Non-Spatial Measles Model-theirs.ipynb

{
 "cells": [
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
     ]
    }
   ],
   "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 seaborn as sb\n",
    "sb.set()\n",
    "import pdb\n",
    "\n",
    "np.random.seed(20090425)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = \"data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import outbreak data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = pd.read_csv(data_dir+\"measles.csv\", index_col=0, encoding='latin-1')\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": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "measles_data = (measles_data.replace({'DISTRICT': {'BRASILANDIA':'BRAZILANDIA'}})\n",
    "                    .drop('AGE', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sao Paulo population by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sp_pop = pd.read_csv(data_dir+'sp_pop.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "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",
       "  </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",
       "\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",
       "\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",
       "\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",
       "\n",
       "                   Total  \n",
       "AGUA RASA          89151  \n",
       "ALTO DE PINHEIROS  46495  \n",
       "ANHANGUERA         26249  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_pop.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot of cumulative cases by district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8198f2208>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Re38w7j0JbSHEvFYudFEY2IbL1zjp0q3CwDZss0xV/SVjtpzL+Q6KqZ24qpqn\nFNiHMu38se0ltvRtJeKp5ctnf45lNUtn9H3OFLZlYWazMMGe6ceyiiX07q7B0D32fUYFvbsbq1zC\nSPZTGegf8x1mLosxC8E6VyS0hRDzWqbnFQCqG6+ZMGRt2yabeBtQ8IcvGPN+quv5oXddO+G7ikaJ\nze0v88yB57CxafY38jfn3UG1OzTuM2cCq1JB7+qk3NGBbVRG3LN1nXJnB+W2NqxiASOTxS6XTlhd\nFE0DdfTPUHV78C5bjuKYWvwpHjeu+obB9411X1Vx1tXh8AdwVPlx1cfgBG4rK6EthJi3KqUExfRH\nuHyNuP2tE5Yt5w5RKfbirT4bzRUccc+2bdLdmwd3RwsuxeNvGfMdtm3zcucbPLrvKXRTp8ZdzZdW\nfJYVtWed9vt/6709FHfvwshkgMEQ1ru6sCoVwMZMpyh3dcEYreCjKZqGIxDEGYngqq9HGSNYx33W\n6cTV0ITiHr22XVFUXPX1qFVVOAJBtOrq03IugYS2EGLeOjypLFB32YT/QBt6muTBRwAIRi8dcc+s\n5EgeepxSdh+au5Zwyy3jvufJ/ZvYdGgzVU4f6xZ+go83X4rfefrsB25bFqV9ezEy6SPXyjqFnTvI\nvPHahOPAisuFp6UF94IW3AsWoHp9I+87VFwNjYOtVods0TpdEtpCiHnJtgzy/R+galX4QmNPKjus\nv+33mEaO6qZ1uP0LjrzDtkgceJhyvg1PoJXaBTfi0HxjvmNbYiebDm0m6g3zrfP/CzWesZeLzQe2\nbWMkE5Ta2jCzWax8jlLbIUr792H0jz0W7Gpsovra63CGI6AoKA4HroYGVI8XGGwFH0+rWUyPhLYQ\nYl7K93+AZZYGl2NNcLhGKXuQUnYfbn8rgeglw9dt22agYxPlfBve6rOJLPrcmK11wzLY2b+bn27/\nJZri4OurvnLKBbZ9zMxmM5tF7+3BrgxNzioVMQYGMPqTWOUy5Y4OrEJ+1HtUXxXBy9fibjkyPKA4\nHLgam/AuWSot5FOAhLYQYt6plBIMdD6LorrwRy6asGy650Xg8ES1I6Gc6nqOXOJtNE+E8IKbxgzs\nXf17+bdt/0HBKKIpDr626issCDTN6neZDkvXyW15l/y2Dym3taF3d427JGkszlgM38pz8CxciFZb\ni+r24F6wAC0cOS3HgQEqZoWiWSKn54kXE9hTXPI1F66PXj7uPQltIcS8k+5+EduqEF50G5pr/Bnb\neqGLcq4sMk0MAAAgAElEQVQNT2AJ7qojYVsudJHtex3NHSa29C9RtdGHimxL7OSBbf8Bts0VjZdw\nacOFLA4tOhFfZ1J6vI/8+1sod3ViFUsUtn+IVSwCoLjdeBYtQnEdWZ+uejy4GhpRnU6csdjgzGa/\nH2ddDEXTUJ2n/oliZVNne/IjipXipGWLZonOXDeduW7Kpj7qvm1bDJTTWPbUf7GZS9efI6EthDiN\nlPMdg2PZ1edMWC4bfwuAQN2x3eJ/AKB2wXocY0wke6dnC7/Y+VtUReW/rL6ds8PLZrH2kzOzWbLv\nvEVx/77BlnRnx4j7jlA1tZ+4lsAll+JqaDzlxpKPDsd0OcPO/j1sS+7EtAxsIFnsJ61nJ3xHxdQx\n7Ilnoh/L5XDh07xj3lsUXEDIHcLrcBOrqsMxyQ53pyoJbSHEvGJWcpiVDJ7gWRN25ZpGgfzAdjR3\nBE9gyfD1wsA29HwH3uqz8QRGLhPL6jme2PcHXut+C7fDxd+svoOzapYc++oTQu/rI/PKn8h98P6I\n7m7F7cZ39koCl1yKZ/ESVLdncDnTKTS+bFgG3fk+OrKd7EntZ0v8Q/QxWryHeRxuaj01E/78HIrK\nyvAKYr7opJ+vqRpNVfVEfZHTfumdhLYQYl4pFzoBRnR3j6UwsA1sE3/kguFwsEydVNdzoDioabx+\nRPlEMcn//e7/JqvnaKyq569WfYVYVd0J+Q5GKkX2rTcpd3cOTxYrHzoIDC6d8i5Ziv+CC6k69zyc\ndXVz3pLuLw1QMsrEi0k6sp0kSwMAmLZJb76Prnwv5lGt4lpPDZHgQgB8mpeWYDMX1K3G7/QD4HI4\nT/twPVEktIUQ84qe7wLA5Zt4b+988gNAoapm1fC1wsCHmJUswdgVaO4jM8BLRpn/88FPyeo5bmxd\nx7qWT+CYYEb6dFWSCVIvvkBq83PY5fKRGw4H3hVnE7ri4/gvuBDVPf7+6SeSbdsYlsH25EfsTu0j\nWRwgrWdoz3aO+4ymajT7G2kONLIg0MjCQDMLAk0SyieIhLYQYl7RC5OHtl7oQi924wmehWOodQeQ\nS7wHKPgjF48o/1r3W/QW4lzVfAU3tF53Qupd7min/Z9+hFUs4ggEqP3s5/GtOBvV5UKrrhl3m8wT\noWgUMazBlnGqnKY928nugX28H99GxRq5/aiCwsra5YS9tVS7gzT7G4n56lAVBVCodgdPyC84YmwS\n2kKIeUMv9lLKHkDzRMbdBMW2bQY6/wgw4gCRUq4NvdiNN7R8xDamlm3xUvurOFWN9YtOTGCbhTyd\n//L/YhWLRP/sS4SuvBrVNXorztmyZ2A/b/e+h3nMMrC8kac920WqnB7zuYinllpPDc2BRi6oW03M\nV4dT1XA6Tv3Z5mcKCW0hxLxg2xb9bU8C1qjx6KMV07sp5w7hDS7DG1wyfC1xcCMAgejIVvbbPVtI\nlPq5vOFi/K4TsyVp8onHMPr7qb3xM9Rct27W3394zXHFqvDb3Y/zevfb45YNuQKsrF2O2zH4S0OV\n08eCQNNwt/bpuk77dCGhLYSYFwoD29ELXfhqVuENnTVuuWz8TWDwpC44vLf4YwBEWj+PJ7B4uOyB\ndBu/2rURt8PFdS1Xz3qdrVKJ1EsvkNr8PM5oHbWfvmnW3p0sDvDRwG529e9la2I7FcsYvrfA38hn\nz7qJGk/NiGfcDhcBl//YV4l5REJbCHHKs22TdPeLoKhUN1wzbrlKKUk5dxC3vwWnN4pt2/S3/x7L\nLFHT/Cl81WcPl81V8vzbtv/AsEz+5ry/mNLSouNhVXQ6/9f/pLhnN4qmUfeVv5jxpiZlU+cPB59n\n18Be2jId2ENnUUe94eFjQVtDLaxvvR6nKv+8n45m9FPNZrPcc8897NmzB1VV+dGPfsSiRYv49re/\nTWdnJ83Nzdx3330EAgEANmzYwMaNG3E4HNxzzz2sXbt2Vr6EEOL0lu17E0MfwB+5eMSs76MZemZ4\n0xR/5EIAUl3PU0zvxu1fNGLyWU7P88C2X5Aqp7lp8ac4JzzxgSPHyywU6Pm3DRT37MZ/4UXEvvKX\nOIb+HTxetm3Tnutka3w77/VtpbcQR1VUFodauCi2htbQIpr9DdKtPUsMy6C3EKdkDM7uz1fy9BUT\nJ3U3tS9HPzPuvRmF9r333stVV13Fv/zLv2AYBsVikX/913/lsssu4xvf+Ab3338/GzZs4O6772bv\n3r0888wzPP300/T09HDHHXfw7LPPyv9oQogJVcr9pLtfRNWqCDVcNWYZQ0/Tu+tBTCOHu2oBvtAK\nyvlOsn2vobnDRFo/P/xvzds9W3h07+9J61nWRFexbha6xc1CnvwH71PYvYvcO28PbzHqO3sl9X/1\nTVTn8U86My2TN7rf4aXO1+jMdQ9fv6r5Cm5Zsh7XGTo5rGgUMS0LG5v+0gCpcoaSUaK3EB+xVvyw\nQqXIB/Ft5I3CHNR2er580QkI7VwuxzvvvMM//uM/Dr5I0wgEAjz//PM89NBDANx666189atf5e67\n72bz5s2sX78eTdNobm6mpaWFrVu3ct555023CkKIM0Cq8zls26C2+eYxZ4yblTzxfb/ENHKEGj5B\nMLYWRVFId78ADG1VOrS15Vs97/HzHb/GqWrcvOQGrlt41YzWExvZDJnXXmXgmacxc4Pbcmo1tbgX\nLMS38hxqb/j0tHYu+6h/Dxv3PElXvgdVUTkvuoqP1V/AktCieTkmndVz7OzfPbzMDCCjZ0kUk9jY\nWLZFXyFBYZJgLRllMpNsfzqWgMvPWdWLJy/I4BK3qC8yfE662+GivqoObYrDDZZlkzPMMY8et7Ap\nmRZFw8Sc5oEl0w7tjo4Oampq+O53v8tHH33EqlWr+N73vkcymSQSiQAQjUbpHzqbtbe3lzVr1gw/\nH4vF6O3tne7HCyHOAOV8J8X0R7h8TfiqV466b1by9O75OUY5QSB6CaH6jwOQ799GKbsfT6B1eKvS\n/tIAD+95Apfq5P+6+FvUz2C3M9uy6H/mKfqfehJb11HcHsI33zp4clbr4mntYHYg3cbe1H72pw+x\nNbEdBYXLGi7mxsXrhserTxWmZdKe60Q3K0NdySUACkaRjmwXXfmeEeu9i0Zp0u5lh+Kgyjn2Mr7D\nnKrGyvByXOpgz0XQ5SfsrcWlOqmvqsPlGN2joSoqTVUNw2vJDcuiq1AeM1TLlkW8qI8K1PTQHD8b\nSJYq5I3BXz70ofLGUeXLpo1pz2w+wTcmuDftNxuGwY4dO/jv//2/c+655/KjH/2I+++/f1R3t3R/\nCyGmwzJ1+tufAkYfqwlgWRXi+381HNjVTYNLqUq5QyTbHkdR3dQ0fQqA17re5nd7Hkc3dT531mdm\nFNgAA5v+QPLRjTgCQWpv/SzBy67A4Z9eC9i2bX696xFe6Xpz+NqS0CI+t+wzLAw0z6ieM2HZFgWj\nSKac5b2+DziU7Rge540XE2T13LjPVrtDBF1HxvBjvijnRVcROGqjG6/mGTq4Q+XwJi1Tbc0eq2JZ\nHMwWKZpjt163pwp8lMozUK7QV9QpmrM3Pl3t0vBrR3pTat0KUY8LTR2dfQoKfqcDv9OBa5pb0047\ntOvr66mvr+fcc88FYN26dTzwwAOEw2ESiQSRSIR4PE5tbS0w2LLu7j4yLtPT00MsFpvuxwshTmOW\nUSRxcCOVYg9V4fNHHewBkO7aPLQEbDXVTetQFIVKKUli/2/Btoku/jxOb5SXOl7jt7sfo0rz8fkV\nn+GyhovH+MSpK+zeReLRh3GEqmn5wf9ACwYnf2gCL3S8witdb9JYVc8NrddR7Q7SGmw5qQ0e27ZJ\nlvoxbYuyWeb5tj+xLbGTklkeUe7wUIJP83JF4yUEnFVEvOHhLnuXw0Wjv364a3mmdcpUDCrW6CC2\nbEiUdLoKZboKZQ5mi5SmEMQK4Hc6uDQcwuMYHZoORSHqdeGeIFBDLo2QS0MBVEXBNcZ7TqRph3Yk\nEqGhoYEDBw7Q2trKG2+8wdKlS1m6dCmPPPII3/zmN3n00Ue59trBtZLXXHMNd999N7fffju9vb20\ntbWxevXqWfsiQojTg2UU6dn9IEa5H0/wLGoXrB9VplKMk42/heaqIbzwRhRFwbZNEgc3YplFahfe\nhCe4mGSxn0f2PEnA5eeu8/96xi3sclcXXf/7X0BRaPjmX884sHcP7OOxvU8TcPr5uzXfIOSe3gzz\n41E29aGzp7soGWVS5TQ7krvoKyZGlIt6wyyrWorL4WR15BxW1J41aff1YbppsSOVI6ObZCsGA+UK\nxzOCWzItegrlKbeIQy6NiyJBgq7xI625ykOL3zPve39n1PH+/e9/n7vvvhvDMFiwYAE//vGPMU2T\nu+66i40bN9LU1MR9990HwNKlS7nhhhv49Kc/jaZp/OAHP5j3f3lCiNk30PlHjHI//ujHqGlah3LM\nRLHBbUo3ATbVzetQhrpUM72vDbbMa9fgD58PwJP7N2HYJrctvXHGgZ17fws9D96PVSwSu/3r+JZP\nf5mYbdtsS+7k5zt+A8DXVn35hAV20SjyXu9Wdqf20ZHtorcQH17ffZhT1VgTPZcq5+CEvWU1S7mw\n7rzj+jfatm3a8yXeiWfY2p9FH6OFfDzCbidLgj682tgt2RqXk8YqNw0+NwHnmbMmXbHtaU5hO0ni\n8eOfKSiEmJ8KqZ0kDvwOp7ee+uVfR1FGz7wupHeR2P8bPIHFRJd8GUVRsMwSndvuQ1GdNJ79t6ia\nh7ZMB//0zr+wINDEf7vozhnNEs+8/ho9P30Axekk9tXbCV52+bTeY9kWT+7fxCudb1AwiqiKylfP\n/gIfq79g2nUbT8UyeLH9Ff5wcDMlc3CimMfhoTnQwAJ/E02BRgLOKqqcPpr8DWNO4pqIbdv0lyt0\nFcp0F8psH8gTLw2eoR1yaVwYCdJc5cbrcBDxuBhjiHdcmqrgnOPjSOdSNDr+L3Bnzq8nQohTlm3b\nFFM7SR56DEV1EW65eczAtqwKqY5nAYWapk8OtwRzyfexLZ1gbC2q5sG2bR7dOziJ7dYln55RYOc+\neJ+enz6A6vXSdNd38C5ectzvqFgG7dlONre/zJa+rQRdAS6OXcAnF32ChqrZmdtj2RbxQoIt8Q9p\ny3TQmesmUeqnSvNxY+snuSC2mqg3fFx/F9mKwYf9OcxjWs3xks62gdyIcWSHorC61s+FkSBLgr6h\nU8DEbJPQFkLMKdu26W97nHz/VlBUoos+j8s7dpClu1/A0AcI1F2K0xsdet4iG38LRdHwRwZbrHtT\nB9id2sfK8HKW1y6ddt303l667/8JitNJ01134108tbW+R9uR3MUD2/4D3RxshS4MNPO3a74+K5O1\nCpUiL7S/zOb2l8ecNHZ18xWsb71+ymPRtm3zdjzDnkyeXMWku1Aet5s75NRYFvLR6HPT6PPQVOXG\nq8kRnSeahLYQYk6le14k378Vl6+RcMstOD2RMcsVUrvI9r2B5qoh1PCJ4evF9B5MPUVV+PzhzVfe\n6H4HgOsXXj2jusV/92vscpn6v/rmcQd20SjRnu3gZzt+hWVbrG26lFXhFaysXT7j86dzlTxP7f8j\nr3e/RcUyCDj9NPkbqfXUcFZNK6vCK/FqnintmmZaNim9QtmyebMvxdvxDHBkpvX1TTWEPSPf43U4\nWOD3SGt6DkhoCyHmjKFnyPS8isNVTXTJl8Y9I7uUO0Ty0CMoqpNI62dR1SMhcvhUr8NnZ5dNnS3x\nrYQ9NSytHr1UbKoyb75B/v0teJctJ3DJZVN+LlfJ8+C2/2T3wN7ha19cditXNk/9HeMpVIq83v02\nfzj4PAWjSNhTy9qmS7iy6XI8mntK79BNi0RJp2zZfJTK8V4iO7xZCEC918WXlzZQ63bKZOFTkIS2\nEGLO5BLvABah2NrxAzt7kPi+X2JjEW39Ai5f45Hnkx8Mneq1aLhL/f2+DymbOtcsuHJaY9m2bZP+\n04v0PfQLVI+Hui99ZcrhVTSK/K/3NtCV72FRcCGLQy0sq1nCqvDZkz88SZ1e7XqTh/c8ScWq4NU8\nfHbpjVzZfPmUNiQxLZu+ks4bfSneTWQ4usfb61BZEw7gVlViPhdragN4pJv7lCWhLYSYE5ZVIZd4\nF9XhxVd77phlKsU48QO/GQ5sb2jZ8L1s/G0GOp5BUd1UNx45rvONnncBuKT+wuOvU7lM30O/IPP6\nq6hVVTTf9R3czQum9Kxt2/znzofpyvewtulSvrjslhlNgLNsi90D+3i9+20+6t9DrpKnSvOxftF1\nXNZ48aR7kCdLOq/0pujIleg9aqvNiMfJ0qAPl6rSVOXm7OoqtDN4pvZ8I6EthJgThYFtWGaRYOyK\nEd3dh9m2TeLQY9hmmXDLrSMC2ygPkOp6DtXhJbb86zjdgzsvJosD7BnYx5JQK1Ff+LjqY+k67f/0\nI8pth3AvaqXxb/4WZ3js8fWx6vrk/k1siX/IktAivnDWzTMK7EOZdn6241f0FQY3PKlxV3NRbA2f\nWXwDYW/NpHV5pTfFsx1JTNvGoSjUe100+NwsCng5LxzAId3e85aEthDipLNtm2zfm4Ay4pzroxUG\ntlEpduOrWUXVMS3xgY5N2FaF2pYbhwMb4M2ed7CxubTh+FvZyScfp9x2iMAllxK7/euozqkfffnH\nQy+y6dBmot4wX1v15WlNNLNsi9e63uLVrjfpyHVj2zaX1F/IFY2XsDg0/ramlm2zN1OgK1+mZFrs\nyxToLJQJOB2sXxBlVY0fx/EskhanNAltIcRJV863USn14as+B801eitQ2zJIdW8GxUF1wzXHPNtO\nMbMbd9VCfDWrhq8niwP8se0lfJqX8+uOb4vkwkc7Gdj0DFokQuwv7jiuwO7K9fD7A88ScgX59gV/\nQ8h9/FubWrbF/R/+gg8TO9AUBwsCTdzYuo6V4eUTf3ahzKMHeuksHFnupQArq6u4eVHdGbVT2JlC\nfqJCiJOumN4NQFX4vDHvZ+NvY+ppAnWXormrR9xLdb0IQKjxEyNan4dP8fqzs7+IV/NMqR757dtI\nv/wShe3bQFGo/9o3UN1Tm4UNUDJK/GzHrzBtkz9fcdu0Ahvg+bY/8WFiB2dVL+aOc7406XuyFYO3\n+tK81D2AYdusrvVzXjiAz+Ggxu2ccA9uMb/JT1YIcdKVMntRVCce/6JR9yxTJ9P7MorDQzD28RH3\n9EIP5dwB3P5WPP6W4es5Pc+2xE4WBpqnvCVo9p236L7/X8GyUDSN+ju+jm/ZxC3bEfW0LX66/Zd0\n5rpZ23Qp50ZGn/c9FS93vsFj+54m6Arw9VVfmXSCWWe+xL/v7qRgWHgdKl9aXM+K6ukdCyrmHwlt\nIcRJZZRTVEpxvMFlw4d9HK2Y3oVllgjGPo5D8464l42/BUCw7pIR17cnP8LG5vy6c6e0PEuP99Hz\n039Ddbtp/Nv/iqd18XG1sAGePfQi25MfcXbtMr5w1s3H9exh2xI7+fWuR/A7q/jb874+bmDbts3W\n/hwvdffTWxzcWe1TzRE+VhfE45DlWWcSCW0hxElVzOwBwBMae3vR/MA2gFGTz0rZ/eQHPkRz1+IJ\nnjXi3tbEDgBWT6G1a9s2vT//d2xdJ/aNv8a34vjXULdnO3nqwLNUu0PcvvLPj3vimWmZvB/fxm92\nP4qmavzdmm/QHGgcs6xt2zzZFueNvjQORWGh38PH62tYWSOt6zORhLYQ4qQqpj8CwHtM8AKYRoFS\nZh9Ob/2I7UyL6d3ED/wOgJrmT41oTeumzo7+XUS9YWK+yY/fzLz8J4of7aRq9XkEPnbJpOWPZds2\nv9v9BJZt8ZUVn8fvOr49xA93q78f/xCAL634LAvGCWyAl7oHeKMvTb3XxVeWNlLrmfokOXHiVSwL\n86jDMouGRbykM9b5mZZtkzMGzxg3Jji69MtyypcQ4lRgGgVK2YO4fI1ortCo+7n424BF1VGzwkvZ\nA8QP/BYFlcjiL+INjjxl682e99BNnYtiaybtGq8MDBD/3a9RvV7qvnr7cW/T2Z7tYuOeJ9iXPsDq\nyDmcHV42+UNHMSyD3+5+jPfjH7Ik1MqXVnx2zHO+bdtmT6bAtv4c7yQyhJwaf7mskZBLAnsiuYpB\nZ75M4ahtWcdj2jZ9RR3dsiYtO5aCYdFdKJMsV6b1/ES+fP742+9KaAshTppiehdg46se3SVtVvJk\n+l5H1arwRwbXWdu2zUDHJrBtoku/hCewaMQztm3zYvsrOBQHH2+aeG9v27bpe+jnWMUidX9xO86a\niTcpOVZWz/GTD35KWs9wVvVivrDs+MaxdVPn/3v/QfalD9Dkb+CvV/8lvjFO39JNi8cP9bElmQWg\nxq3x9eXNZ2xgW7bNgWyRnqOWtVlAoqRTMI4EbtEwOZAtMn77dfZ5HSqtAS9ux5GNdFyqQtTjwjnG\n2ngFhSqng4DTgWuau9BJaAshTprCwHYAvGOEdrr3ZWxLp7rxWlSHe7h8pdRHVe3qUYENsCX+IT2F\nPi6OnT/hMinbskg++Tj5D97Hu+JsQh+/6rjr/rvdj5PWM3xm8af45KJrJn/gKIZl8LPtv2Jf+gBr\noufyFyu/iNvhGlHmcDg91Ranp6jTXOVmXVOEhX4PLsf83Wa0YJjsSReIl3SsY/qMS0OHl5jHJG3Z\ntIbL2/ZgSE/FgioPy0I+gi6NyfpQFCDicU37OFGXqhByaSf9UBUJbSHESWGUByhl9+Oqah6xi9nh\ne7nEO2iuGvzhwSVbllki1fVHUFRC9aNDNqvn+O3ux9BUjRtarxv3c23bpvffHyTz+qtotbXU/+XX\njvsf2pJR5oP4NhqqYlzfcvWUn7Ntm10De3ly/yYOZtpYVr2EO87581GHfKTKFX6zv4dDuRIAl9SF\n+PSCyCm7J7hl2yRKFXqL5THHZi0gXtTpKpQ4kC2NGPOdCk1RiHqcOIe+f9TrYlnIN2L71Rq3k4DT\ngTIUz6rCGXGet4S2EOKkyCW3AOAPj95iNNX9AtjW4IYpQzOxU53PY1ayhOqvQnOP7MrekdzFz3f8\nmlwlz81LbiDmi475mbZtk3x0I5nXX8WzeDFNd34bR2D8ST7j2dm/G8M2WRNdNeU9xQ+3rrcMTTi7\nOHY+f77isyMCO18x+fmeTjryg12/Z1dXcWV9DS0B75jvnG25ikGqbExYRrcsOvJltvZnyVYGy5ZM\ni8oEE6mOFvO6WBMO0OhzD4fwYc5xupJ10yKpG1jHfEbJsogPHX6yK52naExvPPp4FAyTRKmCfRI7\n3v/n9WNvOgQS2kKIk6BS7ieXeAfF4cFXM3JZll7opjCwDae3AV/1OYPli3FyyXdxeqIEY2tHljd1\nHtr5W0pmmc8s/hTXLRy/qzv56Eb6n/49zmgdjXfeNa3ABvhwaEnZVDdQ6ch28Zvdj7I/fYjFoUXc\nsmT9mPuH/74tTke+TIvfw4WRIBdGgiesu9W2bXTLpjNf4tXeFAXDpD1fYorZi0OB6qFxdb/moN7n\npsHnHjGee5gC1LqdNPjco1q/h48J7SqU6S9XMKwsPQWd7kKZojkYwpZ9MiNyctUuDccMDoCZTRLa\nQogTyrIqxPf9EsssUbvwplEneqW6NgNQ03jtcGClel4EINR4zXDL+7A/db5OWs+yruUTE44t597f\nMhjYsXqav/Pf0ALT22K0ZJT5MLGDkCvIwkDzpOV78n38P+/9H3RT5/yh8WvXMePXHySzPNuZYKBs\n0Fzl5hsrmlFnMaxt2yZeqnAoV+SDZJa8YZLRjeFQPKzB52ZJwMtEH60qCiGnxgK/B9843c820JYr\ncihbGg7bjnyJF7sHyFWOtORNG/rLlTG7y6tdGo1DG9xoqkKd14XrmBa4pqhEvU5cqjrcPX6iuVT1\nlJpTIKEthDih8on3MMr9+KMfwx8+f8Q9Q89Qyu7DVdWMJ7h48Fp5gGJqJy5fE97gyCVV6XKGPxzc\njFfzcP0ELWyzkKf3oZ+jaBqNf3snztracctO5tlDL1AwiqxfdMWkreCsnuPBbQ+hmzp/cfYXueSY\n08YyusEbfSle7B7ApSosD/m4cWF01gJ7X6ZAT6HMjlSeA9kiMNjq9ThUfJqDBX4PHofK5bFqmqs8\nw59bNgeXL3UVyvQUBk8LG/5O/z977xkdx3nm+f4qdW50N9BAIxLMUSRFiRIlS7Jylk3JluVxkGzJ\nQZ6dnd0ZX3s+3Dn3zKfde+54vUc743GQx2EsR0UrZyqSFCmKOYMAiIxG5xwqvPdDgwBBAMyWZE/9\nzuHhYb1vVb3oJupfz/M+QTcm9trPBpciTwkKa3Y7aPXWrPRjrvGwyzHrC4HNVGzRtrGx+ZMhLJPs\n2GYkWSPQ/Mlp48cKrRyfl11MHwDAF75omkj+4dBTlIwSn19814zpUseIP/4oZjpNw52fwdnadtbr\nH8wN8/rA2wSdgVMGoI0Uovxg57+TrmT4ZNsnpgl2xbT4t/395HQTr6rwwJI2WjxnVjp1NsZKVV4Y\niHE4U5w4tqjOw5Kgl2VBL5YQJCo6RcNkpFjhlcEEo6UKlfGw7VMFis31u6k/RROSgFNjedA7Zd/a\nryn/KYLDPkxs0baxsfmTUUjtwdSz+BvXoajTRfaYQLsDS487th+QcAemNu/YGz/Arvg+FgbncWXb\n7JXMiocOknn7LRztHdTfcttZrz1dyfCj3b/AtEw+v/jOaS7u48lUcvzbzp+RrmT41Pybuanz2mlz\nNkZT5HSTdU0Bbm5vOOea4YYleG8szZaxzESBj/l+N+uaAgQdGk1uB3uTOR49Lir9eOqdGg3O2hoc\nikSLu2b9tnqdeI8TWlWScNnC+7HBFm0bG5s/CUJYZKMbQZLxN102bdw0ilTy/Ti87RM9tY1qhmpx\nGKdv3hSRNyyDJ448iyzJfH7xXSeN4E48/RQAkfvuR1LP7hEnhOA3Bx8nXclw54LbWNW4Yta5pmXy\ns72PkKqk+dT8m7ll7vUTY5YQHM2V6M2VeGc0hUdVuKU9PGPw1ukyVqqyOZpmVzJH2bRQJIkGp0bA\noSIeJN4AACAASURBVFDUDZ7vjwNQMs2JCO+FdW7m+T04FZkWj5MWj8NuNPJnii3aNjY2fxJK6YMY\nlQTe+gtnLFlazh4BBJ7jLOrs6DsAeOsn3eVCCP5w6CnGinE+2fYJWn3Ns9/zSBelw4fwXLAK9/z5\nZ732TcNb2Z84xNLQopNGpwO8ePR1ujNHWdO4kps7JwPjhBA80RudqGymyRKfmtN41oI9Vqzwm+4R\nYuWpZTPNcdd3oqKjyRJ+rfZYb1A1VoR8XBSuI+T8z1lN7S8RW7RtbGzOO8IySY9sACTqIlfMOKeU\nPQIw0bGrWhwhn9iO5mrEW79qYt7mkffZNPI+c/xt3Llwdne3kU4R/eXPAai/7fazXvuWkQ/43aEn\ncasuvrD0sycNPstUsrzW/yZBZ4AvLbt7ytz3Y1l2JHK0epx8siXE4oDnjK3b4WKF3fEsh7NFoqXq\nRGR2vUPlonAdjW4HEhKyVKvuFXZp5zUK3ebjhy3aNjY2551cbMtExLjmapg2LoRFOXsERatDczVi\n6nlivY8CEGy7CUmqiZslLF7tfxNVUvjmyq9MK/15DKtcZvD736M6OkLo5lvwLF4y47xTES3G+N2h\nJ3CrLv52zTcIu08edf5a/1volsEtc6/HfVzv7w/iWZ7uG8OtyHxpYcsZW7qmELwzkuTVoeSUfOV5\nfjd3zGk8bwFsNn9+2KJtY2NzXhHCIhfbiiQ7Zyw/ClDJHcUyy/jGi6kk+p7CrGYItFwzpYvXgeRh\nxopx1jVfTMgVnPWeY799hOrIMMHrrid89+fPct2C3x58HN0yuG/5X50yJztbzfHO0HuEnEEua1k7\nueZUnid7o7gUmQeWtJ2xYB/JFnn66NhEcJkiwdUt9axp8NPgmj0YzuY/B7Zo29jYnFcq+aOYehZv\nwxoUdXo5TmEZJAdfAsDbsJpS5hDlXC8u/wLqIldNzNMtg2e7a/Ou7bhy2nWOUTx8iOymjTg759J4\nzxfOuqLY7vh+jqRrLTcvalp1yvmv9b2Fbunc1HkN2nhp0t2JHE8cjaLKEvcvaaPN6zrldbJVg52J\nHKmqTm+2xFi5OjE2z+9mfWcTTW5brM8E0zLJVLOUjQrRYgzTOnmp1mNUzCqjxTEMa7K1Z0EvMFaK\nI86wfvq58L9v/39mHbNF28bG5rxSSNZqbR+/L308udj7GJU4vvAlODxtjBz4ISATar95iuA+feQF\nBvLDXNaylg7/zLnWx2qLAzR98ctnHS1uCYvnel5GQmL9gltPOT9eSvDO0GaCzgCXt17KQL7MM31j\nDBUrOGSJLyxoof0Ugp2tGmyMptkUTU3rctXk0rilI8ySgPdD7yL1cUY3dWKlBGWzzO7YfpLl1LQ5\nFoIj6R5y1fx5u68mqyjSx0MuPx6rsLGx+YvAMqsU0/tRHEGc3jkzzjmWhx1ouZpKoR+jksATWoXm\nCk/MiZcSvDW0iSZPmHsW3znjdYx0iuivf0Wp6zDe1RfiXrDwrNe9L3GQ4cIo65ovptnbdNK5Bb3I\nD3f9nKql81fzb6FswiNdwxQMk6VBL7e0h2e1jA3L4qmjY+xN5TGsWn3tgKbgd6gMFio0ujQ+Mzfy\noTUM+bAxLRNTTFqxJaNMtDiGKaaWVy0bFQbzwwzkhhjMDVM0atXdDMs4rcYdXtXDxU2rcSpOmr1N\nJ82xPx5VUmj2NuFUJmMGnIqDkCNApbcHq1KZMl9UK1RHRrCqVfTYGGY2d1r3OSV3n2SN5+cONjY2\nNlDKHEJYOt76lTNaiKaeo1ocwumbi6J6SCd2AeBrmNrV6NX+t7CExW1zb5wx+EyPxxj8/j+jx2K4\nFy2m6Yv3ntO63x16D4BrO6466TzdMnh4z38QLca4Yc7VrGu5mEe6hskbJrd1hLmyOTTjeaPFCjsT\nOboyBUZKVUIOFZ+m4FRkDEtwNF+mw+viq4tb/+wqiJWMEmVjUsyq4y5m3dSJl5NkKjlKRonB/DDR\nYgxLnFlnrqAzQIs3goSEKqs0u8PI2Twdlp9OuWGiNefxeCQHSnE8tW5cRyuGoGgIzHwBIx7jRG+3\nVSygx2LkhDh2ClnJSUZyYZaK6KUySS1A5bja+bqsknAEMCQFCI//OXd+dpIxW7RtbGzOG4VkTYS9\noZld46XMYQDcgcVYZqVmlWsBnL65E3Ny1TzvjWwj7KqfcW+5Gh1l8H/9M0YqSf2n1tPw6TvPyYWc\nLKfYlzhEZ10HHf7Wk859sutZjqR7WdO4kvULbmW4WOFAukCnz8UVkemBcqmKzksDcfakaq5aCVge\n9NLqdfLGcHLCLT7P7+a+Ra3nVHTlw2IgN0RPpo9sJcv2sd2MleKndZ5DdtKiLECjtm2glyT0vIJf\n8aBIky8qwtCRqjp+U8OTq+DQJ9VV6Dp6YhBRKdNHlD3qCLkZKu0BFBUXKc0PSFiSRF5xU+uMogCz\n5Po7Fsx8XANm6TejSKApEkjSh7KVYYu2jY3NecHQc5RzvTg8bTOmeQEU07Va457AEjKjbyOsKr7I\nlVMedu+NbMOwDK7puBLlhA5fZj7P4Pe/h5FKEv7sPdTfevZlSqG2J/5k13MIBFe2Tq/adjxdqW7e\nHtpMizfCfcv/CpDYMJQA4JqW+ik/Q9W0eGskxTujKQwhaPc6+WRziKol2BxNsz9doE5TuLWjlr71\nccivtoRFX3aAilmdNlY2K+wc20N35uiUfWSn4mBpaBF+R63laaUsU8pqiFKAXFrGMlQ0RaNSMYnG\nyxw5H+2v607dae0YAUdNVAEWOGv/lhUZ2e3hxNZmkqIgOZ1T7Haf20FTyI0sS0gSREIe/J5JS1uR\nJRqDbtQP8WXLFm0bG5vzQjG5BxB461fPOG5U0hMdvWppYVtQHAH8TZN1xC1hsXF4C5qssq75oinn\nC8Ng9Oc/xUgmqP/U+nMWbIC3BjexI7aHBYF50+435WfTSzxy4DEkJL687HOYQubh/QMMFyu0eZws\nDtSsvXRF58mjUfryZXRLUKcp3NweZkXIx7vRNK8NJZCAFSEv6zub8Gkf3SO4qJeomBW60j3sSxzk\nSLqXdCUz63wrH8BhhGj3LmSOvx2n4qDZG0FGYW93kv19KbKF4wXfAqpAFVlYNFZSNFVTOK0qIOEL\n+Wmp9yCf0H5TcjhQgyEkRUUNBZEdk/vLkqogOycD/Oq8DhqDM7cWdWoKbudfnsT95f1ENjY2HwmF\n5B6QZDyh5TOO5xM7APDVX0Sy/1kQFqG2m6b01z6Q7CJWSrCu+eIpXbzMYoGhh75PuacHz7LlNHxq\n/Tmvd2/8AI93PYNP83L/ii9Ms+qPoVsGjxx4lEQ5yS2d1zG3bg7P9sUYLlZYGfJxR2cjkiSRquj8\n9OAg6apBs9vB8pCPq5pDdGUKfG/3UQqGScChcv/itg89haugF0mW0xT0Al2pbraN7SJeSkyZ41bd\nXN58CU6zHl2HcgkKBTAMGBuTiI5ChdoWcRcloARMinxA1lkqF2gqxGgqRGnKj+KTLSRZRnG7qFt7\nCZ7FFyO7PTiaW1CDs+fd28zOOYu2ZVl89rOfJRKJ8OMf/5hMJsPf//3fMzQ0RHt7Ow899BB+f811\n8pOf/IQnnngCRVH4x3/8R668cvbcSxsbmz8fqsVR9HIUd2DpjN28jEqKfHwbkuJECINKoR93YOmU\n7l5CCJ7reRmYnped+OOTlHt68F96GZGv3I8kn5s7smrq/PrgY6iywrdW3T9r4ZZsNcePd/2SvtwA\ni4LzuW3ejRzNldgylqbBqfG5+RFUWaZomPzH4WHSVYMb2uq5dtxdvjma5tn+GJoscUUkyJXNIQKn\naHF5LgghMMZzkot6hfe6j7Bn7DA9mT4sYSIMB6JYh1Seh0tegSIpmFUH5aJMXpJ42xIYJ+afjbPA\nZ7FwcBdUK9PGmqpJWstxJEAJBJCdLgLr7yB08y3n/F3ZTOWc//f86le/YsGCBeTztUCLhx9+mMsv\nv5xvfOMbPPzww/zkJz/hO9/5DkeOHOHFF1/khRdeYHR0lPvvv59XXnnFzkG0sfkLoJDcDcycm22Z\nVca6f4dllgh13E4xtRdgWl727vg++nODrGlaNZGXbRaLZDe+Q/rNN9AiEZof+PpZ52Ifz3sj28hV\n89zUeS3zAjOnpmUqWR7a8eOJimw3dt7OCwMJtsQyCOCOOY2AxAv9MbbEMuiW4IpIkOtaa/v58XKV\nFwfieFWFry9tI+L+05QeFUIwVoyxZXQ7mwd3kjgaxsw0IHQn6C5mi2o+1nbE5VBoaXDX9tQtiya/\nhkdUcZayBPMxRHyMYPQooWoGxe3Gf+llqIEAvjUXo/h9U64paQ4Ur/dP8nPa1Din//2jo6O89dZb\nfOtb3+IXv/gFAK+//jq//vWvAbjrrru49957+c53vsOGDRu47bbbUFWV9vZ2Ojs72b17N6tXz7z/\nZWNj8+eBEBaF1F5kxY27bnqudHpkA0Yljr9xHZ7AUlIDL4y345zs/FUrbvIKEhJ3zLtp4vjIj35A\n8cB+kGWavvCl8yLYFbPKa/1vosnqrJXWTMvk5/t+w1gxzg1zrubiyHX89NAwZdOiTlO4e34zYafG\nTw8OMlAoE3KoXNYU5IrmIIYl2J/K8/JgHEMI7u5sPO+CnStW2TtylG1D+zk8FKdccCBMFSt7EZgq\nkiRQNYu2eSbzGhsJOgIggUMWRMpJgn37UCtF9FgMkcwgjYJVrWDlpxckkZwunHM68K25hcAnrkQZ\n95zanD7FskEiWz7tqmqNjbN/xuf0G/A//+f/5B/+4R/I5SYTyhOJBOFwePzGjSSTSQCi0SgXXnjh\nxLxIJEI0Gj2X29vY2HwMKOd6sIw8vvBaJHnqI6VSGCQf24rqDBNsvX48JUzgCU7d9/4guovhwiiX\nNa+dKG5S7u+jeGA/7kWLiXz1azgikfOy3kcP/5FEOcUNc66mzjH94Zgqp3m86xmOpHtZ3XgBc4NX\n8rPDQxiWYH1nI2vDAdJVnR/sH6BomFzY4OfOziZUWeKDeJZXBxPkDRNZgmtaQqwM+WZYxZljmCYv\n7NrN27uHSUadIGRqeUiTuUghv4PrLmrnxrUdODQFM58n/uTj6PEYRipFdXQEhECnZmlLqooSCIAk\noXi9uBcuQna6UEMhXHM6cc7pRGtq+otwcVtCMJYqkcyWATBMwUiiQKVaK/aSL+mMporT8rfPhqpu\nMpIoohsWAkFVP7Ow+We/P3vMxlmL9ptvvkk4HGbZsmVs2bJl1nm2+9vG5i+bfGwbMN01LoQgNV5j\nvH7OHSAp5BM7AfAEJ/eyq6bOMz0voUgKt827YeJ4+vXXAAjdett5E+zu9FHeG9nGHH8bd8y/edr4\ncH6Uf9n5MLlqng5/B5J6JY/1juFUZL68sJllIR+GZfG77lGKhsntHWE+EQliwUTvbMf4/vW6pgDh\nc2zwUTGqbNjbxbaDMQaGKxhVDXCjevM0Nkp0Bpu5sLODOZE6nJpCwOugfPggiV/+lGo0ipGIY44b\nVbLLhXvhIpxz5+FfewlqfQOq339evBfnC9OwSMYLZNOlaeJZKlTJpEscK4hmWgLDrImhYVoUysaE\nJVuqmlSqJub4ccsSFCsGpnX6iuyklp59NmjAfEUeT+OTkB0KmirPUAbmzDnrb2v79u1s2LCBt956\ni0qlQqFQ4Lvf/S7hcJh4PE44HCYWi1FfX2ttF4lEGBkZmTh/dHSUyHn6RbSxsfloqJbGKGUP4/C2\n4/BMrQ9eTO2lWhzGE7oAl28OpUwX1eIQ7sASVMdk4Nfr/W+THLd8G8ZbYerxGNn3NqFFmvFecOrm\nHafL1uh2ANYvuG2iyccxykZlQrDvXHA7vcX59ObLLKxz8+nOJsIuB7pl8dsjIwwXK1wcruOK5hBC\nCJ46WhPsdq+TLy1sPadgMyEE/bEMWwb28vrWMfTM+GelCJo783x63XLWzVswxSDSEwnMeIb4C1tI\nv/YKCIGkqkgOJw3r7yJ0861ImvahGlHFQpXSeApYOlmkrzuJZVoIIJcuU8yfUBIUKOarWGcgrKfD\nsQSxWnjkmf38qiqf6SkzIwTo5qnnnQZn/T/r29/+Nt/+9rcB2Lp1Kz//+c/53ve+xz//8z/z5JNP\n8s1vfpOnnnqK66+/HoDrrruO73znO3z1q18lGo3S39/PqlXn75fRxsbmwycb3QhA4IQCKQDFTK2Q\nSqD5aoSwSI+8Uft3yzWTc/QSr/a/gV/zccvc6yeOJ555GkyThk+vP2+uWdMy2TG2G7/Dx+LQ9MpX\nx4LTbphzDVlrKUfzWVaEvHxxQQuSJFE1LR45Mkx3tsSiOg+f7mwE4LXhJNvjOdo8Th5Y0oZLObsy\npJl8hU37Rnl1ey/pzDF3apDGZp2rLwlxYWcnrb7phk7i+WcnmqYAqA0NtHz9QVwLFp53t7YQglym\nTDZdxjBMEmMFEmN5qpWpXbR03SQ6lJ3V1SzLEh6fA4laNrdpWhimQDhVDFUiZ5hkSvqUcyxAdWsT\ned0hvxOfp3YNRZZoDLnQxkvA+lwaIb8TTZUJ+Z0oZ/E5+OqcuNxna2v/6TjvfpFvfvOb/N3f/R1P\nPPEEbW1tPPTQQwAsXLiQW2+9ldtvvx1VVfmnf/on23VuY/NnjFFJUUztRXM14apbNGVMCEEl34+i\n+VGd9RSSO9FLo3hCq3C4J4Vn4/AWKmaVW+fegFut2UTlo71kN2/E0dqG/5J1nC/2xPdT0Itc3X4F\nsjT1IW5aJhsG3kGVVHqK80lUsrS4HXxmbgRJkiibtZSuvnyZZUEvX1jQjCrLvDIY582RFCGnyn2L\nW89YsA3T4qUt/RzoS3GoP40lBEgmWn2SzoYGrl+5hHWLO2Y8txodJfHHJ8m9vxW1vgHfmotwzZ2H\n7+K1yI7zkwcuhCAezXPkwBhjIzni0ekCPRtNrX6amuuQJHA4VeYtDuP21taVzFfYfCDKzq44Y6nS\ntBYgmiqzemkTQa+DcNBNZ8RHR5Mfj+vj48r/qDgvn8Cll17KpZdeCkAwGOSXv/zljPMefPBBHnzw\nwfNxSxsbm4+Y7NgmQFA3g5VtVJJYRgFPcAWWUSA9vAFJ1gi2Xjc5xzJ4Y+BdnIqDK1pr4iwMg9Ff\n/AyEqEWLnydLcbQwxm8OPo4syXyi5ZJp47vi+0iUkzi0ZSQrCpc2BljXGOClwThlw2KwUCZVNVhV\n7+Nz85pRZIlN0TRvjqRocGp8bUkb/jOobmZZgp7hLM9vPsqu7lqRk6YGjYx/D/5Imu+ue5BGz9RS\nsEYuS/yJxyj39GCVyxjJ2nmuefNp+dbfoDXMXDr2TBBCkEmVOLJ/jKH+9DSRDtS7mTM/RKDeg6rK\nhBq8hCM+3N6pFqmERL5ikMlX6BnJ8kFXnCcPjGJZAtMSpHI117jbqbBkTpA5ET+dET9zIj7CQTeq\nIp2VdfyfAfu1xcbG5oypFAbJx3egOkIzVkCrFPoBcHjbiHX/DssoEGy7EdUxGen8QXQXmWqW6zqu\nwqPVWlGm33id6tAggU9ejWfZzJXVzobHDj9N0Shx77J7aD+hKYgQgmd6NgBQ51rFA0vbcakyPz80\nRG58H1KTJS5rCnDHnEZkSeJItsjz/TH8msIDS9oIOk/fjdo1mOaRlw8xGCsAsGROgEzbBjJmDBn4\n4op7pwm2Vakw/K8PUe7pQXa5kD1ePMuWE7jqanxrLzmjlxvLEkSHMhOWc3wsTy5TnhgzjclI52C9\nmzkL6lm4tIm2ziCOU5QF1Q2LXUfivLN7hL29iSnucb9Hw6HKyBKsnN/AVataWL0wjKba4nwm2KJt\nY2NzRlhmlfjRJwCL+jl3IEnTH7qVfE209XKcamkEb/2F+BsnG3IIIXit/y1kSeaa9lqutFkskHju\nGWS3m/BnPnfe1jucH+VgqotFwflc1rJ22vimkQPEisM41Lk8uOICEIKfHhykaFjc1hFmZb0fv6ZM\nNPTI6QaPdo8iSfClhS2ETkOwC2WdQkln455Rnt/chxCCy1dEWDm/gUPW2/THYqwML+fCxgu4sGnl\nxHlCCPI7thN/7PfosRj+yz9B8/1fP2MPhKGbJGIF+roTHNozSj47GQSmajKBoBtpvClGIOShY16I\n+UsaZxTpZLZMz3CWim4yMJZnYCxPadwaj2fK5Mf3oue31jGvuY76Oifrlkeor3NNu5bNmWOLto2N\nzRmRj3+AWc1Q1/QJXP5508aFMCllu5BVD4XkHmTVS6j9liku9P3JQwwXRlkbuZAGd60HdfKF57EK\nBcKfvQfFd35ymwE2DLwDTC+NCrWa3I93PQFIrF9wA2OlCk8dHcOwBJ+Z28TaxsCU+ZYQPNozOtE/\ne47PfdJ7ZwpV/vB6F+/tn6xJEfQ5+Nb6C1jcEeSloxvY2rONDn8b37jg3on655XhIUpdXWQ3vUu5\n+wgoCqEbbyb82c+dVLCH+9Ps3T7E2Mhk7QyEIJ+rTFi9mkNh2eoW2jqDhCN+AuNdrE6GEIIdXXHe\n3DHEvt7ktD1oh1Zbk8epctMlHVy1qoW2xvP3HX7YZKo6A/kyBaPmabGERaYSJ1UeQ7dqEfG6Kcjr\nxrTP4hgWgrxuoVtn3trsx3d8YdYxW7RtbGxOG2EZ5GLvIckO/JErZpxTzvViGUWQNBA6dZFrkZXJ\nwCghBM+O1xi/cc41QC1lKf3aK6j19QSvv2Gmy54VW0e3s3nkfZo8YVaGp7rbE6UkP9z9K6pmjohv\nHe3+dn5xaAiHIvPFhc2smKEoyuvDSbqzJZYGvDP2zz6eUsXg+7/fyWAsT0eTj9awl4VtAS5f0UzW\nTPBvO3/G/uQhQs4gX1vxZWRJRk+lSD77NJm335y4jnfVahrv+SsczS1Tri+EwLIEerUWqb1vxzB9\n4/vjbo+GcpzbOdJWR2PET1NrHfMWhdEcMwfMWZaoBcONMxwvsP1wjL29SXqGswAsaKvj4sVNuJwK\nrQ1eOpp859RNyxIWhmUyVoxRNEqndY4QYlpkuokgXTEwTiGSJdMiW50qtnnDpDC+FVIySozkuxCi\nhCUKWFYeMKnFr39Y2KJtY2NzHiimD2DqOfxNl6Go061MYZmkh2pFUZDA33gZ/vDUwK9d8X0M5Ia4\nuGk17f5WzFyO0Z89jDAMwnd+9rxEPh9zvz/T8xJu1cU3LrhvSsS4JSx+uOvnjBbH0LQl3DHv+gmX\n9wOL2+jwTXfl7ohneWM4Scihcvf8yEmzX7oG0/zm1cMMxvJcs6aNL9+0GAnozfbxRO+TbB3djiUs\nloYW8aVld+MZTTPwux/VrGrA0d5B8Opr8V6wEjUcplSooucrVEoG3YdiRIezxEdzlIpT06Ka2+u4\n/JoFRNrqTpmdUyjrbN0fZTRZolDW6Y/mGI4Xp4j28axZFOYzn5w/qwUthCBbzQMCS1iMFsYYyA+R\nreZOmAjxcpJsJUfJLBErJhCz2qsfLaqsEXSFUGWNkKuRBlcE13hDHFWWCDhUZi2ZIoFbkc97lpQt\n2jY2NqdNKdsFMGPP7GpxlFjv45jVJCDRvOSbOFzTI5rfG3kfgFvmXo8QgsH//T0qA/14L1yD/7LL\nz8s63xrcxB+7XyDg8PONlffR6mueMn4gebgm2OpCLorcwo5EfsLlPZNg53SDZ/piuBSZryxuw6PO\nbKkmMmUee/MIWw+MAXDlqha+dOMistUsP9z1c4bytQJTTe4wdy28nRW++WQ2vE7/H58Ey8Kz4gI8\ny5YTvO4GZIeDXKbMK7/aPtXdPY6/zklbZxBVlalv9DJvcSNNLf5ZRaJvNMfO7igD6TFKVYOuwQzm\nuFUqqTqat0TjQgeaMvlyozoM6uqr+DwamjrGa7H9EJt+bSFqLyQntvs8GRIKkqTiUCOAipB8SNL0\nDnEnIgNeTUGb5tKX8Kgy2in2+xVJwqsqU3pwO2QZ93jFMkWSWd6wmDZfK6qsTEsP/KixRdvGxua0\nEMKinD2CotWhuZqmjFXyA4wdeQQhagFJwbYbZhTsklHmQLKLVm8zrb5myv19NcFetZrW//K35yXF\nayA3zFNHnsOnefnu2r+dse3mC71vAdDguRBJkujOFU/q8n5lMEHFsvh0Z+OMvbCHYnle3jrA1gNR\nqobF3GY/X7xhMQvbA+Sq+QnBvrDxAq5svYz5Upj8pnfpfelfscpllECAlq8/OCVivq87wevPHqBS\nNmjrDOL2OJBliY759cyZXz9R+MMSVq0lpzD5YLxPdiyfoSc+giksylWToshgyRVQTCS3ADdogell\nOrMz/OzRIlA89ecuSypB13wsoVAyLWS5DkVuQJan13dXJA8BZ91EtTEJmONzMcfnnma3hpwafk1B\nAlRZpsGpoZxiD/5kWNUq5aO9cLwbXTDZ9gxgpIjBEU4vI30SI51Cj8fPem3HaLz/S7OO2aJtY2Nz\nWlQLQ1hmGV9wxRRrzrJ0Ev1PTwi25m7G3zhzUZR98QMYljERIZ3fXqtbXveJK85bTvZzPS9hCJN7\nl90zo2BvHN7B0Ww3itKM19HEgXSBTp9rVpd3b67EB/EszW4HlxwXmKYbFm/trAVm7elJYglBOOBi\n/ZXzuPyCZmRJYldsL7/a/yhls8wVretYL60g8ZPf09fTA4Di91N/480Er70eta6WDpdNl9i/a4Qd\nm/tRFImrb1nMstUtE2vLVwtsjm7hSLqH/uwQyUrq5B+IBhgaLslP0OmhzR9BkVVkRSJVNYiVqhRM\nCUUOUbNjJ5EkDVkOInHqojGS5ERIGjKwus5Dg6v2SuBWFMIurRZ9XylTj0XY7ZjBUp6K0HWqIyNY\nJ/Tvnu39QRgG1ZFhrEp1+qBloY9FMfN5qqMjWKXT2zv/yLBF28bG5lwpZWv7ra7A1PabubHNGJUk\nmrsFvTRCXeSKGdPASkaZ18cjudc0rqylM33wAZKmnbf64oO5YfYmDrIgMJcLwsumjAkh+CA2wO8P\nPQGoNPquJl7RWRuuY31n04zWW6qi82RvFAm4c24Tyrhw9o5k+ckz+xhL1R7+LQ0e7r5mAasXHkGQ\nOwAAIABJREFUhidSww6nuvn5vt+iSDJ3ha9i/u/eY2joWQA8F6zEs2QZgauvQfFMuoS3vtPLBxv7\nAPAHXNx81woam/1YwmJ/4jCbh7eyO74fU9SCpoSuYZVC4x2/wCoEsHIh6pxebluznMaAl0jITVPA\nR1UIMlWDg+kCrw4lOFbiW1ZhVb2HVo9z4jMIOFRaHDKBbHrS8hUCIxbDzGZq96pW0UeGEcbUfXUJ\niZk89GY2S/HAfkqWxcApvsc/FZKqotQFqLviKmT3ySP/j0cIKFb0Geui66ZgNFGgXDUxNDdFXwjO\n0aU+c4hnDVu0bWxsTotyrgeQcfnmThyrRZO/D7ITU88gK248gSXTzrWExY92/YL+3CCXRNbQ7Gki\n8dQTVEeGa2U3Xeeew2tYBo93PQPATZ3XnnB/wWM9R3m3/zdYokqz/3pKVh0XNfi5a27TjBZ2V6bA\nI10jGEJwVXOQOT43u7vjPL+5j57hLJYluGFtO7eu6yTkn+yXLYTgiSPP8ubARiRJ4l75YgI/egZD\n1/GtvZTQDTfiXji17KtlWRzeN8YHG/vwB1ysvLiNpaua0ZwKe+MHeL73FfpzQwB4pRDpvghmKkJL\nXQPrlkXwuDSa6z343Bp+j0ZRFvTkSsQswb5EgeGBJMnKpLj6NYW5fjdtHhdrwv6Jam7CsrDKJVIv\nvUj67TcpztBf+1xwzumc0rHNElAs68zYI0SSoKEJTkNcdUOQKemUfPXEyhKl6nTHdslVh+447loz\nGOST66q18cyNB/rphjXRUeykmED51NNOxV+fZMwWbRsbm1NimRWqxWEc3jZkZVKgCqm9WEYBV2AJ\n5cwhfI2XTuupDbAztpfuTC8rw8u4b/nnyW/eTPKF59AiERo/P3t6y2mvT1j8+sBjdKV7WB1ewYqG\npVPGXxtK8N7wW1giy5zApWSs+TS5HXy6c7pgm0KQrRo82TuGQPC5eRGW+Ny8+v4Af9hQ8za0NXr5\n3LULuGDe9H37twY38cbAuzSpQW7q1qjb+CySx0PLt/4G3+oLp8wt5isM9qV5/51esukysiJx3frF\nJB1RPkju4JW+N4iNB3d1Opegj3TSfUSioc7N1+9exuKOIJIkkazo7E7kOFot05NIMVKc6lJ2KzIL\nfE6azCouVWFVwItaqVJ88SlG+/sYNC1EtYqSjCGJmjjpTg+ZeasQ6uTOd9Xtp+qtbRHoQqLf9FI6\nTRmxZJmywzvlWCJTIlvWZzkDGDqtS08ykQ4/05pOc3N+HK9LpaHOhSSBqkg013txOadvE8iSxLLO\nEHMiPqTz0hLs5NiibWNjc0rK+aOAwOWbLKYihEk2ugmQEGZNJHwNa6ada1omz/W8gizJfGbhHVCp\nEHviUSSHg/Zvfxet/txqZgsh+I/9v2dbdCdz6+bw1RVfmCLEZdNk48gIVf0QDsVP2lxJ2KXx1UWt\nOJTj08AEG4aTbBnLTBTVuDoSZNf7w/xk/yhV3cLtVPn7e1azsC0wbR0Ae+MHeLLrWdwVwaeeOoKv\nZOFetJjmB76B1tg4Ma9UrLJpQzeH99ZURpIlAgshFenn+12vUR7/PBES3tJ80kebOZiv7XmvWtDA\n/Z9sp1zMsHHXCEPJDIXhIWSztuY6CZZj0lLMoBoGYnSUUjaPo1xAtWoWaOb47wcZS5KwkBlz1FOV\nVfrcLbwfXIYhq0zJxjpB944J2ml+U8BUy93tULjmwlbqvOeW5qcqMi0NXpwOmXDATdDnOGcBdWjn\nP13rfGCLto2NzSkp53oBplRAy8W2YVTieEIrKab24vC0TungdYxNI1uJFsf4RMulNHkaif3hd5iZ\nDA3r70JrCJ/z2vYmDrAtupN5dXP4mwu/jkOZKgAfxLJkK7sAE1ldyby6Wpeu4xt8GJbF471Rdifz\neFSFFSEfjS6NwT1x3t01TDjg4qpVLVy5qnWKK/x4ejN9PLz7P5AMk9s2Zmlbdw3eC1biXX3hlId/\nPlvmmd/tIpMqoQUEemOKLs9eyq486OCw/OijrYiqCyvbQLnsY06jh8tWBZlfGEJ95zdEX66llDWN\n/zkZuqRiyA6KjjqyrgBIEi6niiJLJBs6yS5ZQ6Tei6bWhM/lVFgM3HiK6yqyTHO9e6Id5ulwYlGU\nasUgnShiGBaZVJFK+UzjtY8jWaRiCfbtG6NSOon1Pr6Ok44Ded2kYn40+ePf+b+umXXMFm0bG5uT\nkh3bQj72PpLiwultB2oR49nRt5EUF6ojCAi8DRdOOzdfLfBsz8u4FCd3zL+ZUk8PqddeQWuKELrp\nlnNemyUsnu15GQmJLy373ER7z9qY4Jm+GO9FB6hW96HIXj638EouaWqYCCiDWg72I13DDBYqdPpc\nfGVRKy5VYdvBMX6/a5g5TT7+8b6LTylOL+x7BhOLOzfluezev8ezZOm0OclEjmd+v4tSziDW3E20\n4xBI0OptZpXnEgb2e9jflWdJo5u7lnrQYkMYu19H3zwEm2tiUlEUBjsXUfbV0eJykM0ZDFs+YgVz\nohpYVdZIaAEkh8aCJR1curyFdQsbqJQNDN0klShiGhZtJy6wbGDNIJyWJUjFC8SjeYrFk2wGnwRT\nt0gli1gnEcIyguz436eiDGecksX4OZVTzvpo+c5JxmzRtrGxmRWjmiU99Aqy6qVx3ucm9quLqf1Y\nZom6yJVU8kcBCU9wapnQXDXPv+78KQW9yJ0LbsNbthh4+EcgBJGv3I/snNliPRNePPo6Q/kRLm2+\niBbv8QFOgt93j7InmaFU3giY3LPoDi6LTLXsTSH4Xfcog4UKaxr8rO9sQhKCo6NZHnnlEJoq8+D6\nFScVbGFZdL/8BPsd/USSBpd96mszCvb7Bw6w5cV+lKqTaNthOtfU8Wnf5/Bu3o3+9lZUfTdLgBsA\njoLx/rgoKQpi7gJSyIwGwxxYugZvfSPhvMnz24aoWCYeSaKpw43XqSKZFm5DMF+R6Ay6KaUqHHyj\nm+3PHcDQz70Up6zM7Hg2haBKLRarJAQWNXGsjo+VAUmRkY5THUkCWZZBAkmSyJzCQj6RmSL+T+XR\ndjlUOurdp2z9WV/nJORzfgi71GeGLdo2NjazUkztAQSBlqtx+jomjucTHwDgCS4jG92I09uOok6m\nLqUrGf5lx0+JFsf4ZNvlXNd+JUPf+//Q4zHqP7V+RlE7U7ZFd/JC76s0uOpre+XH8dZIij3JLMXS\naxjmIMvqF3NF60VT5ggheKE/ztFciRUhL3fPi9AzkuVHf9xLcrwL1j3XLqSlYWrw1JRrGAabfvMQ\nL/mHwaly/eKb8K9eO3H96HCW3v0jHOjqo5J1IqMhBfZz+cABFrxvYlVeRZgmRdVDLDCP9iYfDXUu\nkGUqdfUM1IXZ6AiRC9YKlFhVk3xPBte2XlJILJAkfLKCsAQkp4ctD8SKqKqM06USCLqpC7lRtVof\nbE07A7c2grqgG1zqRA2STKHKB4fGKFVMYukS0WTxpPax16Wesg3n8rkhLl0WoS3sRTnFXnk44Mbn\nPv2WqH8p2KJtY2MzI0II8sldICl4gysmjleLI1QLg7j8C9ArCUDgqpvM3baExU/3PEK0OMb1cz7J\nXQtuJ7vxXcpHuvBdvJaGT995zmuLFmP89uDjOBUH/2X1/fgdk/Ww96XyvDoYp1R5B8PsZ2loEd9c\ned+0oKKXBhNsHkvT6HJwV2cTG7YP8fvXu7CE4LIVETojfm5Y2z7rGnZ1b+G1Pc/Q06GDULmi8SIu\nmXsFo88+x/Y+mdGik4p1TBidlLwxlkR3sHggiQiEyCgGedXNzvpFHGlcyspFjWwfz/vOmyZGgwct\nVHP3OxNlAt1ZHDl93PKriZ8kSdSHPTQ0+iaagLg8GoGQu1beNOwlUO85ZRevEzEti0LJYE9Pgtc+\nGGRwLI85Y15WDbdTZXFHkEi9Z3xv3IPbqRL0OQkHXDg0hTqP9rEM7JoNy7Iol07ugD+21WDoFtl0\nidJZbh2cyPrPTw/oPIYt2jY2NjNSLQxilON4gsuRj9srzkY3AuBvWkchuQcAd91k3vH7ozs4mu1n\nTdMq7lpwO2Y2S/zJx5AcDho//8VzfnDHSwn+bee/UzGr3L/iizSPu8VNIXh1MMHboynK1Z3o+mEi\nnla+ueor04LTBgtl3h1NEXZpfGNpG+/sGOYPG47g92g8+OkVLJ9bP+v99XiMF175Ka80p6EOOgoO\n/uqS+wgeGOTgL/8H2wNXUHL4cOoFwtUoe1YmKQQyfHve3ewcWcWPdo+RLuhQB7JUy1WWDMH7vYla\nXeygA+eSIJom446VaBgo0OpzIYW8EKq5f5ta6pi/JExDo29KN69zoVg22NEVo3sow7ZDsYm+2Ios\n0d7kw+1QCLo1gm4NkFBViSUdQRrqXDjUk0RaGxaWYZE+ievbsgTpZIlKZfY5pYJONlU6rdYilmlN\nbAUIIahUjBkLo8x+viCVKJyX7YSzwRZtGxubMyYX2wKAL7x24pheTlBMH0BzN+PwtBPvfQzFEUQb\njxo3LIOnu19EkzU+s/B2RLXK8A/+D2Y2S/jue9DqZxfD06Gol/iXHT8lUU5xx7ybWBupBb9ZQvDr\nrmEOZYroRh+V6jb8jgD/fc0DOE8QbEsInuuLIYBbWxt4ZXMfz2/qI+hz8H/fezHhwMzFPMxCgfTe\nnby480m2dUrMHzK4fbQeV7ZM9Zn/lz7Fy7aO26kqbpoXCnJzxtheHiBrFLjQcxX/8nqBWKaWcy1J\ntd7TTk1m0aIGhgMq+nHaK5kWTT05blrWwtJbV571i86xaO1oqkhfNEf/aJ7RRAHdtBhNFLAqJi4B\nmgDDtCY6fDWoCkuDHlRL0OBzks+UMYwy1YrJ8S1BotuGz2pdNmePLdo2NjbTMKrpmji7Ijh9nRPH\ns2ObAEEgciXF1F6EpeNrWDMhKtvHdpOpZrmu4ypCjgDDP/oB5d4e6j5xBaGbbz2nNQkh+M3Bx0mU\nk9zUeS23zpvsu/3mSIpDmSIyBqXyJmRJ5m8vfKDWlOIE3hxJ0V8oU29K/J9ffkBVtwj5nfz3u1fN\nKti54X5ef/Qhhjw680cqfGtbFVkApDFcLlwLFrHLtZZq2YFzRZ7XvG/XUpJNDSPWyeYBN4gyEnDF\nyma+dONiDg6m2dAdpd8nIySBZ6SEZFo0BNxc0uBn5Z0LcJxhn2rDtBiI5jjSm6S7J8nocA5LCCRq\nJcg9gAvQkJg/7WyJiQ4ehoB0GRMYy1ZQPBaGpmP6qgjXpDUsEJSMEoZlIYvj3joEKKaGNH5MsiQU\nUwMx+fIhWzKK4ajNO41wLwsL3VEC6dTWr6Hp6I7SxO10ZxFDObMgt7I3je78qOLMPzXriC3aNjY2\nUzD0HGNHfgsI/E2XTQiyUc1SSO5CdTbgCiwhevjfARnfeKqXEII3Bt5FQuLq9itIvfQChR3bcS9d\nRuS++8/ZLf720GZ2xvawIDCPO+bdNHF8tFjh9aEEMlCs7EKIPNfPuZo2X8u0awwXyrw+lMAtSRx4\nd5CAS+VTn2jnhos7cDpmDszatfM1nj38PNcNZlmerhUw0drbkeavIN+ymMGsg/hYgVymTKUhzV7P\nJigGKPcuRxT9IGTqPBqXrWjm5nVzeDuW5n/s7MWQgKADTbdYkhOsbGugtSNIQ9PUftXVikE8mice\nzZNKFDCPS5kqF6tkUiV0w6KiGxTLJooQKOMiWPN/TH7uQrGQ/DpVUa0Jrpaj5MlQcRXgBMezqepU\nXAWEbCHkmlAqkoJaceHN1iObKq6iH28phFpwTwj0mSAUExEo1+7h0RGO8T1kAaYpY7p1TG8Fy2GA\nLNAUjbC7Hm2GqntTOfaKcoxzK+DzccIWbRsbmymkh17FqMTxN12Gt36ykUc2uhGERV3kE1TyR9FL\nUTzB5ShaLbJ5V3wf/blBVodXEDId9L74PIrPT+tf/1ck9dweNYdT3TzZ9SxezcMDF3wRRVYwhaAn\nU+TJvrFaZ0VjhEp1B0FngFvmXj/jdV4eTCCA0Z0xHJLEd7+wZtbocNMyeentR0ju2MpdB4toJjjX\nXULTTbdzYFhi69u9iOFan2vFoVD05Ojv+AAj1o7etwyEQmvYy5dvXExbi48X++P824EBigiUikFd\n3uCTC5q4dEEjmUSR7oMxtrzdO3F/IWr7vNnUyTtSGVhYxwlzRdEpu/KU/CkqgRhCqb1oGFp13FIF\nl+LEKTsIuoK0O4M4lQaavU2o8uSLS2kMCsMCPQdO2YlTcSFLMv0jWaracZXkNInKXAfCpU6+H4ha\nupp5bJIEKPKUfKyyYZKvzBzoZZgCSxa1nLETjN2jJ/00zi+yU0Fxfbxk8uO1Ghsbm48UUy9QTO9H\nczUSbL1xwjqulsbIx7ehOuvxhlYx1v1rAOoiVyCEoD83yB8OPYUqq3x6/i3EHn8Uq1Si8Z4voHhn\nT5k6HXoyR/nhrp8hgPuXf5GgM8BgvszjR6OMlcajda0Elr4BWZK4f8UXpxRZOcbRXImubJFKsoyr\nYnH/XRfMKNhCCPY8/xtKb7zFgqzOIgHC66Hly1/FsXINrz97kL7uBC6vxtGyQcI0qTYeQmvrxkxG\nUEdXs2RugEuWNXHxwjBv7B3md0Nj6JqMI1UhFC2x0u9m4eIm8qkqj/xg06xRypaqU6zLUHLlKUom\nJa2CWXVhlfxYuSAWCgYCbzhDYxNcsCDA0uZOGt1hJEmimKvSfzBNYrRIJWXUirM4FTJ+B8WKgWmJ\niT3qWXennWApEqVGF5ZTgbmn3x3rVMyWqX/uGfx/udiibWNjM0EhuQuEhbfhognBtowSyb6nAUGo\n7SYKqd1U8n24/PNxeFp4pvslXu7bAMD6+bdi/vL3FHbuQGtuJnDttSe526kRQvDY4afRLYO/XnU/\nyxoWk60a/EfX8ER9cIdUJV95ibJR4gtLP8PC4Lxp17GE4IkjowAo0RL/9LVL8bqm5vjqiTilI0cY\neesVXId7UBTIemUaF62i82sPUjYV/vjrnSTjBbSAk/dzZcpSBSU8hqO1G4fl47vXP0BrKMT+0Qwf\n9Cd4/f1uqi4FR16nrTuLnKm9ZPRSoPdgvLY2xSQTHqFQFycXiCHkSTe1VKrHmV5GarQe64St3Ei9\nmytXNnPlylYCPidCCIb702QHy/QV8hztSTASL2CpEuUGF+VmN7pPw5plG+B0vgtMCyFAEqAoEpIk\nIUs1q/qjKfj58WGmzYFTlUs9G2zRtrGxAUAIi1x8G5KkTrjFTb3AWPdv0EujeOsvRFY9xHr+gKy4\nCbXfyt74AV7u20DY3cA9i++kvStJdOcO3IuX0Po3/w1ZO7dGELtie+nPDXFx02ouCC/DEoJHe0Ym\nBNulyMx1HWFLtsj6+bdyReu6Ga+zZTRNwjAojxb5rzcsnRBsS69SPLCf/Lb3yW6qpbJJwGCjhmIJ\nFrZfQOTBv2V0OMdLT++jUtCJIujPFFFbenC39YBsokgK/23NV+nel+axfQeRSgayIQgDsmEhjT+7\nG5q8NC10MZAfYjA/QtEskG+IsbxlIesCF6HhpJLxE03o9A2V6B4okqfWr/vSZRE8LpXmeg9zIn4C\n4002BgZTPPXaIUZU0GUwXQqmU8Fa6IUl/hO+YwGWqBVjEdTyt2VOWUZMARyKgqzWmpucOLtOUwk6\nVaAm4k0uB61eJ74ZtkWEEKQLVfKnmdMshIB8ForjzUYME9JJMM4gsEwIyKahPH2rQRcqBUudEisA\ntZcQQ1LRqX3OFhJVHFCt1tz/ikZJ82NJGur4933euP3iWYds0baxsQGgmNqHWU3jC69FUd2Yeo6x\nI79GL8fwNVxMqOM2xo48AgjC8+5GddbzxM6foUoKX7/gXhoTVYafeBRJVYnc/7VzdovnqwUe63oG\nWZK5fX4t8GzrWIaeXK3yV8Tt4I52N/+6cytBZ4BrO66c8TpjxTLP9cewLMHaOi/zW2sR5WY+z+BD\n36dytLaPXAp62NkBPe1OLhnUWMxcRlffyIv/sgm9aiIQDCMYU00cnXtRGkbwO3xcEbkMdTDCS7/o\ng4qJA7BUCadTxeNUcThUGhq9uNpNot5+nhh4C8tloXpUruu4iqvbvsLuQzn2bk+yvzdJsVKYWPui\n9gB3X7NgoqvY0aMptucKPB1PUjxeOdsmXdZCiNpLwnhBFUkIVCGhyTXLGAmQwakpyLKEW5Gpd2o1\nIRagGgJ5PKfZqFqUsxU0S4CwiKXLZAvVKVb1/8/em0dJdtV3np973xJ7RC6RW+VWWXtJpaqSVKUF\nbWhBQiAJhEHGYAzYgN2eNgNj7Gm7zxmfmZ4zM2fcPqZpj2lhMI1Ng0FICIT2DS1oL9WStW+57xmR\nGXu87d75I7KyKqsqpaqS6MYQn3OyTtS7L9578SLzfe/vd3+LVD6VwEM7JWLVAqAZBoYXxsNumURu\nikghs9j2s8Erkg7eeq3+7VBIAmnhGmHKVgLOEoEeSIuSnUIJg7KVxDOWTmKUMCiEmt++9unZ8DSh\nUhnDLyF0cMawBgIzhF6YuEghsC2D86xzcwZ10a5Tpw5aK/JTLwKCZOvV+G6O6aP/jO9kSbRcSUPn\nrTjFIZziIOHEasKJPg7PHWO6PMsV7ZcRf30/w9//b6A16Y98FLvl7XpPvTVe4PHtfd9j3slx56r3\n0xZtoeD5PDw8A9QE+3fXNPP13ffiKY87V92GZZxZ0nK+7PJ3O4fQtiQ0XuFjd25Ca407OsrEN76O\nOzFOfNt29lgZftZbIOTDLbHtaLWBp49l4aUxfDRzaMx2Hy99HDM0hDACVptrucK9nmOPZygX52rr\nvq0RNl/cxi3be5BSknMKvDm9m90zr3MkcxwykLDj/M7632JVoo9X92b462f2MZGp9btsiNtcv6WH\n1Z0petviNKfC+FrTPz7Pz/eMMZ0wa+vKSiEU6BNW8mlKoAKFN+dRGsrjzr192pLUirQ7T9qdxzyL\nAAGgocEv0B5UCSuXJjeHqQOSXgl5Fue4J21cI3zq20HUnMhupIGZU1IJFxGCkkwwZ6apyCh6QYi1\nlLAQJKcBLQ28QHI2oT5frJhJrDFMLGyecTTTNojELEAjKyXCNthuEVEpIwXUypefOTkVQtCQCBEN\nGW+bNaHKZdzJSbRa5r6ffk3ntFedOnV+rSlMv4xXnSHWtBWtFdNH/onAy5Fsu5ZUx421Zg6TzwGQ\n6ngvAC+OvQLApdkYM9/7LkYqRcfn/4joho3v6FqUVtzb/x0Ozh1hU/MGbu19L55SfOPAKAEQNSV/\nsL6THx2+j4nSFDd2XctVHdvOOE4mV+Fv3jgOSRt73uV//cDF+MODjHzj6/iztfXkUE8vB0pDPLxZ\nE3MFf771T3jq0UmyM1nKEmaVwm/0iFy0lymv1vs6ObeCdXObcWYl/UyAKSl0x1CG4M4NHVy8tZNM\nJcuL46/y3OgvcIKaG/ji5g1cs+IK1qRWMZ3x+eoP9jEwUcCQguu3dPCBq3ppaYhQ9gOeGZhhx4Ex\nCn5A1gRtSkiHaq7tQCEMiTYgcAO0r3BmyjhzDqro0ZYM09oQwQx84lYZmfaJlOex3ZOWrVQB0WIW\n03cJV4vEi7PIcxSNxWNEIgjbxmxZgxtPkzcbcCKNIAVVT3B01iBQFy6qdshYrPYmYMGtHyCCmrWe\njCrClsZULlEnh+TM/G2JJiGrGEIRER5huUxZ0vmzb/azWbxsBu26aPf8SpQWOb17+HnwZ19adqgu\n2nXq/IbjuzlyE88hzRjhxComD38THTikOm4k1X4dUOun7RSHCCfXEIp18uzIi+yY3k1HrJ3Ewy/h\nGQbdf/bvsNvPzI0+X3bN7OVA9jAbm9bxuU2fYrri8Z3D4+Q8H1MI/s3GbgZzR3h9aie9iW7uXvPB\nM44xX3T4f588gNGbIO5pvnxlN5WnHyPz8ENo18Xu6uIgMzy3tUg+biC04BOb7mHXzhLZmRIzwmci\nPUn3KpcZjpFxPZomLiI5sYJ4YOMALR0JppotxtNh7JzDXY0pujcmeWHsZe4/8hCe8olbMe5adTsb\nm9fRFm0hX3b5Lz/ez76BLADv2dTOPTeuwTAFjx6a5HB/maIJWLL2dDZlrc7pCaRAOQpnLM/GcISW\nRJhLUy7G5HGUVUZN78PfN31e91uYJnZPD+GeHkJd3cjw8tHhZmMjZlMzhSrs2TvHsUMzeG5wUp1O\nEb9oWNDbFQUB2g/QlTI6UHjZWYJ8HrWMCIb8Cq2lYSJ+4V3tsBUApbNsV0IQGGeXQiUkgR1B2SGC\nZBRtGgRWGCIWpvUOen+fghaSwAzXvAnnQF2069T5Dacw8xpa+yTT15IdeQi0prn3w4vBaFprchML\nVnb7DRyeO8aPjvyUlJ3gk4lr8Ma/TmL7Fe+KYGuteXzwGQSCe9Z9iNdmijw8UrOKDQGfXddJg23w\nd0cfQgrJ7278GIZcGg1d8QK+9sIRRGcMQ8Mfb+pg8v/+D3gzJ8VstDTJQ+9vJCRsNjet4QOrb2N2\nyONw/wAVu8z0JS8gZUA+00rv1JVEvCRqwVDtWtlIoj3Gc04FpylMJFvlw70p3lQv8c1fvAlAxAxz\nz7q72da2BaENfr5rnJf3vs7IQuONjb2N3Hx5F/myyz+8fpzZkECYEsLijPVVgaZ1dJiuwWM0zUyR\nCFySpsJCEZSKeMUip4ZkCcte9BoL0wQhEYaBMOSSowrTqPl3hUBXq1QOH6Z8+DC+F5xRp1tr8NyT\n9buV0jRo2CZq1qzQCqk8xIK1LtBIHSD2XshvwX9fpNbItwpq8xaWF4qZ5ff570hdtOvU+Q1GBS7F\nzE6kGaU0tw+tPNJ9HyPacNLF7RQGcErDhJNrMSPt/Gjf1xAI/nDzZ7B/8DAekLrhnaV2Qa2YyYPH\nHmG0OM5lrZsJSPLo6DCS2jrm/7ypl3TY5qXx15kuz3Jt51WsiLcvOcbQfIlv7R/FT4eQSnN3SjL3\n/5wU7Mi6DYTXr+PB+AEQOb6w9TNsaFrLswf2sfOZY0RIMr56J916Je0j6ylP1jTUF4KC2aF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4b5QOf7uKn7uiW9uauTE8weeoldM/O8MLCS1OoE0Z4kWhqUgLbxIW488gThbTEK5RDB8Rb6Vm7G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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8198efb38>"
      ]
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "Age distribution of cases, by confirmation status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b6Nix4199jSIiIiKXLaPReNFnwl1YuPzZOBHxHGXeM3fq1Cm2b9/O7bffDoDJ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\niJzQ/weog27dpNnvCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb81a6cef28>"
      ]
     },
     "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": [
    "### Vaccination Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>3933136</td>\n",
       "      <td>0.73</td>\n",
       "      <td>124610790</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3952137</td>\n",
       "      <td>0.66</td>\n",
       "      <td>127525420</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>3952735</td>\n",
       "      <td>0.68</td>\n",
       "      <td>130455659</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>3935224</td>\n",
       "      <td>0.73</td>\n",
       "      <td>133364277</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BIRTHS   VAX        POP  SIA\n",
       "YEAR                               \n",
       "1980  3896442  0.57  121740438  0.0\n",
       "1981  3933136  0.73  124610790  0.0\n",
       "1982  3952137  0.66  127525420  0.0\n",
       "1983  3952735  0.68  130455659  0.0\n",
       "1984  3935224  0.73  133364277  0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccination_data = pd.read_csv('data/BrazilVaxRecords.csv', index_col=0)\n",
    "vaccination_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate residual susceptibility from routine vaccination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "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": true
   },
   "outputs": [],
   "source": [
    "vacc_susc = (1 - vax_97*0.9)[::-1]\n",
    "vacc_susc[0] = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Susceptiblity accounting for SIAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Compilation of cases into 2-week intervals by age class\n",
    "\n",
    "Age classes are defined in 5-year intervals. We will combine 40+ ages into a single class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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.YEAR_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": 15,
   "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 lab-confirmed and clinical-confirmed subsets, with no missing county information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lab_subset = measles_data[(CONFIRMED | DISCARDED) & measles_data.COUNTY.notnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
    "confirmed = (lab_subset.CONCLUSION=='CONFIRMED').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clinic_subset = measles_data[CLINICAL & measles_data.COUNTY.notnull()].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram of lab subset, by outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nWSYlJWnSpEkyxig3N1cPPfSQOnXqpNatW2vcuHGKiYlRo0aNNH/+fElSy5Yt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\nAPjQD6VwINPGJnnFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb819971278>"
      ]
     },
     "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": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41547, 15)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab_subset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age_slice_endpoints = [g[1:-1].split(',') for g in age_groups]\n",
    "age_slices = [slice(int(i[0]), int(i[1])) for i in age_slice_endpoints]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get index from full cross-tabulation to use as index for each district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dates_index = measles_data.groupby(['ONSET', 'AGE_GROUP']).size().unstack().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup of Sao Paulo population data\n",
    "\n",
    "Match age groupings, exclude invalid districts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unique_districts = measles_data.DISTRICT.dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "excludes = ['BOM RETIRO']\n",
    "\n",
    "N = sp_pop.drop(excludes).ix[unique_districts].sum().drop('Total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "N_age = N.iloc[:8]\n",
    "N_age.index = age_groups[:-1]\n",
    "N_age[age_groups[-1]] = N.iloc[8:].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8182af7b8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mz+fT+PHjtWfPHqWlpUmSJk2apA0bNthv9ocPH9aKFSskSRMnTtSePXs0derULtu/laam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\nEQAAADAQAQAAAAMRAAAAMBABAAAAA/0/iWoz93emaMYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb818253ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_age.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile bi-weekly confirmed and unconfirmed data by Sao Paulo district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# All confirmed cases, by district\n",
    "confirmed_data = lab_subset[lab_subset.CONCLUSION=='CONFIRMED']\n",
    "confirmed_counts = (confirmed_data.groupby(['ONSET', 'AGE_GROUP'])\n",
    "                    .size()\n",
    "                    .unstack()\n",
    "                    .reindex(dates_index)\n",
    "                    .fillna(0)\n",
    "                    .sum())\n",
    "\n",
    "all_confirmed_cases = (confirmed_counts.reindex_axis(measles_data['AGE_GROUP'].unique())\n",
    "                       .fillna(0).values.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "confirmed_counts_2w = (confirmed_data\n",
    "                        .groupby(['ONSET', 'AGE_GROUP'])\n",
    "                        .size()\n",
    "                        .unstack()\n",
    "                        .reindex(dates_index)\n",
    "                        .fillna(0)\n",
    "                        .resample('2W')\n",
    "                        .sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 9)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_counts_2w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# All clinical cases, by district\n",
    "clinical_counts = (clinic_subset.groupby(['ONSET', 'AGE_GROUP'])\n",
    "                   .size()\n",
    "                   .unstack()\n",
    "                   .reindex(dates_index)\n",
    "                   .fillna(0)\n",
    "                   .sum())\n",
    "\n",
    "all_clinical_cases = (clinical_counts.reindex_axis(measles_data['AGE_GROUP'].unique())\n",
    "                      .fillna(0).values.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clinical_counts_2w = (clinic_subset\n",
    "                        .groupby(['ONSET', 'AGE_GROUP'])\n",
    "                        .size()\n",
    "                        .unstack()\n",
    "                        .reindex(dates_index)\n",
    "                        .fillna(0)\n",
    "                        .resample('2W')\n",
    "                        .sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>AGE_GROUP</th>\n",
       "      <th>[0, 5)</th>\n",
       "      <th>[5, 10)</th>\n",
       "      <th>[10, 15)</th>\n",
       "      <th>[15, 20)</th>\n",
       "      <th>[20, 25)</th>\n",
       "      <th>[25, 30)</th>\n",
       "      <th>[30, 35)</th>\n",
       "      <th>[35, 40)</th>\n",
       "      <th>[40, 100)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ONSET</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>1997-01-05</th>\n",
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       "      <th>1997-01-19</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1997-02-02</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1997-02-16</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-02</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AGE_GROUP   [0, 5)  [5, 10)  [10, 15)  [15, 20)  [20, 25)  [25, 30)  [30, 35)  \\\n",
       "ONSET                                                                           \n",
       "1997-01-05     0.0      0.0       0.0       0.0       1.0       0.0       0.0   \n",
       "1997-01-19     0.0      1.0       0.0       0.0       3.0       4.0       0.0   \n",
       "1997-02-02     4.0      1.0       0.0       0.0       2.0       1.0       0.0   \n",
       "1997-02-16     4.0      0.0       0.0       0.0       2.0       1.0       1.0   \n",
       "1997-03-02     9.0      0.0       0.0       2.0       4.0       5.0       1.0   \n",
       "\n",
       "AGE_GROUP   [35, 40)  [40, 100)  \n",
       "ONSET                            \n",
       "1997-01-05       0.0        0.0  \n",
       "1997-01-19       0.0        0.0  \n",
       "1997-02-02       0.0        0.0  \n",
       "1997-02-16       0.0        0.0  \n",
       "1997-03-02       0.0        1.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_counts_2w.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>AGE_GROUP</th>\n",
       "      <th>[0, 5)</th>\n",
       "      <th>[5, 10)</th>\n",
       "      <th>[10, 15)</th>\n",
       "      <th>[15, 20)</th>\n",
       "      <th>[20, 25)</th>\n",
       "      <th>[25, 30)</th>\n",
       "      <th>[30, 35)</th>\n",
       "      <th>[35, 40)</th>\n",
       "      <th>[40, 100)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ONSET</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-01-05</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1997-01-19</th>\n",
       "      <td>30.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1997-02-02</th>\n",
       "      <td>22.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1997-02-16</th>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-02</th>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "AGE_GROUP   [0, 5)  [5, 10)  [10, 15)  [15, 20)  [20, 25)  [25, 30)  [30, 35)  \\\n",
       "ONSET                                                                           \n",
       "1997-01-05     3.0      1.0       1.0       0.0       0.0       0.0       0.0   \n",
       "1997-01-19    30.0      3.0       1.0       1.0       1.0       3.0       2.0   \n",
       "1997-02-02    22.0      4.0       0.0       2.0       1.0       1.0       1.0   \n",
       "1997-02-16    21.0      2.0       2.0       2.0       2.0       1.0       1.0   \n",
       "1997-03-02    24.0      5.0       2.0       5.0       2.0       2.0       2.0   \n",
       "\n",
       "AGE_GROUP   [35, 40)  [40, 100)  \n",
       "ONSET                            \n",
       "1997-01-05       0.0        0.0  \n",
       "1997-01-19       1.0        0.0  \n",
       "1997-02-02       0.0        1.0  \n",
       "1997-02-16       0.0        2.0  \n",
       "1997-03-02       1.0        0.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_counts_2w.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check shape of data frame\n",
    "\n",
    "- 28 bi-monthly intervals, 9 age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert clinical_counts_2w.shape == (28, len(age_groups))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Disease Transmission Model\n",
    "\n",
    "We will extend a simple SIR disease model, to account for confirmation status, which will be fit using MCMC.\n",
    "\n",
    "This model fits the series of 2-week infection totals for each age group $a$ as a set of Poisson random variables:\n",
    "\n",
    "\\\\[Pr(I_{a}(t) | \\lambda_a(t)) = \\text{Poisson}(\\lambda_a(t)) \\\\]\n",
    "\n",
    "Where the age-specific outbreak intensity at time $t$ is modeled as:\n",
    "\n",
    "\\\\[\\lambda_a(t) = S_a(t-1) \\frac{I(t-1)\\mathbf{B}}{N_a} \\\\]\n",
    "\n",
    "where $S_a(t-1)$ is the number of susceptibles in age group $a$ in the previous time period, $I(t-1)$ an age-specific vector of the number of infected individuals in the previous time period, $\\mathbf{B}$ a matrix of transmission coefficients (both within- and between-ages), and $N_a$ an estimate of the population of age-$a$ people in Sao Paulo.\n",
    "\n",
    "The matrix $B$ was constructed from a scalar transmission parameter $\\beta$, which was given a vague half-Cauchy prior (scale=25). This was used to represent within-age-group transmission, and hence placed on the diagonal of a square transmission matrix of size $A$. Off-diagonal elements, representing transmission between age groups were scaled by a decay parameter $\\delta$ which was used to scale the transmission to adjacent groups according to:\n",
    "\n",
    "\\\\[\\beta \\delta^{|a-b|}\\\\]\n",
    "\n",
    "where a and b are indices of two age group. The resulting transmission matrix is parameterized as follows:\n",
    "\n",
    "$$\\begin{aligned}\n",
    "\\mathbf{B} = \\left[{\n",
    "\\begin{array}{c}\n",
    "  {\\beta} & {\\beta \\delta} & {\\beta \\delta^2}& \\ldots & {\\beta \\delta^{A-2}} & {\\beta \\delta^{A-1}}  \\\\\n",
    "  {\\beta \\delta} & {\\beta} &  \\beta \\delta & \\ldots & {\\beta \\delta^{A-3}}  & {\\beta \\delta^{A-2}} \\\\\n",
    "  {\\beta \\delta^2} & \\beta \\delta & {\\beta} & \\ldots & {\\beta \\delta^{A-4}} & {\\beta \\delta^{A-3}} \\\\\n",
    "  \\vdots & \\vdots & \\vdots &  & \\vdots & \\vdots\\\\\n",
    "  {\\beta \\delta^{A-2}} & {\\beta \\delta^{A-3}} & {\\beta \\delta^{A-4}} & \\ldots &  {\\beta} & \\beta \\delta  \\\\\n",
    "{\\beta \\delta^{A-1}} & {\\beta \\delta^{A-2}} & \\beta \\delta^{A-3} & \\ldots & \\beta \\delta &  {\\beta} \n",
    "\\end{array}\n",
    "}\\right]\n",
    "\\end{aligned}$$\n",
    "\n",
    "The basic reproductive number $R_0$ was calculated as the largest real-valued eigenvalue of the matrix $\\mathbf{B}$. To impose a mild constraint on $R_0$, we applied a Gaussian prior distribution whose 1st and 99th quantiles are 8 and 24, respectively, a reasonable range for a measles outbreak:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymc import MCMC, Matplot, AdaptiveMetropolis, MAP, Slicer\n",
    "from pymc import (Uniform, DiscreteUniform, Beta, Binomial, Normal, \n",
    "                  CompletedDirichlet, Pareto,\n",
    "                  Poisson, NegativeBinomial, negative_binomial_like, poisson_like,\n",
    "                  Lognormal, Exponential, binomial_like,\n",
    "                  TruncatedNormal, Binomial, Gamma, HalfCauchy, normal_like,\n",
    "                  MvNormalCov, Bernoulli, Uninformative, \n",
    "                  Multinomial, rmultinomial, rbinomial,\n",
    "                  Dirichlet, multinomial_like, uniform_like)\n",
    "from pymc import (Lambda, observed, invlogit, deterministic, potential, stochastic, logit)\n",
    "\n",
    "def measles_model(obs_date, confirmation=True, migrant=False, constrain_R=True):\n",
    "    \n",
    "    \n",
    "    '''\n",
    "    Truncate data at observation period\n",
    "    '''\n",
    "    obs_index = clinical_counts_2w.index <= obs_date\n",
    "    confirmed_obs_t = confirmed_counts_2w[obs_index].values.astype(int)\n",
    "    clinical_obs_t = clinical_counts_2w[obs_index].values.astype(int)\n",
    "                        \n",
    "    n_periods, n_age_groups = confirmed_obs_t.shape\n",
    "\n",
    "    # Index for observation date, used to index out values of interest \n",
    "    # from the model.\n",
    "    t_obs = obs_index.sum() - 1\n",
    "    \n",
    "    lab_index = (lab_subset.NOTIFICATION > obs_date).values\n",
    "    confirmed_t = confirmed[lab_index]\n",
    "    age_index_t = age_index[lab_index]\n",
    "    \n",
    "    '''\n",
    "    Confirmation sub-model\n",
    "    '''\n",
    "    \n",
    "    if confirmation:\n",
    "\n",
    "        # Specify priors on age-specific means\n",
    "        age_classes = np.unique(age_index)\n",
    "\n",
    "        μ = Normal(\"μ\", mu=0, tau=0.0001, value=[0]*len(age_classes))\n",
    "        σ = HalfCauchy('σ', 0, 25, value=1)\n",
    "        var = σ**2\n",
    "        ρ = Uniform('ρ', -1, 1, value=0)\n",
    "\n",
    "        # Build variance-covariance matrix with first-order correlation \n",
    "        # among age classes\n",
    "        @deterministic\n",
    "        def Σ(var=var, cor=ρ):\n",
    "            I = np.eye(len(age_classes))*var\n",
    "            E = np.diag(np.ones(len(age_classes)-1), k=-1)*var*cor\n",
    "            return I + E + E.T\n",
    "\n",
    "        # Age-specific probabilities of confirmation as multivariate normal \n",
    "        # random variables\n",
    "        β_age = MvNormalCov(\"β_age\", mu=μ, C=Σ, value=[1]*len(age_classes))\n",
    "        p_age = Lambda('p_age', lambda b=β_age: invlogit(b))\n",
    "\n",
    "        @deterministic(trace=False)\n",
    "        def p_confirm(b=β_age):\n",
    "            return invlogit(b[age_index_t])\n",
    "\n",
    "\n",
    "        # Confirmation likelihood\n",
    "        lab_confirmed = Bernoulli('lab_confirmed', p=p_confirm, value=confirmed_t, \n",
    "                                observed=True)\n",
    "\n",
    "    if confirmation:\n",
    "        \n",
    "        @stochastic(dtype=int)\n",
    "        def clinical_cases(value=(clinical_obs_t*0.5).astype(int), \n",
    "                            n=clinical_obs_t, p=p_age):\n",
    "            # Binomial confirmation process\n",
    "            return np.sum([binomial_like(xi, ni, p) for xi,ni in zip(value,n)])\n",
    "        I = Lambda('I', lambda clinical=clinical_cases: \n",
    "                           clinical + confirmed_obs_t.astype(int))\n",
    "\n",
    "        assert I.value.shape == (t_obs +1, n_age_groups)\n",
    "        \n",
    "        age_dist_init = np.sum(I.value, 0)/ float(I.value.sum())\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        I = confirmed_obs_t + clinical_obs_t\n",
    "\n",
    "        assert I.shape == (t_obs +1, n_age_groups)\n",
    "        \n",
    "        age_dist_init = np.sum(I, 0) / float(I.sum())\n",
    "        \n",
    "    \n",
    "    # Calcuate age distribution from observed distribution of infecteds to date\n",
    "    _age_dist = Dirichlet('_age_dist', np.ones(n_age_groups), \n",
    "                         value=age_dist_init[:-1]/age_dist_init.sum())\n",
    "    age_dist = CompletedDirichlet('age_dist', _age_dist)\n",
    "    \n",
    "    @potential\n",
    "    def age_dist_like(p=age_dist, I=I):\n",
    "        return multinomial_like(I.sum(0), I.sum(), p)\n",
    "\n",
    "    \n",
    "    '''\n",
    "    Disease transmission model\n",
    "    '''\n",
    "\n",
    "    # Transmission parameter\n",
    "    β = HalfCauchy('β', 0, 25, value=5) #[1]*n_age_groups) \n",
    "    decay = Beta('decay', 1, 5, value=0.8)\n",
    "\n",
    "    @deterministic\n",
    "    def B(b=β, d=decay):\n",
    "        b = np.ones(n_age_groups)*b\n",
    "        B = b*np.eye(n_age_groups)\n",
    "        for i in range(1, n_age_groups):\n",
    "            B += np.diag(np.ones(n_age_groups-i)*b[i:]*d**i, k=-i) \n",
    "            B += np.diag(np.ones(n_age_groups-i)*b[:-i]*d**i, k=i)\n",
    "        return B\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",
    "    @deterministic\n",
    "    def R0(B=B):\n",
    "        evs = np.linalg.eigvals(B)\n",
    "        return max(evs[np.isreal(evs)])\n",
    "    \n",
    "    if constrain_R:\n",
    "        @potential\n",
    "        def constrain_R0(R0=R0):\n",
    "            # Weakly-informative prior to constrain R0 to be within the \n",
    "            # typical measles range\n",
    "            return normal_like(R0, 16, 3.4**-2)\n",
    "\n",
    "\n",
    "    A = Lambda('A', lambda R0=R0: 75./(R0 - 1))\n",
    "    lt_sum = downsample(np.tril(FOI_mat).sum(0)[::-1])\n",
    "    natural_susc = Lambda('natural_susc', lambda A=A: np.exp((-1/A) * lt_sum))\n",
    "\n",
    "    @deterministic\n",
    "    def p_μ(natural_susc=natural_susc): \n",
    "        return downsample(sia_susc) * downsample(vacc_susc) * natural_susc\n",
    "    \n",
    "    if True:\n",
    "        # Following Stan manual chapter 16.2\n",
    "        λ_p = Pareto('λ_p', 1.5, 0.1, value=0.5)\n",
    "\n",
    "        a = Lambda('a', lambda mu=p_μ, lam=λ_p: mu*lam, trace=False)\n",
    "        b = Lambda('b', lambda mu=p_μ, lam=λ_p: (1-mu)*lam, trace=False)\n",
    "\n",
    "        p_susceptible = Beta('p_susceptible', a, b, value=p_μ.value)\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        p_σ = HalfCauchy('p_σ', 0, 5, value=1)\n",
    "        \n",
    "        m = Lambda('m', lambda p=p_μ: logit(p))\n",
    "        \n",
    "        θ_p = Normal('theta_p', m, p_σ)\n",
    "        \n",
    "        p_susceptible = Lambda('p_susceptible', lambda θ_p=θ_p: invlogit(θ_p))\n",
    "        \n",
    "\n",
    "    # Estimated total initial susceptibles\n",
    "    S_0 = Binomial('S_0', n=N_age.values.astype(int), p=p_susceptible)\n",
    "    \n",
    "    '''\n",
    "    Model of migrant influx of susceptibles\n",
    "    '''\n",
    "    if migrant:\n",
    "    \n",
    "        # Data augmentation for migrant susceptibles\n",
    "        imaginary_migrants = 1000000\n",
    "        N_migrant = DiscreteUniform('N_migrant', 0, imaginary_migrants, value=100000)\n",
    "        μ_age = Uniform('μ_age', 15, 35, value=25)\n",
    "        σ_age = Uniform('σ_age', 1, 10, value=5)\n",
    "        M_age = Normal('M_age', μ_age, σ_age**-2, \n",
    "                       size=imaginary_migrants, trace=False)\n",
    "\n",
    "        @deterministic\n",
    "        def M_0(M=M_age, N=N_migrant):\n",
    "            # Take first N augmented susceptibles\n",
    "            M_real = M[:N]\n",
    "            # Drop into age groups\n",
    "            M_group = pd.cut(M_real, \n",
    "                             [0, 5, 10, 15, 20, 25, 30, 35, 40, 100], \n",
    "                             right=False)\n",
    "            return M_group.value_counts().values\n",
    "\n",
    "        p_migrant = Lambda('p_migrant', lambda M_0=M_0, S_0=S_0: M_0/(M_0 + S_0))\n",
    "\n",
    "        I_migrant = [Binomial('I_migrant_%i' % i, I[i], p_migrant) \n",
    "                             for i in range(t_obs + 1)]\n",
    "\n",
    "        I_local = Lambda('I_local', \n",
    "                lambda I=I, I_m=I_migrant: \n",
    "                         np.array([Ii - Imi for Ii,Imi in zip(I,I_m)]))\n",
    "        S = Lambda('S', lambda I=I, S_0=S_0, M_0=M_0: S_0 + M_0 - I.cumsum(0))\n",
    "        S_local = Lambda('S_local', lambda I=I_local, S_0=S_0: S_0 - I.cumsum(0))\n",
    "\n",
    "\n",
    "    else:\n",
    "\n",
    "        # Remaining susceptibles at each 2-week period\n",
    "        S = Lambda('S', lambda I=I, S_0=S_0: S_0 - I.cumsum(axis=0))\n",
    "    \n",
    "    # Check shape\n",
    "    assert S.value.shape == (t_obs+1., n_age_groups)\n",
    "    \n",
    "\n",
    "    # Susceptibles at time t, by age\n",
    "    S_age = Lambda('S_age', lambda S=S: S[-1].astype(int))\n",
    "    \n",
    "    # Force of infection\n",
    "    @deterministic\n",
    "    def λ(B=B, I=I, S=S): \n",
    "        return S * (I.dot(B) / N_age.values)\n",
    "\n",
    "    # Check shape\n",
    "    assert λ.value.shape == (t_obs+1, n_age_groups)\n",
    "    \n",
    "    \n",
    "    # FOI in observation period\n",
    "    λ_t = Lambda('λ_t', lambda lam=λ: lam[-1])\n",
    "    \n",
    "    # Effective reproductive number\n",
    "    R_t = Lambda('R_t', lambda S=S, R0=R0: S.sum(1) * R0 / N_age.sum())\n",
    "    \n",
    "    R_t_local = Lambda('R_t_local', lambda S=S_local, R0=R0: S.sum(1) * R0 / N_age.sum())\n",
    "    \n",
    "    # Poisson likelihood for observed cases\n",
    "    @potential\n",
    "    def new_cases(I=I, lam=λ):\n",
    "        return poisson_like(I[1:], lam[:-1])\n",
    "    \n",
    "    '''\n",
    "    Vaccination targets\n",
    "    '''\n",
    "    \n",
    "    @deterministic\n",
    "    def vacc_5(S=S_age):\n",
    "        # Vaccination of 5 and under\n",
    "        p = [0.95] + [0]*(n_age_groups - 1)\n",
    "        return rbinomial(S, p)\n",
    "    \n",
    "    # Proportion of susceptibles vaccinated\n",
    "    pct_5 = Lambda('pct_5', \n",
    "                lambda V=vacc_5, S=S_age: V.sum()/S.sum())\n",
    "\n",
    "\n",
    "    @deterministic\n",
    "    def vacc_15(S=S_age):\n",
    "        # Vaccination of 15 and under\n",
    "        p = [0.95]*3 + [0]*(n_age_groups - 3)\n",
    "        return rbinomial(S, p)\n",
    "    \n",
    "    # Proportion of susceptibles vaccinated\n",
    "    pct_15 = Lambda('pct_15', \n",
    "            lambda V=vacc_15, S=S_age: V.sum()/S.sum())\n",
    "    \n",
    "    @deterministic\n",
    "    def vacc_30(S=S_age):\n",
    "        # Vaccination of 30 and under\n",
    "        p = [0.95]*6 + [0]*(n_age_groups - 6)\n",
    "        return rbinomial(S, p)\n",
    "    \n",
    "    # Proportion of 30 and under susceptibles vaccinated\n",
    "    pct_30 = Lambda('pct_30', \n",
    "            lambda V=vacc_30, S=S_age: V.sum()/S.sum())\n",
    "    \n",
    "    @deterministic\n",
    "    def vacc_adult(S=S_age):\n",
    "        # Vaccination of adults under 30 (and young kids)\n",
    "        p = [0.95, 0, 0, 0, 0.95, 0.95] + [0]*(n_age_groups - 6)\n",
    "        return rbinomial(S, p)\n",
    "    \n",
    "    # Proportion of adults under 30 (and young kids)\n",
    "    pct_adult = Lambda('pct_adult', \n",
    "            lambda V=vacc_adult, S=S_age: V.sum()/S.sum())\n",
    "\n",
    "    return locals()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model execution\n",
    "\n",
    "Run models for June 15 and July 15 observation points, both with and without clinical confirmation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_iterations = 50000\n",
    "n_burn = 40000\n",
    "migrant = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `backgroundjobs` to run the models each in their own thread:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.lib import backgroundjobs as bg\n",
    "\n",
    "jobs = bg.BackgroundJobManager()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_june = MCMC(measles_model('1997-06-15', migrant=migrant))\n",
    "model_july = MCMC(measles_model('1997-07-15', migrant=migrant))\n",
    "model_june_noconf = MCMC(measles_model('1997-06-15', confirmation=False, migrant=migrant))\n",
    "model_july_noconf = MCMC(measles_model('1997-07-15', confirmation=False, migrant=migrant))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting job # 0 in a separate thread.\n",
      "Starting job # 2 in a separate thread.\n",
      "Starting job # 3 in a separate thread.\n",
      "Starting job # 4 in a separate thread.\n"
     ]
    }
   ],
   "source": [
    "for model in model_june, model_july, model_june_noconf, model_july_noconf:\n",
    "    jobs.new(model.sample, n_iterations, n_burn, kw=dict(progress_bar=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed jobs:\n",
      "0 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x7fb8185d4390>>\n",
      "2 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x7fb81a51c978>>\n",
      "3 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x7fb819c212e8>>\n",
      "4 : <bound method MCMC.sample of <pymc.MCMC.MCMC object at 0x7fb819c29550>>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "jobs.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of model output\n",
    "\n",
    "Estimate of R0 for june (with confirmation submodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting R0\n"
     ]
    },
    {
     "data": {
      "image/png": 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mAJKSvsKiRZ8YVL5BNa9KkZMFAKmpRzF27GgMHtwfK1cuw/vvfwQHBwc4OjrhpZcm4pVX\nJiEmJgoXL9LbhXzw1VfLlHlZxLY12p4sbzdHnetr6upfbU65AQD4Ye8VBPm5IrS5F4VYhBCDde36\nqLJGloKjoxN+//1P5eeAgECsXJnI6ngTJ05R+xwUFIRDh04oP+/de1D5s7aq8AAwadJUTJo0ldU5\niW2gnCz+aLQ9WfowvUG4ZMPp2nUqq2jokBBCCCFMGm2QxSY2oqR2QgghhBhLEEFWeUU11v9xGXdy\nSzg7ZkFJBSZ9fIBxnWrwRTWzCCG2iPJ2hIvuLX8YnJOVkJCAlJQU+Pr6YufOnWrr1q5di08++QTH\njx+Hl5fhc20Za/+p20g5ewfnbtzH0um6J5Jm60L6A63rKKwihNg6ytsRLrq3/GFwT9bIkSOxZs0a\njeXZ2dn4+++/0bRpU04aZgjF/IGFJRXcHVRXJKWyLvMed71nhBBCCBEOg4OsiIgIeHh4aCxfvHgx\n3n77bU4aZRF68q1qdKxXXbNm10WOGkQIIYQQIeEkJys5ORmBgYFo164d633KK6txL+8hF6dXMiRP\nvYZh2+JSlvMZquZk0dghIcQGUd6OcNG95Q+T62SVlZUhMTERa9euVS5j81bexIX7UFhSgTXvPoEm\n3i4mtcHFxaH2BxEglbqz2sfV1UH5s2KfifE7VI7JXEfrTn4ZmvvXn0NcVzleKnVHTY1c+ZnP2H6H\nlnLvwUN88sNJTB3ZCW2be7Pez9auwxRCuhZiGZS3I1x0b/nD5CArIyMDWVlZGD58OORyOXJycjBq\n1Chs3rwZvr6+WvdT5E/9uOsixkYz94CVV1ZDLBLB3o65w+3n/dcQ2twLDx/W5WLJAZmsiFW7i4rL\nlT8z7VNSUq6xDAAuXJPBWVIfSCkCygOpN7Fs0znMGNUJXdtKWbXBFkml7qy/Q0tZ8/tFXMnIw+Lv\nTuCTV3qz2scWr8NYQrsWQghpLIwKslR7qkJDQ/H3338rP0dFRWHbtm3w9PRkdaz8YuZgBgBeWVpb\nsXjSkDAkn8rEgK5B6Nu5NrG+oLgcf568jT9P3sbQ3sEmXQMA3L2vnsD+sLyKcT+RCLiVXf/AKymr\n3W7b4XQAwMot502acJpoR0OzhBBC+MTgnKz4+HiMGTMG6enpiIyMxJYtW9TWi0Qig4p4Ojnoj/PW\n7LqEm9lF+G7PZeWyaqakKh32/XMbmw9cV35uuPuWg/+pfU4+lcl4HJFIhFNX72ksv3Of27cMF35/\nEom/XeD0mHyl0m/IuL6yqhpV1TWWag4hvEB5O8JF95Y/DO7JWrp0qc71ycnJBh2vc4j2IUVdDO3V\n2Jh8rcH+6gdgm0vl7ChhfNZzXR0+/W4h0u8WYuqwDpwel5fqbo22b3jemhNwcbLDe+O6W6xJhNg6\nytsRLrq3/MHbCaKZSizUyOWoqq6BnUR/B11Ng64stvnqnq6OGg/7A6duo6KSelLMRQTdNycnr9RC\nLSGEEELYs4lpde7ll+KnP6+iVEseFBNtPUdf/JqGB4Vl2H/yNjanXNc6jPTb3zfVjiVhGWWJGDZb\n9tNpVvsS01BOFiGEED6xiZ6slb+mISu3BC5OdhjRtzWrfVQ7oiqr6gOpf9Mf4O2vjyl7unzcnTDw\n0WY6j1VSVsV6uLA254zVpoQr/K+KQYjFKXJ2aGhJeOje8odNBFl5RbVvGD4sM64na++J22rrVIcS\nFcfWpUYuh5ipi4r5xKiuoaFBQohtowewcNG95Q+rDxfK5VAOE97Lr8+t+e9OIeP29wvKlPuxO77+\nDeVy9onvcgAnLmm+XWjLcvIeIv0u8/fJB9SRRQghhI+sHmQB9W+Npd24r1y2SaXcgqqVW9I0tjWV\nIZXarTFUWFlVg+uZBRoBY41cjtSLOXqnA/rkpzNY+P1JczbRIrh+g5MQQggxJ5sIspiUaUmCl9X1\nZGkLwowhN2C4UK61kID5bPjzChb/eArHLmSrLT9xKQeJv13AF3WBpzZshkxtmUhPCQdCiCaqpSRc\ndG/5w+o5WVqDFq0xj2GP2j2pGXhmQIjuNsjB+u3CI2l3DTo/W+UV1XCwF0PEEOydvpoLALiRVYje\nHQOVy+/VlS64nllgljbZDoqyCDEU5e0IF91b/rB+T5bWGIs56DHHiJEhJRy4yMfKkhWrDfE9LKvC\nK8sO4otfdfdINVa6OhlpCJEQQoitsnqQpfURacFs5xq5HE6O3HXqZT94qPXhn3oxB++tOYH4L+vn\ne3xQWDsEek5fnlnD78TA+EKIAYnwrogQQohQWD3IYsJUzd2c5GBf8Z2NhKTj+Ot0FuM6xXyEqrW9\nCh5W6DxeSV2vl+LNSltRWl6FtBu5FgveGM9CURYhjChvR7jo3vKH1XOych481Fj2+ueHUaKlZpYx\nz9Qf913Ruw3btwvZOpx2R28RVIWlG8/qXM/09qUx5OC2g/DLbedx8WYepsd1wqPtpBweWZ2yzVqm\nUjKXh2VVcHaUMObJEWLrKG9HuOje8ofBPVkJCQno3bs3YmNjlcs+//xzDBs2DMOHD8f48eORnZ2t\n4wjqVKe3UdAWYAG1CeKbUwx7s1Bbr5IqCdcPUpbP/n3/3Na/kYr1e7UHjKXlVbp7lTiORy7ezAMA\nZD8o4fS4h9PuYO+JjPoFVghy7t4vwWsrDuGHfVctfm5zycotUetBJYQQYl4GB1kjR47EmjVr1Ja9\n/PLL+O2337Bjxw4MHDgQK1eu5KyBTPYcz9C/kYG47q3IuFfMaruNydcMOm7KmSxlDpdqzFRQUoHp\nyw/hy23/sjrOhj+vYuKSv3Avr74nUS6X4/LNByirYF953xy+230Zv/ylGUgzxYhcdGRdupWnVggX\nAK7czgdQ+30LwX93CvHet6n4eju73w9CCCGmMzjIioiIgIeHh9oyV1dX5c+lpaXw9vY2ukFH/zVP\niQR9bDW1JydPczi18GGFRo/VoXN3AACnr8rw7e8XGXu0FOUyCh9WIPlUJgAg5ewd5fprmQV4a+Vh\nfL5Z+1uOxy9m404utz1X2uxJvYWs3BLlcCFzQGXanausqsanP5/BO98c4/KwNifjXhEA4Oz1XCu3\nhFgK5e0IF91b/uAsJ2v58uXYsWMHnJycsHnzZqOP8/vRW1w1iT05YG9n3ncA7heUKavVG2LR+lMa\nyz5YdxJdQvwQHOCuXLbt0H/Kn4/+m42yimq8FN1O7bqST2ZicI8W6oVeVYKJLFlt75uiF6ehvKJy\nJP12EU28nbFkai/l8geF5il2uvnADWw+cAMDugUBAGMPW8PASy6XG9QrWVUtsGhKC8oqa3wob0e4\n6N7yB2dB1qxZszBr1iwkJSVh8eLF+Oijj4w6jrVyjB3MHGTtPHqT9RCiKm1T5py9nqsWZDV0+qoM\n3m6OGNS9Pvl+41/XMbhHC7Xt/jiRgdFRuou1KiiCHEURVIUDZ7Lw3KC2sJOY5zvMqwvimAIieYMg\n8b01JzB56CMYNkD7d6PKkN83uVyO9XuvICzYGz3C/NnvaAMoeZ+YU1FxMS5dvqy2zMvLBfn5mj3x\nTKqrqm3zVXdCTMT524WxsbGYMmWK0fvfvc/uP0ouZeWVYs2uS5wfVyqtf9A7OdnrXK9rmTauLg46\n198vLoeHh7PG8atE6v+c+fm54XZOEY5dzNHZjjKVnOmG6z29XODCcI2GWr/7osay/JL6EhcNz1uq\n0iuXekUGANiw/yqGDWjL6rt8WFYfxKpu7+bupLb8dk4RDp7OwsGzd3Dw7B0M6ccuMOWKIb8XTNwb\nXA8hXPor5TB+/jsfYolx/wbYe4Vx3CJCbINRQVbDfJ9bt24hODgYALB//360b9/e9JZZ0KrN58xy\nXJmsSPlzaalmLSzV9QpXbsjg4+GksZxJcYnuYbrMnCJM//SAxjkfNEjylsmYt2soL69E63qZrEhv\nkFX0sALr9lzGsMdbae2F28zwIkBlZbXW86oGWRV1P1dXy/HdzgtwdZRgQNcgnW3653J9BX/VYxcV\nlaktX/xdKjJl2q/fnKRSd73nO3zuDn5KvoYlUx6Dp5ujxvqG12MtFOBZjiJnx1JDS46uPpDYmf6H\nFtHP0veWGM/gICs+Ph6pqanIz89HZGQkZsyYgYMHDyI9PR0SiQTNmzfHggULzNBUfmnfwkvtM9vM\nn+Wbz2HhpJ6ctCHXjMVLL958oPa5hsUFHjidhTPXcvGgsBzzJ3RnfS5ttbBWbklTq2+m+LlGLsfW\nujIf+oKsEi3DsQ2pBli26Ls9tUM1p6/KMKAbu/psRNjoASxcdG/5w+Aga+nSpRrLRo0axUljhORy\nRm3yuFwux728Usa3/corqjWWWeLNvYbZOdVsIiSo5z991qCAKpuioA/repqyGd6Y1HUMBzsJ4/Iz\n19TflFPMP1nD8noAHUVoeToFkSL36oe9V9AmyENtQnFCCCGWZfWK70J37EI2vv2dOd9r/ynDCpGa\ny5RPU0w+htyAwEabB1p63rqHNcGtHP1DXIoAw6Agy4YTwiuralBQXG7YEJuodgj1wJksHDiTBV8P\nJ7Rr4U1vFxJCiBXQCx1mpmsqnIcMle3lciC3oJRha8sqLq3EX6czWVcI5yDGwtELzDMFMPUCFjHM\n96jolNLXlN+P3sT05QdRVlGl7P3SOKeeY1jCxz+dxtvfHENuvmG/D6o9kw17HEnjQbWUhIvuLX9Q\nT5YZlZZX4cSle1rX70llrlxv6hyFXJj5+WEAtTlLsY+3AqC73AEXk0Q38XZmXL7lYH0NMEUdrNe/\nOKKxHdMUTUy21tUUu5VdBG93zSTx2vPoPkZeUbnWfbny351CALXzezZx1/0mqYIIQEWl5jC0aldW\n2o37CG/jy0ELiS2jvB3honvLH9STZUbr9lzWvxGDgypV2Lm26cB1/K9hdXMdrmcVKn/WFXg0HKLL\nvFeMKZ+mKAMFVUy5aADg7KA/5q+ukaOq2vrz7/28/yquZOQpK+3bCpFIhCqVe1FdI9cIgFeY6W1a\nQggh6ijIMqPrWQVG7XfbiKKlbP2hpfdMG7nKwJmuzp2GSesffP8Pqqpr8OH6k4zbFxRrlp9g0xkm\nl8sxa6VmL5YuJWWV+P3oTbWaWMrjNTi2gr5E/tKKanz80xms23OZk148czpwJgsiysoihBCLoyDL\njNiWBzBW9gPLFm7VFUycvpqLRT+cRFlFFcorq/VOV5OTp5lnxCZYOf/fA5Qw5LLp8tOfV7H10H/Y\ndEB90mm5XP2cFZW1PWRZuSXI1BPoqg7JLdtkOz1DTKFUqkqRWWuprKrG9sP/6d+QcIbydoSL7i1/\nUE6WGVWwTBo3lq58L3PQFQMpApgDZ7Jw4HSW2rqyiio4sRgKZJM8v2rref0bNXCvLnH8foO3F+VQ\n78n6724hwoK98d63qXqPqTrd0YX0Bzq2NJ/TV2W4nlWA0QNUqs+LoHGjbKGjLfXiPfz2901MHtnZ\n2k1pNChvR7jo3vIH9WQRndyd6ys4s+lp2nzghkYR1MNpd1mdyxzDblXVNTrHOVXP+enPZzg/vzmt\n2noef6RmqA2DiiDSuNzalwUs27aGCvTMTkAIIUJEQRbRqXVTT+XPxsZA1XVDhw8K64OvLIaiq2yL\nohpi1dbzyqDjws08JO28oFxXWVWNS7fy9B7jox9Pcd4uLql+bRKJSCOo5OprzSsqx5mrMvyw74pZ\n8wYJIUQoKMgirMlNqB51L+8hTtZN4AwAFxmG2BJ/u6CxzFR375eoBYfHL9TnJ+04ko49x9VfBNiw\n76rGMa5lGvcCgzU42Us07hKbavwVldX4/o/LOnPRElYfx8qt53HgdBYW/aD5QkNxaSXSbuQy9kja\nwpBlY0N5O8JF95Y/KMjSYdCjNAec4gG9J/UWfj96y+jjnG0wBQ4AFJZU4I2VRxh7tbgiggglDG8V\nAkD6Xc0q8smnMzk5b8qZLHy57Twqq5jLVZiLSCTSCHLYDMMmn87EwbN3MP+7E1q3US29UVFZg0xZ\nMZJ2XlAW1V2+6RxWbE7DxZuavYPmiLESEhLQu3dvxMbGKpetWrUK/fr1Q1xcHOLi4nDo0CHlusTE\nRAwePBgxMTE4cqT+DdULFy4gNjYW0dHRWLRokRlaah2vvvom5e4IFN1b/qAgSweJhF57V0yXs/nA\nDZy+KtPwvQtNAAAgAElEQVSztXZ/nVFPhpcDeGPlERSWVLBKNDfFPYY3GU2leBNR4XCaer2s9Xuv\n4NQVmUUmllYNophyr9j0Im1J+Y/1tgrLN53D8Qs52Huitjcw/W5tTTTGt17N0JU1cuRIrFmzRmP5\nhAkTsG3bNmzbtg39+vUDANy4cQN79uzB7t27sXr1arz//vvK723BggVYtGgR9u7di5s3b+Lw4cOc\nt5UQ0jhRkEV04iKf59SVeyhvUIXcYrWlzBQn3y9UT+7/bvdl3L1fG1CpFkvVdZlpN+7j15QbrM+Z\nk/eQsRyD6nBmbU+W+no2w4VstmlI0UPYcOqlaoZisea42xEREfDw8NA8F8O1JCcn46mnnoKdnR2a\nNWuG4OBgpKWlQSaToaSkBOHh4QCAESNGYP/+/WZoLSGkMTI4yGLqov/kk08QExOD4cOHY8aMGSgu\n5n9S7LNRIfo3IqzcuFNo0KTNXDJHL5Y2c1enoryiWpnoD+jOY1ux+Rx2H7/FWJiVyZzE40j87QJy\nGvQUqZa1EIs1A5qaGs23C4+k3UUhw/yPCjuOpOPn/ddYtashxlttwdv/448/Yvjw4Zg7dy6KimqH\nhHNychAYGKjcxt/fHzk5OcjJyUFAQIDGciGgvB3honvLHwYHWUxd9H369MGuXbuwY8cOBAcHIzEx\nkbMGGiKifRPOjhXdowVnx+IzY3o42BBqIvQXW9LUamixuU5D48+H5dqLsTJVdmcKcNfuvoSNydqD\nqB1H0vHnydu6G6Kl3bKCUpy+KsPKLWmorKpBVm4JCnQEdFx6/vnnkZycjB07dsDPzw9LliyxyHlt\nEeXtCBfdW/4wuBhpREQEsrLU82t69+6t/LlLly7Yu3ev6S0zgpjjoSGaigQ4fO4OOrU2fTLhsGBv\nixdPtYZLt/Lw5TaVgqkspwpqyKSE+QbHy8krxbe/X9LY7Nx1zZcRgNpSDWxoK7Z74HR9Qdo3Vx0x\nuEK/KXx8fJQ/jx49GtOmTQNQ20N19259vbbs7Gz4+/trLM/JyYG/vz/r80ml7hy0mjvWao+HhxMA\nywTSXBKJRPDzc7Pa92Zrvz+A7bXJ1tpjKM4rvv/6668YMmQI14dlRWTtiosClJNXivlrtb9xxlbD\nAOuslge8ENzMrn9r0diyF3tPaO9F0vVrLjfgjGItB4r/8m+WR9DP3AFWwwBVJpNBKpUCAP7880+E\nhoYCAKKiojB79myMHz8eOTk5yMjIQHh4OEQiEdzd3ZGWloZOnTph+/btGDt2LOvzy2Sab6hai1Tq\nbrX2FBaWgY8pvnK5HLm5xQCcLH5ua94vbWytTbbYHkNxGmR9/fXXsLe3V8vXsiSmhFtCrOleXina\nNvMyeL+7942cl7LhXEE6SLju+rWw+Ph4pKamIj8/H5GRkZgxYwZSU1Nx6dIliMViBAUF4YMPPgAA\nhISEICYmBkOGDIGdnR3mz5+v/KNs3rx5mDNnDsrLy9GvXz/lG4l8p8jZoWEl4aF7yx+cBVlbt27F\nwYMHsX79eq4OabDAJu7AFePLDKiSSt3h7OLAybGIfnzvEtbmh71X0L97C6zadA5jnwpDy0DNt+F8\nfd3g61n7l7TiwX8rR/2vN9Xvx9XNSev39eepLEwe0ZFV2+zsxHq/9+zCcvh6OqGpn5vWbe4Xl1vl\n/i1dulRj2ahRo7RuP3XqVEydOlVjeceOHbFz505O22YL6AEsXHRv+cOoIKthF/2hQ4ewZs0a/Pjj\nj3BwsGxgMmNkJ6yse7uqrJS56KQxZLIilFooWbexc3Wys6kuYS5VVNVg3W//4sTFbNy8U4Al03pp\nbHP/fjHe/fpviEQifDCpBwDATWXOSAA4kVafB/ngQQlkbsz/nZ2/kYsHD9jX5tL3vSd8VTt0uPad\nKK3b/HMxR7D3jxBCTGFwkMXURZ+YmIjKykpMnDgRANC5c2csWLCA67Yy6hoqVf7MNiVrZL/W6NrW\nD++tMT3XiJiupKyKcToboVDUzarQkczesOp9w6G8zzae5bxd2nKyCCGEcMPgIMvQLnpLYvvMcHa0\nQ5BU+/AHG0N6BeOfy/csWodJyLiazsaWaUuV0vc7lH2/RK3AqUgk0lnjim3ZDbEBOVknLumuHbX4\nB9ueRLsxorwd4aJ7yx+cv11oTVy8XSgC8MyAkPoPdUb0aYXtR9KVn0f1b4MrGfm4l1cKqbczZBRs\nEa10/17eVpmUOUtWjF3HbuHSrfr5/1ZsPAMHO/U3tz78XnOCZqbj6eLkIGG1HQB8s0P35N3Xs/gz\niXZjQQ9g4aJ7yx/CCrIM3H7++O54f90/asveHReBVg2Skx3sxBjWpxXaBHki+VQmpg7roLbe3cWB\ngiyilWKYsKCYufepqqa+l2rF5nO4X6hZp0r1DwixSITcgjKNbZRYvl3oQS92EEKIWdlMYRN/HxeT\nj2FoT1ZwALs3ohTPrA6tfDDz6XA41vUAKCoSebg4YP747gadmzQe+soxVKlMw6Otmrvqr7aHlqR3\ng1FKFiGEmJXNBFlThz1i8jHYxli6tjMoTlM8G0XsAzbS+Oj7lWJT362sQiVpnqM5if797wGOpN3V\nvyHhJZrfTrjo3vKHzQwXtgzQrB+kjZ1ErJYIzJVeHfzRwp99sKQSYxGiVcO5A2vkcrU3+1R7sthQ\nC7hMtHb3JfQJD9S/IeEdytsRLrq3/GEzQZYhAn1d0KtDANxd7PVvbIDJseq5VvqCJ0WHAh+m8wlp\n5onrmZScbA0ZKonoO/9Ox7bD6fjs1fr5PoOkrgYdb8F3/+hcb2g/1/bD/xm4ByGEEDasFmR98lpf\nvL3qsFH7igA82bOF8vNbz3WFRCzC+f/us96fG4ooi7MDmo2EB4GgNbk526OYw2K22mw7XPuG6uaU\nG8plhrzlZw6//X3TqucnxGpEdvhl2y64uBj2h45CRNeO6PRIe44bRYTEakFWWCsf43duEC+EBXsD\nAOsgqwXL/ClP19oE40AtSflyC8ZY7Vt44XJGvtH7G1ITqTF6tJ0UB8/esdj5Ui/W151STbEqLTd9\nKLDhjAykcaJaSvo5+7TEqfsA2D06NFRWp1klyKJ7yx82NVwYHOCOW9n6p+cQsQxrvNwckN/gtflF\nk3si0JfdXy0DujVDRVUNencMYFyvzMmyQC9Rp9a+pgVZFGPpJKQE8P/uFFq7CcQG0ANYuOje8odV\n3y5cPqOPUfuxzcUKCfLUWMY2wAIAezsxhvZuCR8PJ1bbNzOxirw2kV2D8ET35lrXiwB8PlP3dyli\niLJaN2X/soHQ2dlZ7z8FrjueDgsoYCSEED6zapClGI7TZlT/1ozLn40KYVxeUKLeazXhqTDjGsaW\nBUZlAnxc8FJ0O9hJtN+qhLGPwtVJd+DJlJMlxDStx7X0Ourj7sztSxSGoOE9QggRJqvXybKTiJVv\nV6k+84P93TGkV0vG3ih3LZWqMxoMNTo7mnc0VFGMVBGssAlaRvRpxbi8pZY8MYmk/qADH23GfFAR\nNBLDurb1w7DHWyo/M+VkNfEyvQCsrTF26NaakyWznWuQEENQLSXhonvLH1bPyfo6vp/ywaj6nFME\nMP4+zprzoml5HtZY+FnlYC9R+382j+lhfVqheRM3rNx6Xm35m892wczPNd+2dLCrf/NseJ9WSD6l\nOZGyiCFLbcaocAD1b44xBRGdWvvg2IVsFq3mD7GRfzYwDadair55AQkxBuXtCBfdW/4w+JGUkJCA\n3r17IzY2Vrnsjz/+wNChQxEWFoYLFwx7YEjEYpUAgN2DTttWTM/JxVMeM6g9hpg0JAxd2/rh5WEd\nDdrPz8tZY5mzo+Zr/B1b++DlofVDnhItgYBIpL8HR3X18D6tENG+CZzM3NNnHcb2ZHHcDEIIIY2e\nwUHWyJEjsWbNGrVloaGhWLVqFbp353D+PnmD/1ehNaBgWBzAwZyI2vh7u2DGqPD6oMmEaX2YrunN\n0V3UEvXZjmj5eWom6ldU1VfIH96nFV4d0ZGz0hPtmnux2s6QqYeY3uh8JrKN3v2MDZasOVxoLr06\n+Fu7CYQQ0qgZHGRFRETAw0P9rbTWrVujZcuWJifwqj3njHjmaSvt0L6F/iDAk6tJd1lgaiWby9UW\nXDZczBRkuTEkdnMVVzDlvjF952wDoH6dA/HyUM25LD30vCgBGD/sJ8AYCyVlVQj0FV7eHWGH8naE\ni+4tf9jseJHiIWtI2KbtQflYhwC9Naa46MkY0bc1vvg1DV1C/HD2eq5B+7JJ2NYWP7CtG8a0p7m0\nCvTQ+M7T7+qvgabN05FtWAVC+t5Y1Ubf99+7YwCO/su//LWG8yaSxoPydoSL7i1/WP3tQlUDugYB\nAKYN76Cz5pS2HjPV5+QrI+rzpNi8vcVFTk6XED+s+d8AtK+rQN/Q0N4tARhf+YFt1XamgIHpK2gT\nZL46WYN11PUyhrbg6X/Pd0WoynClud4uZOpZ4wN6cZEQQqzHqj1ZUql6js6IKHcM7t0KLio1n1oE\nemr0IPj4uMHL3VHjePb29cnjT/Wtz99xc63ftuE5FezsJFrXsaG6r5tb/fmcHe1QWl4FAAgPbQKp\n1B0lVZpPPqZz62pPh9a+uFA3jZCXl4vatjOe7ar8HCR1RZasBJ1Dpco3CRXrpAC+eWcgpi1JZnuZ\njOwbzL339osRaB3sa/TxnJ0dNK7dw8OJMYDq82gL3MguxtXb+cp9DRXSzFNvcGbK74a1ODjYWfWt\nSUIIaeyMCrJ05V4ZkpclkzEPH5UUlSl/7h8eAHl1NX7567pyWe79YlSWVWjsV62S3K167EKV42k7\np7xGrnWdPlKpu9q+xcXlyp+dHCTKICsv/yFksiI8eFCicQyZrAg9H/FXm9NOV3uqq+rnuHuQVwKZ\nU32g4yCqv5Z547rjYVkl3F0cMO7JdugS4qd23Ly8h4ZcKqOKuutTaN/MA7m5xg8NlpZWalz75fT7\nGr2bAT4ukMmK1L7vo+eyDDpXeBtfzHw6HIt/OKVzO2N/N6ypvLwKLw8Nw6L1uq+NCBPNbydcdG/5\nw+Dhwvj4eIwZMwbp6emIjIzEli1bsH//fvTv3x/nzp3DtGnT8PLLL3PWQEd7CaJ7tMDMurpPALSP\ngWj5o51N3GdvpmlVVINORVVxbYHoqH7MFe6Z6Op5UV1jbyeGp5sjxGIR+ncJgqebo9Zt2RrSK1jt\nM9PVaGtflxA/I87IPHHy1GEdAKjfu5sMc1/qGrp0cbKDWCTi5duFbKZFatPUE+9P7GGB1hBb8+qr\nb9JDWKDo3vKHwT1ZS5cuZVw+aNAgkxujS5e2+h/OLQM8cCNLc3JcNjlZjg6adaq4oHpqtjlVbOg6\nlCF5SQ237dUpEMfO6577btjjrTCqfxtMXPIX6/MovBrXEVM+TTF4v+rqGrWA8MXBocqSENE9WmDn\n0Zta99X15qhdXfVS1a+hRRM3ZNwrNriNluag5w8Dxe8I/8JHQggRBptKfNdn2vAO6Nc5UOur/HF9\na6esiWgnVVsuZ/GGVcdWPqY3kIHqmRW9JaqBVxNvZ8SP6aKxrT6qwZEpyc0NC5zOeq4bgNo8Je3n\nVv+sev7/Pd/V+MY0OP4LT4Qql4nFIrVoQbUJLk66/1bQ9falIvBVCzZ5EpXou+/m+sOBEEIIO7wK\nsnqE+WN8TJjWnhoXJ3skvRWJV+M6qS3XFWO9Ny4CgyKaYdjjzHMKGkO1NpFcLke/zoEAgKZ+mjWL\nFk9+DB1a1gZ4bIYsh/QKxoi+rdR6xeQmzFTt3eAFAmdHO3wd3x/vvNBN6z7aOspaBbqjXQvmNyv1\n7duwDYD6XI2hzbxMKFWhvz1sOhnjx3SBv7czfD1q65AxFUy1JcogjCdBI+EW1VISLrq3/GGzdbKM\nZSfRDFR0JeO3CvRAq0BuSxl0bOUDqZcTZPllkMuB8TFheHFwO2XbvD1UAhuVB6CXmyO6tvXDmWva\na2yN6l/71uQXv6bVLzShJ4spYHW0190DwnY4spnUDZky9WE3NoHS0AY5XwDQrZ0U51Rrj+log6er\nAwpKNF+MYFJYt53qNTVso6JntENLH3w0tZdy+a5jN1mdw1gujnZ42OClAlX6boPi995WYqwOLXUH\n4IRblLMjXHRv+YNXPVnGYpOTxSWRSIQ+nWp7r9xdapPdVYM/DxcHLJnWCytm9NFIuB6k0nuj+xwc\nNVYHJy3DTQ1P7VMXNCp6eBTmT4jQvzMD1RIeCtrmbWzI0Mr9vnXV8dV6EUXA60/Xv2gx4amwhrvZ\nhME9Wuhcb+0aWSFB6kPOA7qx+90mhBChEFxPFhNrVL0e3KMF8osrMCiC+cHShGGSaABo6lc7V2FX\nPYn+ajlZRrZRH60vcTaI8J6JbANPVwe14T2gdvLviHZSnLwiM7oNCyf1QG5BGWMPpaqwYG9cupUH\nqZcz7uWV6j2unUSMquoaZa+V6hXZiUXorPIWpLaA1txBjL7DN3xT89moELVSJw+K6kpbWOvNSZXT\n9nzEH+FtjK+dRgghfNQoerK6tK0d7okzoESCqRztJRgb3U5tgmc2PN0cseqNvpg+spPO7dSem2Z6\n2OvLORr2eEsM7R0MFyd7jOjbGu4umr1I5ZX1tcs6tPQ2eOgqSOqmDHh0lq1QrGL5XUgkioR3zXVD\n6irz88noASEaQW5lXS01qw0XqtyLqcM66A2UCbcob0e46N7yR6PoyWrexA2JsyPNVguLa0zDZQ0Z\nO30ME205TM8/0RYHzmgv7jmir/6g9XxdVXoA6BYqNanduuYPr4+x5BpdTIynlDN/bCZ10+gh0pZH\nZu7+UUO+qdDmXjYXxCimQnqsg7+VW9I4Ud6OcNG95Q/b+lfZjPgSYLGlmqJkytuFADBn7KOMyyVi\njr8zDgPDhvezU+vaoajwNn4mTSpuJ2Foo9bhQvOGWVxMjq5vnTm9GN0O0T2a47mBba3TAEIIsTJh\nRR6NiJijOlmAdd8+mza8g0YFeW1Ug4UeYeq9I4O6N8f88d0x5DHNYzF3ZKl/adK6HDnV8hu69geA\nzm1qe7yG9+Gu/AeXvN2d9G/EMUWwC9T2ZD0b1ZZxGJkQQhqDRjFcKETdQqU4rjLPoSksFWQ1PE+g\nrwt6hPmjR5g/hvZuiaKSCvhpeSEAUK+Y37AnSywSKSvAMwWd3ds3wakrMo03TRVDgSP7tYbU0wl9\nwgNZX09wgDu+ie8PB3sJdhxJZ72fPoMimmH/yUyj9m0V6IH0u7WzHox7sh1nbWLySEtvXLyZp7Zs\ncI/makPExHpofjvhonvLHxRk8VS3UKn+jViy2LuXdTGSRCxCdY1c7Roc7SVw1BFgAbW9JI+GStG/\na1MDzyvCKyM64kZWARYpJoJucNHOjnZ6SyIwcdBTU8zSmvq6IP1uIbzcHOBTV1LDXKOaHgw9VNrK\nfhDLowewcNG95Q8aLuQr1ZwsE5+ilgqyTO0xs5OIMX1kJ3RsZWQpALU8NvaN4iKnadjjLY3Yy/g7\no1pXrKq6RseWxmOcGNxmSp8SQoj1UZDFU/QoM5xqJXtFXMrue2T/bWsrmurmrP+NUSaTYx/Ruf6R\nuirqUh29gGzqhhmDKbhXlI0ghBBiRJCVkJCA3r17IzY2VrmsoKAAEydORHR0NCZNmoSioiJOG0k0\nqZZCMHnIykKlwRVtHlE3kXfXttwNearS1rMX5OeKuL6tMPelR6Hsh+G4J6thrSplm9gfQvXMaN7E\nTecWbz7bBSvf6MsQxNU32pgZDzxVJmFvOCuBLrkFZQafi5gH1VISLrq3/GFwkDVy5EisWbNGbVlS\nUhJ69eqFvXv3omfPnkhMTOSsgUS/lnUJ33wxpFdLJM7uj9ZNuZ0zUhtFiCASiRD7eCu0aaoy3QvH\n8eVIlYK3I1TfOjTqPPp3EotEcFWpq8a0h0+D6Y5Cm3vpPa6iB83T1QFjo0OZW8dwMrFYhHnjI/D2\nc131noOY16uvvkm5OwJF95Y/DA6yIiIi4OGh/nBMTk5GXFwcACAuLg779+/npnWEFVMLk7J5/it6\nn0yh2iNib2e+BGk2HTeKSvwuTtrf/Yju0RzNpK6s500E1HsVRUw5YBaieu7WTT0gFonwZM8WWPtO\nFN55oRue7NECrlquPcDHBWHB3pjwVHu8Ny4C/bsEMSa0BzME9xKxCC0DPNA+mCaDJoQQTnKyHjx4\nAD+/2ppBUqkUDx484OKwxEBdQvwYH3x6sYgAFBNem8JaRTGZhgRnjOqEmMdaYNCjzbXu9mxUW3ww\nqSerIHbGqE6YNryD9g30RH5c9eop3tjs36X+DUyxSIRv/zcAoweEKJeNjgrBx9N6Mx7DwU4MkUiE\nvuFNlb1gH0zqobbN2MGhjEOjhgwtEkKI0JmlhAPbnhWplF/DXLpY81oU5174yuNG7V/ZIApRvZb3\nJvbEuesyhLb2w/yXH4O9RGz0tXp6OlvkexI16Hlyc3PUOK9U6o6wkCacnXMww3W5ujrW/+zmqLFe\n1efxAxAbvwMAENbaD/tPZqJzWyl8vLXPfcn0XUZL3dEjvCm83ByN7uFsFuDO+H2FtvDC1Yx8AMDo\n6DDGfS11j4l+VEtJuOje8gcnQZavry9yc3Ph5+cHmUwGHx8fVvvJZMJIkJdK3a16LaaeW1RdgyCp\nKx57xF/jeK2auKJVE1fk5hYj2M/FpPO1lLpa5HuqqVHvNSopLrfK/bl7r1j5c1FRuc5tVdvXuaU3\nXhvZCWHB3rifV8Jqn4ZyyyoNaGm9uL6tENk1iPHYkrqgLaSZp9ZzFxaW6mwXBWCWQw9g4aJ7yx9G\nDRc2fHsrKioKW7duBQBs27YNAwcONL1lxGLsJGIsnNQTQ3q1NNs54vq2MrqMgeEsnQHFLOOecYGd\nWCxCt1ApnB01/wZSdE6Za1gu9vFW+qfBsY2vlxBCbJ7BQVZ8fDzGjBmD9PR0REZGYsuWLZgyZQqO\nHj2K6OhoHD9+HFOmTDFHWwkPPTOgDQCgK4cV6vnI2Em8HRuU5+jevnaIU+rF/byE742L0LmeTVxn\noWoghBDCCwYPFy5dupRx+bp160xtCzHQkz1awMtdd66PtcX0DMbg7s0hEVuu7u2YqLb4bs9li53P\nnBoWGVUMhUok3H+frQLZJd8bGzASy6K8HeGie8sfNHchj42OCtG/kQ2wZIAFAH07N8WIqFAMf+s3\nAKaXuDCUnUSMquoatfpVpsQlwQHuuJVdO/RYXRdkcTVcOPDRZkg+xW4yajZnpJcLbQc9gIWL7i1/\n0LQ6RJDEBtS24trH03rhrTFd1HoZdcVYIc08daxV31dxTH8f3ZNps9XGmNIRKg0a92Q79VXUyUUI\nIUoUZBHCMW93R4S19FGLOHRN4q26ztNNe9L5o+2kGNWvDUb0bYWXottp3c4QhgRFz0a1hb+PC14Y\nXF8Bvn+XIE7aQQghQkTDhUSwBnQNwoEzWaymkbGkZwa0weYDN5Sfa2pq/z/prUjG4TbVyaxdnOww\n7HHTq+8rtGvB/rtp1sQNH015jLNzE/OivB3honvLHxRkEcF6cXAo4vq1tmDpCHWqnUSt65LKe4Q1\nQUzPYER2CULibxeQduO+cgJnO63J7Iooi/shUKbpckxBOVm2gx7AwkX3lj8oyCKCJRKJrBZgAepD\ncWEtffDBxB4I8K0t6KpaA0vXUGLD43CN60NTThYhhNSjnCxCzKTh5NPNmrip9VYpyzHoeftSdbiQ\na1wERa+O6Gj6QQghRIAoyCLETDz0VE4f0K02afzJni1YHc9Wh+Ii2tfPAWnpchlEu6++WqbM3SHC\nQveWP2i4kBAr6dpWiqS3InXkYinY/hjcm6M746/TWQhv42vtppA6lLcjXHRv+YOCLEKsSH+ABbjW\n5ZU1nGKHC/Z23HRmd2zti46tKcAihBBVFGQRYuNmvxCBtb+dx6j+bTg/tqO9BAljH4WPjU/PRAgh\nfERBFiFmwtUcf4F+rpgS24GTYzEJCdJdcZ7wE9VSEi66t/zBWZD1/fff49dffwUAPPPMM3jppZe4\nOjQhhBAD0QNYuOje8gcnCRnXrl3Dr7/+ii1btmD79u1ISUnB7du3uTg0IbzVp1MggvxcMfPpcGs3\nxeZNnjwZ27dvx8OHD63dFEII4QwnQdaNGzfQuXNnODg4QCKRICIiAvv27ePi0ITwlruLAxa+3BNd\nQvys3RSb9+mnn6K0tBSvvfYaZs+ejUOHDqFGMd8QIYTwFCdBVtu2bXHy5EkUFBSgtLQUhw4dwt27\nd7k4NCGkEfDy8sJzzz2Hd955B9XV1ViyZAnGjx+PDRs2WLtpvEW1lISL7i1/cJKT1aZNG0yePBkT\nJkyAq6srwsLCIJFw/7o5IUSYkpKS8Pfff6Nt27aYMGECwsNrh1jHjx+PF154wcqt4yfK2xEuurf8\nwVni+6hRozBq1CgAwPLlyxEQEKB3H6nUnavTWx1di+0RynUAwroWJq1bt8aECRNgb68+1+TKlSut\n1CJCCDEdZ9PqPHjwAABw584d/Pnnn4iNjeXq0IQQgTt//rwywJLL5Vi+fDkAwN1d2MElIUTYOOvJ\nmjFjBgoKCmBnZ4f58+fDzc2Nq0MTQgTuzJkzyp9FIpHaZ2IcqqUkXHRv+YOzIIsSVAkhxhKLxbhy\n5QratWuHK1euQC63/fkabR09gIWL7i1/UMV3QojVffDBB/j0008hk8nQpEkTLFy40NpNIoQQk1GQ\nRQixuhYtWlCSOyFEcCjIIoRY3ebNm7Fp0ya10i8bN260Yov4j/J2hIvuLX9w9nahIQ4dOoQnn3wS\n0dHRSEpKskYTdMrOzsZLL72EIUOGIDY2FuvXrwcAFBQUYOLEiYiOjsakSZNQVFSk3CcxMRGDBw9G\nTEwMjhw5olx+4cIFxMbGIjo6GosWLbL4tQBATU0N4uLiMG3aNAD8vY6ioiLMnDkTMTExGDJkCM6d\nO8fba0lMTFT+fsXHx6OiooI315KQkIDevXurvUFsatuXL1+ODRs2YOPGjVi/fj0CAwMxePBgPPvs\nswM9iCQAACAASURBVLhz545FrktoXn31TXoICxTdW/6weJBVU1ODhQsXYs2aNfj999+xa9cu3Lhx\nw9LN0EkikWDOnDnYtWsXNm7ciA0bNuDGjRtISkpCr169sHfvXvTs2ROJiYkAgOvXr2PPnj3YvXs3\nVq9ejffff1+ZuLtgwQIsWrQIe/fuxc2bN3H48GGLX8/69evRpk0b5We+XseiRYvQv39/7NmzBzt2\n7EDr1q15eS1ZWVnYtGkTtm3bhp07d6K6uhq7du3izbWMHDkSa9asUVtmatsdHR2VU3H9+uuv8PT0\nxL59+zBu3Dh8+umnZr8mQggxB4sHWWlpaQgODkZQUBDs7e0xZMgQJCcnW7oZOkmlUoSFhQEAXF1d\n0aZNG+Tk5CA5ORlxcXEAgLi4OOzfvx8A8Ndff+Gpp56CnZ0dmjVrhuDgYKSlpUEmk6GkpERZvXrE\niBHKfSwlOzsbBw8exDPPPKNcxsfrKC4uxsmTJ5UFb+3s7ODu7s7La3Fzc4O9vT1KS0tRVVWFsrIy\n+Pv78+ZaIiIi4OHhobbM1LZLJBLMnz8fo0ePxrJly3Dq1CkAQHR0NI4dO2b2ayKEEHOweJCVk5OD\nwMBA5Wd/f3/cu3fP0s1gLTMzE5cvX0bnzp1x//59+PnVTvYrlUqVBViZriknJwc5OTlqle8Vyy1p\n8eLFePvttyESiZTL+HgdmZmZ8Pb2xpw5cxAXF4f33nsPpaWlvLwWT09PTJw4EZGRkejXrx/c3d3R\nu3dvXl6LwoMHD0xq+5IlS9C9e3ds2rQJgYGB+PbbbwHUBl8eHh7Iz8+34NUIA81vJ1x0b/mDEt91\nKCkpwcyZM5GQkABXV1e1QAWAxmdbk5KSAj8/P4SFhSE1NVXrdrZ+HQBQVVWFixcvYt68eejUqRMW\nL16MpKQk3t0TALh9+zbWrVuHAwcOwN3dHa+//jp+++03Xl6LNoa2PTMzE+fPn8fEiRMhl8vx448/\nIj4+HgC01sxKSEhASkoKfH19sXPnTgC1uWGzZs1CVlYWmjVrhhUrViirxicmJmLLli2QSCSYO3cu\n+vTpA6A2N+ydd95BRUUF+vXrh7lz5xp72TbF0JydtH8v4lbGbaPOdfrsWQDdjNqXGI7ysfjD4kGW\nv7+/WiJrTk4OmjRpYulm6FVVVYWZM2di+PDhGDRoEADA19cXubm58PPzg0wmg4+PD4Daa7p7965y\n3+zsbPj7+2ssz8nJgb+/v8Wu4fTp0/jrr79w8OBBlJeXo6SkBG+99Rb8/Px4dR0AEBAQgICAAHTq\n1AkAMHjwYKxevZp39wSonUKmW7du8PLyAgAMGjQIZ86c4eW1KJja9qSkJPTp0wd37tyBv78/jh49\nivj4eFRXV6O4uFj5XakaOXIkxo4di7ffflvtOL169cLkyZORlJSExMREzJ49Wy03LDs7GxMmTMC+\nffsgEomUuWHh4eGYPHkyDh8+jL59+5rx27JNO/48juvFgfo3ZNQFkgbzThJCrDBc2KlTJ2RkZCAr\nKwsVFRXYtWsXBg4caOlm6JWQkICQkBCMGzdOuSwqKgpbt24FAGzbtk3Z7qioKOzevRsVFRW4ffs2\nMjIyEB4eDqlUCnd3d6SlpUEul2P79u0WvdY333wTKSkpSE5OxrJly9CzZ098+umnGDBgAK+uAwD8\n/PwQGBiI9PR0AMDx48cREhLCu3sC1E6GfO7cOZSXl0Mul/PyWhr2Lpna9tzcXAwZMgQikQhRUVHK\nFII//vgDjz32GGMbzJEbZo0cPVshFoshsXc0+n+EEE0W78mSSCR47733lMMCTz/9tNqbb7bg1KlT\n2LlzJ0JDQzFixAiIRCLMmjULkydPxhtvvIEtW7YgKCgIK1asAACEhIQoywoo5m5UDJfMmzcPc+bM\nQXl5Ofr164d+/fpZ89IAAFOmTOHldbz77ruYPXs2qqqq0Lx5c3z00Ueorq7m3bW0b98ew4cPx8iR\nIyEWi/HII49g9OjRKCkp4cW1xMfHIzU1Ffn5+YiMjMSMGTMwZcoUvP7660a3vVWrVtizZw9ycnJw\n5coVeHl5YfDgwfDy8sKyZexzT3TlhnXp0kW5nSI3TCKR2ExeG9eolpJw0b3lD5GcJgkjhNiAa9eu\n4fr162jdujXatWvHap+srCxMmzZNmZPVo0cPnDhxQrm+Z8+eSE1NxcKFC9GlSxdlba+5c+eif//+\naNq0KZYtW4a1a9cCAE6ePIlvv/0W33zzDcdXZ/v+9+E3uJhn7HCh9fy+bAQAYOib2y1+7oEhhXjj\nlbEWPy/hD0p8J4RY3S+//KL8+ezZszh79iyeffZZg49j6bw2maxI/0YWIpW6m9Se8rJKDlvTOJQ8\nrDD6Ozf1fpmDrbXJFttjKKtUfCeEEFUODg5wcHCAvb09rl27pqyTpQ/XuWHWyNEjhAgX9WQRQqxO\nkawO1L41OHXqVL37mCM3zFbyJrlAeTvCRfeWPyjIIoRYnepwYU5Ojtrch9osXbqUcfm6desYl0+d\nOpUxeOvYsaMyp0tI6AEsXHRv+YOCLEKI1Tk4OACoLWLaoUMHvPzyy1ZuESGEmI6CLEKI1fXs2VPt\nc35+vnIqnaZNm1qjSYQQYjIKsgghVjd79mzk5uYiNDQUV65cQWBgIPz8/CASibQOCxLdKG9HuOje\n8gcFWYQQq/P19cW6devg4OCAiooKzJ4926AipEQTPYCFi+4tf1AJB0KI1WVmZqK8vBwAUF5ejszM\nTCu3iBBCTEc9WYQQq4uPj8fkyZMB1Ca/x8fHW7lFhBBiOgqyCCFW16dPH/Tp08fazRAUytsRLrq3\n/EFBFiHE6lJTU/H111/j/v372L59O5YsWYK5c+dau1m8Rg9g4aJ7yx+Uk0UIsboVK1bgm2++gZeX\nFyQSCa5cuWLtJhFCiMkoyCKEWJ1YLIaTk5Nyqpuamhort4gQQkxHQRYhxOqefvppTJ48Gbdv38b0\n6dPxzDPPWLtJvPfVV8uUuTtEWOje8ofVcrKqqqqRl/fQWqfnlLe3C12LjRHKdQDCuhap1F1jmVwu\nR8eOHTFgwABkZGSgefPm8Pb2tkLrhIXydoSL7i1/WK0ny85OYq1Tc46uxfYI5ToAYV0LE5FIhM8+\n+wxeXl4IDw+nAIsQIhj0diEhxOr8/PywatUqhIeHQyyu/duPSjoQW3f2cibmL11r1L4BPo54ZcIL\nHLeI2BoKsgghVnPy5ElEREQgMDAQDx8+RFpamnIdBVmmoVpK5lfm0QW3K43bt/zef0afl+4tf1CQ\nRQixmi+++ALr16/Ha6+9hpdeegnr16+3dpMEgx7AwkX3lj/o7UJCCCGEEDOgnixCiNVcv34d8fHx\nkMvlaj+LRCIsXbrU2s0jhBCT6A2yEhISkJKSAl9fX+zcuZNxmw8//BCHDh2Cs7MzlixZgrCwMM4b\nSggRns2bN1u7CYJFeTvCRfeWP/QGWSNHjsTYsWPx9ttvM64/ePAgMjIysG/fPpw7dw7z58/Hpk2b\nOG8oIUR4goKCrN0EwaIHsHDRveUPvTlZERER8PDw0Lo+OTkZI0aMAAB07twZRUVFyM3N5a6FhBBC\nCCE8ZHLi+7179xAQEKD87O/vj5ycHFMPSwghhBDCa/R2ISGECBDNbydcdG/5w+S3C5s0aYLs7Gzl\n5+zsbPj7++vdr2XLlrh586app7cZTHOy8ZVQrkUo1wEI61qIZVDejnDRveUPVkGWXC7Xum7gwIHY\nsGEDnnrqKZw9exYeHh7w8/NjdXKZrIhdK22cVOpO12JjhHIdgPCuhRBCGgu9QVZ8fDxSU1ORn5+P\nyMhIzJgxA5WVlRCJRHj22WfRv39/HDx4EE888QScnZ3x0UcfWaLdhBBCCCE2TW+QxaYg4Lx58zhp\nDCGEEG5QLSXhonvLH1TxnRBCBIgewMJF95Y/6O1CQgghhBAzsPmerMWL38eZM6fh7u4GkUiMN96Y\njU6dOgMA7t69gwUL5qKwsAChoe3x3nsfwM7O5i+JEEIIIY0AL3qyXnvtdaxduwHTp7+Ozz6rT6z/\n+uuVeP75sfj5563w9PTE77/vsEh7qqurdX4mhBBro1pKwkX3lj941e3TsWM4MjNvKz+fPn0S77+/\nGADw1FOxSEr6GiNGjFLbJzv7LhYunIeysjIAwKxZb6Njx04AgB9/XIc///wDYrEYjz32OKZOnY5r\n167gs8+WoLy8HEFBQZgzZz7c3NwwY8ZUtG0bivPnz+GJJ2Jw/fpVODg44OrVK+jVqycmTHjFQt8C\nIYToR3k7wkX3lj94FWQdPXoYrVu3AQAUFOTDzc0dIpEIACCVNsH9+zKNfby9fbBixVewt7dHVlYm\nFixIwOrV63Hs2N84duxvfPvtD7C3t0dhYSEA4MMPF+DNN/+Hzp274LvvVuO771ZjxoxZAICqqiqs\nXr0eQO0wpkx2D0lJ6wRVx4gQQggh3LB6kPXoox0BAKdO/at1my+//BzffLMKOTk5+Oqrbw06flVV\nJZYt+wTXr1+FWCxGZmZm3fn+QUzMUNjb2wMAPDw8UFJSjJKSYnTu3AUAEBMTi7lz31Iea+DAwWrH\nHjBgkEFtIYQQQkjjwYucrOnTX8fPP2/F9Omv47vvkgAAnp5eKC4uUlajl8nuwc+vica+v/zyE3x9\nffH99xuxdu0GVFdXGd0OJydntc/Ozs5atiSEEOuivB3honvLH7wIshRGjRoNmUyGf/9NAwB06xaB\nlJRkAMDu3TvRt28/jX1KSorh61s7zc/evbtRVVUbZHXv3hN//LEL5eXlAIDCwgK4urrB3d0daWln\nAQB//PE7unTpZvbrIoQQrr366puUuyNQdG/5g1dBFgCMGzcR331XO2Q4bdpr+OmnH/DccyNRUJCP\noUNHaGwfF/cMdu/+HRMmPI/09P/g6uoGAOjZsxd69XocL788FhMnvoCNGzcAABISFuDLLz/H+PHP\n4+rVK5gwYTIAKHO/CCGEEELYEMl1zf5sRi1btsQ//5xnlZNl64SU+C6UaxHKdQDCuxYhsaX7Yurv\nyaLP1+FGaQsOW2QZv/+/vbuPiuK89wD+3Re0VlFUdlcOGkzWWm2Dmmib1sPZRpby4vISKlFiaxPX\nijn3RqMxtlUvmorVezVBe9KTBI5aEmNjEoWkKWmPca1sTauNSc1qjW01klWQBVEJKoKwz/3Dy15R\nYBfY2dkZvp+/3IeZ2e+zg/Bj5jczhbf+uE5/5h2Zk/SMUfM5/vtnP5E7Rgfh9rMmHPP0lOKOZBER\nkX/s21Ev7lvlkP3qwnZqOKJFRBQu2LOjXty3ysEjWUREREQSYJFFREREJAEWWUREKsS+HfXivlWO\nsOnJIiKi4GHfjnpx3ypHQEeynE4nUlNTkZKSguLi4ru+fvnyZfzkJz9BVlYWMjIyUFpaGvSgRERE\nREri90iW1+tFQUEBSkpKYDQakZOTA6vVCrPZ7Ftm165dmDhxIrZt24ZLly4hLS0NmZmZ0Ot7d6CM\nVxoSERGR0vk9kuVyuRAXF4fY2FhERETAZrPB4XB0WCY6OhrXrl0DAFy7dg1RUVG9LrCIiKjv2Lej\nXty3yuG3EvJ4PIiJifG9NplMOH78eIdlZs+ejccffxwJCQm4fv06tmzZEvykREQUMPbtqBf3rXIE\n5erCoqIiTJgwAYcOHcI777yDdevW+Y5sEREREfVHfo9kmUwmVFdX+157PB4YjcYOy3zyySd48skn\nAQD33HMPRo8ejc8//xzx8fHdbttgiIRWq+ly7FvfurV+ZWWl/5nITE3PZFPLXNQyD0BdcyEi6i/8\nFlnx8fFwu92oqqqCwWBAeXk5Cgs7ngs2m83461//iqlTp+LixYuorKzEmDFj/L55XV0jvF4R0Fg4\nC7eHWPaFWuailnkA6psLhUZ7zw5PLakP961y+C2ydDod8vPzYbfbIYRATk4OzGYzdu/eDY1Ggzlz\n5iAvLw+rVq1CZmYmhBBYsWIFoqKiQpGfiIg6wV/A6sV9qxwBXQJosVhgsVg6jOXm5vr+PWLECLzy\nyivBTUZERESkYIp5rM7Uqff77p9FREREFO4UU2QREVHgeC8l9eK+VQ7eMZSISIXYt6Ne3LfKobgj\nWTxtSEREREqguCKLiIiISAlYZBERqRD7dtSL+1Y5FN2T1X7a8OOPT8ichIgovLBvR724b5WDR7KI\niIiIJMAii4iIiEgCqiiyeMUhEVFH7NtRL+5b5VB0TxYREXWOfTvqxX2rHKo4kkVEREQUblRXZPHU\nIREREYUD1RVZRETEvh01475VDlX3ZPE+WkTUX7FvR724b5WDR7KIiIiIJBBQkeV0OpGamoqUlBQU\nFxd3usyRI0fwyCOPID09HfPmzQtqyL5inxYRERGFmt/ThV6vFwUFBSgpKYHRaEROTg6sVivMZrNv\nmcbGRqxbtw47duyAyWTCpUuXJA1NRETda+/Z4akl9eG+VQ6/RZbL5UJcXBxiY2MBADabDQ6Ho0OR\n9d577yE5ORkmkwkAMGLECIni9h37tIioP+AvYPXivlUOv6cLPR4PYmJifK9NJhNqa2s7LFNZWYmG\nhgbMmzcPs2bNwjvvvBP8pEREREQKEpSrC9va2nDy5Em8+uqruH79OnJzc/HAAw8gLi4uGJuXBI9o\nERERkZT8FlkmkwnV1dW+1x6PB0aj8a5lhg8fjoEDB2LgwIGYNm0aTp065bfIMhgiodVqJBnryTrB\nEKzthAO1zEUt8wDUNZdQSExMxJAhQ6DVaqHX67Fnzx40NDRg2bJlqKqqwujRo7F161ZERt76XIuK\nirB3717odDqsXr0aCQkJMs+g79i3o17ct8rht8iKj4+H2+1GVVUVDAYDysvLUVjY8SZoVqsV69ev\nR1tbG1paWuByuTB//ny/b15X1wivV0gy1pN1+npUy2CIRF1dY6/WDTdqmYta5gGoby6hoNFosHPn\nTgwbNsw3VlxcjO9+97tYuHAhiouLUVRUhGeffRanT5/GH/7wB7z//vuoqanB/PnzsW/fPmg0mm7e\nIfzxF7B6cd8qh9+eLJ1Oh/z8fNjtdqSnp8Nms8FsNmP37t148803AQBmsxkJCQnIzMzE7NmzMXv2\nbIwbN07y8MHGWz0QqYMQAl6vt8OYw+FAdnY2ACA7Oxv79+8HABw4cAAzZ86EXq/H6NGjERcXB5fL\nFfLMRKQ+AfVkWSwWWCyWDmO5ubkdXi9YsAALFiwIXjIiol7SaDSw2+3QarXIzc3Fo48+ivr6ekRH\nRwMADAaD71YzHo8HU6ZM8a1rMpng8XhkyU39R/01HVb9z/ZerXv9agNWL5kHg8EQ5FQUbKp+rE5f\nsDGeSLneeOMNGI1GXLp0CXa7Hffee+9dp/+UfjrQH/bthLe2r8ahRvhfrjPTYo7h7bd3ct8qAIss\nIlKd9otzRowYgaSkJLhcLowcORIXL15EdHQ06urqfPfzM5lMuHDhgm/dmpoa3z3//Am3CxJuz7N2\n7doerTvwKxFAU7ATkRQOVo7E9vyZknz/hfP3tBKxyPKDR7SIlKWpqQlerxeDBw/G9evXcejQITz1\n1FNITExEaWkp8vLyUFZWBqvVCuDWlYjPPvssnnjiCXg8HrjdbkyaNCmg9wqnCxL6eoFE842bQUxD\nkhJAff1VREYG9/sv3C6yCcc8PcUiqwdYcBGFv4sXL+Kpp56CRqNBW1sbMjIykJCQgPvvvx9Lly7F\n3r17ERsbi61btwIAxo0bh7S0NNhsNuj1eqxdu1b1pxKJKDRYZPUCiy2i8DVmzBi8++67d41HRUWh\npKSk03UWLVqERYsWSZwstNiTpV4P31uP998v475VABZZfTR16v3QajX46KPjckchIvLhL2D1Onh2\nJDb9p8X/giQ7v/fJosDcfo8t3m+LiIiIWGRJjAUXERFR/8QiK0RYbBFRKL30UqGvL4vUpb0ni8If\ne7JkcGexxQZ6Igo29mSpF3uylINFVpjorPDiVYxERETKxSJLAW4vtrorxjob81essZAjIiKSBous\nfqS7Yq3961qtBl6vuGs5FmFEysL7ZKkX75OlHCyyKCB9PZom1ba7Gmu/d1lfjvIRKRl/AasXe7KU\ng0UWSa6roiacrrYMtNBjMUbhLMe+DAOj7unVupoBURgQFeRARP0ciyyiHvBXjBHJ6asjxkIfHS93\nDCL6PyyyiIKoLxcmdLWOUh7bxNOw4WXa0GMAgKNfTpE5CQUbe7KUI6Aiy+l0YsOGDRBCYNasWcjL\ny+t0OZfLhcceewxbtmxBcnJyUIMS9XeBFnB3Lt/Xq1IDXae7zO3Lud1fdLksBReLK/ViT5Zy+C2y\nvF4vCgoKUFJSAqPRiJycHFitVpjN5ruWe+GFF5CQkCBZWCLyL5x63YiI+jO/j9VxuVyIi4tDbGws\nIiIiYLPZ4HA47lpu586dSElJwYgRIyQJSkRERKQkfossj8eDmJgY32uTyYTa2tq7ltm/fz/mzp0b\n/IRERNRj04Ye8/Vlkbrw2YXKEZTG9w0bNmDFihW+10KIgNYzGCKh1WokGZNy252Ndfa+oc7Q2Viw\n5hIOuZhBXblIWuzJUi/2ZCmH3yLLZDKhurra99rj8cBoNHZY5sSJE1i2bBmEELh8+TKcTif0ej2s\nVmu3266ra/TdXTzYY1Ju+86x2++SLleGrsaCNRe5c/V0TKvVyJ4hkLFgzUWOXH0dIyJSO79FVnx8\nPNxuN6qqqmAwGFBeXo7CwsIOy9zeo7Vy5UrMmDHDb4FFREREpGZ+e7J0Oh3y8/Nht9uRnp4Om80G\ns9mM3bt348033wxFRiIi6iH2ZKkXe7KUI6CeLIvFAoul4/nf3NzcTpfduHFj31MREVGfsCdLvdiT\npRx+j2QRERERUc+xyCIiIiKSAIssIiIVYk+WerEnSzn4gGgiIhViT5Z6sSdLOXgki4iIiEgCLLKI\niIiIJMAii4hIhdiTpV7syVIO9mQREakQe7LUiz1ZysEjWUREREQSYJFFREREJAEWWUREKsSeLPVi\nT5ZysCeLiEiF2JOlXuzJUg4eySIiIiKSAIssIiIiIgmwyCIiUiH2ZKkXe7KUgz1ZREQqxJ4s9WJP\nlnIEdCTL6XQiNTUVKSkpKC4uvuvr7733HjIzM5GZmYnHHnsM//znP4MelIiIiEhJ/B7J8nq9KCgo\nQElJCYxGI3JycmC1WmE2m33LjBkzBrt27UJkZCScTify8/Px1ltvSRqciIiIKJz5PZLlcrkQFxeH\n2NhYREREwGazweFwdFhmypQpiIyM9P3b4/FIk5aIiALCniz1Yk+WcvgtsjweD2JiYnyvTSYTamtr\nu1z+7bffhsXCc8VERHI6+uUU9mWp1MGzIzFzZrbcMSgAQW18P3z4MEpLS/Hb3/42mJslIiIiUhy/\nRZbJZEJ1dbXvtcfjgdFovGu5U6dOYc2aNdi2bRuGDRsW0JsbDJHQajWSjEm57c7GOnvfUGfobCxY\ncwmHXMygrlxERGrnt8iKj4+H2+1GVVUVDAYDysvLUVhY2GGZ6upqLFmyBJs2bcI999wT8JvX1TXC\n6xWSjEm57TvHtFpNp+8bygxdjQVrLnLn6umYVquRPUMgY8Gaixy5+jpG0mrvx+IpQ/Vp78n6j/94\nRu4o5IffIkun0yE/Px92ux1CCOTk5MBsNmP37t3QaDSYM2cOXnrpJTQ0NOAXv/gFhBDQ6/XYs2dP\nKPITEVEnWFypF++TpRwB9WRZLJa7mtlzc3N9/16/fj3Wr18f3GRERERECsbH6hARERFJgEUWEZEK\n8T5Z6sX7ZCkHn11IRKRC7MlSL/ZkKQePZBERERFJgEUWERERkQRYZBERqRB7stSLPVnKwZ4sIiIV\nYk+Wev3Fcx+GCzeWrnulV+s/ON6EH+fy2YehwCKLiIhIQQYMGopruL/X6zdcrQliGuoOTxcSERER\nSYBFFhGRCrEnS724b5WDpwuJiFSIPVnqxX2rHCyyiIjChMdTiw+PfNSrdSMjv4I2r+APdaIwwv+P\nRERhYn/FIXzw7yG9XLsNAwzxQc1DRH3DIouIKExooIFWF5wfy+09Ozy1pD7ct8rBIouISIX4C1i9\nuG+Vg1cXEhEREUkgoCLL6XQiNTUVKSkpKC4u7nSZ9evXIzk5GVlZWfjss8+CGpKIiIhIafwWWV6v\nFwUFBdi+fTt+//vfo7y8HGfOnOmwTEVFBdxuN/bt24d169Zh7dq1kgUmIiL/eC8l9eK+VQ6/PVku\nlwtxcXGIjY0FANhsNjgcDpjNZt8yDocDjzzyCABg8uTJaGxsxMWLFxEdHS1RbCIi6g77dtSL+1Y5\n/B7J8ng8iImJ8b02mUyora3tsExtbS1GjRrVYRmPxxPEmERERETKItvVhefPH8LUqYNRXX2ow3iw\nxqTc9t1jGgBC5gydjwVrLvLn6umYJgwyBOtz8D8XeXL1bYyISO00QgjR3QLHjh3Diy++iO3btwOA\nr/E9Ly/Pt8yaNWvwne98BzNnzgQApKam4vXXX+/2dOHYsX2NTkRKU1kpd4LgqqtrDOr2fvtWGfZ/\nPiwo2+ov91L6feGtVpX0Z96ROUno9HXfmlqO4vuW79w1PnTYIHzZ0NTtupGRQ/CtqQ/26n17ymCI\nDPr/sb4wGCJ7vI7fI1nx8fFwu92oqqqCwWBAeXk5CgsLOyxjtVqxa9cuzJw5E8eOHcPQoUP99mNV\nVgb/B5Rcwu0boS/UMhe1zANQ11yAnv+Qot5Re3HVn/V131bhm9hx8EonX+lsrKMhzR+HrMhSA79F\nlk6nQ35+Pux2O4QQyMnJgdlsxu7du6HRaDBnzhx873vfQ0VFBb7//e9j0KBB2LhxYyiyExERUQ/p\nBwzq9boRYmAQk6hfQD1ZFosFFoulw1hubm6H12vWrAleKiIiIiKF42N1iIhUqL/0ZPVHcu7b5uZm\nHPrLX3q9/tQHHsCgQb0/kqY0LLKIiFSIxZV6yblvm4dMxMt/7N0tmm5crcd/DfoqHnig/3xvMrWf\nPAAACkRJREFUssgiIiKigOj0A6DTD+jVut62liCnCX98QDQRERGRBFhkEREBcDqdSE1NRUpKiu9+\ngErG59upF/etcvB0IRH1e16vFwUFBSgpKYHRaEROTg6sVmuHZ7QG6mxlJS7W1/cqxxfuSgCTe7Xu\nndiTpV7ct8rBIouI+j2Xy4W4uDjExsYCAGw2GxwOR6+KrKJd5fjiurFXOTTae/CVwb1alYjCEIss\nIur3PB4PYmJifK9NJhOOHz/eq20NGDgIg3QjgxWNSDX0EYPw+rsVKHV8EtDyAwfq0dzc6nv9zftM\nyP2BTap4kmCRRUQURG03GuBt6F2B1hc6vRZtrV7f62/f2wYA+NtZXcizdJZHat767j/zUOfxpy95\npNq3Un9GOgD1AOqvBfaYsDvzDP+qNLmkJGuR1ZuHLYYrziX8qGUegLrmEo5MJhOqq6t9rz0eD4xG\n/6f8Otsvxb/i0y9CquS/5E5A1CVeXUhE/V58fDzcbjeqqqrQ0tKC8vJyWK1WuWMRkcLxdCER9Xs6\nnQ75+fmw2+0QQiAnJ6dXTe9ERLfTCCGE3CGIiIiI1IanC4mIiIgkwCKLiIiISAIssoiIiIgkIEuR\npdRnhNXU1ODHP/4xbDYbMjIy8NprrwEAGhoaYLfbkZKSggULFqCxMbB7gIQDr9eL7OxsPPnkkwCU\nO5fGxkYsWbIEaWlpsNls+PTTTxU5l6KiIt/31/Lly9HS0qKYeaxatQrTp09HRkaGb6y77EVFRUhO\nTkZaWhoOHTokR+ReefXVV5GRkdHhZ0Co9fSzliPPH//4R6Snp2PixIn4xz/+EbIsXeXZtGkT0tLS\nkJWVhcWLF+Pq1auy5vnVr36FzMxMZGVl4YknnkBNTY2sedrt2LEDEyZMwJUrV0KWp6tMv/71r2Gx\nWJCdnY3s7Gw4nU5Z8wDAzp07kZaWhoyMDDz//PP+NyRCrK2tTSQlJYnz58+LlpYWkZmZKU6fPh3q\nGL1SW1srTp48KYQQ4urVqyI5OVmcPn1abNq0SRQXFwshhCgqKhKbN2+WM2aP/OY3vxHLly8XixYt\nEkIIxc7lZz/7mdizZ48QQoibN2+KL7/8UnFzOX/+vEhMTBTNzc1CCCGefvppUVpaqph5fPTRR+Lk\nyZMiPT3dN9ZV9n//+98iKytL3Lx5U5w7d04kJSUJr9crS+6e+Ne//iXS09NFc3OzaG1tFfPnzxdu\ntzvkOXryWcuV58yZM+Ls2bNi3rx54sSJEyHL0lWeDz/8ULS1tQkhhNi8ebN4/vnnZc1z9epV379f\ne+01sWrVKlnzCCHEhQsXhN1uFzNmzBCXL18OWZ6uMr344otix44dIc3RXZ7Dhw+L+fPni5s3bwoh\nhKivr/e7nZAfybr9GWERERG+Z4QpgcFgwMSJEwEAgwcPhtlshsfjgcPhQHZ2NgAgOzsb+/fvlzNm\nwGpqalBRUYFHH33UN6bEuVy9ehVHjx7FrFmzAAB6vR6RkZGKm8uQIUMQERGBpqYmtLa24saNGzCZ\nTIqZx7Rp0zB06NAOY11lP3DgAGbOnAm9Xo/Ro0cjLi4OLpcr5Jl76syZM5g8eTIGDBgAnU6HadOm\nYd++fSHP0ZPPWq489913H8aOHQshwwXsneWZPn06tNpbv/KmTJkS0iNHneUZPPj/H1LZ1NSE4cOH\ny5oHADZs2ICf/vSnIctxu64yyfH9A3Se54033sDChQuh19+6+9WIESP8bifkRVZnzwirra0NdYw+\nO3/+PE6dOoXJkyejvr4e0dHRAG4VYpcuXZI5XWDa/0NpNBrfmBLncv78eQwfPhwrV65EdnY28vPz\n0dTUpLi5DBs2DHa7HQ8//DAsFgsiIyMxffp0xc3jdpcuXeo0e2c/BzwejywZe+JrX/sajh49ioaG\nBjQ1NcHpdOLChQtyxwLQ9WdNd9uzZw8sFovcMbBlyxY8/PDDKC0txaJFi2TN4nA4EBMTg69//euy\n5rjT66+/jqysLKxevVr2VonKykocPXoUs2fPxrx58wJ6vikb33vh2rVrWLJkCVatWoXBgwd3KFIA\n3PU6HB08eBDR0dGYOHFit38pKGEura2tOHnyJObOnYuysjIMGjQIxcXFitsv586dQ0lJCf70pz/h\nz3/+M5qamvC73/1OcfPojpKzA4DZbMbChQsxf/585OXlYeLEidDp5Hk2oD9K/6yl8vLLLyMiIqLT\nfqRQW7ZsGQ4ePIgf/OAH2LBhg2w5bty4gaKiIixevNg3JtcRpNvNnTsXDocD7777LqKjo7Fx40ZZ\n87S1taGhoQFvvfUWVqxYgaVLl/pdJ+RFVm+fERYuWltbsWTJEmRlZSEpKQkAMHLkSFy8eBEAUFdX\nF9AhRLl98sknOHDgAKxWK5YvX44jR45gxYoViI6OVtxcRo0ahVGjRiE+Ph4AkJycjJMnTypuvxw/\nfhwPPvggoqKioNPpkJSUhL///e+Km8ftuspuMpk6HAGqqamByWSSJWNPzZo1C6Wlpdi5cyeGDh2K\nsWPHyh0JgDJ/DoVaaWkpKioq8MILL8gdpYOMjAycOHFCtvdvf6RUVlYWEhMT4fF4MGvWLNTX18uW\nCbh1Oq79j4XZs2cHdORISqNGjUJycjIAYNKkSdBqtbh8+XK364S8yFL6M8JWrVqFcePG4fHHH/eN\nJSYmorS0FABQVlamiPk888wzOHjwIBwOBwoLC/HQQw9h8+bNmDFjhuLmEh0djZiYGJw9exYAcPjw\nYYwbN05x++W+++7Dp59+iubmZgghFDmPO//67Sp7YmIi3n//fbS0tODcuXNwu92YNGlSyPP2Rvtp\nuOrqanzwwQeyHREJ9LOWK0+gX5PKne/pdDqxfft2vPzyyxgwYIDseb744gvfv/fv348JEybIlmf8\n+PH48MMP4XA4cODAAZhMJpSVlWHkyJGyZQJu/bHQ7oMPPsD48eNlzZOUlITDhw8DAM6ePYvW1la/\nvXSyPFbH6XTil7/8pe8ZYXl5eaGO0Csff/wxfvSjH2H8+PHQaDTQaDRYtmwZJk2ahKVLl+LChQuI\njY3F1q1bO23gC1d/+9vfsGPHDrzyyiu4cuWKIudy6tQprF69Gq2trRgzZgw2btyItrY2xc1l27Zt\nKCsrg1arxTe+8Q2sX78e165dU8Q82o+IXrlyBdHR0Vi8eDGSkpLw9NNPd5q9qKgIe/bsgV6vx+rV\nq5GQkCDzDALzwx/+EA0NDdDr9Vi5ciUeeuihkGfo6WctR55hw4ahoKAAly9fxtChQzFhwgRs27ZN\ntjxFRUW4efMmoqKiAACTJ0/Gc889J1ueiooKnD17FjqdDmPGjMFzzz0XsqKmszztFw4BgNVqxd69\ne32flVyZjhw5gs8++wxarRaxsbFYt26dr+9QjjxZWVlYuXIlTp06hYiICPz85z/Ht7/97W63w2cX\nEhEREUmAje9EREREEmCRRURERCQBFllEREREEmCRRURERCQBFllEREREEmCRRURERCQBFllERERE\nEmCRRURERCSB/wVVRkHx422KiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8186287b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if model_june.R0.value.shape:\n",
    "    Matplot.summary_plot(model_june.R0, custom_labels=age_groups)\n",
    "else:\n",
    "    Matplot.plot(model_june.R0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate of R0 for june (no confirmation submodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting R0\n"
     ]
    },
    {
     "data": {
      "image/png": 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VMu/fnFOFEvtAOUf2hfqTfVY5Qvbh+pM4mJFt1FyPihePCyqlKJgs2nBa43tb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\nJMaEjIiIiEhiTMiIiIiIJPb/AYxAbpDX5l0hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb819932a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if model_june.R0.value.shape:\n",
    "    Matplot.summary_plot(model_june_noconf.R0, custom_labels=age_groups)\n",
    "else:\n",
    "    Matplot.plot(model_june_noconf.R0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate of R0 for july (with confirmation submodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting R0\n"
     ]
    },
    {
     "data": {
      "image/png": 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F/eSOJcU8e+TT1cWRtd3KE7NL1zMmqHJ2Nu5ry5PlbTGyNvP5mv824bNc90dP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\nRCQzJmREREREMvv/K4P1KmolEUMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8186b9b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if model_july.β.shape:\n",
    "    Matplot.summary_plot(model_july.R0, custom_labels=age_groups)\n",
    "else:\n",
    "    Matplot.plot(model_july.R0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate of R0 for july (no confirmation submodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting R0\n"
     ]
    },
    {
     "data": {
      "image/png": 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zD5b8FveCpDm5+lX02WKDt9dnsQZqDLQf1KZ6emz1DGf7HHWwp9GE1PTz2HXU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\nERGRyJiQEREREYmMCRkRERGRyP4HoVKqMHRwXioAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb819993860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if model_july_noconf.β.shape:\n",
    "    Matplot.summary_plot(model_july_noconf.R0, custom_labels=age_groups)\n",
    "else:\n",
    "    Matplot.plot(model_july_noconf.R0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lab confirmation rates, June model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb8180688d0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DMeh63O5xcjr5rgUAACQWPp0A6MHvb5bf32x1GQAAAKZhJAgYRpxOpzIzs2LS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8180624a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_age = pd.DataFrame(model_june.p_age.trace(), columns=age_groups)\n",
    "\n",
    "f, axes = plt.subplots(figsize=(14,6))\n",
    "sb.boxplot(data=p_age, linewidth=0.3, fliersize=0, ax=axes,\n",
    "          color=sb.color_palette(\"coolwarm\", 5)[0],\n",
    "          order=age_group.categories)\n",
    "axes.set_ylabel('Confirmation rate')\n",
    "axes.set_xlabel('Age group')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of **local** population susceptible, June model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
     ]
    },
    {
     "data": {
      "image/png": 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68cXeKiriFhoAQNdk/CdUVVWVFixYoOzsbAUEBDR4jMViaXVfgYGBkqRZs2ap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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb819988f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(model_june.p_susceptible, custom_labels=age_groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proportion of **local** population susceptible, June model with no confirmation correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
     ]
    },
    {
     "data": {
      "image/png": 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atGmaPXt28L1lWe2GlwEDYuRwRHVmWu06fPi/jfYXrspKqanpzOumJpsqK2M1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb81874ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(model_june_noconf.p_susceptible, custom_labels=age_groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic intensity estimates at June or July observation time, by age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
     ]
    },
    {
     "data": {
      "image/png": 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NAgCAwJSVWbV6dS+VlV17tzwGtETSmvr6es2ZM0cFBQUKCwtr9RiLxdLptsLD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\n/g/cA5xBA6Pe4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8186362b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(model_june.λ_t, custom_labels=age_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
     ]
    },
    {
     "data": {
      "image/png": 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F0q8tkoZUV1dr9uzZysvLU2hoaIPn2Gy2Zo8VFhYmSZo8ebJmzZolm82mlStX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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8185a95f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(model_july.λ_t, custom_labels=age_groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time series of epidemic intensities for lab- versus clinical-confirmation models, for each age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ll4mJiSEvL4/Zs2c3R90EQRAEQbgIjhwxcfCgmehonSlTbISGQmGhxv79JhyOlq7d5cGv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\njcWFDGOMMcY01v8AsM4FzCB7T+wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8186b5908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lam_june = model_june.λ.stats()\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, sharey=True)\n",
    "\n",
    "axes[0].plot(lam_june['quantiles'][50], 'b-', alpha=0.4)\n",
    "axes[0].set_ylabel('Epidemic intensity')\n",
    "axes[0].set_xlabel('time (2-week periods)')\n",
    "axes[0].set_title('Lab confirmation')\n",
    "\n",
    "lam_june_noconf = model_june_noconf.λ.stats()\n",
    "\n",
    "axes[1].plot(lam_june_noconf['quantiles'][50], 'b-', alpha=0.4)\n",
    "axes[1].set_ylabel('Epidemic intensity')\n",
    "axes[1].set_xlabel('time (2-week periods)')\n",
    "axes[1].set_title('Clinical confirmation')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb81073df98>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb819b2acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rt_values = pd.DataFrame(np.c_[model_june.R_t.trace()[:, -1],\n",
    "                          model_june.R_t_local.trace()[:, -1]],\n",
    "                        columns=['June, total', 'June, local'])\n",
    "\n",
    "ax = Rt_values.boxplot(return_type='axes');\n",
    "ax.set_ylabel('R(t)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb8185725f8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M1MaNG7Vp0ybt37+/sXUBAABucOuGv2nTprn12E/FxMSoU6dO9U7v2rWr+vbtq4CA2icg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\nhD8AADZD+AMAYDOEPwAANvP/AFmnd+8INV+7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8186b92e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rt_values = pd.DataFrame(np.c_[model_june.R_t.trace()[:, -1],\n",
    "                          model_june_noconf.R_t.trace()[:, -1],\n",
    "                         model_july.R_t.trace()[:, -1],\n",
    "                         model_july_noconf.R_t.trace()[:, -1]],\n",
    "                        columns=['June, confirmation', 'June, no confirmation',\n",
    "                                'July, confirmation', 'July, no confirmation'])\n",
    "\n",
    "ax = Rt_values.boxplot(return_type='axes', figsize=(14,6));\n",
    "ax.set_ylabel('R(t)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_june = pd.DataFrame(model_june.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\n",
    "S_age_june.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_june['Confirmation'] = 'Lab'\n",
    "\n",
    "S_age_june_noconf = pd.DataFrame(model_june_noconf.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\n",
    "S_age_june_noconf.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_june_noconf['Confirmation'] = 'Clinical'\n",
    "\n",
    "S_age_june = pd.concat([S_age_june, S_age_june_noconf], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S_age_july = pd.DataFrame(model_july.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\n",
    "S_age_july.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_july['Confirmation'] = 'Lab'\n",
    "\n",
    "S_age_july_noconf = pd.DataFrame(model_july_noconf.S_age.trace().squeeze(), columns=age_groups).unstack().reset_index()\n",
    "S_age_july_noconf.columns = 'Age', 'Iteration', 'S'\n",
    "S_age_july_noconf['Confirmation'] = 'Clinical'\n",
    "\n",
    "S_age_july = pd.concat([S_age_july, S_age_july_noconf], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numbers of suscepibles in each age group, under lab vs clinical confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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//u8DS8j867/+63XPe+yxx3TkyBHV1tZq6tSpeuSRR3TkyBG99957cjgcGjFi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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8181ef208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.factorplot(\"Age\", \"S\", \"Confirmation\", S_age_june, kind=\"box\",\n",
    "                   palette=\"hls\", size=6, aspect=2, linewidth=0.3, fliersize=0, \n",
    "                  order=age_group.categories)\n",
    "g.despine(offset=10, trim=True)\n",
    "g.set_axis_labels(\"Age Group\", \"Susceptibles\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccination coverage by strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pct_5:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.243            0.011            0.001            [ 0.222  0.265]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.221            0.236           0.244          0.251         0.264\n",
      "\t\n",
      "\n",
      "pct_15:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.335            0.012            0.0              [ 0.31   0.356]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.311            0.327           0.335          0.343         0.358\n",
      "\t\n",
      "\n",
      "pct_30:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.858            0.002            0.0              [ 0.854  0.862]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.853            0.856           0.858          0.859         0.861\n",
      "\t\n",
      "\n",
      "pct_adult:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.669            0.007            0.001            [ 0.657  0.678]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.657            0.663           0.671          0.675         0.678\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_june.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "june_coverage = pd.DataFrame({name: model_june.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\n",
    "june_coverage['Month'] = 'June'\n",
    "june_coverage['Confirmation'] = 'Lab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "june_noconf_coverage = pd.DataFrame({name: model_june_noconf.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\n",
    "june_noconf_coverage['Month'] = 'June'\n",
    "june_noconf_coverage['Confirmation'] = 'Clinical'\n",
    "\n",
    "july_coverage = pd.DataFrame({name: model_july.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\n",
    "july_coverage['Month'] = 'July'\n",
    "july_coverage['Confirmation'] = 'Lab'\n",
    "\n",
    "july_noconf_coverage = pd.DataFrame({name: model_july_noconf.trace(name)[:9999] for name in ['pct_5', 'pct_15', 'pct_30', 'pct_adult']})\n",
    "july_noconf_coverage['Month'] = 'July'\n",
    "july_noconf_coverage['Confirmation'] = 'Clinical'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coverage = pd.concat([june_coverage, june_noconf_coverage, july_coverage, july_noconf_coverage], \n",
    "                     ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb7de0b3710>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QiIFWo3FfQOMGECjPLX3ar/0D8c7buRNdFNu7R7v35523zoElE8BVwpIJtAVLJgAgsFrq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\nCMQAAACwtf8BUgywJkvc1jkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb7de0b3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot(row=\"Month\", col=\"Confirmation\", data=coverage, kind='box',\n",
    "              row_order=['June', 'July'],\n",
    "              order=['pct_5', 'pct_15', 'pct_30', 'pct_adult'],\n",
    "               palette=\"YlGnBu_d\", linewidth=0.7, fliersize=0, aspect=1.25).despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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66KOPjC4JCDi73S673R7oMgCYiKHT8hwOhxITE5WRkaHY2FilpKQoLS1N8fHx\nrm3Ky8sVEREhSTp+/Lhmzpypd9991yf7Z1oemqKsrHd08ODHkqTbb79TSUnJAa4IQHPiblqeoaf0\n8/LyFBcXp06dOkmSkpKSlJ2dXSvwr4S9JF26dEnR0dFGlgR4bdeuHTp6NM/n7ZaUnHctHzz4sT7/\n/JhP2+/e/VYNGzbCp20CCH6GBr7NZlOHDh1c61arVUeOHLlqu927d+vFF1/U2bNnlZ6ebmRJAACY\nUpMYtDdkyBANGTJEhw4d0rx587Rz585AlwRo2LARhvSUFy2qfXc97rYHwB8MDXyr1aqioiLXus1m\nU2xsrNvt+/Tpo5qaGhUXF/vk1H50dGuFhYU2uh3ASO6utwGALxka+AkJCSooKFBhYaFiYmKUlZWl\ntLS0WtsUFBTo+uuvlyQdPXpUknx2Hb+4+JJP2gGMxOBSAL4UkEF7oaGhWrBggaZMmSKn06mUlBTF\nx8dr/fr1slgsmjhxonbu3KktW7YoPDxcERERWrlypZElAQBgSjwtD/Cz555bqKqqSklSixYtNX/+\nbwJcEYDmhKflNTMnTuTrxIn8QJeBBrBYLHUuA4CRCPwg9ac/vak//enNQJeBBmjXrl2dywBgJAI/\nCJ04kS+7vUp2exW9/CA0fPioOpcBwEgEfhD6bs+eXn7w6dIlXlZre1mt7dWlS7znHwAAH2gSN95B\n/djtVXUuI3jQswfgbwQ+EAD07AH4G6f0AQAwAQIfAAAT4MY7BjPiEasXLpSppqZG0uW7GV5zjW/v\nxc7jVQEgeHHjnWbkuwHv67AHADRP9PCD1JIl/ylJWrBgWYArAQA0JQF5eA6MQ88eAFAfnNIHAMAE\nCHwAAEyAwAcAwAQIfAAATIDABwDABJiWJyk9fbVKS0sMrsa3rtQbFdU2wJXUT1RUW02d+qtAlwEA\nzRbT8v6F0tISlZSUKCQ8eKa6OS3hkqSyS44AV+I9h7353hcBAJo6Av8fQsIj9cObZwS6jGbt7Kev\nBLoEADAtAl9SeXm5HPYqAslgDnuZystbBLoMADAlBu0BAGAC9PAlRUREqNrZklP6Bjv76SuKiOBv\nTAAIBAL/Hxz2sqA6pe+oqZAkhYS2CnAl3rs8aC+4ZhUAQHNB4Cv4prZJUmmpXZIU2bp1gCupj7ZB\n+bsGgOaAefhBauXKVEnS7NlPBbgSAEBT4m4ePhdUAQAwAQIfAAATIPABADABAh8AABNglH6QCraH\n/QAAAospZZPuAAAJNklEQVTAD1LNeHIFAMAATMsz2K5dO3T0aJ5P2ywtLXEFvsVi8fnc9u7db9Ww\nYSN82iYAwD+YlteMfPdvtGb89xoAwIfo4QehRYue+t56aoAqAQA0NQHr4efk5Gj48OFKTEzUmjVr\nrnp/27ZtGj16tEaPHq37779fx48fN7okAABMx9BBew6HQ0uWLFFGRoZiY2OVkpKiwYMHKz4+3rVN\n586dtW7dOkVGRionJ0cLFizQhg0bjCwLAADTMbSHn5eXp7i4OHXq1Enh4eFKSkpSdnZ2rW169uyp\nyMhI17LNZjOyJAAATMnQwLfZbOrQoYNr3Wq16vTp026337hxowYMGGBkSc1CYmJSncsAALjTZEbp\nf/zxx9q8ebPmzp0b6FKavLvuurvOZQAA3DH0Gr7ValVRUZFr3WazKTY29qrtjh07pmeffVavvfaa\n2rb13ZxydyMVm4Px48dLat6fEQDgO4YGfkJCggoKClRYWKiYmBhlZWUpLS2t1jZFRUWaNWuWnn/+\neV1//fVGltOsDBo0KNAlAACCiOHz8HNycrRs2TI5nU6lpKRo2rRpWr9+vSwWiyZOnKhnnnlG7777\nrjp27Cin06mwsDBt2rTJyJIAADCdZn3jHQAAcFmTGbQHAACMQ+ADAGACBD4AACZA4AMAYAIEPgAA\nJkDgAwBgAgQ+AAAmQOADAGACBD4AACZA4AMAYAIEPgAAJkDgAwBgAgQ+AAAmQOD7UWFhoUaNGlXr\ntZdffllvvPFGvdqZPHmyjh492uA6MjMzddddd2ns2LEaO3YsjyN2o6kcr0OHDmncuHHq3r27du3a\nVeu9bt26aezYsUpOTtaMGTMavI/mqKkcv/Xr12vUqFFKTk7WpEmTdOzYMdd7mZmZSkxMVGJiot55\n550G76OpaCq/cyO+4zIyMpSUlKQxY8booYce0rfffltrf56OY1FRkcaNG6exY8dq5MiRevPNN13v\nffPNN5owYYISExM1Z84cVVdXN7reuoQZ0iqaFIfDoZCQ2n/bJSUl6ZlnnglQRfhXvn+8OnbsqNTU\nVL3++utXbRsREaHMzEx/lgcPvn/8Ro0apUmTJkmS9uzZo9TUVGVkZKikpES//e1vlZmZKafTqXHj\nxmnw4MGKjIwMVOlBq7HfcQcOHFBmZqaWL1/udpubb75ZmzdvVsuWLfXWW2/p+eef18qVK70+jrGx\nsXr77bcVHh6u8vJyJSUlKTExUe3bt9cLL7yghx56SPfee68WLlyoTZs2uf6f8SUCvwmZPHmyevTo\nof3796usrEzLli1T7969VVlZqaefflrHjx9Xly5dVFVV5fqZDz/8UC+99JKqqqp0/fXXa/ny5YqI\niNCgQYM0YsQI7du3Tw8//LBGjBhRa19Op9PfH6/Z8dfx6tixoyTJYrFcVQPHseH8dfzatGnjWr50\n6ZKio6MlSbm5uerXr58rGPr166cPPvjgqn+rzUkwf8f17dvXtdyzZ09t27ZNkvfHMSzsn3FbUVGh\n8PBwtWrVSpL08ccfKy0tTZI0duxYvfTSS4YEPqf0m5iamhpt3LhRTz/9tF5++WVJ0ltvvaWIiAhl\nZWXp0Ucf1SeffCJJKi4u1urVq5WRkaHNmzere/futU6dRUdHa/PmzXV+gezatUujR4/WY489plOn\nTvnnwzVD/jpe7tjtdo0bN06TJk3S7t27ffvhTMBfx2/dunUaOnSoUlNTNWfOHEmSzWZThw4dXNtY\nrVbZbDYjP26T0By+4zZt2qQBAwZIqt9xPHXqlEaPHq2BAwfqwQcfVLt27VRcXKy2bdu6zlC0b99e\np0+f9mm9V9DD96O6emjff33YsGGSpFtuuUVFRUWSpIMHD+rBBx+UJN1000266aabJEl///vf9cUX\nX+j++++X0+lUdXW1evXq5WrLXXAMGjRII0eOVHh4uN5++209+eSTta4n4bKmcrz+lT179ig2NlZf\nf/21fv7zn+umm25S586d691Oc9SUjt8DDzygBx54QFlZWZo/f77Wrl3buA/XRDWV37m333ETJkyQ\n3W7XxYsXVVJSorFjx0qS5s6dq379+tXZ9pYtW3T06NEGHcP27dtr69atOnPmjP793/9dd999tyIj\nI/12po7A96N27dqppKSk1mvnz5/Xdddd51pv0aKFJCkkJMTjwA2n06l+/frpxRdfrPP9iIiIOl9v\n27ata/m+++7TihUrvKrfbJrK8fpXYmNjJUmdO3fWHXfcoc8++4zA/4emePxGjBihhQsXSrrcE9y/\nf7/rvVOnTunOO+/02EZT1lR+595+x23YsEGSd9fwJWnfvn1as2aN/vjHPyo8PFyS++OYl5enZ599\nVhaLRbNmzdLAgQNd28TExKh379767LPPlJiYqLKyMtc4hFOnTslqtf7LOhqKU/p+1Lp1a8XGxurj\njz+WdPkfQm5urnr37v0vf+722293XS/6/PPPdfz4cUlSjx499Le//U0FBQWSpPLycp08edJjHWfO\nnHEtZ2dn64YbbmjIx2n2msrx+q7v9gRKS0td1zrPnTunw4cPKz4+vl7tNWdN5fh99dVXruX33ntP\nN954oySpf//+2rdvn8rKylRSUqJ9+/apf//+9f6cTUlT+Z0b8R336aefauHChVq9erVrHIbk/jje\neuuteuedd5SZmamBAwfKZrOpsrJSklRSUqLDhw+7/l+444479Je//EXS5RH/gwcPbnS9daGH72f/\n9V//pd/85jdKTU2VxWLRo48+6uqRuTsddv/99+vpp59WUlKS4uPjdcstt0iSrr32Wi1fvlxz5sxR\nVVWVLBaLHn/8cf3oRz9y25YkrV27Vnv27FFYWJjatm3r8a9aM2sKx+vIkSOaOXOmSktL9d577+nl\nl1/Wtm3blJ+fr2effVahoaFyOBx65JFHCPzvaQrH749//KM++ugjhYeHKzo62vXvrW3btpoxY4bG\njx8vi8WimTNnKioqyse/Af9rCr9zI77jVqxYofLycj322GNyOp3q2LGjXnnlFa+PY35+vlJTUxUS\nEiKLxaLp06erS5cukqQnnnhCc+bM0apVq9StWzelpKQ0ut66WJwM8wUAoNnjlD4AACZA4AMAYAIE\nPgAAJkDgAwBgAgQ+AAAmQOADAGACBD4AACZA4AMAYAL/DyoXDQKcYTWhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb7ddb056a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = sb.boxplot(data=june_coverage, order=['pct_5', 'pct_15', 'pct_30', 'pct_adult'], \n",
    "                  color=sb.color_palette(\"coolwarm\", 5)[0])\n",
    "axes.set_xticklabels(['Under 5', 'Under 15', 'Under 30', 'Under 5 + 20-30'])\n",
    "axes.set_ylabel('% susceptibles vaccinated')\n",
    "sb.despine(offset=10, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pct_5:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.199            0.006            0.0              [ 0.19   0.211]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.184            0.193           0.2            0.201         0.21\n",
      "\t\n",
      "\n",
      "pct_15:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.299            0.009            0.0              [ 0.287  0.315]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.278            0.29            0.3            0.301         0.314\n",
      "\t\n",
      "\n",
      "pct_30:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.861            0.002            0.0              [ 0.858  0.865]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.857            0.859           0.861          0.862         0.864\n",
      "\t\n",
      "\n",
      "pct_adult:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.61             0.001            0.0              [ 0.608  0.612]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.608            0.61            0.61           0.611         0.613\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_june_noconf.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pct_5:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.192            0.004            0.0              [ 0.185  0.199]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.185            0.189           0.194          0.194         0.2\n",
      "\t\n",
      "\n",
      "pct_15:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.29             0.006            0.0              [ 0.279  0.3  ]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.279            0.286           0.291          0.293         0.3\n",
      "\t\n",
      "\n",
      "pct_30:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.859            0.001            0.0              [ 0.856  0.861]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.856            0.858           0.859          0.86          0.861\n",
      "\t\n",
      "\n",
      "pct_adult:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.611            0.001            0.0              [ 0.609  0.612]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.609            0.61            0.611          0.611         0.612\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_july.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pct_5:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.194            0.004            0.0              [ 0.187  0.202]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.187            0.191           0.192          0.197         0.202\n",
      "\t\n",
      "\n",
      "pct_15:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.291            0.005            0.0              [ 0.282  0.303]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.282            0.288           0.289          0.296         0.303\n",
      "\t\n",
      "\n",
      "pct_30:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.859            0.001            0.0              [ 0.857  0.861]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.857            0.858           0.858          0.86          0.861\n",
      "\t\n",
      "\n",
      "pct_adult:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.611            0.001            0.0              [ 0.609  0.612]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.609            0.61            0.611          0.611         0.612\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_july_noconf.summary(['pct_5', 'pct_15', 'pct_30', 'pct_adult'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initial migrant susceptibles (June model, with confirmation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "N_migrant:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t578091.263       49677.094        3718.281     [ 482694.  657868.]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t486002.0         532774.0        574789.0       612655.0      666188.0\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "model_june.summary(['N_migrant'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By age group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
     ]
    },
    {
     "data": {
      "image/png": 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AgHEEDAAAYBwBAwAAGEfAAAAAxhEwDEhNHaSbb7452MMAAKDLIGAAAADjCBgAAMA4AgYA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\n7v8Daxv+nVVx29gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8181efc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(model_june.M_0, custom_labels=age_group.categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "june_r = pd.DataFrame({'local': model_june.trace('R_t_local')[:, -1],\n",
    "                             'total': model_june.trace('R_t')[:, -1]})\n",
    "june_r['Month'] = 'June'\n",
    "june_r['Confirmation'] = 'Lab'\n",
    "\n",
    "june_noconf_r = pd.DataFrame({'local': model_june_noconf.trace('R_t_local')[:, -1],\n",
    "                             'total': model_june_noconf.trace('R_t')[:, -1]})\n",
    "june_noconf_r['Month'] = 'June'\n",
    "june_noconf_r['Confirmation'] = 'Clinical'\n",
    "\n",
    "july_r = pd.DataFrame({'local': model_july.trace('R_t_local')[:, -1],\n",
    "                             'total': model_july.trace('R_t')[:, -1]})\n",
    "july_r['Month'] = 'July'\n",
    "july_r['Confirmation'] = 'Lab'\n",
    "\n",
    "july_noconf_r = pd.DataFrame({'local': model_july_noconf.trace('R_t_local')[:, -1],\n",
    "                             'total': model_july_noconf.trace('R_t')[:, -1]})\n",
    "july_noconf_r['Month'] = 'July'\n",
    "july_noconf_r['Confirmation'] = 'Clinical'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r_estimates = pd.concat([june_r, june_noconf_r, july_r, july_noconf_r], \n",
    "                     ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dBgzwZbNKp0sAAABNFOH8G63bdVBxU1/WAklS6xKWtQAAAGfYvqwlJydHo0aN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\nwjkAAABgCMI5AAAAYAjCOQAAAGCI/w+c2MRkt4flFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb7ddca98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.factorplot(row=\"Month\", col=\"Confirmation\", data=r_estimates, kind='box',\n",
    "              row_order=['June', 'July'],\n",
    "              order=['local', 'total'], margin_titles=True,\n",
    "            palette=\"YlGnBu_d\", linewidth=0.7, fliersize=0, aspect=1.25).despine(left=True)\n",
    "g.set_ylabels(r\"$R_t$\")\n",
    "for ax in g.axes.ravel():\n",
    "    ax.hlines(1, -1, 2, linestyles='dashed')"
   ]
  }
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