCCd2r/b7cdrm6upq5c+cCfPT/\nOh/ExpjJWJ0sAFT1xx3dj6q+ISI3Ao8DtZjnRmLi6BuAm0VkMRZ3tRhojrkVfg0YD2wH/iQin1XV\nPyQe59prr/3o/ciRVQwfXsWmTfZEftMms/i0xwkn2OR04EB71dbGXWubmjpmydqxw+IfDz44efKE\nd981YTJ8eOvSDxUVlt1u4kRb7tfPxMNf/mLug8GaHtzr+vdvLZACwcVNNT4RHj3arDpRkRUsIVEr\nfVlZapHV1GTWtldescyMtbU2oV68uK0lq6kpnuUQWsc2lZTE952M/v3NDTEZGzbA88/D5MnWzk2b\n7DyVlbUWtHV1Vj9MtX0376efjteZGj7crFah/cGqF+1TlIYGO24QWSG2KlNLVklJ1yxZ9fVt3Syh\nrYtjNLNgMqI10sDu36OOah2jFYhaslTt+GG90lI7f3vsYTGAUdfcxOyCffvGr9W6dXbvh4cLTs/j\n9dft3jjooMzWr66ubjNn6SyiycqeR1cQWaKq09N9lmS764HPY5mf+mHWrPsxv/cqVd0gIqOBJ1V1\nYpLtNV3bHKc3o2qugVdfDeefbzWviuEf/a5d9vT4oIPg1lt7ZpxZMSMiqGpOr5qI3Ab0x+KEfwV8\nGnP563KGwdjYtUZV/yfFOquAqZjV62RVvSz2+eeBWar6pYT1W41nmzfD3/9uE05Vc0c7+eTM2vfM\nM+Z2tX69WUvOOccsUwsX2m9maprKlqtXm4vcgQea0Hj/fRNxQ4ZYW/72N7OSBwtTYM0aK/x71FHm\n4qdq6bVVrah4SFyxfbutN2GCtevEE9u24b77zMLw6KNWL2/NGnv172+CYvx4E0a1tTZJPuII227t\nWrPQDBwYP18bNtgk6ogj7Jzu3g0zZrSeRL/8sonZAw4wS8iyZSYy337bxMh555n73nPPwWmnmXVo\n3TpzX45aPAIffGBum8mu2dNPW5bX8eOtT++9Z9d340b7e8wxtt5zz9m1OPhgE2SJ/+c2b7Z1Skut\nbSefDEuX2nUvKbFtGhpM+M6c2VoQNzVZ4ooZM2D//e2zu++2fQ0eDIcdlrwQcZRgzfzgA7vfUj0E\nSGTtWjvn/frZdY6iap4Kn/pU3OL12GN2HyejsREef9zE/YQJ9r699q9YYffMm2/acaK/h8WL7bwM\nHWpCfNaseAKXhQvtfikthepqs5Jt326WwAUL7LcxYULyhwZO96ChwX4bybIUL1xo1zD8FjpKV8a0\nTJ5P1IrIYZGDzcDcIlKiqteo6lhV3QcLBP6Hqn4eeBC4JLbaJcADHW927yRbyrkn4X1OzltvwfHH\nm8j461/h5z/v+QIr02tdUQEPP2yTqauvjhfh7IkU4/2dJ45S1YuBLar6I2AW0OnCBSIyMvZ3HHAu\n8H8J3w8RkT6x95cD81W1BlgBzBKR/iIimNdH2spvw4fb7/u002xy0JGsmgMG2ERy06Z4goaOWLKC\nBaauzibcQ4bY5H3XLnjiiXjCgUTCBDP8HxKJW5miVp1gyRoypG1Wvui+tm+PW476xLK5vfmm7eup\np8zaMXYP7RpZAAAgAElEQVRsa3e69mKydu2yyfd++5l4TJxoRa1d4VyFbI59+sSz6AXLS7CstGdh\nCRkSk1mQamriWQdLYrFdoaByoiVr0CCzuiWrg1Zba8c55BDbz5AhcUtLWZm9DjvMBEFi1sKwvHWr\nvaIZFTN1F4zGZHXUzSpcp2SWLBG7ri++GG9Tqv2Xl9s5COnf28ssCHFLXRgzosefOtXO16hRJni3\nbrWx9emnk2cXDO6CdXV2Xz7QgZnq5s2Zr1sMNDdbYp9cMm+e1eVLRqYZOHNBJiLra8A9IvKMiDyD\npV//SieOFaZKNwAni8ib2FPIGzqxL8fplTQ3Wyr2I4+0p6jPP28DQ7ExeLA95X70Ufje93q20HJy\nQnjQt0tExmAeE3t2YX/3isir2EPAK1V1h4hcISJXxL6fDCwTkTeAT2BZc1HVJcCdWJmSV2Lr/m8m\nBxw50gb+iROTp0FvjwED7El8Y6P9ToLISpU9LcrWrfa3rs5E0uGHmwvWO++Ym9SsWcm3q6iIW0IC\n/frZcnRSGSbxe+5pLnvJGDzYshWGSXBoe58+9r+vosL2OW6cTcgDYf3ohKm01NpfUWHWnYMPbiuO\noiIr/N261drRp49NwnbvjifoSOcuWFZm2yZzGaytjSfKCMdtaDBhsGGDWXjAzv/HP26CO1n9rDAx\n3HNPmD27dRKJUHsqnIv2RNbKlWYleuut+PoNDR1zF4yKj0wZMMDuyWQiC+yee//9eNKLdPvfYw+7\nH0L6/sS6ZYEQcxaI3ichocmgQbbeokV2vzQ323VpLyarrq5j9c1qa83a7MRZtcosibmiqcn+B7T3\n8GD37sKJrEyKEb8kIpOwp4QKrFDVDiUiVdX5WMYmVHUL9rTPSSDEOxQT3uc4K1fC5z9v/+BfeKG1\n+0dvoKPXevhwe7J+wgk2GF93Xc9zHSzG+ztPPByrXfX/gNi0lV91dmeq+vEkn90Wef8c7VjKVPUn\nwE86e+yDD+7Y+hUV5o41dmw8e15Li00iMrFk1dXZ7yhYGgYONEvQypU26W+v9tOAAeb6F/0N9u9v\nlq21a+OfZWIpmTnTHqBExUw05ffhh8NDD7XNmhr2G43HiU6M2yOalS9M6nfuNPeh7dtt8l5XF7fW\npbNkgf1/2rzZBEAgiJswoQsiq6Ulbn1Zt84enIXjBUtaIiG7Y+L+oK3ISrSENTTErWhTp1qJj379\nbHn37syzCwYh39GYrFD8t737oLzc1nn//cxEX58+tv5DD6VeN1h2o8dJxrhxZs2aMMGOv21b67IA\nUUtWR7PdBmuhY7S0WK2zjmQ+7Shbt9r/ofYybTY0pP7/kEsyzd81A9gntv6hMf/EO3PXLMcpHlRh\n7lz41rfMavOVr3R8UOut7LEH/OMfNrlTheuv73lCy8k+kQQX94nIX7GMt0VR1L6y0ibnBx5o8UXB\nklVentnkbvduE0yhWKuICQCR9t37AolxMIcdZpPTt96Kf5aJyOrTB846q3UShyCCSkqsPRde2Pa3\nHibNUWGTqciKJokoL7dJX2WluV2GJBEh/ioTkTVoUNtJXdSKFbZvabFzEj6vrbVjhcQMffsmL1Lc\n0NBWJIS+Hn54/FpFz11g9277vqEhbq3p29eshw0NmWcXBGt3R8ejkhI7l+2JHLBruGCBnZ9M/qfP\nmGH9StX2VJasKNMjGQWCZTa637597ZyF8zpkiInxVKnmAy0tuRUUmfDnP1vsXzK3386yfr2d3z0z\n8BdYvdp+GyNHWrzkwIH2/yacv5oa+z1Gf8ddYcsW62s0kU2Ubu0uKCK/B/4TOBoTW4fHXk6WKcb4\njWLv844dNpn46U9NTFx1Ve8VWJ291nvsYRatxx6zwsXJ4iC6K8V4f+cDEXlFRK4RkQmx+lZFIbDA\nJiznnGOTiuBm19KSfNIP9n20MG5Tk02Ao2Jo6NDOWc4HDjTBF51YNjZmNomPplQOIii6XbKJd5i0\nJ7NkpZpEJYvJAvvfEkTOrl2tLVklJakn/4n1m6CtyIq6Cw4aZNakykpLrBHcIPv2TW/JCoSxIbhu\nhn4ncxesrIQzz7Q+HHywifLo+U5HaNOAAZ1LfT1gQGqRtd9+ZjlNlvQjGSNH2jkbPbr9dYIlK9xH\nqY4fCJbb4E4I8eyC9fV2T5x+eua1s8JvoZAu7vX15v6bipYWs6qmq/cW2LTJXpnw3nsmrhYssOs2\nY0br3+CaNRbnFmXLFovTSza+h1hNsPP69tutz+/OnfHrmOy8R12B800m07nDgKNjhYi/El65bpjj\n9HaWLbMnkpWV9s8lXVawYmaPPeDJJy2j15w57o7hMBtoxuKFF4rIN2NJK4qKMHFpbjahtGNH20nG\nihXxAqwhNiFxEnrEEZ3PvAWt03JnmlghSllZPI12uvXOP7/1pD9TS1ZUZEUTOgR3vbq61pasdA+7\nKiri9a8CW7e2dreMiqx+/eDcc+1427bFY23bE1mpLFnRtiUTWe1NKnfubLt9e+yxh8ULjhtnySI6\nysCBqUXOsGEmmKZNs0yD2SBYsoI4zURkBUtWYuKLUNA4uKymsxRviz3micb+1dRY7FchiNaAS8aG\nDSb2o1boVBa4jljotmyxpD7nnmuJWQYPtt9uTY2dw1272v52Fi8299Fk4vDlly05D5ib50svxe9l\naF1EPLGNod2Z3Au5IBORtRxI8ezAyRbFGL9RrH2+6y6LNfrud+G229rGHvRGunqthwwxa9aHH8In\nP9n2n3R3pBjv73ygqqtV9UZVPQy4CDgYWFXgZuWdqCWrb1+bHCb+LnbsaF38t2/fuJgJf9NZbdKR\nDZEV/ZuKRKtKZ0TWtGlwyim2XFFh4ijRkpXOepOsIPHGja3dqUpK4sVxQ9+OPtpERTSJRzJ3wWSW\nrExFVir3qEwzWR5xhGX1C0WEO8qUKa3T6KciW0VigyUrMZYtFX36mJCMFs4uL7drVlsbvydSJZZp\naLDyARC3xLz+upVDWLGi8/3pLCHmMBU1NdanEM+3YoWl/Q8PZBIJsYXp2LXL1k3MAFlaamLp3Xfj\nLrOBxkb7De67b/sxcLt3m5BdvNiu2Ycfxr+LFpNObGMhXQUhs5isEcBrIvIiEJ63qKrOzl2zHKd3\n0tIC114Ld95p/5SnTSt0i3oWFRWWSvfSS02kPvhgx9JfO70HERkPXACcj1m1vlXI9hSCqMgKab5X\nrbKnwbNm2W+jpiY+eayvby2ysvV0Nyo4Qmr4jhBcBzsqzqC1i1d7JIqswYPj8WX77msT4sSYrHTW\nniAsw7lvarKJYmK8WDjngcR2dtWSlSwmq71rcPDBrVPi55Ko22S+CJasvn3NMpvp5DrUAIsmYikr\ns8QmUXfCxPMcaGiIC7CoyCpUbFbfvvG4v/YentTWmstxEDvr15tb39KlyYv2Zuqmv2yZidZEQiHx\n4JpbVxdvX0jLHyzaydi1y9LtH3igbfPOO5bQ5eCD41bwZJasQousTCxZ1wLnANcBN0VeTpYpxviN\nYupzXR1cdBHce281L7xQfAIrW9e6Tx8TqSefbIVRE327uxPFdH/nExF5AfgzNoZ9WlVnqmrRjUuJ\nImuvvWySM3SoTUDWrbNg8Pp6e+3ebeKgrMwmKp0RNckIgqOuzuItomnXO9KXzrQnCLSOxGRFRUpZ\nmSXWOeKI1pa9dCKrpKS1BW/nTpsoJroyqqZuWyqR1Z4lKzpxDpaskJofzHqZLEvklCnpk5v0ZILY\nLSkxwdBR62xpqYnk8Nv48MN4XbhUlqzGRrvOwXUXTPxWVWXPStcRgstwMgtpoLbWHsIEkbVzp1ks\no32Ikom74JYt5oYYTSwSCCKrsdGOGUoEgImskGClPTEXEvYccIAVPh8yxATXyy+3rnMW3V7VXCIL\nWUQ6kxTu1bEnhvup6t9FpCKT7RzHibNtmwUhjx0LP/tZ59wvnDgi8G//BuPHw7HH8pH7pVM0XKKq\nbxS6EYUmmiK8pMTcs0aOtMn/Aw/YhAdsUvPgg/Z0P1iysiWwwCYxGzeasJs8OW4R6ghdaVNpaccs\nWYkCauDA1u5NmbgLgk36du60/tbWtnWRysTKlpjCfft2SxywY0dbS1ayrIclJRbb+9RTlrFRpH2R\n1dsJlqzOChsRe3gHdi+G1OCQOiYrWLiiLnXhgQZklpUwm4SSDqlSl9fWxjOUhvizAQPiVunEezkT\nq9wbb1gMVrLfcfgN1tbGrcnBRTdYsqK/02TbBq+Vigq751Ut5i0akxUVWaHERSG99jPJLvjPwJ+A\nUDNkL+wJopNlijF+oxj6vH49HHecPVn7wx/glFOqCt2kgpCLa33ppSawLroIfvnLrO++yxTD/V0I\nXGAZUUtWmFiGAruDBllygeHDbZ3mZquFFSxZ2QwEnzDBnvrvv79Nsjrbl86KrP79Uz+tTieyEsnE\nkgWWuCHUCAtP4xOPC6nTXoeskAsXWszpE0/Es7hlYskCc3msrITnnjN3r2hyk2Iik9T7mTJkiP1G\nwjVN5y4I8d8ixFP0p9ouV7S02O88VRHlmpp4QorNm+O1zfr3Tx7vnIkl64MP2ndHDfdjKBwezc4Z\nHlC0Z8kKv4PEEhLB4hgykyZuv2NH5+q8ZZNMDv0l4BhgB4CqvgmkjYIQkX4i8oKILBGR10TkP2Kf\nDxOReSLypog8LiKVXemA43RnVq2yehXnn29p2ntrevZCcsIJlir2F7+AK69M7SLhOO0hIleJyDIR\nWS4iVyX5fqiI/FlElsbGtimxzw8UkcWR13YR+Wo+2pzoLhjl4IMt1uSUU+xp9h572GT8gAOyL7L6\n9zdL/eTJnd9HV0TW6ad3zZKVSKaWrHHjLJD/ySfNbTlRZAUxlMp9sqzMtnv7bbtmZ55p1y2a4j7a\nj/baPmuWTULfe6+w7lGFJJzvbLjoHXssnH12fJ/p3AWhratdeXnymLlANtO8v/tuXFSpmshqbyxU\ntTb17Wv33saNJvYheWkCSC+ygtUo0QIWCNdkxw5rW8jO2dhoDxUGDWpfZKmaFStZXa3ychNZwV0w\nXIN33jErc+hXochkyrdbVT/SwyJSBqS9NVS1HjheVadjmZ+OF5FjgKuBeap6APBEbNmhOOM3enOf\n33kHjj8evvENyyIY/ln35j6nIpf93m8/e4r7/vsmuoKbVKEp1mvd0xCRg4DLsBqQ04AzRSSxctQ1\nwCJVnQZcDNwMoKorVPUQVT0EK3myizx5e4SJX3Nz28n3mDHxCU+/fvaE+dBD41aObKc07urENtsu\njFFyZckaNAiOPNKelifWyAqcdlp6171hw2xfo0fb9enb1+onJutHe+3q29dSrp91Fpx0Uvq290ay\nacmC1r+RVBapZO6CYZs+fZJvt2wZ3HOPiaNs8NZb8bi8lhYT2omWrIYGi1F6+un4b62iwixQ4d5N\nljUT0mcX/PDDtpamKOF4TU2tRdaKFWaFHTWqfXfB5marqZbsf1ZZWTx7ZxBpW7bACy9YvFZHk/Bk\nm0xuxfki8l2gQkROxlwHH8pk56oajI59gFJgK1bf5I7Y53dgSTUcp1exapVN9r/9bfjSlwrdmuJg\nyBCrdH/KKeaa+dxzhW6RkytEZICIfF9EfhVb3l9EzuzCLicCL8QKGzcD84HzEtaZBDwJJqyA8SIy\nImGdk4CVqvpeF9qSMYkxWe1RUdE64UFwY+pO5FJkRbOONTenF4SZiiwwYXTIIXEXzUQqM/DVGTXK\nkpakI9NU+8XqMZFNS1YiISZrx4623wURFXUXDNbQ9sTZqlWWbGXx4vhnqvZ65ZXWgiaTzH7RYwd3\nwYYGs/IsWWKi7v77LRnOrl2tRVZwF4TOuQs2Nlqbx49vv33RaxIVWTt32m9IpH1LVqrfbBBeUUvW\n7t0mrkpLWxcuLwSZ/BSvBj4AlgFXAH8DvpfJzkWkRESWABuBJ1X1VWCUqm6MrbIR8BQAMYoxfqM3\n9nn1arNgfetb8MUvtv2+N/Y5E/LR75IS+MEPLD7r7LPh1luz65LRUYr1WueB3wINwFGx5XVYBtzO\nshw4NubOXgGcgcUfR1lKTHiJyExg7yTrXAj8Xxfa0SH69LGn15s2pZ5YH31061iJysrOZQDMJZ1N\n4Z4J/frZRPDtty2xRLbcBaPrZ2Kxao999sks22wqS5aTfUtWlLIyEwR/+5vdQ1GSWbKiddASRda2\nbSYq9t7bBEEYo1auNEH06qvxOlBr1sDdd6dvX3Oz7Sc8cAlZK1essHTyb75pDyDHj7fvoyKrqSlu\nyWov22V7Iuv99+HZZ82Vb++9229f9LcdFVkh6QUkF1lR0ZqMqMgK29fXm3vhpz9taeoLSSbZBZuB\n/429OoSqtgDTRWQI8JiIHJ/wvYpIu1OgOXPmMD4mjSsrK5k+ffpHk5bghuPLvtydlidNquKUU+Cs\ns6pj8Qndq33FsjxoUDU//Sn85CdVPP88fOYz1fTv333a15uWq6urmTt3LsBH/6/zwARVPV9ELgRQ\n1VrpQjVdVX1DRG4EHgdqgcVA4jPVG4CbRWQx9tBxMVafCwAR6QOcBXw72TGuvfbaj95XVVV9dC67\nwogRMHWquRylqkuUKF4GDy68G00iubSulZSYsHz55fhyuvXTrZNIZzIqdhQXWakZONBEQrZdYcGu\n7yuvmJDZsKG1oI5asqIp3MHu6cTYqHfeMUEiEs8u2a+fuZyGIsLr15tQCPtbvdpiANu7/sGSFWpP\n9Yk9gFm71gTQ7t1m1Rk71jLy9ekT71f0b3u1qtpL7f7GG7bvI49s99QB8Qygoa9BZEXjuEpK2h4j\nneU5FCEOMYzBkpUqRjMd1dXVH41xXUU0zWNeEVmV5GNV1X07dCCR7wN1mN97lapuEJHRmIVrYpL1\nNV3behvV1dVZGXh7Er2pz9u3W6rQs8+2gsPt0Zv63BEK0e9du8yauHgx3HuvBf3nk2K81iKCqnZe\n8WR2jGeBE4FnVfWQWPzUXao6M0v7vx5Yo6r/k2KdVcBUVa2JLZ8NfFFVT02ybtGNZx3lgw9s8pWr\nQPVFi0yMzppl2f5SafKaGrM2ZOLCl0+2brVU7WefXeiWFB+qlrlRxK5D9N/6ggWWcOSoo+y+efVV\nE/WnnWZZI4cMscybYElSPvjAEpxUVMAjj9g9OXQoPP+87XvbNls+9VSzvr70kgmRCRMsuczWrW3r\nnd1/v7nJjx4Nf/mL7fPFF+33NGWKuTlOnGhJLqqrLX7qpJOsLX//O8yebQ9q1q83q9dxx7Xe/1//\nagJm9uy2nx9zTHor7uuvm1Dcvt3iq/bc0yx0JSWWGEzELPLLllndukB9vZ2jc89Nvt/nnzcXyPPO\ns+swdqz1adCg7I33XRnTMnkmcnjkdSwW7PuHDBq1R8gcKCL9gZOxJ38PApfEVrsEeKDjzXac7kVd\nnf3zOeYY+OEPC90aJ1BRAXPnWlzcMcfAffcVukVOlrgWeBTYS0T+D/gH7ViQMkVERsb+jgPOJcHt\nT0SGxKxViMjlwPwgsGJcBNzVlTYUMyNG5DYT2JgxliAnxH+kYuDA7iewwC1ZhUTEiuxOmWKJFWoi\nv/yQqS9Yk0JWQYjXqwL7u3mzZcMMlqNoQev6enswGBKphOK9kyZZjPcbb9gDw0cfbeuymGjJGjPG\nhNS++5qr8MSYKSOaKAKsHSLxjJSlpcmzKCazZKkmrw+XjJAEpLzc+lxSYu1raYn/HkuSuAums2RF\nLeDBElZfH69RVmgycRf8MOGjn4nIIuD7aTYdDdwhIiWYmPudqj4Rc7W4R0QuBVYD53e82b2TYnvi\nDb2jzy0t8PnP2z+ym29OP4D3hj53hkL1WwSuuAIOO8x8tBcsgBtvzI1LSSLFeq1zjao+HhuHZsU+\n+mqSsaqj3Csiw4FG4EpV3SEiV8SOdxswGZgbc3FfDlwaNhSRAVjSi8u72AYnR4wa1fOLwA8Y0LU0\n+U7XKSszC8nLL5v7XUODWX+GDYsLkajIKi+3zH/r1pl4GjmytShJFFkNDWYVqqw0t8TGxni9rkmT\nzBo0cqSJrGA9Uo3Hg4WYLJG21qjQntAPMJE1c2ZcvCdLVb9zZ/KYrPp6218m8YulpbbuiBFx1+aT\nTor3HdoXWanciKPHLy2Nx2R1xV0wm6QVWSJyGPGU7SXADCxTYEpUdRlwaJLPt2CDkeP0Cr71LTNP\nP/64P2XszsyYYQPjxRebq8c999jTPqfnkDAeAayP/R0nIuNUdVFn962qH0/y2W2R988BB7azbS2Q\npIqL42SP0lJzGXMKy+TJ8MwzcfFx0kkW+xSsScFiA/FC0armUhjcBgNBZDU1xQVHebnFUW3dGq95\nBSayJk6E5ctbZzkM4iea+KI9gmAJf0WsjdHvE0XWI4+03U9zs7ngZhqLOGSICcYDI/9BS0pab58s\nhXtTU/qYrKglq6UlHvfVHcgkzPQm4oNaE259yhnFGL/R0/t8yy3w8MOWXSfTJyc9vc+dpTv0e9gw\nePBBs2TNmAG/+11ua8p0hz73MqLjUTKOT/Gd4zhOlykpsbiiKBs2xK1JI0bEa0b17WsuqtA6w2eg\nb1/LKLhkSfyzYLnatKm1VQxMFA0ebKIOLCYqDDFRS1Z7JIqsRBKFTntZBd95xzIfZpqldPjw1HW0\noPPuglFLVnOzuVn2GJGlqlV5aIfj9Dj++le47jpzPyt0LQYnc0pK4DvfsRoln/ucuRJ+73u5qa3i\nZBcfjxzH6Y6UlpqlprnZkjqkSmceZdgwc/+rqDBXQIiLrJDePNG1fcgQs2bt2mUWrVDXKhqT1R7p\nRFaiJSsqsETioufNN+Hkk83ili06I7ISLVkNDbZNT3IX/FfaPjkMl1BV9b+y3qoipRifePfUPi9b\nBnPmmFUkamrPhJ7a567S3fp9wgnmPnjhhWaJ/P3vs19To7v1ubcQS6Z0JXAMNj49DfxSVetTbug4\njpMDysrMgpLOkpTInnvaa/NmqzkF5m44YIAllUiWkn7IEHOJ2xirOBtEVibugiUlqWvSJaZwj74P\nSTNCQo5sCixo310wVUzWwIHxchSlpRarlo9yCpmSya1wGPBFYAxWePFfsFirgUAOcwE5Tvdk0ybL\nJHjzzelrQzjdm9Gj4Ykn4NBD7fXMM4VukZMhd2KJKH4O3AJMAX5X0BY5jlO0BIHQUZEVGD4cPvWp\neDxXqGNVW9tWZIlYEpc337Tlujr7m4m7ILS2/iQSMvSFihPBqlVaGs88uHKlCcNs0xlL1ogRNnaH\n7Wtqep7IGgscqqr/qqrfwETXOFX9kar+KLfNKy6yVfysJ9HT+lxfb/UaPv95+MxnOrePntbnbNFd\n+11WBv/xH3DbbTbI3Xhj23/0naW79rkXMEVVL1XVJ1X1H6p6GSa0HMdx8k5ws2tu7loCrGgMVnAZ\nTJYJd++9LZU8dFxkpSr8HS3qC/G/wQL29tuWwCMXNSeTiax0iS+ilJZaMpJUhdnzTSa3wkgspW2g\nMfaZ4xQVqvDP/2zBq6mKDTs9k9NPt6KPDz4IZ5xhGSOdbssiEfnIjiwis4CXC9gex3GKmH794u6C\nXYnvjYqsEOudTGTttRccfrjFc0XdBdPFZEFqSxakFlnvvWe1wnIRh57MXbC2NnPRVFJibow9zZJ1\nJ/CiiFwrIj8CXgDuyG2zipNijN/oSX3+yU+skvsdd3TtSVVP6nM26Qn9HjsWqqut6OQhh9j7rtAT\n+txDmQEsEJF3RWQ18CwwQ0SWicgrhW2a4zjFxrBhZllqaura/GDw4LioCFkJ26vpuN9+MHRodt0F\nw/fBTTCIntJSE3Dbt8fblW2CONRIFogdO+IxV+kI/e5OlqxMsgteJyKPYgHGAHNUdXEmOxeRsZhI\nG4kFJ/+vqv5cRIYBdwN7E0sJr6rbOtF+x8kLDz4Iv/gFPP9893pK4mSf8nJzH6yqgosuMuvl97+f\nelBy8s6p2dyZiFwFXIYldfqVqt6c8P1Q4HZgX6Ae+IKqvhr7rhL4NeauqLHvns9m+xzH6d6Ul5sY\n2LKlayJr5sz4+1A8O5VlrE+f1iIrE0vWsGGphUg0+UXUkrVzp/Uxl+nRg8tg6HO06HI6Ro+2rMHj\nx+eseR0m01uhAtgZG3jWisg+GW7XCHxdVacAs4Avicgk4GpgnqoeADwRWy56ijF+oyf0eelSuOwy\nuP9+M9F3lZ7Q51zQ0/r9iU9YscXnnjPB9e67Hd9HT+tzT0FVVwPbgcHAsPBS1dWx7zJGRA7CBNbh\nwDTgTBFJLPl6DbBIVacBFwNREXYz8DdVnQQcDLze4Q45jtPjCRaebJUD6dvXst+mok+fjmUXBDjs\nMLOAtUdIcAGtE18cf7ylbc8lUZfBlhZzFxyUYYq9igrL9twVkZtt0jZFRK4FvkVcCPUBfp/JzlV1\ng6ouib2vwQafMcBs4i6HdwDndKjVjpMn1q+Hs86yosPRJ0xOcTB6NDz6KJx9tvm/3313oVvkAIjI\nvwGvAL/AChSHV2eYCLygqvWq2gzMB85LWGcS8CSAqq4AxovICBEZAhyrqrfHvmtS1e2dbIfjOD2Y\nSZPsb8gMmA3SWaX69IkLq0zdBdOR6C4oYvvcc8/s9i0ZIbvh+vXmrj9iRPcSTR1FVBNLYCWsILIU\nOAR4WVUPiX32iqoe3KEDiYzHBq+DgDWqOjT2uQBbwnJkfU3XNsfJJbt2wXHHwTnnwHe/W+jWOIVm\n4UL47Gdh1ixzHc3UT7zYEBFUNc3UoMvHeBM4SFUbsrCvicBfgCMxV8AngBdV9arIOtcB/VX1GyIy\nE1gAzMTcA28DXsOsYC8DV6nqroRj+HjmOE7Wef99eOops+J87GMmhN59F445Jv227TF/PuwT81dr\najJvngED4JRTstPmVDz4oD3c3LTJROv48YUXWV0Z0zJp+m5V/Sipooh0OKRMRAYC92GDz87od7GR\nx0cfp1vR0mJp2idPhmuuKXRrnO7AjBnmPtivnyXGWLCg0C0qal4FUji8ZI6qvgHcCDwOPAIsBhKT\n+H+gIpgAACAASURBVN8AVIrIYuDLsXWasbjmQ4H/VtVDgVrc/d1xnDwRkmLsv3/mMVnpKC2FZcts\njGtuthiuXNTFSsbu3ZYm/vjju5/rX2fIJJT7TyJyGzbA/DPwBSzINyNEpBwTWL9T1QdiH28UkT1V\ndYOIjAY2Jdt2zpw5jI9FsFVWVjJ9+vSPsnWFWIfetLxkyRK+9rWvdZv25GM5fNZd2lNVVYUqfPrT\n1axcCS+8UIVIdvef2PdC9zdfy73l/r7tNvj3f6/mrLPgi1+s4oc/hGefTb5++Kw7tT/by9XV1cyd\nOxfgo//XeeB6YLGILAd2xz5TVZ3dmZ3F3P1uBxCR64E1Cd/vxMY+YuusAt4BBgJrVfWl2Ff30o7I\nujZS96Gqquqjc+k4jtNZRo6E2bOt5Mj27dlxFxw2zFK1g4mswYPh4A75rnWepiYrzFzIBGPV1dWt\nxvCukNJdMObKNxbzWQ+GwsdUdV5GO7ft7wA2q+rXI5//JPbZjSJyNVCpqlcnbFt07hXV1dVFN/B2\nxz7/5Cfw+9+bCb6yMvv77459zge9rd8bN1pClHXr4He/M6tnIr2tz5mQJ3fB14FfAsuJW51UVed3\ncn8jVXWTiIwDHgOOUNUdke+HAHWq2iAilwNHq+qc2HdPAZep6puxGOb+qvrthP0X3XjmOE7+WLPG\nhNHo0Sa4jjii8/tqbjaXwY0bYepUs45NnZq9tqbirrvg6KNh3Lj8HC8TujKmZSKylqnqQZ1s2DHA\nU1iAcjjQd4AXgXuAcbSTwt0HJacQ3Hkn/OAHZiYfM6bQrXG6O6rwq1+ZS+n3vgdf/WrPd2/oKnkS\nWS+p6uFZ3N9TwHDiGXGfFJErAFT1tljh47nYOLYcuDQkuBCRaZh3Rx9gJfBPickvfDxzHCeXvPee\nxWLtuSds3WqJmrrK3XfDgQdasotkDxFzQTbcHbNNzkRWbOd3ALeq6oudOUBn8UHJyTf33w9XXglP\nPhnPEuQ4mfD223DxxdC/P8yda0WNi5U8iaz/wtwEHyTuLoiqLsrlcTuLj2eO4+SS99+HlSvNkrV9\nu8UQd5X77rOHzUOHmtgqVnKd+GIW8JyIvCMiy2KvVzpzMCc12fIB7Ul0lz7/5S/wxS/CI4/kXmB1\nlz7nm97c7/32M/fSE06wGiR//KN93pv7XGAOxcam6+l6CnfHcZwejUjmdbIypbwc6uuzV/erGGk3\n8YWIjFPVNcAnMBeJbmbAc5zs8Ne/wj//s/095JBCt8bpqZSVWar/T3wCPvc5eOghuOiiQreqd6Kq\nVYVug+M4TnehJFInK1vudmVlLrK6SrvugiKyOFIX6z5V/WReG+buFU4euOsuuOoqmxB3JVDUcaLs\n2gXf+hY8/LDF+X3844VuUf7Ih7tg7DhnApOBfuEzVf1xro/bGXw8cxwnl2zaZGnXR4+GxkaYNq3r\n+5w3D2pr4dBDu1ciinyTa3dBgH07s3PH6c7cdJNNhJ94wgWWk10qKuCWW+DWW+GCC8zC1dhY6Fb1\nHmJlRc4Hvop5WZwP7F3QRjmO4xSIXLkL7t5tFi2ncxR5HqzuRTHGbxSiz01N8PWvw+23w7PP5i81\naaAYrzMUZ78HDKhmyRIrYnzssfDOO4VuUa/hKFW9GNiiqj/C4rOKODTbcZxiJuoumE2R1dICAwZk\nZ3/FSKpLcbCI7BSRncDU8D722pFiO8fptqxbZ8kJXnsNnn66uLPAOflh1CiL97vgArOY3nVXoVvU\nK6iL/d0lImOAJmDPArbHcRynYASRlc0U6OXltq+BA7Ozv2IkbQr3QuE+7E62mTfP0mxfeaXVNfJg\nTiffLF4MF15oxRZ/8Yve+YQwTyncvw/cApwA/DeWnOlXqvr9XB63s/h45jhOLtm+3Txz9tjDUq7v\nt1/X97l4MaxdC2ed1fV99WTyEZPlOD2WDz+Ef/on+MIX4Pe/h+9/3wWWUxgOOQReftmeOM6YAUuX\nFrpFPRNV/TdV3aqq92FF7Q/srgLLcRwn14jYuFJXZ/Uas0FZGQwalJ19FSs5FVkicruIbBSRZZHP\nhonIPBF5U0QeF5HKXLahJ1GMMSu57HNTE/zmN3DQQTBkiLkInnhizg6XMcV4naE4+52szwMHWsHi\na66Bk06C//5vc/Fw0iMiM0VkdGT5EuBPwL+JyLAu7vuqWB3I5SJyVZLvh4rIn0VkqYi8ICJTIt+t\nFpFXRGSxiLzYlXY4juN0lOAumE2RVVFhVjGn8+TakvVb4NSEz64G5qnqAcATsWXHyRpNTZY2e+JE\ns1w9/DD87Gf+RMbpXnz+87BgAfz61/CpT8HWrYVuUY/gNmA3gIh8HLgBuAPYAfxvZ3cqIgcBlwGH\nA9OAM0VkQsJq1wCLVHUacDFwc+Q7BapU9RBVndnZdjiO43SGkF2wrg769Uu/fiZMmJCdVPDFTE5F\nlqo+DSROHWZjgyKxv+fksg09iaqqqkI3Ie9ks8+bNsENN8D++9vE9de/hiefNLes7kQxXmcozn6n\n6/MBB8Bzz8Fee5kr4TPP5KddPZgSVd0Se38BcJuq3qeq3wP278J+JwIvqGq9qjYD84HzEtaZBDwJ\noKorgPEiMiLyfc5rgzmO4ySjpASamy3lerZEltN1ChGTNUpVN8bebwRGFaANTi+hrg7uvdcsAQce\nCG+9BX/6Ezz1FBThnN7pgfTtCzffbHW1PvUpuPZas8Y6SSkVkfLY+5OIiZ4YXanmshw4NubOXgGc\nAeyVsM5SYsJLRGZidbnCOgr8XUQWisjlXWiH4zhOhykpgfp66NMneyncna5T0BJjqqoi0m40wpw5\ncxg/fjwAlZWVTJ8+/aMnwyHWoTctL1myhK997Wvdpj35WA6fdWT7bdvgppuqWbAAFi2q4vDDYfr0\nau68E846q3v1L9lyYt8L3Z58Lfv9nXr9gQNh8eIqLrnE7ufvfAc++9nu1Z/27ue5c+cCfPT/Oofc\nBcwXkQ+BXcDTACKyP7CtsztV1TdE5EbgcaAWWAy0JKx2A3CziCwGlsXWaY59d4yqrotZtuaJyBsx\nT46PuPbaaz96X1VV9dG5dBzH6SohbXu24rGKmerq6lZjeFfIeQp3ERkPPKSqU2PLb2C+6xtiAcxP\nqurEJNsVXcrb6urqoht4M+3z22/DQw/Za+FCs1LNnm2pRUf1MFtoMV5nKM5+d6bPLS3w85/DddfB\n9dfDZZdlr+5JPsh1CncRORKrifW4qtbGPjsAGKiqi7J0jOuBNar6PynWWQVMVdWahM9/CNSo6k2R\nz4puPHMcJ380NZkXz9ixcMwxhW5N76IrY1ohRNZPgM2qeqOIXA1Uqmqb5Bc+KBU3qpbq+s9/ttfW\nrXDmmSaqTjyxd9YXcpwor70Gn/scjB4N//M/Padwdj7qZOUCERmpqptEZBzwGHCEqu6IfD8EqFPV\nhphL4NGqOifmXliqqjtFZABmDfuRqj4e2dbHM8dxckZzM9xzDxx1FOy9d6Fb07voypiWU3dBEbkL\nOA7YQ0TeA36AuVzcIyKXAquB83PZBqfnoArLlsFdd8Ef/2jVxs89F26/HWbOdD9jp7iYPBmefx5u\nvBEOPRR+/GO44gr/HeSQe0VkONAIXKmqO0TkCgBVvQ2YDMyNubgvBy6NbTcK+LOYubEM+ENUYDmO\n4+SaMC6MGVPYdjitybklq7MU45O/YnWnmjKlij/8AX77W9i2DS680F7Tp/csN6lMKcbrDMXZ72z1\n+bXX4m6Dt9ximQi7Kz3VkpVLinE8cxzH6Q10ZUzzZ6JOQVCF+fPt6fz++5tr4E9/CqtW2ZP7Qw7p\nnQLLcTrD5Mnw9NMwZw6ceipceSVs3lzoVjmO4ziO0x5uyXLySk2NFQq+5RZb/pd/gYsvhsrKwrbL\ncXoKW7fCD35gLrXf+AZ89avdK0bRLVlt8fHMcRynZ+KWLKfbs3IlfP3rFpD5xBNw663w6qs2QXSB\n5TiZM3Qo/OIXsGABLF1qluCf/xx27Sp0yxzHcRzHCbjI6kZkKy9/d6GlBebNs4yAs2ZZkbxFi+C+\n++D4480dsLf1OROKsc9QnP3OZZ8POMCsWQ8/DNXVsM8+8O//Dlu25OyQjuM4juNkiIssJ+ts3Wrx\nVRMnwr/+K5x9Nrz7rsVaeWpRx8kuhx4K999vQuvtt2HCBLj0UotzdBzHcRynMHhMlpMVmpvhH/+A\nuXPhr3+1mlZf/KLVbPAEFo6TPzZtgt/8xmprDR8Ol1wCF10EI0fm5/gek9UWH88cx3F6Jt26GHFn\n8UGp+9PSAs89B/fea69Royz72UUX2eTOcZzC0dwMTz5piWYefNDqz512Wu6P6yKrLT6eOY7j9Ew8\n8UUvoSfErKxbB7/7nWUEHDPGrFVDh8Jjj8HChfDlL3dMYPWEPmebYuwzFGe/C9nn0lI46SQTWWvX\nwnHHFawpjuM4jlN0FExkicipIvKGiLwlIt8uVDu6E0uWLCl0Ez6isRFWrIAHHoD/+A/41Kdg3DiY\nOtU+O+ooy272yiuWTnry5M4dpzv1OV8UY5+hOPvdXfo8cCBUVBS6Fd0XEblKRJaJyHIRuSrJ90NF\n5M8islREXhCRKQnfl4rIYhF5KH+t7n0U44OYjuLnKDP8PKXHz1HuKSvEQUWkFLgFOAl4H3hJRB5U\n1dcL0Z7uwrZt25J+rmquec3N9mpqslf0fXuvxkZ7NTTYq77eXrt2Wc2qmhrYts0ykm3eDBs22FPv\nDz4wUTVxor3OOw9uuMGC6rMZY9Ven3szxdhnKM5+F2OfexoichBwGXA40Ag8KiIPq+rKyGrXAItU\n9VwRORC4FRu/AlcBrwGD8tTsXkl1dTVVVVWFbka3xs9RZvh5So+fo9xTEJEFzATeVtXVACLyR+Bs\noFeJrFtvhSVLTNDs2hUXOLt32ysInyCEtm61Ir3JRBRAWZm5AJWX29/wPvp54vuyMkudXl5uf/v3\nh7597e+gQVbEdORImDQJhg2D0aNhr71gzz1tW8dxnF7OROAFVa0HEJH5wHnA/4usMwm4AUBVV4jI\neBEZoaofiMhewOnAdcA38tt0x3Ecp7tSqGn0GOC9yPJa4IgCtSVn7LWXiZuKChM1/ftDv34mcvr1\ni4uf8PrmN1dz661xcRRepaVQ0kuj51avXl3oJuSdYuwzFGe/i7HPPZDlwHUiMgyoB84AXkxYZykm\nvJ4RkZnA3sBewAfAT4H/DxictxY7juM43Z6CZBcUkU8Cp6rq5bHlzwFHqOpXIut4KibHcZweSE/L\nLigiXwCuBGqBV4Hdqvr1yPeDgJuBQ4BlmPXrcmAscJqqfklEqoB/VdWzkuzfxzPHcZweSmfHtEJZ\nst7HBqfAWMya9RE9bZB2HMdxeiaqejtwO4CIXA+sSfh+J/CFsCwiq4B3gAuA2SJyOtAPGCwid6rq\nxQnb+3jmOI5TZBTKklUGrABOBNZhrhkXFXviC8dxHCf/iMhIVd0kIuOAxzDPih2R74cAdaraICKX\nA0er6pyEfRwHfDOZJctxHMcpPgpiyVLVJhH5MjaYlQK/cYHlOI7jFIh75f9n783D7KqqvP/PqkrN\nmUMIgRADgUAYAwREBiloQEAE1FdQEYmozfuCA6/dKrZ2g7YDjT/a6XlbERuCCjQCgiCDSYDCCMiU\nBEJCCGMSCJnHSio1rt8f6+zcU7fuVFV3qKq7Ps9zn7rn3DPsvc+pe/f3rElkHJZd8ApV3SYilwOo\n6o3AIcDsyO3vZeDzaY7jboGO4zgOUCJLluM4juM4juM4zlCl6DnryrXoY3/6LSJvi8hLUb+Ts14N\nWPrZ59EicreIvCIiS0Xk+OK2vm/0tc8iclB0fcNrq4h8pfg96D39vM7fEpEl0f63i0hNcVvfN/rZ\n54z7DiRE5GYRWSsii2PrxorIXBFZLiJzRGR0mn1TFpzPdf+hQrpxKDd6ey9F3w2vRWN3ZmlaXXxE\nZF8ReTz6Xnw5/A74WCUQkdroe3VRND/4UbTexygJSZoz+xj1RFLMsfM2TqpatBdwGJaZqRZzE5wL\nTE3a5sfAv0bvDwLmJX3+NeA24P5itr2U/QbeAsaWuh9F7vOtwGXR+2HAqFL3qdB9jm1TAbwH7Fvq\nPhWyz8AULHlATbR8J3BpqftU4D5n3XcgvYCTiTLqxdZdD3wjev9N4LoU+1UCr0fXuApYBEzPdf+h\n8so0DuX26s29hLlnLorGbEo0hhWl7kORxmkvYEb0fjgWvz7dx6rHONVHf4cBfwdO8jFKOU7d5sw+\nRinHqMccO1/jVGxL1u6ij6raCYSij3GmA4+DFX0EpojIeABJFH38DTCYsjX1q98Rg6m/0I8+iwWZ\nn6yW8QtV7VDVrUVse1/Jx3UGOB14Q1VXMfDpT5+3YTEw9WLJcOqxzKMDnb72ec9ofbZ9BwyqOh/Y\nnLT6POwhCNHfC1LsurvgvKq2A6HgfK77DxUyjUNZ0ct76XzgDlVtV9W3sYnMccVoZ6lR1TWquih6\n3wy8gtUW9bGKoao7o7fV2MOMzfgYdSPNnNnHKDXJc+y8jFOxRdbLwMmRGa4eK/o4KWmbUPQR6V70\nERJFH7uK09y80d9+KzBPRJ4Xy2w1GOhPn/cD1ovILSKyQERuio4x0OnvdQ58Eri9wG3NF33us6pu\nAm7A0mWvBrao6ryitbzv9LXP+2BWrGz7DnQmqOra6P1aYEKKbVIVnN+nF/sPFTKNg5P+Xtib7mVd\nynLcRGQKZv17Bh+rbohIhYgswsbicVVdgo9RMqnmzD5GPUk1x87LOBVVZKnqMuA/gDnAw8BCegqm\n64DRIrIQ+FLYRkTOBdap6kIGmVWnH/3ujD47SVWPAs4GrhSRk4vS8H7Qzz4PA44G/ktVj8YKhF5d\npKb3mTxcZ0SkGvgIcFcx2txf+tNnEZkKXIWZ3PcGhovIxUVqep/pT59z3HfQoOY/kSp7UvI6SbVd\nhv2HCkO5b3klh3uhrMZSRIYD9wBfVavTthsfK1DVLlWdgT2k+qCInJr0eVmPUS5z5nIfoxgnZppj\n92ecip74QlVvVtWZqnoKsAXzN45/vl1VL1PVo9QKOo7H4jZOwIo+vgXcAZwmIr8tdvv7Sj/6jaqu\njv6uB+5lkJhw+9Hnd4B3VPW5aNO7MdE14OnPdY44G3ghutaDgn70eSbwlKpuVNUO4I/Y//mAp5//\nzxn3HQSsFZG9AERkIrAuxTbJBecnkXAFzWX/oULyOOxL96eg5U66eyHT/TPkEZEqTGD9TlXvi1b7\nWKUgCiV4EDgGH6M4qebMv8PHqAeq+l70Nz7Hzss4lSK74J7R38nAR0lyixKRUdHTfCKz3RPRhOVf\nVHVfVd0Pc6d6LJq8DAr62O9mEakXkRHR+gbgTMzlaMDT1z6r6hpglYhMizY9HVhSvJb3nb72ObbJ\np7AvxEFDP/r8KnC8iNSJiGDXeWlRG99H+nOds+07CLgfuDR6fylwX4ptngcOFJEp0ThcFO2X6/5D\nhUzj4KS/F+4HPiki1SKyH3AgMGgy6/aH6Lvwv4GlqvrT2Ec+VhEisodE2d5EpA44A/MK8DGKSDNn\nvgQfo25kmGPnZ5y0+Fk8/opNmBcBp0brLgcuj95/AJt8LcMsGD2yygGnMIiyC/an31h80qLo9TLw\nrVL3pRjXGjgSeA6LbfljqvtgIL762ecGYAMwotT9KGKfvxHtuxgLLq0qdX+K0Oce+w7UFyb4VwNt\nWGzR54CxwDxgOeb2ODradm/gwdi+Z0dj8Hr8eyvd/kP1lW4cyu3Vm3sp2v5fojFbBnyo1O0v4jid\nhLkQL8KEw0LgLB+rbmN0OLAgGqOXgK9H632MUo/X7jmzj1GPsUk5x87XOHkxYsdxHMdxHMdxnDxS\ndHdBx3Ecx3Ecx3GcoYyLLMdxHMdxHMdxnDziIstxHMdxHMdxHCePuMhyHMdxHMdxHMfJIy6yHMdx\nHMdxHMdx8oiLLMdxHMdxHMdxnDziIstxHMdxHMdxHCePuMhyHMdxHMdxHMfJIy6yHMdxHMdxHMdx\n8oiLLMdxHMdxHMdxnDziIstxHMdxHMdxHCePuMhynCIiIk0isklEqkvdFsdxHMfpD/6b5jjpcZHl\nOEVCRKYAxwHrgPNK2hjHcRzH6Qf+m+Y4mXGR5TjF47PAPOB3wKUlbovjOI7j9Af/TXOcDIiqlroN\njlMWiMjrwDXAs8ASYJKqrittqxzHcRyn9/hvmuNkxi1ZjlMEROQkYB/gflV9DVgKfLq0rXIcx3Gc\n3uO/aY6THRdZjlMcLgXmqOr2aPku3L3CcRzHGZz4b5rjZMHdBR2nwIhIHbAGe6jRHK2uAUYDM1T1\npVK1zXEcx3F6g/+mOU5uuCXLcQrPBUAHMB04MnpNB+ZjgcOO4ziOM1jw3zTHyQG3ZDlOgRGRh4GX\nVfXrSes/AfwMCxbuKknjHMdxHKcX+G+a4+RGwUWWiIwGfgMcCijwOeA14E7gfcDbwIWquqWgDXEc\nx3GcFIjIt4DPAF3AYuBzqtoa+/x84HvR513A11X1seizrwJfAAS4SVV/VuTmO47jOAOQYrgL/gx4\nSFWnA0cAy4CrgbmqOg14NFp2HMdxnKISFVT9InC0qh4OVAKfTNpsnqoeqapHAbOAX0f7HoYJrGMx\nl6lzRWRqcVruOI7jDGQKKrJEZBRwsqreDKCqHaq6FasMfmu02a2Yf6/jOI7jFJttQDtQLyLDgHrg\n3fgGqrojtjgc2BC9nw48o6q7VLUTeAL4WOGb7DiO4wx0Cm3J2g9YLyK3iMgCEblJRBqACaq6Ntpm\nLTChwO1wHMdxnB6o6ibgBmAlsBrYoqrzkrcTkQtE5BXgYeAr0erFwMkiMlZE6oEPA5OK03LHcRxn\nIFPQmCwRmQk8DZygqs+JyE+B7cCXVHVMbLtNqjo2aV/PyOE4jjMIUVUpdRtyJXLvewA4GdiK1fu5\nW1VvS7P9ycBvVPWgaPky4ApgB7AEaFXV/5u0j/+eOY7jDFL6+ptWaEvWO8A7qvpctHw3cDSwRkT2\nAhCRicC6VDuralm9Lr300pK3wfvsffZ+e5/78xqEzASeUtWNqtoB/BE4Id3GqjofGCYi46Llm1V1\npqqeAmwBXk2zn7+yvK655pqSt2Ggv3yMfJx8jIr76g8FFVmqugZYJSLTolWnY0/6HiBRGfxS4L5C\ntmOwMGXKlFI3oeh4n8uHcux3OfZ5ELIMOF5E6kREsN+ppfENRGRq9BkicjSAqm6MlveM/k4GPgrc\nXsS2O47jOAOUYUU4x5eB20SkGngDS+FeCfxBRD5PlMK9CO1wHMdxnG6o6osi8lvgeSw9+wLgJhG5\nPPr8RuDjwGdFpB1opnv2wbsjq1Y7cIWqbitqBxzHcZwBScFFlqq+iKW3Teb0Qp97sDF69OhSN6Ho\neJ/Lh3Lsdzn2eTCiqtcD1yetvjHL5+GzDxawaWVFY2NjqZsw4PExyg0fp+z4GBWeYtTJcnJkxowZ\npW5C0fE+lw/l2O9y7LPj9BWf9GXHxyg3fJyy42NUeAqaXbA/iIgO1LY5juM4qRERdBBlFywG/nvm\nOI4zOOnPb5pbshzHcRzHcRzHcfKIi6wBRFNTU6mbUHS8z+VDOfa7HPvsOI7jOE5xsgs6TtHYsAFe\negnWroV162D8eDj4YJg2DYYPL3XrHMdxHMdxnHLAY7KcQU97O9xzD/z+9zB/PsyYAXvtZQJr3Tp4\n9VV44w1obITPfAbOOw/q60vdascZmnhMVk/898xxHGdw0p/fNBdZzqDmySfh8sth3Dj4whfgggtg\nxIie2zU3w733mhBbtAi++U34P/8H6uqK32bHGcq4yOqJ/545juMMTjzxxRChHOM3+trnlhYTVxde\nCP/2b9DUBJdcklpggbkKXnIJ/OUv8OijZvGaNg1++1so9tynHK8zlGe/y7HPjuM4juMUQWSJyNsi\n8pKILBSRZ6N1Y0VkrogsF5E5IuIVO52cWbcOTjsNtm6FpUtNaEkvnjEcdphZte66C376UzjjDHj9\n9cK113GcgY2IfEtElojIYhG5XURqkj4/X0RejH7HXhCR03Ld13EcpxisX1/qFjjJFNxdUETeAo5R\n1U2xddcDG1T1ehH5JjBGVa9O2s/dK5wevPIKfPjDFlv13e/2TlyloqMDfvYz+NGP4Nvfhq9+FSrc\nvus4fWawuQuKyBTgMWC6qraKyJ3AQ6p6a2ybBlXdEb0/HLhXVQ/IZd9oH/89cxynYHR1wZ132vxo\n5MhSt2ZoMRjcBZMbdx4QfoRuBS4oUjucQczSpWbBuuYa+N73+i+wAIYNg3/6J3jmGbj7brNqrVzZ\n/+M6jjNo2Aa0A/UiMgyoB96NbxAEVsRwYEOu+zqO4xSa8AznrbdK2w6nO8UQWQrME5HnReSL0boJ\nqro2er8WmFCEdgx4yjF+I9c+v/46nHkm/PjHcOml+W/H1Knw17/C6afDzJnwhz/k/xyBcrzOUJ79\nLsc+DzYiL4sbgJXAamCLqs5L3k5ELhCRV4CHga/0Zl/HcZxC0tVlf9etK207nO4Uo07Wiar6noiM\nB+aKyLL4h6qqIpLSj2LWrFlMmTIFgNGjRzNjxgwaGxuBxORlKC0vWrRoQLWnGMuBTNuvXAknndTE\nxRfDZz5T2PZ861uNnHEGnH9+E7fcAnfd1cjw4QNnvAbzst/fpW9PIZabmpqYPXs2wO7v68GEiEwF\nrgKmAFuBu0TkYlW9Lb6dqt4H3CciJwO/Aw7KdV+Aa6+9dvf7xsbG3WPpOI7TX4LIamkpbTuGAk1N\nTT1+w/tKUVO4i8g1QDPwRaBRVdeIyETgcVU9OGlb92F32LYNTjzRrFf//M/FO29zM3zlK/C3v8Ht\nt5t1y3Gc7JQyJktE6oF9VfXVXuxzEXCGqn4hWr4EOF5Vr8ywzxvA+4F/yGVf/z1zHKeQtLTAQw9Z\nnHlvk4E5mRmwMVkiUi8iI6L3DcCZwGLgfiA4fV0K3FfIdjiDk44OuOgiOPlki5sqJsOHw8035E9i\nKQAAIABJREFUw/e/D+ecA9ddB52dxW2D4zi5IyLnAQuBv0TLR4nI/Tnsugw4XkTqRESA04GlScee\nGn2GiBwNoKobgFez7es4jlNoVC3GvKoKWltL3RonUOiYrAnAfBFZBDwD/FlV5wDXAWeIyHLgtGi5\n7MmXeXIwkanPV11lJvCf/7x0T2UuvBCef96eEJ1+en6SYpTjdYby7Hc59rmEXItZlzYDqOpCYP9s\nO6nqi8BvgeeBl6LVN4nI5SJyebT8cWCxiCwEfgZ8Mtp3UYp9f52PzjiO4+RKV5dlRq6ry5/L4KpV\n8GrOPgFOKgoak6WqbwEzUqzfhD3xc5yU/OpX8Pjj8NRT9nSmlEyebG358Y/NbfCnP4VPf7q0bXIc\npwftqrpFuj+R6cplR1W9Hrg+afWNWT7P+pnjOE4xCCKrvh527oQxY/p/zK1bYeNGOOig/h+rXClq\nTFZvcB/28mX+fPhf/wuefBIOOKDUrenOggVw8cVwxBHwX/8F48aVukWOM7AoVUyWiNwMPApcDXwM\nywBYpar/u9htScZ/zxzHKSRbtsDTT9ucZOzY/MydFiyAd9+Fj3yk/8cazAzYmCzH6S3vvGNxWLfe\nOvAEFsDRR9sXzz77mNB68MFSt8hxnIgvA4cCrcAdWA2rq0raIsdxnCIQLFk1NdDWlp9jdnTAjh0e\nj94fXGQNIMoxfiPe51274GMfg69+Fc46q3RtykZdHfznf8Jtt8GXvwyXXWZPkXKlHK8zlGe/y7HP\npUJVd6jqv6jqzOj1bVXdVep2OY7jFBpVi12vqEikc+8vbW123G3b8nO8csRFljMgUIUrroApU+Ab\n3yh1a3KjsRFeeglqa82q9cgjpW6R45QvIvJ4itdjpW6X4zhOoQmWLBGbT+WD9naorLQYL6dvZI3J\nEpHDVXVxkdoTP6/7sJcRv/ylxTg9/bSlTx9sPPoofP7zloHwhhtg1KhSt8hxSkMJY7Li1exqsYyA\nHar69WK3JRn/PXMcp5CsXQtLlsBee5k4OvLI/h9zzhzzMJoxwxKAlSuFjsn6pYg8JyJXiIhPHZ28\n8+STcO21cO+9g1NgAfzDP5hVq7LSrFrz5pW6RY5TXqjq87HX31T1/wKNpW6X4zhOoenqyr+7YHu7\nhUd0dOTneOVIVpGlqicBFwOTgQUicoeInFnwlpUh5Ri/cdddTVx4Idxyy8BMdNEbRo6EG2+Em26y\nOK0vfcmCRpMpx+sM5dnvcuxzqRCRsbHXHiJyFjCy1O1yHMcpNKomsPItsmprPfFFf8gpJktVlwPf\nAb4JnAL8TEReFZGPZ9tXRCpFZKGIPBAtjxWRuSKyXETmiMjo/nTAGbzs3Anf+Y4lujjnnFK3Jn+c\neSa8+KLVmDjqKHj22VK3yHHKggXAC9HraeCfgM/nurOIfEtElojIYhG5XURqkj4/X0RejH7PXhCR\n06L1B0XrwmuriHwlj/1yHMfJSIjJckvWwCKXmKwjgVnAucBc4DequkBE9gb+rqoZPTVF5GvAMcAI\nVT1PRK4HNqjq9SLyTWCMql6dYj/3YR/CqFpB38pK+N3vzMw9FLn7brjySrjqKkvoUVlZ6hY5TmEp\nVUxWfxCRKcBjwHRVbRWRO4GHVPXW2DYNqrojen84cK+qHpB0nArgXeA4VV0VW++/Z47jFIyVK2HV\nKovJ2rgRjjuuf8d75x3429/g0ENtfnbYYflpZzZeew0mTTJxN1Doz2/asBy2+Tnw38C3VXV3jhFV\nXS0i38nSsEnAOcAPgK9Fq8/DrGEAtwJNWPFIp4z44Q/h9dfhr38dugILrKjy+98Pl1xiQaS33w4T\nJ5a6VY4zdIg8KtIqGFX9Yw6H2Qa0A/Ui0gnUY2Ipfpy48+9wYEOK45wOvBEXWI7jOIUmnync33sP\n5s+395WVZtEqFs8/D+vWwYknFu+chSQXd8EPA7cFgRW5/zUAqOpvs+z7E+DrQPyST1DVtdH7tcCE\n3jV56FIu8Rt33AG//jXcfz8880xTqZtTcPbd17IPNjbCMcfAT37SVOomlYRyub/jlGOfS8BHsryy\noqqbgBuAlcBqYIuq9khfIyIXiMgrwMNAKpfATwK396EPjuM4vWbbNptTdXTkz13wrbdgWGSCGTbM\nQjvefTfzPvlk5cqhEweWiyVrHvZ0rjlargf+ApyQaScRORdYp6oLRaQx1TaqqiLiPhRlxPz5FoP1\n6KNm0Xn11VK3qDhUVsI118AJJ8BFF0FrK3zzm0Pbiuc4xUBVZ/X3GCIyFbgKmAJsBe4SkYtV9bak\nc90H3CciJwO/Aw6KHaMaE3Xf7G97HMdxciEk11q+HMaNy4/I6ugw98B162zusm4dNDfDPvv0v73Z\naGgwV8Enn4TDD4cxYwp/zkKSi8iqVdUgsFDV7SJSn8N+JwDnicg5WM2SkSLyO2CtiOylqmtEZCKw\nLt0BZs2axZQpUwAYPXo0M2bMoLGxEUg8IR5qy4GB0p58Lq9cCd/4RiO//z1s3NhEU9PAal8xls84\no5HFixs57bQm5s6FP/+5kbq6gdM+v799ubfLTU1NzJ49G2D393WpiB7uHYL95gCgqt/LYdeZwFOq\nujE6zh+x37DbUm2sqvNFZJiIjAv7AGcDL6jq+lT7XHvttbvfNzY27h5Lx3GGBl1d8PDDcMYZUF1d\nnHO2tdnf7dth/Pj8iKz2dhNs06fDihVWK6u2Nvt++aCzE44/3qxZ8+fbw/hjjy3OuQNNTU095ix9\nJZfEF08CX1HVF6LlmcAvVPUDOZ9E5BTgn1X1I1Hii42q+h8icjUw2hNfDH1WrYKTToLvfhdmzSp1\na0pPSwt84Qv29OlPf4K99y51ixwnP5SwGPGNQB1wGnAT8AngGVXNmmEwSvB0G3AssAuYDTyrqv8v\nts1U4M3IA+No4C5VnRr7/H+Ah+PJMmKf+e+Z4wxxliyxepnnnAOjilRV9rXXYMECE1YHHWSJL157\nDU45Jfu+6Zgzx0Ibxo2zBBjz51t/jjvO/lZV5a/9ydxzD3zkIyZSOzrgj3+02PaKnHKhF4ZCFyO+\nCviDiPxNRP4G3Al8uQ/nCr8w1wFniMhy7Mfwuj4ca0iSL+U80NiwwdKaf/WrPQXWUO1zJpqamqir\ng9//Hi64wFwIX3ml1K0qPOV6rZ2icYKqfhbYpKrfBY4n5s6XCVV9Efgt8DzwUrT6JhG5XEQuj5Y/\nDiwWkYXAz7D4K8AyD2Ju9bkk2XAcZwjyzjv2N1/xRK2tFm+VbZv6yLdMxNz7sp1/wQLYsiX95x0d\n3WOywI45d669CklIRR/O3dBgroq5sHMnvPHGwIrnyuouqKrPich07MdKgVdVtVe5RlT1CeCJ6P0m\n7MfIKQO2boWzz4aPfhS+9rXs25cTIvDtb1u60lNPtSc2J2SMdHQcJwMt0d+dIrIPsBHYK9edVfV6\n4Pqk1Tdm+Tx8tgPYo1etdRxnyNDWZi57Y8f2z13v6adh6lTYc09Ys8bWdXamL//S1mYxTM3NJk5E\nLNNgJjZutOOPTlOlNi6ywnm7umD4cJvTtbQULsV6cl9HjTJBODKHsvLvvZeoSzp1auZti0WuBriZ\nwBFYvatPichnC9ek8mWo+ehv2wZnnWUpzH/wg9TbDLU+50Jyny+9FGbPhvPPNzP9UMWvtVNg/iwi\nY4AfYwWJ3wayPAd2HMfpPxs3msAaNqx/ImvHjkQyi5DRL8RdxVG1dOfr1ycsWRU5Zhdsb89cYDiV\nyOrsNAE3blzhMg12ddk54gnBRo/ObHVL3h8S4zcQyGrJEpHfA/sDi4C4ES5b+nanjGluNr/kGTPg\nF7/wLHrZOOssuPde+NjHTHCdc06pW+Q4g4tYgot7RORBLGlTjj/PjuM4fae11aw7ra39c1dra0vU\npdq82f62t/e0HL3+usVeAUyICiHlKrI6OjLXvkrlLtjVZf3abz9YtMjak+9sg3FXwUB9vQnJXOjs\nhJqagSWycrFkHQOcqKpXqOqXw6vQDStHhkr8xrZt5iI4fTr8v/+XWWANlT73hnR9Pukkqx02axY8\n8EBRm1QU/Fo7hUREXhKRfxGRqaq6ywWW4zjFIgiTXGKi0rFjh4mstjazVO3YYe5yqQTRqlUJkROP\nyeqvJUvV9g8WrLglq7MTJk+2pBhvvNH7/mUjlcgaNiyz1a2ry9wXQxtHjhx8IutlYGKhG+IMDTZt\ngtNPt/oGN95Y2owwg5Hjj4cHH4TPfx7m9SiF6jhOBs7DvC3+ICLPi8g/i8jkUjfKcZyhT4gl6m0K\n9c5OS5H+5pv2kDVYslpaLMNeXV1Pd8H2dptrHRSl9emtu2BHR3rhErdiQU9LVmWlibt16/KfYCJV\n7Fk20bp6tblNhjYORpE1HlgqInNE5IHodX+hG1aODPb4jXXr4LTT4OSTzYKVi8Aa7H3uC9n6fOyx\nlsb005+GZ54pTpuKgV9rp5Co6tuq+h+qegzwKSyO+K0SN8txnDIgiJOKChNBmdzx4rz5poUKLFtm\ny52dJqqamy3RRHV1z2Nt3myxSmPH2nJcZGVLfNHZaWIkk8iKC534+44OO0d1tSXOWLzYxF6+iFvQ\nAtksWXH3ys5Oy0a4a1f/a4Xli1yKEV8b/VVAYu8dZzcrVlgBvk9+0mpheQxW/zj5ZLjlFkuGMW+e\nVV93HCczIjIFuAi4ELNqfaOU7XEcZ2iybp3F/6ia4OnsTLgLvvWWJcI47rju+2zbZpapeJ2p1lZL\nJjFmjGXuAxMNQWQF0RZnyxY7Z1UVzJxp20FulqxwrHQisLOze/vioie4I4LNSf7yF8vod/bZ6c8X\n6Ogw8RPamu7cqdwFM1my2tsTn3d1Wdvr6swS2NCQvV2FJqutQVWbsCxNVdH7Z4GFBW1VmTJY4zeW\nLjVRcOWV8L3v9U5gDdY+94dc+/zhD8MNN9jf994rbJuKgV9rp5CIyDPAvdjv2idU9ThVvaHEzXKc\nvNLaWuoWOOvWwd/+ZiLjscfgz382gRQsWbt2Ja7TqlXw0EMWBjBnjhUrjtPSYskkjj0WRoywdW1t\ndrwRI0w0BHfBLVss4cXWrYlixwcemHDp643IiluH4oKrvb2nsDrgABOU8fVjx5q4ytVi9M47ljDj\nnXfS75NKZFVWZrZkxePLwv61tXYNBgJZRZaI/CNwF4l6IZOwHzLH4e9/NxfBH/zAig07+eXii+EL\nX7AK6APJz9hxBiCXqupRqvojVX2z1I1xBhY7d9rkeDDT3AyPPlrqVjgLF5oouvBCywTc3m4CqLLS\nXnEXti1bYPx4i7c+/XSLIYoTrzlVV5ewXK1eDRMnJtwFu7rgqafsofZbb3WvcRXEVXLii7fe6unO\nFwRJ+NvWZrFgwcUwWOTiHHtswkoXZ8QIm5dkq8sVztfeDs89Z/XEAJYvh/nzE9ukcxfMZMlqa0v0\nJexfW5tIhlFqconJuhI4CdgGoKrLgT2z7SQitSLyjIgsEpGlIvKjaP1YEZkrIsujOK805dDKj8EW\nv/Hggzb5/+//hksu6dsxBluf80Fv+/yd78Chh8JnPjOwKpn3Fr/WTiFR1WV93VdEviUiS0RksYjc\nLiI1SZ+fLyIvishCEXlBRE6LfTZaRO4WkVei37rj+9OPcmXXrtzjWPrCW29ZgHyqmkPJdHQkJoID\niWz1jZzC8t57sHKl/Q5PmmTramtNRLW0JCxZcZHV3m7JGMaNM2GkahawcB137eoushoazK2wvd1c\nCKuq7LxPP23HOeccS3gxZkz3toWkG0FkbdxohXmXLjVxHh4wtLebGAvta2629gbLW3Lii0BFReqk\nFDU19gAjGx0dicyJLS02Di+8YJatQKrsgtksWfEkHsGSVVc3iCxZQKuq7jZQi8gwcojJUtVdwKmq\nOgMLQD5VRE4Crgbmquo04NFo2RlkzJ5tGfAeeMDc2ZzCIQK//rU9kfq3fyt1axxnaBHFcX0ROFpV\nDwcqgU8mbTZPVY9U1aOAWcCvY5/9DHhIVadjv3WvFLrNcTZvhiVLCitQisHixfD224U7/rp15mZ1\nzz3Zn3KvXg0vvli4tvSVkLTASY2qCYtC3Efr1sETT1gyqn337R4WUVWVyIxXWWntiIuseIzTqaea\nMHszsrUnW7IaGkw07LOPnaOqyn77hw2z+Kthw+DII3sKoSDwKirs/Fu2mBBcs8bu5UWLEu2pqUkI\nk+AhE/72RmSFNi9ZkltGw5aWRMr1XbtsHOLukKmyC/bGkhX2H1TugsATIvJtoF5EzsBcB3Oq4qOq\nQd9WYz9cm7E0u7dG628FLuhVi4cwgyF+Q9VcA6+9FpqazATeHwZDn/NNX/pcUwN33QW//71lIhqM\n+LV2BijbgHbsN24YUA+8G99AVePOusOBDQAiMgo4WVVvjrbrUNWtRWk1Nvl6/HGL89jai7Pu3Nn9\nCfJAoDcZ2XqLqj3ZDymvs1mDQk2g3lAMC1NXl4usTGzbZvWbChHDvHmzue91dNjfONXV9jcIHUgI\nh7a2xOdgLnbTp1s7VRNiAyxLYIjLSi70e9xxie1SEc4tYvdIa6slmZg82axvW7aYkFq/3s7TW5EV\nBGQy++1nQu7ll9O3LRw3PNzYtcvO19Bg/Q1W41TugrnGZIX6XoMuJguzNK0HFgOXAw8B38nl4CJS\nISKLgLXA46q6BJigqmujTdYCE3rdaqckdHbCFVfA3Xeb6frgg0vdovJizz1t7C+/HF4p6rNyxxn4\niEiDiPyriNwULR8oIudm209VNwE3ACuB1cAWVe1RpU5ELhCRV4CHga9Eq/cD1ovILSKyQERuEpH6\nfPUpGzt3mqvSnntmnnz/7W+wdm1iecMGeO21wrevN2Sq3dNfdu2yydrRR1vCgGxCpbdipqvLkh9s\n29a39q1caW5d2ejocJGViSCMC1Gfs73dkj2ccoq5/sUJlqp43FI6SxbY/+yuXfDuuybAQnunToUj\njoD994cJ0cz4fe+DCy7InlBs2LDuMVm7dtnD2UMOsWPsv78l4Ahzh7jIqqxMiKzW1u6iMJBOZB14\nIJx5prnjvvtuz88D8f/tlhb77koWWakSX4TldPd93IoVt2Rt2AAPP5zfFPN9IWsKd1XtxFwjfp1t\n2xT7dgEzoqd9fxGRU5M+VxFJ63o4a9YspkyZAsDo0aOZMWPG7hiH8IR4qC0HBkp7wvIjjzTx/e9D\nXV0jTzwBCxY08eqrA6d9g2m5sbGxX/v/x3/Ahz7UxK9+BeecU/r+9GY5MFDa48v9X25qamL27NkA\nu7+vS8QtwAvACdHyauBu4M+ZdhKRqcBVwBRgK3CXiFysqrfFt1PV+4D7RORk4HfAQdhv6NHAl1T1\nORH5KfZgsodj77XXXrv7ffge6C8hSD3b5Hv7dpt0hInbQIztCYHxhSAezJ+tuCkkCq/mwrvvmuWk\npcXGeeTI7p9v2pSoZ5SO1attu0MOyd6ubEkGVFMnLygH4skb8k1Hh4mWvffu+Vl1JEpCXBQk7qH2\n9p6iRcREz5NPmgtgIFyz978/sa6iIuFOmIm4Fa2iwkTWmDFmzToh+kY86CBLPFFZmRA2O3fCHnsk\nRNbOnYm6W3HCsVNRWwtHHWXWuWQLXCB+TVpa7Hz19SZA333XhGCqmCxI/M+m+izuahgXWZs32/r1\n67P//yXT1NTUY87SV0Sz/MeKSKpijqqq+/fqRCL/CrQAXwAaVXWNiEzELFw9bCIiotna5hSHDRss\nwcWBB8JvfpP6KYdTXC6/3J6a3n671yRzBhYigqoW/a4UkRdU9RgRWRjFTiEiL6rqkVn2uwg4Q1W/\nEC1fAhyvqldm2OcN4DigCnhaVfeL1p8EXK2q5yZtX5Dfs9dft8lES4s9BY9PcFassED7UaPMxbi+\n3gTAjBlmOXnjDQuizxctLTa56ev30Zw51tb4BDNXkl2yktm61Sa055wDc+fahHCPPdJv/+qrNn5n\nnpn5vFu3WgrvcePs+/jAAxMuiYE77oCzzuqZqCDOgw9aAoJjjzUrx7BhcN998KlPdd9uxQrL6HvR\nRT2P0dxs1/T1121i/aEPZW77UGT9eqsrue++cNJJ/TtWcG8L7nvPPWfX8IADem771lt2Xc4919rw\nzDO2/qMftfvt1FNT14dKJyr6wsqV9hAlhBaMHWtuialEoSrceafVNX3kEYvd2rABGhvt/2Tffc3N\nMM4TT1h7Tz215/HAxuqhh+BjH0v9HfDkk9bGqiobx1Gj7Ptov/0S/5MtLTZ+yd8B995rQvO003r+\nn//pTyYozz3XLPbHHmvHXrnSruGWLbauP/TnNy2Xy3ts7HUyFuR7W8Y9rFF7hMyBIlIHnIHV17of\nuDTa7FLgvt43e2iSL+WcT958056CnHoq3Hpr/gXWQOxzoclHn3/6U3Mv+XWv7culw6+1U2Bao98a\nYLeFKpeqQsuA40WkTkQEOB3o5rwlIlOjzxCRowFUdaOqrgFWici0aNPTgSX970puhCe3FRU9LVkv\nv2xxs3feaS5AmzaZxeW11/JvNdq61YTC88/nls45Ff2xrj3ySOZkFvE4k1RjlUyulqwVK8wi8cEP\nwrRpPTMShvOsWJH+GCHb2qGHWpzcvHkWZxfaHSdT4ounn7btDzts4MSjFJt8WrL++lcTDeGYqdz+\nAmFeFLcmhX1SWbIC+RJYYKKopiZx3JaWxHIyItamEBs1cWJ3y1Yqy1lFRWp3wUBdnR0zXVbOcC+P\nHNndXbCqysThtm2ZRefmzSbAkmlrs3N3dHRPPrLffia2ehOrWgiyXmJV3RB7vaOqPwVyySc3EXgs\nisl6BnhAVR8FrgPOEJHlwGnRsjMAee45exp01VXwwx+6xWQgUVdnT6u+8x2r2eE4DtcCjwCTROR2\n4DHgm9l2UtUXgd8CzwOhVOhNInK5iFweLX8cWCwiC7EHjfHsg18GbhORF7Hsgj/MQ19yIkz8gnDo\n6jJrxvbtNoHaudPWVVebVWvaNFufb5G1YoVNarZutSf6faE/bQpCJR3xrGX5FFnvvZewHg4f3rOW\nYThPpvpczc1mLTnsMBNrZ58Nhx9u1yw53XxoU3L7n3/exu/oo82S1tFRnkWLu7psntIfkRXGLdR/\nCnF26RJCQHeRFb/PQir3YrtuimQWWWD33KZNNmZjx9r2nZ3p3QXTxWTFmTjRrHqpCCJr9Gg7R3Nz\n4jwjRthyquyCkLgmyf9HwTW2tjbhMh3ff+RI+07asiVzuwtJ1ksvIseQSNleAczEMgVmRFUXY77q\nyes3YU/7nCTy4aOfL/78Z/jc56wG1nnnFe48A6nPxSJffZ42DX7+c3MdeeGFhFvDQMWvtVNIVHWO\niCwAQs7Tr6jqhhz3vR64Pmn1jVk+D5+9iHl6FJ3OThNZIWZh0SKb5NTUwF572aR7zhxbPusss5Rs\n3pwoeKqan4dnLS3m5jZjhlm0Nm7smRwgG/1JfNHZmVlUxCdvqWKy3nrL3ItOOcWWc018sWNH4nt3\n+PCeT/HDeTK1rbW1+2S4ttbctV5+2Sbp8QlvOF7cWtjRYR4n8eQIo0fbxDLE4JULXV0maML1662l\nqKvLCvN+4hM2ruPH2//LqFGZLVlhfTwmq7bWRExFRWEScWSioqLnfZXM8OGWDKehwe6bESNMkMTr\ndsUJqekzccghZlXeb7+esYmdnSZG6+psvLZts3OHtqxebfd6KpEVzrt6taWv37LFXANHjbJXiEtN\njtuqrbXvwWefze76WyhyufQ3xF4/Ao4BLixko5zS8qtfwRe/aEKrkALL6T+f+hScfDJ8+culbonj\nlAYROUZEjo7c+CYD70WvycG1b6iSbMnauNHiGfbeG445xiY0I0cmJk0htXEQM/lKfhFcjCorLYA9\nnskwV/pqyQoT6lxFVrIla8sWE6erVyeOkYslK0zqwkR2xIieFrWQgCJTAeR0k+FcLVltbbZ/3CVt\n5MjUmQ7vvhuWL8/cr8GMaqJm1f33527RCuPZ2ZmwiHR0WNbOkEAhmyUriKkwye/oSFgYS0U6UQgm\nbNatSwidkSMt82BdXWpRmM1dEEwkHXGEeUEl09GRcCkcPtzGMtyzIcNguuQWob0jR1oM5Ouvm3ga\nORJOPLG7yEpu45FHltaqm4u7YKOqnhq9zlDVL6rqq8VoXLlR6viNri64+mr4yU/sKUFfApB7S6n7\nXAry3eef/9xcdG7LGilZWvxaOwXihqTX/xe9wvKQJR6T1dlpT6InTDALVqipM3Jk4n0QWfH00vkg\n/vS7qqr3xw1CqS8T0lysRckia9kyi1cDWLXK4qrGjEm4++VS9Dc5dkXExj5eoynuyhSexq9a1d2l\nKh8iKznmp7Y29Xi0t1tSj1zYutWO3ZeaYWB9XL269/v1h2DJCkkrcplchyQQ8QydYcI+dmxCrGay\nZNXUmCCDxH32wQ9aIpRS0NKS3UI9fLhZ2oLImj7d2p4uYUguIgvMirVpU897pqPDhNHo0SbG4hba\nhga7Vtu2pRdZtbUmqEaPNpfoSZMsWcbIkYmCxamslzU1pRVZubgL/hMJd8Hdq6O/qqr/mfdWOUVn\n1y5zD1y1Cp56qveuHk7paGiwDFZnnmnFoadOLXWLHKd4qGpjqdtQKkK8R0WFPQmuru45Edx778Rk\nPUw4gpgJYmjVKnuaPHp039rR0pIQHMOGZY6PSkWuljVVi0E9OmafzEVkdXR0F1kbYk6kra02UWto\nMOE0dmxulqwQuB9nwgQLzt8/yr0cJv1VVXYNtm+HxYtNwFRXWzxXvBhtnP6IrJqa1AkIamos9iUX\nN9GmJuvjYYfZ2GVLL5/M6tU2rqmy20GiD83NNmb5+N0K4x3Eci5iv7k5sW0Qwm1t1ue6usS9nElk\nVVYmsu6FSf64cZbBslT1RIPoS8e4cfZgYfx4Wx471uYP6aiszM21uLLSXPgWLTIhd8ABiYLChx5q\n9+CaNd3/1ysqLJZw2bL047XHHrZdSBAT2g12zdvbU1uygmUzn5kce0Mu4XjHYP7m92OsYKOCAAAg\nAElEQVTi6lzgOWAIG51LQ6niN9avh/PPt+w08+Zlriqeb8oxZqUQfT7qKEuC8elPmxUyk5tAqfBr\n7RSSKLPgFcBJ2IPB+cAvVXVI5Vrr6IC33za3vOCOVllp3+OjRvXcfuLExPuqqkShUpHEJHT58sST\n4t4SLFBhol9V1XuLVHu7Tb6y7dfWZpaYadMSKbF7a8mqrLSn6Dt32pPz4G4X1oU+5SKykhMEjBxp\n1yb5vEH0zJ1r43PCCRZHu88+1u5U6b37K7LWrk1MWrdts+tdVWXny6WO1vDhiTFK1b5sbN+eub7T\n0qU2Wd6yxcRNNpEVklokJzeIo9rdPTOTm2YgJEXo7EyIrNZWG6v6ehNZr7xi/zO5JLAIYiRM6Psy\ndvkg/n+fipEjLU4zV3qTWXqPPRIie+FCs5LF3S3Hju15DQ891KxgqcbrE59IbF9XZ/PVuOCrqbHr\npJpaSFVX2zXNpd5YvslFZO0LHK2q2wFE5BrgIVW9uKAtc4rCsmXw4Q9bvYR///fSKH0nP3zlKxbk\n/m//Bj/6Ualb4zhF57fANuDn2APBT2NFgz9Rykblmw0bLOZhy5bulqxdu3JLflNba0/66+oSk9Aw\n0U1nVQkkW0BeeMGehsfrY4WnytlYssQmzIcfnij0un27naOry2oNvf/93Sdj4bjr1vVeZMVTuIfj\nrF9v+1VX24Q6WECCiMn09Lu5uacla/jwhGUknLeiImFBAhunyZOtfyGWrLfuggsX2pP/8eMTIjF5\n33XrTMSMG2dxLCNHJkRsLiIrbmnqy+R0+/bMtcHWrrWHAsHKkY2mJttn5Eibs6Siq8vEUdwilYp3\n3zVr4sEHJ1J8x9vQ2mrjU1Njx3jxRVufi8iqqCh9IehzzumZeKK/9MYid/jhZgENKd3nzOkujvfd\n115xKirSC9Lk8Uy2qI0bZw+K0v2vhutYCpGVy5R6TyD+ldkerXPyTLHjN+bONb/h73wHfvCD0gis\ncoxZKVSfReCWW6ye2WOPFeQU/cKvtVNgDlXVz6vq46r6WFRc+NBSNyobq1YlLCi50Nxsk4o1axKT\n5ZAuOpfJXW2tiYxQW2bnTtt/8mSbqKQTSK2tlkwgzsaNFnsTt+iEmJhMvPiiuc299potd3TY5Djs\nu2iRpYVPHpe4OAqEc2WqDZUck9XebuOQyZIV9kvHhg32RD5OEK5hv3DeeP2gsWPtu7q21s7XF5G1\neXPieMHqEidMKgHmzzerZ1tbQszmImqCQAyptXtDtjIBXV0WtzNzJhx3XG5t2bgRzjgjs5gO7oKB\ndCIr3HsrVybupRCHBQmRFa5Tb2q/VVaWXmSNGpX/kju9yZJYVdU9qcX+++cWz9VX9tjDHipkElml\nisvKZch+CzwrIteKyHexmle3FrZZTqH5r/+CSy6xWkuf+1ypW+Pkiz33hNmz4dJLu8cdOE4ZsEBE\nPhAWROR44IUSticjoYbVc8+lry2Tih07zM2svd0m2sFdMNd6PCHuqq7OJh4vv2zrDjzQXPGeeir1\nfs3Nifo2gV27bHITj+ENiS82bUqfye6998xlLoiD4ErU2WluZCtX2uRs3bru32Nh0h4XX52dJpC2\nbjULxYoVPSfFySILEkkNgsipq0sItXT1qAIhk2M8LgRsYtvQkBijuLtgc7N9PweXzPp6E5vr12cW\nWVu2WFrsUBMojEMYi1SWrLA8caJZFI46KuEmWFOTm2jq7EyIxlzS2cfZvr27O2oyS5aYlStefiAT\nGzeacNhjD9s23XGDu2AglchqabH/oZkzzSK4fbtdx3jiiyCyoPfWj/p6c31zEhxyiAn9QlFbm9mC\nPqBFlqr+APgcsBnYBMxS1aIVWywnihG/0d4OX/oS/OIX8OSTibogpaIcY1YK3eczzzT3z1mzevcE\nrtD4tXYKzEzgSRFZISJvA08BM0VksYi8lHnX4rJ1KzzwgLnRqPYu5Xlzs7nVTJjQ3V0QchNZQRDV\n19sEftUqi5kYMwY+/nFbF9JWxwnCJnymmkgKsMceie2CNWrDBnNXS8XOnSbswsSnrc0m3OPHm/A5\n4QQTWa+/bjWgAqlitzo7zTWqsdGyrD79dM/zJsdkQU+RFZJTQHZL1qZNds5Usa/Dhycy0iVbsuKp\n1uvrTUzG48vi1NXZffL883ZNQoprsP63tVms0NKlqWOywETWtGm2HKw8uVgawxgEgdFbS1Zra8Ji\nmoyqtTlkscsmsjo6TIyGos8h3Xe6NqcTWYsXw0svmQV4r73seB/4gLUj3FNhXOLWwbo6Ewmn51jd\ntaKidFkFByo1NblZLPvDGWfYA4V05y+VyMrVqFkPbFfVm0VkvIjsp6pZn72JyL6YJWxPLBD516r6\ncxEZC9wJvA94G7hQVUtYk7k82LgRLrzQbri//z11kLQzNPjBD8wV9Cc/ga99rdStcZyi0Isw7p6I\nyLeAzwBdwGLgc6raGvv8fOB70eddwNdV9bHos7exeLBOoF1VM04pXn3VJm4jRpi4mTMncx2enTtt\nwj1xoj2Fb2iwieKKFd1FVi4uOcHFrabGfhPCscCOc8ABNqkdN84mLcHtaOdOe79pk8VTBPdE1e4i\nKyS+aGlJnWUwWCJGjEhk/QrWsHgGu7feMvEZjxGLuznGj1dZafuffbaJu0WLrC87d8KUKaktWaNG\n2VgG60d1dUIUxGOyUrFjR/r4kalTYcECs4pVViay1G3b1j17Yyi8etRRqd2cxo9PXNv3vc8sXnE3\nxPb2hKtbstgL+8VjxkItp1xFVrBkhfe9IdRESmVJCvdNuK7ZYrI2bbJrFO6NUPQ52VUTEjF0QbjF\nz9/SYklJxo2zBAuQuO/D9uFej1uy3vc+G0fPuDywGTXKYsFSMaAtWSJyLfAN4OpoVTXw+xyP3w78\nX1U9FDgeuFJEpkfHmquq04BHY8cuawoZv7FkiQURH3OMPUEdKAKrHGNWitHn6mr4n/+B666zaucD\nAb/WTiFR1beBrcBIYGx4qerb0WdpEZEpwBexJE+HA5XAJ5M2m6eqR6rqUcAs4Nfx0wONqnpUNoEF\nNkEeNcoEwKhR5kaWzuoDFmM5f74Jre3bE5YsSLgLhvfZGD064aq3c2fPCfr++5u4WbHCLEmBnTtt\n4h8sWS0tJhTOO6+7S1Vw20knskK695Dtrq3N3PwmTeq+XXBrix8jk8gCa8/kyeautXy5Hffpp3um\ncA/tjCdmiMdAJVuykj0CQt9TMWkSHHusjd9LLyWyGSanAK+vN6GQLo5ExDxNPvhBuz82bOherDXu\nJpjqutfUdBdZIeYtuGVmIy6yeusu2NGR6HMy8ZpqkN2StXOnCfIggEaNMpfWTZt6bhuyy4UMf+3t\nJuCDlaqhwZaDuArEi9lCd5E1ebILrMHOwQebRbcU5BKT9VHgfGAHgKq+C+SQwwhUdY2qLoreNwOv\nAPsA55GI67oVuKB3zXZ6w733mivFNdfA9dcXNgDRGThMmQK//KW5DqZy/3GcoYSI/DvwEvALuhcn\nzoVt2EPBehEZhnlvvBvfQFV3xBaHA8lRjzmHmidbrfbbr7tbXJyuLptoTppkFq+DDzYrQEMD/MM/\ndA9Iz0VkidjT+XQiq64OLrjAXJ6CW9bOnfZ+0qTE5DaIpWRXtXgx2Pb2nlaKuEAJqcYrKnpmQwsC\nIllk1dd3P2YqC+D++1vdog98wKw98W3iYxWfbAfxEbIbggmZe+4xoRYnuRBxMhMnmkWrpaW7RSk+\nVvvsk/7Je6C62to1erS5DHZ2Jq5XELKnnppwpYvzgQ90t5yFGmq5ZvPLhyUrLrJWrYInnuheUw1s\nfDKJuGC5DRx2mP22bUnh+xTSvFdU2P9IWxs8+qi5VXZ2mvg++uieAjmTyHIGP6nqBxaLXG6jVlXt\nkugxgog0ZNk+JdGTwqOwxBkTVDV4oa8FJvTlmEONfMdvdHXB974HN98MDz9sgZ4DjXKMWSlmnz/+\ncaub9ZnPmAWzlCn6/Vo7BeYiYKqq5lAdpzuquklEbgBWAi3AX1R1XvJ2InIB8CNgInBm/BDAPBHp\nBG5U1ZsynS+5aOakSYkkCMnJFEK69cMOs6fqcYtPKDjaG5EVCFakVJOPeIzSnDkmsNraLHYruOFt\n2ZJaaFRWmlDZscMmvI88YkIgTJTjAqW62sRlKpEQBElI2LBzZ3ZLVqp+VFdbW5MtWZWVNqZBaIkk\nrHAh3fTmzdbvZItMS0t260YQicGSFe8T2Hgkp4BPx8iR5m5YV5eoddXWZlahdMeYkDSrCqnNcxVZ\nXV0Jl76+iKyamoRgrahIuFDuuWd3F9AgitJdx507u1scRcyaFeLektscXDSDyAKLY+zosHVTpvTc\nL4iscP5UGRsdpy/k8pV8l4jcCIwWkX8ELgN+05uTiMhw4B7gq6q6XWK5JVVVRSRleP6sWbOYEv1H\njB49mhkzZuyetAQ3HF9Ovfzgg0388Icg0sizz8KyZU00NQ2c9vly8Zavvx6OOaaJyy6D2bNL3x5f\nHlrLTU1NzJ49G2D393WJWAKMwR7c9QoRmQpcBUzBXA7vEpGLVfW2+Haqeh9wn4icjNXgOij66ERV\nfU9ExgNzRWSZqs6P73vttdfufj98eCNHHNG4e7kiioVauTK1yGposIl2uto3yUkdcqE6mvCnm0xW\nV9vktLkZPvaxRM2l4cPhT3+yCWu6ZwjDhiWsYNu3274HHZToT9yStXp16oD1mhqbUFdXmxVk2TKL\n/Ro1KmFxEsksssD2efvtnmMUJuNxMVJdnUjBHvpQW9szniOTu2D8WOE8tbV2jcO63hJSYgfB0dxs\n7Uy2CmVrT+hXNtG0Y0eivhektzTNnw/HH9/zHgoiK2SaDDEx48fbvZB8jweXwfh13L7d4vLWr+8p\nwkeOTG35DfdEiIML92BLS+baYEFch364Jau8aWpq2v0b119EM6QfE1ND+wIHk3hq9xdVnZvzCUSq\ngD8DD6vqT6N1yzD/9TUiMhF4XFUPTtpPM7VtKNLU1LR7EtMfli+3itiNjfCzn/X9i70Y5KvPg4lS\n9HnNGrNk/upXcO65RT31bvxalwcigqrmuUpLTuc9FvgT8DIQpsWqquflsO9FwBlRbS1E5BLgeFW9\nMsM+bwDHqerGpPXXAM2qekNsXbffs4cftslp/An96tX23Z18u7zxhiWoyJSda+1ai9s67bSeFox0\nbNxoVqpDDoEjj0x9zKeest+PePHXMHEdPjx9LZ677upuLdlzT3NtBPjrX82aMHmyJWBavRo++tGe\nx1q3zs5fW2vud8uX22R7770t497559ukeMkSm0AfcUTqtqxcaZl0zzjDBNeKFXbcc8/tWbz5kUcs\ndvnJJ23CHkTt1q0WexZ44AGzzqVLfhHG6c9/trE95BDbZ+ZM60tfePzxRKzRO+/Ydenqgk/kWGr7\n2WcT7pahePT++/ecH2zZYteorc0y1T79tI3vOed0366rC/7wh56Fb7dtM0E8ZowleDnlFDvnPfeY\nu95LL5nLXhDdAPfdBx/6UEIwPvusCbM33zTr1znndI8j37rVPDSSixIvXGjHeOutRJxjiOmqrOz5\nPxdYvtyuV329JU0Bi6s74IDcxtYZ2vTnNy0Xrf6Qqh4GzOntwSOR9t/A0iCwIu4HLgX+I/p7X2+P\n7aTm4YetRtL3vw//+I+lbo0zUNhrL/tBvOAC81HPFgvgOIOQ3wLXYSIrPHvP9UndMuBfRaQO2AWc\nDnRLGRNZu96MvC+OBlDVjSJSD1RGXhoN2APJ72Y6Waqn6smpqXfssBiWCROyu5VV9MFdMJslq6rK\nJrjJQiR5ORUdHTbJPfxwa9OjjybiZdavTwjG6moTTanE2ogRlsWwudkm7h0dNj7xosVVVdktWcG9\nMoxRprEKlqyurkTa9fe9r3uK/ZC6PpsFKe4uGNqRzhKZC/vtZ6Lu1Vdtua0tt2sRCNe5stKEaXu7\n3Vf77tt9u5CwpLra2nviiSbwkgmZGZNdD599NlF7Kliy2trseAccYCIreeySk1+sWGF9PfhgexiQ\nLGaHD7fzJxPPLlhfn6h7tnOnLaf7/wjnj7dhID+cdgYPGb+Sox+TF0TkOFXtS46yE7GUuC+JyMJo\n3bewH8I/iMjniVK49+HYQ47+PPFWtaQWP/+5JboIBQ8HOuX2lB9K1+cTTjDL5rnn2tPJvfcu7vn9\nWjsFpllVf96XHVX1RRH5LfA8JtAWADeJyOXR5zcCHwc+KyLtQDOJ7IN7AX+M3OCHAbepasaHkqmE\nQUODTW7DRPGNN0xgbN9uVpNM9Ca7YCBsm8ldEFIXys3GKaeY1Sgco6HBrA+qZkUJMTkHHpjeGlZX\nZ9lwn37a9gVLulFd3T0NeXBNS0dFhT1cChP7ILJSCbMgCkK9pS1bTGiETHVVVdaGYBnJRFVVwnUN\nLFV7fwieuK+9lkgWEU+dn41g+QqucbW1qeOadu1KxDaBnau9HR56qLs1KxRcThZZra12jGHDEuMZ\naoXV1MBFF/WMDY6LrLY2O+aWLTbO06f3bGNoW/L/UdxdMNwTIUtlaFMq4okvROw4LrKcfJDLV/Lx\nwGdEZAVRhkFMf6UxzidQ1b+RPoNhjqXdnGy0tMDnP29fvs880zMVruMEPvUpc6U491xzCcnk7uI4\ng4z5IvIjzFNidxSNqi7IZWdVvR64Pmn1jVk+R1XfBGb0pqGpMuJVVJgY2bQp4dYWHpaFBBfpyCQc\n0hHEVbrJZFjfl8lm8gOccePMIhGyJAZyscTU1CQm9GBpz0MtrsWLzbJz7LGZj5GcMjz+N07ckhUS\nRdTVmSDZtcvWrV2bm0tmiCdLFhT9paLC7p22tu51xbIR6oWtWWPLe+2VXmSF84CNU2trItlGuB+C\nJSk5KUhINhEXWfF4tFTjERdZcQtVpri3cK3i1zY8oAhjFAR5ZaXde7mIrOpq66uLLCcfpP33F5HJ\n0dsPAfsDpwEfiV5Zfdyd3tOXQLt337U6GmCT5sEmsPIVXDiYKHWfv/Ute0J8wQWp69gUilL3uxSU\nY59LyNHYQ8Ef0vsU7kUlnYvbtGn2PR5qO+2zT+rMe8n0xV0wFKxNt08QYX2xZCUzYQIsXWrWuWT3\ntGxUV9tEfcwY+96qrLQ2r1plCS16S0VFdwtT8rlaW22yXl9vYzBiREJkvfaaCbvkOkvpqKnJf8mU\n0PZPfap37ocheUpoz8SJCQthnJDkI5UYjf9eBDGUbMmKi6wghFpbM99HcZG1c2difS4iK06wZO21\nl8VwVVcnkoYk9yXOsGHWxuCCCvm57x0n01fyn4CjVPVtEblHVT9erEY5ufHccxYwfOWVcPXV6d0u\nHCeOiCXAuPRSCx7/059yz1DlOAMVVW0sdRtyQbV7Udk4Bx5oMUAPPJB7Agvom7tg2D6du2BIaZ6P\nJ/pTpphYaWszS1RvCJPdadMsUQNYu5YsgZNPTh/TlY4Qs5OKUaPM0tPVZckZgoWsrs4SIuzaZa6b\n2SyLgerq/IusTO3PhbjIWrAAXn7ZBFh9vd1zwZKVnPIeTACFBBTNzQlLVaCjI5GJMG7Jgsz3UbLI\nGjXKjp9J6KQSWcGSFVwM45bEYOFKRW2tFXvesCFxf7oly8kHuX4l71/QVjhA7+I37roLrrgCbrrJ\nLBKDlXKMWRkIfa6shNmz4ZJL7P65777CC62B0O9iU459LiUici5wCLC7Eo+qfq90LepJiHdJJwyq\nqy0jXW/+H/viLhjOlakeUIijyQfZ6kplagN0b2ewnuyzT+8fLmYSKePHm+hIFsEzZ1qmu5UrcxdY\noX39SXaRiv6KrCBmampMML75psX9bdxoIjaVyApxSnFLVnOzFTuOi6y46KmqSoisbDFO8bTyO3ea\ncG5tzXxtM4ms+DahDlemBxCjRlkc4RNPWJsrKrxOlpMfvBLAIEPVMgf+5jcwdy7M6FUkgOMkGDYM\nfvc7mDXLfmz/9KfePT13nIFEVM+xDnNtvwn4BPBMSRuVgmzZ8KD3qaNDzafeCo6jjuqeGjuZqqrS\nu02F88cn6Zs22d++eG8Ed8NUjBiREA3xY9fWWvxTb2KgoPfb50J/RVZcmAQXTDBx8/DDiTimuFgJ\nBYyTRdaUKd3dBePZMSsr7f4JyVwyiayKioTIamkxIbt/lkf7oVh2HNXu7Y4nH8lm5Q2p3aur3Yrl\n5I9MIZlHiMh2EdkOHB7eR68U4ZJOf8kWv9HaCp/9rLmS/P3vQ0NglWPMykDqcxBaH/qQ1RBZurRw\n5xpI/S4W5djnEnKCqn4W2KSq38Xisw7Ksk/RSZX0or9UVVl9p94yaVLmxAx1ddmL7haaVJasvfe2\n9N59obY2c0xVLjFwpaS/ImvqVHOzTKa+3tLENzd3j90K56yoSIiskIGxoSEhstassVpt4X6JW7Li\nCTNSEc8W2daWm7BPZ8mKi+MgmHJx2wyW45D8wnHyQdqvelXNsyex0x82bLD4q732gqam0v/wOUMH\nEfjud+3p+SmnwA03mBuhx/g5g4zwnH2niOwDbMTSqw8ocrFk9RYRi+XKN42Npf8eCBPuuMjqT4mS\nmprMhZ2nT7cMrAOV4P7WV6qr0yfICkWCX3opEVsFibpTK1bYHKShwTLTVlUlMhSuW2d/6+rMKhbS\ntoeU7JlKhsRjsnLN7JeLu2C8VlkuDzZGj7ZX8nEdp6/kObmo0x/SxW8sW2ZWhpNOgjvvHFoCqxxj\nVgZqny+5BObNs3prF15oPvr5ZKD2u5CUY59LyAMiMgb4MVbn6m3gjpK2KAUdHfkXWYWi1AIL+pdK\nvi+MGmWZ+wYq/bVkZaOhoaflp7LSXAMPPdQyOjY3m8iKW6DWr7e/bW2W8VgkYcnavj1zuv7KSnMB\n7ezsmyXrnXdMACaLrIMOsldwgczG2Wfbw8ZQk8xx+ouLrAHOY4+ZdeFf/gV+9KP819xwnDhHHgnP\nP29plg89FH75y54peh1nIKKq/66qm1X1HmAycJCq/mup25VMZ2f+3QWHMlVVNvH3RARGoUUW2Fgn\nuwtWV5u1dN26hEUrFDbu6jKRVFFhcVHB5bKqyhJptLTYNUzHhAlWfHjBguzp3gNBZG3YAPPnw4sv\nJlK4x7cJyVtyHbOGBsvy6Tj5oKBTdhG5WUTWisji2LqxIjJXRJaLyBwRGV3INgwmkuM3fvMb+OQn\n4Y474LLLStOmQlOOMSsDvc+1tfCf/wmPPGJZLI88Ev74x4Q7R18Z6P0uBOXY52IjIseJyMTY8qXA\nXcC/i0hOCcNF5FsiskREFovI7SJSk/T5+SLyoogsFJEXROS0pM8ro88eyHauQrgLDnXOO88fMAaK\nJbKSE19UV5sr4IgRFn81ZUqiKPSmTRbHdf75cPrp3Y+zZYsJl0zXb++94bTTzE2zvT03QT1ihFnP\nnnrK3BxFelqyAhMm2ENDxyk2hf7augU4K2nd1cBcVZ0GPBotOzE6O+FrXzO3rfnz7cvHcYrNjBnw\n6KNw3XV2Lx58sFm2mptL3TLH6caNQCuAiHwQuA64FdgG/DrbziIyBfgicLSqHg5UAp9M2myeqh6p\nqkcBs1Ic96vAUkCznc9FltMf0hVSzif775+oNQUJkQUmoj7yERNCwV1w/XrYYw97QBcvPRD2yeQq\nGKiqsn0rK3MT1HvsYZa1gw6yGmotLdaWVC6u1dW9S73vOPmioCJLVecDm5NWn4f9ABL9HcRVnvJL\nY2MjW7faU7uXXrIMggcNuNxY+aUcY1YGU59F7Af16afh5pthzhxzJbzsMnsAEA+OzsZg6ne+KMc+\nl4AKVY2SenMRcKOq3qOq3wFycfzZBrQD9SIyDKgH3o1voKo7YovDgQ1hQUQmAecAvwEyRjF1dloR\neXfBdfrK3ntbhsBCUlPTPfb7mGOshhjYb0Jw52togB074I03Upf/qK21h8ShqHM2RozoXcmAI4+0\nOZKItXf7drd4OgOLUtyOE1R1bfR+LeCVeSJeecWyHu23n9WrGJuTo4vjFB4RS/t77712n06fDv/7\nf9u9+o1vwMKF5g/vOCWgUkSCg9HpwOOxz7JGP0UC7QZgJbAa2KKq85K3E5ELROQV4GHgK7GPfgJ8\nHcj6yGHrVsu8VohMgE55MHx43ws795WxY1OLl5oaKx0wdWr6jIUTJuReWHvEiL4nOBkxomcRaccp\nNSUNv1VVFZG0U7NZs2YxJUrzMnr0aGbMmLH7yXCIdRgqy9//fhPXXbeIX/ziKj73udK3p1jLYd1A\naU8xlpP7Xur29HZ5r73g2GObmDkTxo1r5I474Jxzmqiqgssua+TTn4bVq3vuv2jRIq666qqSt7+Y\ny2HdQGlPoe7n2bNnA+z+vi4ydwBPiMgGYCcwH0BEDgS2ZNtZRKYCVwFTgK3AXSJysareFt9OVe8D\n7hORk4HficjBwIeBdaq6UEQaM53n2muvZfNmc7edOLGRAw7IuLnjDAr22Sd/tcVGjOhe0Lg3jBwJ\nq1cPjIyYzuCmqamp2294fxAt8OPnyN/9gcjXHRFZBjSq6pooWPlxVe1RVlBEtNBtGwi0t8PVV8Pd\nd8M3v9nEFVc0lrpJRaWpqWn3xK1cGIp9VjU3qDvugP/5H5g82Qpnf/rTMGaMbTMU+52NcuyziKCq\nRZ3qiMgHsJpYc4Jrn4hMA4ar6oIs+14EnKGqX4iWLwGOV9UrM+zzBvB+4GvAJUAHUAuMBO6JiiLH\nt///27v7WKuqM4/j3x9cEAEjESu29U4gjFrs2LEOQjvKQI01ODOKSlM11NKOsSSOVTPxjflj0rRp\nrE0mziTjTGesCrVqX7RSjDFSFKbUZmyxXLjlxXcE3/CFgoqC9+Izf6x9vId7D29nn7v3OZzfJzm5\n++y9zz3refY9d6911t5rRUSwenW6j8U34ZsN9PbbaeqQCRMO/rWbNsHjj6fL20ePbnzZrH3lOaeV\n0cj6PvBWRNws6UZgTEQMGPyiHRpZL76YRg8cOxYWLiz+EgCzwdDbm+bbWrAgjT+zYzkAAAwuSURB\nVFA4axbMmwef/7y/ZWwHZTSy8pD0l8DdwGnATmAB8LuIuLVqn4nA89nVF6cCP4+Iif1+z3Tg2og4\nt8Z7RESwYkUala2zc/DiMWtH774LDz6YzjeH0lyiVr4857TBHsL9XuC3wImSNkv6Omnkpy9Keho4\nM3veViJSBXTyZJg9GxYvdgPLDh0dHTBzZurReuYZOPnk1Ks1dWrq6erpKbuEZn0iYjXwI2AlsCZb\nfZukeZLmZc9nA92SVgH/zsDRBz/6dXt7ny1b0ihslZ5dM2ucSu/VEN+TZU1k0Huy6nWo9mS9+ipc\ncUUajefHP4bPfKZvWzteWuSY28Pu3XDTTctZunQGL7wA114Ll1126H/j2I7HutV6soogKRYtCqZO\nTZO4mlnj7W2eLLM8mrYny/rs3g233poaVSedlO5fqW5gmR3Khg6FM86A5cvTBMePPZbmYrn55vpv\ndDZrJeed5waW2WByA8uajXuyCrBiRZpc+PDD02SuvunZDNauhe9+N92/dc01cOWVaYQoa23uyRro\nUDqfmZm1E/dkNan16+H88+HSS1MlcvlyN7DMKj79abjnHvj1r2HdujTXyne+A9v2O+i2mZmZWXNz\nI2sQrFkDF10E06fD6afDhg0wZ87+u7IbNS5/K3HM7WNvcX/qU+n+xMcfT/cqTpwI8+engQJaXbse\nazMzs3bnRlaDfPghPPwwnHNOGlltyhR4/nm47joYMaLs0pk1vxNOSKNurlyZ5kuZNCkN/b52bdkl\nMzMzMzs4vierAV55BWbMSEOIXnVVmvvKDSuzfF5/HX7wg/SYNAm+8Y00B4o/W83N92QN1ErnMzMz\n69PUkxHXq5VOShFptMDTTvNkq2aN9sEHcP/9cOed8OST8KUvwYUXpi82Djus7NJZf25kDdRK5zMz\nM+vjgS9KJqXLA/M2sNrx/g3H3D7qjXv4cLjkEliyBLq60j1b3/42jBsHF1wAt9ySLjFsxkmO2/VY\nm5mZtbvSGlmSZkraIOkZSTeUVY5m0tXVVXYRCueY20cj4u7shOuvT4NkPP106tV66imYOxeOPBJO\nOSWN5vmtb6Wer0cfhe5ueO211CNWtHY91q1I0nxJayV1S7pH0mH9ts+StFrSKklPSjozWz9C0hOS\nuiStk3RTORG0Pn8psX/O0YFxnvbPORp8HWW8qaShwH8AZwEvA7+XtDgi1pdRnmaxrQ3HrnbM7aPR\ncR9zTBq1c86c9HzHjjRtQnd3GnRm2TLYtAnefBPeeAO2bk0jfB5xBIwale7tGjEiXXI4fDgMG5Ye\nleWOjvSoLFevq37UWl/5XcuWbeNjH0vvUXm/ESPSnHmjRvU9xozxRJplkjQeuByYFBG7JP0UuBhY\nWLXb0oj4Zbb/ycADwJ9HxE5JX4iI9yR1AL+RdEZE/KbYKFrf8uXLmTFjRtnFaGrO0YFxnvbPORp8\npTSygCnAsxGxEUDST4BZQFs3ssysfqNGweTJ6VFLBOzaBe+8A++9B++/nx49PamX64MP+pZ7eqC3\nN/3cvTv9rCxX1vf29i3v3Dlw354eePFFeOih9L67dqX9Ku+7Y0ff47nn4Kijis2X7eFtoAcYKWk3\nMJL0BeBHImJH1dPRwJtV297LFocDQ4Gtg1paMzNremU1sj4JbK56/hIwtaSyNI2NGzeWXYTCOeb2\nUXbcUl9PUlG+9rWNLFhQ3PtZfSJiq6R/BTYB7wOPRMTS/vtJOh+4Cfg4cHbV+iHAH4CJwH9FxLpC\nCm5mZk2rlNEFJc0GZkbE5dnzrwBTI+KbVft4KCYzsxbUaqMLSpoIPAhMA7YDPwfui4i797L/NOCH\nEXFiv/VHAo8AN0bE8qr1Pp+ZmbWoes9pZfVkvQx0Vj3vJPVmfaTVTtJmZtayJgO/jYi3ACT9Avhr\noGYjKyJWSOqQNLbymmz9dkkPZb9vedV6n8/MzNpMWbdarwSOlzRe0nDgImBxSWUxM7P2tgH4nKTD\nJYk0KNMel/xJmphtQ9KpABHxlqSjJY3J1h8OfBFYVWjpzcys6ZTSkxURvZKuJF1WMRS4vd1HFjQz\ns3JExGpJPyJ9Afgh6f6q2yTNy7b/NzAb+KqkHuBd0uiDkO7PWpjdlzUEuCsiHi06BjMzay6l3JNl\nZmZmZmZ2qCr8csF2nPCx3pirtg/Ntj1YbMnzyRO3pI2S1mTbfld86euTM+Yxku6TtD77G/9c8REc\nvByf6ROzdZXHdklXlRPFwcl5nPf52maVM+ars9f9UdLVxZe+PJJmStog6RlJN5RdnrJIukPSFknd\nVeuOkvQrSU9LWlK57DLbNj/L2QZJZ9f+rYceSZ2SlmWftT9W/ic6V332Vh90jgbqX390jgaqVd9s\nWJ4iorAHMB54Hjgse/5TYG6/fUZVLZ9Mmk+r8nxk9rMD+D/gjCLLX0bM2bp/It2AvbjseAo81i8A\nR5UdR8ExLwT+IVvuAI4sO6bBjrlq/RDgVaCz7JgGM+YDeW0zPnLG/BdANzCCdHn4r4CJZcdUUN6G\nAs9m+RsGdJEmPC69bCXkYhrwWaC7at33geuz5RuA72XLJ2W5Gpbl7llgSNkxFJSnY4FTsuXRwFPA\nJOdqQJ4G1Aedo5p52qP+6BzVzNGA+maj8lR0T1b1hI8dtMeEj7lilnQc8LfAD4FWGqEqV9yZVooX\ncsSsNPTztIi4I9uvNyK2F1LqfBpxnCENNPBcRGyusa3Z5Il5v69tUnlingQ8ERE7I2I38L/AhYNf\n5KYwhdTY3BgRPcBPgFkll6kUEbEC+FO/1eeRvlwi+3l+tjwLuDcieiJiI6kiM6WIcpYtIl6LiK5s\n+V1gPWluUeeqSo364J9wjvawl/qjc1Rb//pmQ/JUaCMrIrYClQkfXwG2xV4mfJS0HngYuKpq/RBJ\nXcAWYFm0wISPeWMGbgGuI92M3TIaEHcASyWtlHR5EWXOK2fME4A3JN0p6Q+SbpM0sqiy16sBx7ni\nYuCewSxro+SJ+UBf22xyHuduYFp2+cVI4O+A44opeek+CVR/cfBSts6ScRGxJVveAozLlj/BntO6\ntGXeJI0n9f49gXO1hxr1wbU4R/3Vqj86RwPVqm82JE+FNrKUJny8htTF9glgtKQ5/feLiEURMQk4\nF7irav2HEXEK6QT9N5JmFFHuPOqNWcnfA69HxCparFcn77EGTo+IzwLnAP+oNPlnU8sZcwdwKvCf\nEXEqsAO4sYhy59GA44zSNA7nkiaAbXp5Yj7Q1zabPDFHxAbgZmAJqfG1ihb70igHjyx1gCJdi7Ov\nfLVVLiWNBu4Hro6Id6q3OVc164Nf6Le9rXN0IPXHds9RlX3WN/PkqejLBT+a8DEieoHKhI81ZZcX\ndEga22/9dqAy4WOzqytmYGy233mSXgDuBc5UGma4FeQ61hHxavbzDeABWqPbOk/MLwEvRcTvs833\nkRpdza4Rn+lzgCezY90K6o356IN9bRPJ+3m+IyImR8R0YBvpPpN28DLQWfW8kz2/BW13WyQdCyDp\n48Dr2fr+eTuO1ristiEkDSM1sO6KiEXZaueqhqr64F/hHFWrVX+8C+dogL3UNxuSp6IbWe044WO9\nMb8ZEf8cEZ0RMYF0OdVjEfHVgstfrzzHeqSkI7L1o4CzSZccNbu6Y46I14DNkk7Idj0LWFtc0etW\nd8xVu1xCOgm0iro/06TGxT5f26RyHWdJx2Q//wy4gBa5NLQBVgLHSxqf9dheBCwuuUzNZDEwN1ue\nCyyqWn+xpOGSJgDHAy0zymwe2WfodmBdRPxb1SbnKrOP+qBzlNlL/fFSnKM97KO+2ZA8FToZcbTh\nhI85Yx7w6woockPkjPtY4BdZfa0DuDsilhQcwkFrwLH+JnB3Vhl7Dvh6keWvR96Ys39qZwEtcd8d\n5Is5IrpqvPZ/io/i4DTgb/u+rFerB7giIt4uNICSRESvpCuBR0g3598eEetLLlYpJN0LTAeOlrQZ\n+Bfge8DPJF0GbAS+DBAR6yT9jNSQ7yX9zbTM+S+n04GvAGskVb5Ino9zVa1mfTDLl3NUWyVe/x3t\naRzwQP/6pqSVNCBPnozYzMzMzMysgQqfjNjMzMzMzOxQ5kaWmZmZmZlZA7mRZWZmZmZm1kBuZJmZ\nmZmZmTWQG1lmZmZmZmYN5EaWmZmZmZlZA7mRZWZmZmZm1kD/DxpDaSDpttKkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a017710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traceplot(trace[burn:], vars=['beta', 'A'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  8.355            0.007            0.001            [8.340, 8.369]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  8.341          8.350          8.354          8.360          8.370\n",
      "\n"
     ]
    }
   ],
   "source": [
    "summary(trace[burn:], vars=['A'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.gridspec.GridSpec at 0x11a9456d8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OTpIs+CBGSSOgFz2g+ZI8EPjPwJ+wyKNeNX6qiq1btzM9vY/p6X1s23YxY/TIeal3+tQD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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a937d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(trace[burn:], vars=['p_age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}