notebooks/ob18/c3/r.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 3 from Bjornstad (2018): $R_0$\n",
    "\n",
    "Let us explore using weekly measles-data from the 2003 outbreak in Niamey, Niger . The data is available as `niamey` in the `epimdr`-package. The `tot_cases`-column represents the total incidence across the city for each week of the outbreak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: shiny\n",
      "Loading required package: deSolve\n",
      "Loading required package: phaseR\n",
      "Loading required package: polspline\n"
     ]
    }
   ],
   "source": [
    "library(epimdr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>absweek</th><th scope=col>week</th><th scope=col>tot_cases</th><th scope=col>tot_mort</th><th scope=col>lethality</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1       </td><td>45      </td><td>11      </td><td>0       </td><td>0.000000</td></tr>\n",
       "\t<tr><td>2       </td><td>46      </td><td>12      </td><td>1       </td><td>8.333333</td></tr>\n",
       "\t<tr><td>3       </td><td>47      </td><td>15      </td><td>0       </td><td>0.000000</td></tr>\n",
       "\t<tr><td>4       </td><td>48      </td><td>14      </td><td>1       </td><td>7.142857</td></tr>\n",
       "\t<tr><td>5       </td><td>49      </td><td>30      </td><td>0       </td><td>0.000000</td></tr>\n",
       "\t<tr><td>6       </td><td>50      </td><td>41      </td><td>1       </td><td>2.439024</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " absweek & week & tot\\_cases & tot\\_mort & lethality\\\\\n",
       "\\hline\n",
       "\t 1        & 45       & 11       & 0        & 0.000000\\\\\n",
       "\t 2        & 46       & 12       & 1        & 8.333333\\\\\n",
       "\t 3        & 47       & 15       & 0        & 0.000000\\\\\n",
       "\t 4        & 48       & 14       & 1        & 7.142857\\\\\n",
       "\t 5        & 49       & 30       & 0        & 0.000000\\\\\n",
       "\t 6        & 50       & 41       & 1        & 2.439024\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| absweek | week | tot_cases | tot_mort | lethality |\n",
       "|---|---|---|---|---|\n",
       "| 1        | 45       | 11       | 0        | 0.000000 |\n",
       "| 2        | 46       | 12       | 1        | 8.333333 |\n",
       "| 3        | 47       | 15       | 0        | 0.000000 |\n",
       "| 4        | 48       | 14       | 1        | 7.142857 |\n",
       "| 5        | 49       | 30       | 0        | 0.000000 |\n",
       "| 6        | 50       | 41       | 1        | 2.439024 |\n",
       "\n"
      ],
      "text/plain": [
       "  absweek week tot_cases tot_mort lethality\n",
       "1 1       45   11        0        0.000000 \n",
       "2 2       46   12        1        8.333333 \n",
       "3 3       47   15        0        0.000000 \n",
       "4 4       48   14        1        7.142857 \n",
       "5 5       49   30        0        0.000000 \n",
       "6 6       50   41        1        2.439024 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(niamey)\n",
    "head(niamey[, 1:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do a visual inspection to identify the initial period of exponential growth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAA2FdRUVFQUCAQCHg8nouLC4KdXOLi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nw7RpUFgIly+DsTHpNIqKw+Ho6enhNV+o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\nwg4AAACAIf4PnK6J0dpxOFIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mar = c(5,5,2,5))\n",
    "plot(niamey$absweek, niamey$tot_cases, type = \"b\", \n",
    "     xlab = \"Week\", ylab = \"Incidence\")\n",
    "par(new = TRUE)\n",
    "plot(niamey$absweek, niamey$cum_cases, type = \"l\", \n",
    "     col = \"red\", axes = FALSE, xlab = NA, ylab = NA, log = \"y\")\n",
    "axis(side = 4)\n",
    "mtext(side = 4, line = 4, \"Cumulative incidence\")\n",
    "legend(\"topleft\", legend = c(\"Cases\", \"Cumulative\"),\n",
    "       lty = c(1,1), pch = c(1,NA), col = c(\"black\", \"red\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cumulative incidence looks pretty log-linear for the first 6 weeks or so (Fig. \\[fig:niameyweekly\\]). The data is weekly and the serial interval for measles is around 10-12 days, thus V is around 1.5-1.8 weeks; We calculate $R_0$ assuming either $1.5$ or $1.8$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1.69423334567417</li>\n",
       "\t<li>1.833080014809</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1.69423334567417\n",
       "\\item 1.833080014809\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1.69423334567417\n",
       "2. 1.833080014809\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1.694233 1.833080"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit=lm(log(cum_cases)~absweek, subset=absweek<7, \n",
    "     data=niamey)\n",
    "r=fit$coef[\"absweek\"]\n",
    "V=c(1.5, 1.8)\n",
    "V*r+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So a fast-and-furious estimate of the reproductive ratio for this outbreak places it in the 1.5-2 range. Measles exhibits recurrent epidemics in the presence of various vaccination campaigns in Niger, so this number represents an estimate of the *effective* reproductive ratio, $R_E$, at the beginning of this epidemic.\n",
    "\n",
    "In their analysis of the SARS epidemics, showed that for an infection with distinct latent and infectious periods a more refined estimate is given by $R = V r + 1 + f (1-f) (V r)^2$, where f is the ratio of infectious period to serial interval. For measles the infectious period is around 5 days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1.81445011031601</li>\n",
       "\t<li>1.99919772595046</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1.81445011031601\n",
       "\\item 1.99919772595046\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1.81445011031601\n",
       "2. 1.99919772595046\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1.814450 1.999198"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "V = c(1.5, 1.8)\n",
    "f = (5/7)/V\n",
    "V * r + 1 + f * (1 - f) * (V * r)^2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maximum likelihood: the chain-binomial model\n",
    "\n",
    "We employ a standard recipe, for doing a ‘non-standard’ maximum likelihood analysis . The first step is to write a function for the likelihood. Conditional on some parameters, the function returns the negative log-likelihood of observing the data given the model. The likelihood, which is the probability of observing data given a model and some parameter values, is the working-horse of a large part of statistics. R has inbuilt `dxxxx`-functions to calculate the likelihood for any conceivable probability distribution. The function to calculate a binomial likelihood is `dbinom`. We can thus define a likelihood-function for the chain-binomial model [7]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "llik.cb = function(S0, beta, I) {\n",
    "    n = length(I)\n",
    "    S = floor(S0 - cumsum(I[-n]))\n",
    "    p = 1 - exp(-beta * (I[-n])/S0)\n",
    "    L = -sum(dbinom(I[-1], S, p, log = TRUE))\n",
    "    return(L)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the the real statistical analysis (below), the two parameters will be estimated simultaneously. However, in order to ease into the idea of likelihood estimation we will consider the two sequentially and visualize the likelihood by plotting it over a grid of potential values. We illustrate with the data on measles from one of the three different reporting centers in Niamey, Niger from 2003 . We first need to aggregate the data into 2-week intervals which is roughly the serial interval for measles. The epidemic in district 1 lasted for 30 weeks (the 31st week is a zero)[8]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5920"
      ],
      "text/latex": [
       "5920"
      ],
      "text/markdown": [
       "5920"
      ],
      "text/plain": [
       "[1] 5920"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoweek = rep(1:15, each = 2)\n",
    "y = sapply(split(niamey$cases_1[1:30], twoweek), sum)\n",
    "sum(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In district 1 there were 5920 cases during the epidemics, so $S_0$ needs to be at least that number. In the above parameterization $R_E \\simeq \\beta$, lets initially assume a candidate value of 6500 for $S_0$ and calculate the likelihood for each candidate value of $\\beta$ between 1 and 10 by 0.1 (fig. \\[fig:profbeta\\]):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "2.3"
      ],
      "text/latex": [
       "2.3"
      ],
      "text/markdown": [
       "2.3"
      ],
      "text/plain": [
       "[1] 2.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FQ6J8QUIVCj4xlr142NpHNCTBECNdrs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\nAYIEQMH/AZEg+mLWdoL1AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S0cand = 6500       \n",
    "betacand = seq(0,10, by = .1)\n",
    "ll = rep(NA, length(betacand))\n",
    "for(i in 1:length(betacand)){\n",
    "     ll[i] = llik.cb(S0 = S0cand, beta = betacand[i],\n",
    "     I = y)}\n",
    "plot(ll ~ betacand, ylab = \"Neg log-lik\", \n",
    "     xlab = expression(beta)) \n",
    "betacand[which.min(ll)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We follow the convention of using the negative log-likelihood in the profile. Intuitively, one may think that it would be more natural to consider the *likelihood* itself (the probability of observing the data, given particular parameter values). However, since this would be a product of small numbers (one for each observation), computers are not precise enough to distinguish the joint probability from zero if the data set is large. If our $S_0$ guess is right then $\\beta$ should be around 2.3. We can do a similar check for $S_0$ (assuming $\\beta$ is 2.3). The grid-value associated with the highest likelihood value is 7084.8 (fig. \\[fig:profRE\\]), so our original $S_0$ guess was good but not perfect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "7084.8"
      ],
      "text/latex": [
       "7084.8"
      ],
      "text/markdown": [
       "7084.8"
      ],
      "text/plain": [
       "[1] 7084.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PXXlGlRWdcYR2mGHCAkewws8pPw9XRsTbzkANlDSHY4o/3BvEz0+1zeQUCxEJId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\nCYAAQgIggJAACCAkAAIICYAAQgIg8H9Cb94FgCunLAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "betacand = 2.3\n",
    "S0cand = seq(5920,8000, length = 101)\n",
    "ll = rep(NA, length = 101)\n",
    "for(i in 1:101){\n",
    "     ll[i] = llik.cb(S0 = S0cand[i], beta = betacand, \n",
    "     I = y)}\n",
    "plot(ll ~ S0cand, ylab = \"Neg log-lik\", \n",
    "     xlab = expression(S[0]))\n",
    "S0cand[which.min(ll)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a proper analysis we minimize the negative log-likelihood by varying both parameters simultaneously. We can do this using the generic `optim`-function or the `mle2`-function in the `bbmle`-package. The `mle2`-function uses `optim` to find maximum likelihood estimates, but also provides confidence intervals, profile likelihoods and a variety of other useful measures . We summarize the basic pertinent likelihood theory for these other measures in section \\[sec:c7lik\\]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: stats4\n",
      "Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Maximum likelihood estimation\n",
       "\n",
       "Call:\n",
       "mle2(minuslogl = llik.cb, start = list(S0 = 7085, beta = 2.3), \n",
       "    method = \"Nelder-Mead\", data = list(I = y))\n",
       "\n",
       "Coefficients:\n",
       "       Estimate Std. Error z value     Pr(z)    \n",
       "S0   7.8158e+03 1.3022e+02  60.019 < 2.2e-16 ***\n",
       "beta 1.8931e+00 3.6968e-02  51.209 < 2.2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "-2 log L: 841.831 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.89311094280269), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 7815.82916929596), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>2.5 %</th><th scope=col>97.5 %</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>S0</th><td>7577.967212</td><td>8088.641095</td></tr>\n",
       "\t<tr><th scope=row>beta</th><td>   1.820943</td><td>   1.966336</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & 2.5 \\% & 97.5 \\%\\\\\n",
       "\\hline\n",
       "\tS0 & 7577.967212 & 8088.641095\\\\\n",
       "\tbeta &    1.820943 &    1.966336\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 2.5 % | 97.5 % |\n",
       "|---|---|---|\n",
       "| S0 | 7577.967212 | 8088.641095 |\n",
       "| beta |    1.820943 |    1.966336 |\n",
       "\n"
      ],
      "text/plain": [
       "     2.5 %       97.5 %     \n",
       "S0   7577.967212 8088.641095\n",
       "beta    1.820943    1.966336"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(bbmle)\n",
    "fit = mle2(llik.cb, start = list(S0 = 7085, beta = 2.3), \n",
    "    method = \"Nelder-Mead\",data = list(I = y))\n",
    "summary(fit)\n",
    "confint(fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the joint MLE estimates are $S_0 = 7816$ (CI: <span>7578, 8088</span>) and $\\beta = 1.89$ (CI: {1.82, 1.97}).\n",
    "\n",
    "Applying statistical tools to biological models – like the chain-binomial – can usefully highlight uncertainties due to parametric interdependencies. In the case of a ‘simple epidemic’ like the measles outbreak considered here, for example, it is conceivable that similar epidemic trajectories can arise from having a large number of initial susceptibles and a low transmission rate, or a more moderate number of susceptibles and a higher transmission rate. We can quantify this through considering the correlation matrix among the parameters of our likelihood analysis; `vcov` calculates their variance-covariance matrix from which we can calculate standard errors according to `sqrt(diag(vcov(fit)))` and `cov2cor` converts this to a correlation matrix. As intuition suggested there is a strong negative correlation between the estimates of the $\\beta$ and $S_0$ parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>S0</th><th scope=col>beta</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>S0</th><td> 1.0000000</td><td>-0.7444261</td></tr>\n",
       "\t<tr><th scope=row>beta</th><td>-0.7444261</td><td> 1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & S0 & beta\\\\\n",
       "\\hline\n",
       "\tS0 &  1.0000000 & -0.7444261\\\\\n",
       "\tbeta & -0.7444261 &  1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | S0 | beta |\n",
       "|---|---|---|\n",
       "| S0 |  1.0000000 | -0.7444261 |\n",
       "| beta | -0.7444261 |  1.0000000 |\n",
       "\n"
      ],
      "text/plain": [
       "     S0         beta      \n",
       "S0    1.0000000 -0.7444261\n",
       "beta -0.7444261  1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cov2cor(vcov(fit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic simulation\n",
    "\n",
    "The chain-binomial is both a statistical model for estimation and a stochastic model for dynamics. We can thus write a function to simulate dynamics using the estimated parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.cb=function(S0, beta, I0){\n",
    "   I=I0\n",
    "   S=S0\n",
    "   i=1\n",
    "   while(!any(I==0)){\n",
    "        i=i+1\n",
    "        I[i]=rbinom(1, size=S[i-1], prob=1-\n",
    "           exp(-beta*I[i-1]/S0))\n",
    "        S[i]=S[i-1]-I[i]\n",
    "   }\n",
    "   out=data.frame(S=S, I=I)\n",
    "   return(out)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We superimpose 100 stochastic simulations on the observed epidemic. The simulations from the chain-binomial model brackets the observed epidemic nicely (fig. \\[fig:cbsim\\]), suggesting that the model is a reasonable first approximation to the underlying dynamics. We will revisit on this case study in the context of outbreak-response vaccination in section \\[sec:orv\\]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eabW7duPX/+vN/v7+rqOnXq1DvvvHPq1Kmuri6fz9fZ2bl///4jR4785Cc/udE3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\nr3oVISSdTvf7fY7j/H4/JZp+v5/sExqPx/MkQSculyufz49GI+xhT5w4kc/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DKrM7oDnBI9Bvp3e4uq5fvnx5YWFB0zRFUeAsIYTYbLZu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JUif/u35CMfIb/5m2x0x8AAMGLHwMDw9JDL\n5Xiev2a0R6fTURTF5XKNx+PJZCKKYrVaHY1GVGBHCEHCsCiKoCDr6+utVguiOmxg4aKgxlUA\n+jC6yGs2m2Q/TYMQMhwOkdmG7aGiKHc/9JD8+teTWGxnZwcdEpDN2e128D+Uos7OzqJeFrvI\nQCDQ6/Wul2CXSCSgNrvrrrvohfl8Xpbl69Hc5w2Y2BkMBuoYDQQCe3t7Pp8PJBjFrJQrg5Bp\nmoYN8jSFnXafUA5HZXayLE+fDGp2yT6xg8bOaDRKkoQPa29vz+/3o8zX4XC4XC581mD8hBC3\n2007f30+nyRJsNAaDAaERedyudtvv50Q8uijj/rf/e6r6+sX/+IvfiiKmtVKIhHy5jeThx4i\n3/wm+dznbtjpMjDcTGDEjoGB4ekhk8mEQqHpPgOK8+fPE0Li8fjGxgYhxOv14re7qqqRSAS8\nCre02+3QYJ0+fVpVVQRwEEIGgwHibaltArBYLJTYQTRGCHG5XGislySp2+2aTCZwhYXt7XA+\nL73nPYSQnZ2dWCxWLBZtNttwOATzMxgMsiwLguDxeCBNE0WxVCoZjUYEuaFS9sC7Q9AJx3Gr\nq6vTFwYCAcp7XihgX+l0Ouk2NhAIoHhjOByCxgmCQOtcEfVM9leo5Ce9xrIs41tKBKmRghBC\nwwVR+xYIBOjnQu81Ho8xmVNV1efzYVhLCEFMHe5OrR74SwAKy0AgUCgUBEGIRqO5XC4cDm9v\nb0uS5HA46g8+SDY2Um9+MzKN8YCEEHLkCHnTm8inP/2cnyoDw80IRuwYGBieHtLp9PUGVFev\nXuU4bnFxcWdnB0G4IASSJPl8PmSa4JbIKzYYDNi1oRYWPMBkMtG5DgWk9BgmTYd0wHURiUQ0\nTYMVgOO4lzz0UOaee0g83m63a7Wa2WxGNt78/Hwmk4HgTNO0yWSC1LRiseh0OhHtFo1Gr+n5\nVRQlk8lgHjntGvlpENgB4XBYEARK7CwWi9PpNBqN2InzPM/zvMlkohzU4XAgWPjwQ1E3Qzab\nxUc2TfvOnTtHwwLxOGD5+K8oiv1+f9rRbDQa8UGMRqPFxUVKFrGCJ/syO/A8+CcIIQg9WVlZ\nQX/J6uqqmE6TmZnAHXekUqlAIPAT3tgzZ8j+ozEw/IyDETsGBoangclkUiqVruecqFQquq6H\nQqFSqaTrOh0LQWBXKBSQRUIIabfbmCdtbGygkFRVVeSuQTB3YGI3Ho8P1M8TQqrVKi7Bs8Bv\nu5ROh3M55U//lBCys7ODZq1wOJxOp0Oh0GAwoHoyt9tdKpXcbvdkMjGbzQglhnMCJGn6BWSz\nWdBKmDSB0WhULBZfcIEdAJldtVqFKZUQEggEJpPJYDBwu912u30ymUznxVitVkmSms3mNE+l\nBA6bdEgPdV2npgdCCE6J3gWJKmR/h4uzhVEDt+90Opqm8TxfLBaniR18NoSQW265Rdf1H//4\nx4QQVINomraystLpdObm5prNZqVSueuuu1SeV4fD5eVlGobyf9/8eEx+8i8BBoafWTBix8DA\n8DSQz+dB3Q5f1ev1JpMJesAQLFyr1YbDoSAIkUgEGbNgAAaDAV+cOXOmXq+DaqCxXpbl4XDY\n6/WowA6Zt9MOWRAXo9GIsLTJZAJ3J9y1L/72tzeOHw/+/M8TQnZ2dpaXl1Op1MLCQj6fB/+j\nIr9IJNLtdiHX63T+f/beNEaSvD4T/kdEZuR9Vd53ZmXW0Vd19wCDGZgZwMYfMOYDsBhbC2vJ\nu1i2V4JFrFhki5VssPwaIb/rF2TZWFqBV97hBZvFeDErm2vGnsGGmem768j7vu8zzvfD4/o7\nXN1Dj/W6mz7i+YBqoiKzsiKyyad+v+eY+P3+9XqNlfFt97CgLBcuXKAHS6USx3G3vRr3HrFY\nbD6fa7vFwuFwv99HgAvGlovFgo7oaDEXneFRGwq+ttlskiShOQ2eZThUVqtVPB7H14SQVqtF\nOyrwtBzHORwOu92Ou9ZoNHw+n91ur9frGxsb2KoTQsbjMfgiZHZ0YieKYqfTgZel3W4HAoH9\n/X2n0zne3OSWS+fNm4FAgGGYZrNJ/04g3/oWeeyxe3GVdei476ETOx06dPwLUC6XI5EInZlp\ngYlLIpHAKMXtdsuyLEkSFdjRpDer1Yq13d7eHhquwO0Wi4XRaOR5Hh0G9Jnx4+hgCfwMvAHB\nKJPJxGw2L5fLVLGYqFavvfOd8IFia9xqtTAIrNfrkORjD2s2myFKMxqN4/FYVdVQKMRx3G09\nv/v7+4qimEwmbV5dPp9PpVInZns/LsB54PV6KbGLRCKdTicajZJj2Ry23mBv8/kcQcHYq1LQ\nva3L5SKE4OHkOGIG98VsNoMfE0Km02k8Hsc94jhOVVVZlnGzcHA8HkejUZZl6/U6NvW4YizL\nUm+sw+EAS7PZbG63m4ae5HK53d1dzPb8Fy/u7+yov/Zru5FIu922WCz/+PA/+zPyF39BfvVX\n7+rl1aHjQYFO7HTo0PEvwI8Q2EFXl0gkbty4QQix2+1gBiaTKRAIlEolyszQTMDzPM7U6vpd\nLpfX69WyOgSdUDk/GCFmSCCFmUyGlii8+Xvfu3r2bPjNb8ZLxT7XYDAg06Tb7ZpMJhy0Wq3j\n8Rg2WLg1EeRRqVQ4jjvh+V0sFii60CZ9kB93k9gJIKbYaDSe8E+4XK75fD4ajViWRd8uokxW\nqxV0jXQ3SvewiqIoigJaRj0K2HQD+/v7lA6iaARcDQ9Bg9l8PofxmWEYu92+WCzwVMlkkt5f\nSuw2NzdVVcX0NBaLYfiazWax9m00GpPJ5PHHH//Ln/3Z1WDw5K/9WupP//Txdlv97/+dfOAD\n5Od+jvzO75Cf+Im7dWV16HigoBM7HTp0vFpAJ3dbgZ2qqu12mxASi8WguEe1FMMwqVRKkqR6\nvU61X9PpVFGUSCTy8ssvE0IkSRJFEatVfEEZBvRwiqLQagRQB1VV5/M5Ajto/1W8UklUKs89\n9VQ2myWE5HK5ZDJZr9cTiUShUHC73RjUoSBhZ2enXC6jgoIQks1ma7UaBHa3en7RUk8IOX/+\nPD04Ho8Hg8F9IrADYrHYarUaDAbj8ZgQYrFY3G43x3Gj0UhRFJfLhaEjdq+qqnq9XovF8k8L\nTQ1UVQWXrVQquObL5ZISslKpFI/H6ZmgiRgHEkJYll0ul+v1GrtyzPAEQZjP5+PxOJFIwFys\nKEoul8NtfeKJJwgh3//+94nGP5FMJg0Gw2KxcDqd+/v7sVhMcLv/53/6T+x/+A87+/tv+oM/\nSH/xi2Q6Jd/6FvnP//muX1wdOh4Q6MROhw4drxb1el2WZfqJrkWr1cKHeigUmkwmmL4gjZYK\n7DC/Qb2EqqpveMMb2u02NV0iTG48HmuDiGHk1GZtYLzEcRxEY4qitFotVNq/9TvfuX7mzDAY\nhOiNCuwikUiv1+v1evDDEkIURYEirdfrITMPFoFEInHbBLuXX34Z60stjcvn8w6Hw+fz/etd\n4P+/iMfj3W7XZrNph3bL5RIRwRsbG1AxUt7McRzcx7dth0O+4Hg8hkEYejgAgc94FMMwtVoN\nAYR4D+D+8jxvt9tBIhuNhtPpNBqNtVotFArRVf5yuURetN/vZxgGBDoajcJCC3Um3cYyDBOL\nxeqjkfTxjz/76U9/6fOf/78/9CHpy2XZeIIAACAASURBVF8mTz99ty6oDh0PIHRip0OHjleL\nUqkUCoW0aSMUWKp6PB4Mh0wm02q1Wi6XENiVy2VKgMxmM1LrYrEYwuQwM1NVNRAI0Ep4gE7s\ntBVVeBII7Px+/2g0MpvNruvXk6XSc08+mU6nWZYdjUb9fj8Wi3U6HZZlbTZbu912OBzgIjzP\nC4LgdrvxXULIcrmMRqOQ5N9K7LAZ9Hq92hCWfD6/ubl5op3ix4tYLKYoSjAY1MrsWq1WMBik\n3V+r1QrzPHgpMK47IbMDoF8URfH06dPk2N2i/S7uBSGk0+lQHzFkdqCSNF2l0WgkEgmTyQSZ\nHfrfCCFWqxVdYeR4OU4ICYfDHMfRbWwul9vZ2SmVSqvV6uLFi4qiHBwcbG1tNZtN0Pq7cil1\n6HhgoRM7HTp0vFqUy+UfkWBHCEkmk9euXSOEOJ1Ol8uFaJJgMFgsFrUOA0mSrFYrPBYgbTzP\ncxxns9lQCa995sVigTUfOR4sMQyzXC4xiEKQysbGxtPf/vaNM2e6weDW1hZej9PpnE6nJpOp\n3+8nEonVaoXaCYZhTp8+XSqVYL+w2WygnrQb44TLFfF1qqqC3wCqqpZKpftHYAcgpvifXAXH\n/gkQvtlshrkjEqGxzoY3FpM2anQlxyYJGCboxFQrsysUCvSeiqKYSCRwd+ieFxtYapX1er2S\nJGHHurm5SW8lJXbRaBQ9chzHhUIh6p+YTCZIZjk8PNze3iaE/PCHP9za2prP59RmoUOHDgqd\n2OnQoeNVQVEU1H3e+i1BELrdLoI/EAuCGDNCSDqdliSp0WhQgR2S5JLJJAR20OlDnrVer0/k\nEjMMY7FYaKYd2ADI2Xq9pn2y8VotXSw+++STqqqCbCHbtlgsplKpYrGIDSx+NCHkzJkz5XIZ\nP06SpFQq1Ww20SR2q+f3hRdeAMs5deoUPdhsNheLxX0lsAMQFzKdTtGxEYlEFEWx2WzT6bTd\nbsPOYrFY0B4hCAJySXB9tI2x+BpXnlZEaK9Mo9HAipZqH8ER8VQsyyJaBVVvDMMYDIbVaoUx\nWzKZBKFfr9fVahUPecMb3kAI+fu//3ui8U+43W6v15vP57e2tvb395F0XavVTCaTz+ez2Ww4\nTYcOHRQ6sdOhQ8erQqPREAQhkUjc+i3Mh2RZjsViCA2eTCaCIGCiBoEdiB2NSXvjG9/YaDTw\ncHgmNjY2Op0OlnEAEuycTic9gmEewzDwwCJBgxCy+8wzN0+f7gSDDocDk6FSqZTNZkulktfr\nhZbObrfDdGkwGMxm82q16vf7i8ViuVwajUaDwRCNRm+bYIfCDJ7ng8Gg9lcOBoO0XOv+QSwW\nA4GDzM5kMqGQAxHQoVAI3hG6e3U4HFar9YTGDlyNYRisxYvFIn5TGGlxjqIoPp+P3tBarQZS\nTjkiWsLws1RVHQwGaIprt9vQ56EajmEY9Nviyl+/fp0c+yfwMmjoSS6XkyTp3LlzqqoeHh5i\nDKlP7HToOAGd2OnQoeNVoVwuQ6p167ewTeM4zmAw4KN6uVzO53NFUZBgFwqFaDMprLIWi4UK\n7GBNjcVikiRNp1OtJVaSJLolJMfEjmVZuFYjkchwOMx0u4nDw2efeophGOxhS6WSoih+v7/f\n7yuKEggExuNxMBjUjuscDgcC7VwuV7/fT6VSqqo2Go0Tu+Z6vY7RYCqV0srp7p8msROIxWLI\njdP6J4bDocPhQFcbIWS9XoN+EUJkWcamG02vNJ0Y0sblcgn/BBwzqGKjP0vrwygWi7FYDAQR\ne16GYaxWq9PpBDUvFovxeBwyO47jNjY28FSBQADvH3TQYdAYjUaXyyWIOEJPEomEqqqFQmFn\nZ0dV1UuXLqFpYzQa3dbVq0PHIwud2OnQoeNV4bZ2UeDw8FBV1Wg0itGdw+Gw2WwQckHID7cE\nTpYkyW63F4tFqO+xpEOsndPp1PokCCHYKhJNxBrHcbIs4+Dm5qYsy09/97v7p061g0GGYege\nNplM1mo1i8XSbrchEXO73Xjms2fPQh8GM8TW1lahUEin06imOOH5feGFFwghkOVpX1W1Wr0P\n97CEkEAggIreUqmEixYOhxuNRiwWM5lMyBkRRZFuSKfTKVzM+M9bw5bNZrMgCBC3QTZHNN1i\ndNS3WCyozA7jPVVVkURNeyZisRgstISQZDJJgwmpzC4YDEqSJAiCx+Ox2Ww4ExHQCKPZ398P\nBAJWq7VUKvn9ftfNm+/+6lf1bawOHVroxE6HDh13hqqq1Wr1tsRuMBhMJhOO4+LxOBoCeJ7H\nRzsS7BqNBp2prFYrQsj29vb+/j5NOAOrG41GiD3TPrnP56OzJZAGRVEcDgeI3XK5DDca8Rs3\nnnvTm/AtzNuOjo62trYgsKtWq5PJxGAwtNttbCETiUSlUsFoUBTFaDTa7/eRYBcOh7UiP0VR\njo6OYPPUzucQgHfbrfSPHQzDQFc3n8+73S4hJBKJdLtd9MaC5uIiw1YMD4rL5dIyNu0XmOTR\npTOYH55hNpvRHg6k4mG8SuvgRFEUBAHrdVVVLRbLer0GXdvd3cU5s9ms1+vhHNS1wX8TjUbB\n2DiOSyaTR0dHu7u7+BNid3eXYZjhcOiQpFPXrzf0bawOHRroxE6HDh13RqvVWq1Wt6UyuVwO\nkcKxWKzRaCBEA2yM+kypcg604LHHHoOsihwTiM3NzVqtdmKnxvM8VocANneqqkKzbzAYSqXS\nW77zndyZM81IhBASjUatVutwOBwMBhDYwQY7HA5RLa+q6qlTp+r1OiorsPWTZdlisQQCgVtH\nkrlcThRFuG61WXr5fJ6WaN2HiMfjnU5nY2MD21hcLovFslgs2u12LBYDx8JvpKqqy+VyOp1Y\nkVNOBnAcBy9tsVgEmcNp+C7LsloFJAw0hBCqp1wsFpPJZLFY0KQVlmX7/T59L2Fk6HA4MLTb\n29sjhMBVQ2OKCSFbW1tHR0fb29uLxaJarW5tbamqeu3aNePWFidJvWvX7u4F1aHjgYJO7HTo\n0HFnIIhOS24oaGqJ1+uF7ZQ2zUNgFw6HabUXOR68YSdIj4RCIQzzKKCsx8ET0n6whFgsxl+7\ntpXLffeNb8Q52I0eHh7SugVBEPx+vyzL1IZ54cKFYrFotVpR0hCNRlFadVvP76VLlyA+o+Ml\n4L5qErsVYNjJZBJpdvBPQNEoiiJ2zdqKWKPRSMd1GGrSp4LSjhBSKBTAkiVJokGGuNf05Js3\nb0YiEax0jUYjBqKEEIvFAv53cHAA20Sj0eB5HqYNURQ3Nze1Ms1Op4PfgqZeI/RksVjE4/H9\n/f1MJoOcFNuZM4RhljdvagP2dOh4xKETOx06dNwZrySwk2W5XC4LgmCz2dBAZTKZzGYzBHah\nUKhUKsEUSQjBQMjpdGL8oz2IGtMTT451G/nna0GWZbFhzGazT33726Vz52qhEM6hAjvsYe12\nO8I1iKbDNJ1OFwoF6PzA2EqlUjqdbjQakiRpR5Lr9frw8BCvU0vjZrNZp9O5PwV2AGwoHo+H\nyuwikUi73caiGbvm1WoF8k0Imc/nw+GQ3g56vwghsiybTCaw5HQ6jauhXdd2Oh06wBsMBrQH\nlv4NgDgVkMhWq4WWMOxYqZ3F5XIVCgV87fV6BUEA51ZVtdlsEkLcbvfGxga8sTdv3jQajYlE\nguM4mefnNput28V7T4cOHUQndjp06LgjfoTArlKpYPaGUQohhAaUYFAHgR0IAeY3p06dop/i\nCP71er2VSuVEsQFt/SKafDVVVUOhEFaozKVLWwcHV975TnyL+mpLpdLW1hbKTFut1nA4dLlc\nGCtGo1G8JEEQFosFpoyTyQQCu2AwqC1guHbtGiaFLMtqCV+hULBYLKFQ6C5c6X8dWCwWr9fL\nMMxqtUIxA/wT8XjcYDD0ej2LxYL2NvyC8/l8uVzSrTdNMCGaeglBEKhfWBAEegJipfG1LMtg\nY9pSCqPRiCY3MHiv1wvrCSEkk8ngtMlksl6vEX+zs7NDjvf7iKzD8yD05NSpU6PRqNPpbG1t\nsSw7n89Hbrd/PtdDT3TooNCJnQ4dOu6Abrc7n89fSWDndDoNBkM8HoelQJZluj5DsC2mKUgt\nJoTs7e1RgR0hxGAw7OzsVCoV2liA4xzHoYWMUjr8L9LLTCZT+I/+6Gh7++BY47W5uclxHMy2\n2EJaLBaz2SyK4s7ODj74n3zySQwLA4FAo9Ewm83j8djlcmG4dYK5Xr58GT86lUphZQzch01i\ntwJpdn6/HzI7tOWGQiHwp0gkQghB0xrON5lMHo8H0TParl7E0WFRS5Nu6NAOy3St3YQ+lhbL\nCoIwn89RNIcTVFXF7Th79izOqVaroVAI21j4JyCzo/4JQkg2my2Xy1arNRAI7O/vb21tCYJQ\nrVZnPp9vPteNsTp0UOjETocOHXdAuVz2eDwoLTiBXC4HMVw4HB6Pxwgixo4vk8mgyAFGCmxa\nUUiAz3iwJVmWA4EAHQJRw6Ysy9Bv4TRKpPCfFwhJX736/E/+JM5hGAa70aOjo1QqNZlMptPp\ncrkEcUEqCl5SoVDgeT6ZTM7nc5RSQGBXrVa1CXbD4RCNCAaDIZvN0uOIUrufBXYAnAfpdFrr\nn+B5XpblZrOJ7BKkohBCjEajxWKB+xU+ZeoL0RaLgbiTYz6HKawgCLRThBBy9epVGuMMwodE\nQ1mWMQ09Ojry+XzL5XI0GrlcLo7jWJYdj8eZTAbEzuPx0NxprX8ilUqxLFsqlXZ3d/f3930+\nn8PhmEwmUizm7Pf1iZ0OHRQ6sdOhQ8cd8EoCO6jNQONQC4aPfFEUOY4Lh8PoYyXH8zasPkul\nEm0GI4RwHAexPGY5eGZFUcxmMz7dqRmWZVmLxYJp3+v/z/85ymZnx9lytwrsXC5XtVqFHxPT\nI4fDwXFcoVBYr9cYPp09e7ZUKm1ubjabTUEQtAl2ly9fdjgciHzT0rhutzubzdLp9L/2Nf5X\nRjweH41GoVCoUqkoisLzvNfrHY1GDodDFEWU4UqSBO4liqIkSQiUxsNpiAkhhGVZv99PCMnn\n8+j/VVUVPmhCiKIotPCNEIKVPZa5oIMon3A6nXgn1Ot1zFZxc7G1lyQJOYIoiHM4HOv1ej6f\nI2wZbzCDwUBDT5rN5mg0wiR1HQ7but1Op4NFvw4dOnRip0OHjjugUqncltjlcjmURPn9fhgw\nrVary+VC5RfUbHSihpkZBHb4Gql16XQ6l8tZrVZtUakoislkElFzNIADGcitVsvf6bi/973n\nnnySumXNZrPP5+v3+8PhEAK7cDg8m80Qq/Hiiy8SQh577LHpdNrr9ViWbbfbDMPY7fbVapVK\npcrlciAQoHtJVVWvXLmC+DqHwwFaAxQKBa/Xqw1huT+BmGJ4TkGhIpFIs9mE52AwGKDaFZRa\nVdX5fN5ut7V8jgI0jhAyHA6z2SytAKE3y+l08jyPeyFJEljjiRMsFovBYGBZdr1eQ4eHGVsk\nEtFudfEuAm8uFot+vx9NFXgeyOzC4bDb7T48PIzH4wzDjD0ex2BAFAU2Cx06dOjETocOHT8K\n/X5/Op2+ErHz+XxGozEej+fzedpDparq5uYmBHagDuR4Fbu1tUUrYgkhLMtms9l+v2+1WulB\n0Ah8/GulbKqqplIpWZZ/6rnnqru71URiNBrhW2gSOzo68ng8EMwZjUakaezu7uI1PP7448Vi\nkeO4dDqN5XK1Wg0EAna7/cRIslqtjkaj2WxmNBq1e1hyHzeJnQDDMCDBaP4ghEQiEfgnWJZt\nNptgq+jJJYSAxQYCAcTUaQMFVVWdTCYYXkKcRwjBaI2eQFNR6FORY20lOc5Mmc/nOD6bzRRF\nwava3t7GwStXriSTSWxjz5w5Qwi5evUqwpa1/onRaNTr9RBwHYvFRFGschwny9Hj7a0OHTp0\nYqdDh44fBdSq3jrLgdoMwqxYLAbyNJ/PsTg7c+ZMqVSKRCJQ09NME1AEcszzZFl2u92yLNPm\neGTnkmP1vdamiqGOv9vdunbtO298I3a++BaslCic6HQ6CNKDNxMdoxzHWa1WvOBIJLJarXZ2\ndiCwU1X1xEjy8uXL8M+eCDqRZRmhd3fhMv/rI5FIVKtVKrODfyIYDIqiWKvVwKgMBgMWpmiS\npQF1lPDhjnS73Y2NDXIscKTHcSuXy6WW512/fh0nE43MDqpHcLh8Pm+327vdrizL9PKWy2Uq\ns8MVxs5d65/weDw09KRcLjudTqPRWGVZwjC+2UyX2enQAejETocOHT8K5XJZ6yqgaDQa+MBG\nYi0NK5EkCYOWUql0YmWJWDuQAwx4XC7X0dERIQR0EDM/URRRdUo0Jllotg4ODp7+7nenjz9e\njEa1Q750Oi2KYqVSgcDO6/XWarXlcmm32y9dukQIwX4wn8+LoggWcvbs2Uqlkk6n2+22tlRD\nkqQbN26AUMqyrJXTVatVSZJuezXuQyQSiWazGY/H8bJDoRBYOMdxrVZrb28PzbBQueF2rFYr\nDFwVRaGWZNwRzERrtRpdf1OSJwgCngQ8L5fL4RKxLAvWuFgs1uu10WgEcWw0Gpi8djodm82G\nPJTJZJLJZAaDwXA4hJhyvV6jXrZer9NxYDabPTo6SiaTJpMpn8+Hw2HGal06nY5+X5/Y6dAB\n6MROhw4dPwqv5JzI5XLBYHA8HhuNRkzXTCaT0+nkOM7tdkNgt1qtaEwJIWRnZwfhwOTYDLGz\ns5PP51FpQI6XevP5fHNzE9HEtChWVdVEIrF6+eXTN24cvPe9NN+YEGKxWKxWK+ZSyWSyWCz6\nfD4kJG9vb1+9epUQ8trXvrbX681mM6fTWSgUjEYjSEwymSyXy16vFxSEEHJwcKAoSrfbNRgM\nkUhESx8LhUI0GqVjrfsc4LJGoxGlGkaj0efzYTmLqwoFHjjTbDZbLpetVotWhNFwEyAajZLj\nVg8cMRgM9BYgKgWcb71ex2IxzGipoxnvDfgnED7MMAyomNvthpLS6/W6XC4M7WDjLZVK2Lci\nkpoch55IkrS1tbW/v59IJIxG48znM9br1GahQ8cjDp3Y6dCh4xUxGo3G4/FtiV0+n4dOKxaL\nYepG40ui0Wi1WmUYptlsakNMEolEv98Hk8CRzc3N0WhEOQQMDQzDZLPZE530hJCtra0n/uZv\nqtnsP5hM5LiQlBCCl3d0dJROpw0GQ6VSYRgGts1sNgv3RiqVKhQKoHq9Xi8Wi2FTbDKZTiTY\nXb58eXd3F4T1hJzuQRHYAUajMRgMNptN2JPJscwOuSHNZtPtdiuKgnuEiJPJZAJHAtFcXmAw\nGDAMg7kajmA0i6/BkulcjXI+SsgcDofJZMLyF0+OVT4hBLtyQkgul6PdYghkOTg4sNlsbreb\nrlnRflEul3d3d2GkWK/XA6fT2u3yPK+Vb+rQ8chCJ3Y6dOh4RSAS1uv1nji+Wq3q9TrCabEs\nQ88BRPcQ2NHeCEIIPtGHw6FWqk8IwQJXO2ixWCwgEOS4loqyBz6fP3P9evH97x8MBhzH0RgU\nRNrmcrlsNosx4WAwEASB8gye5+12e6FQUBTFZrMpirK3t1coFCCw0ybYzefzfD6PodRqtdLS\nuNVq1Ww2HxSBHYA9rDbNDv4JVVUbjQZ+O1EUQawDgQBqJDANHQ6H2ljmcrlssVgURaH+CW3K\nCUosKAvP5/N00kkTVZB4Byt0pVLheR50jbaQvfzyy5lMplgsKopy6tQpQgiYnzbNjoaeZLNZ\nFNfKsty12TzjsZb/6dDxKEMndjp06HhFQGB3a8sCpl/T6VSSJJfLhZWroigYsyFwxOPxaBVy\noVCoWCyC2OG0VCp1/fp1MEL6zOi2unHjBtF0TjAM43K5LJ/5TCmZdL7jHWieoLPAbDbb7XZH\no1E2my2VSj6fr9PpcBwXjUafffZZQggIHEopYPJIp9PNZjOdTp8o1bhy5YrD4YCSDJxV+ysb\njUZsJB8UwD+RSqVqtRo8rYPBIBAIqKpaLpf39vYIIQaDAetXZBRj8AZdndYx0+12ITccj8f0\nIPbphBCkxpDjW3Z0dATWqJXZjUYjQRDwlkD7xXQ6Xa1W4XAYt7her2cyGUEQ6vU6dvrr9Xow\nGESjUS1j29raOjo6MhqNiL5zu91zn88zHvM8r8vsdOggOrHToUPHj8ArCexQq1WtVlEghrow\nq9VqMBisVqssy41GQxsYq6rq9vZ2sViEugvno5rCZrNR/mcwGBD/C/qFLSohRFGUc2Zz/IUX\nnn3zm1F+CmoI8RbHcblczuv1bmxsFItFt9vNcRzP85lMBs+DjBVEEJdKJZfL1Wq1OI5DDZq2\nVOPKlSt7e3u1Ws1sNqfTaWoUwK984sj9j0QisVqtMAStVCrwT4zHY6vV2ul0QFJVVUWA32Qy\nEUWx3W7TYRv8E8BsNsMU7ejoiF4EOnklx1HDMDsvFgs8uSzL2ImLoijLssViQU2tLMvY4zca\nDa/Xi0fNZjOz2RyJRLCN9fl8DMMUi8VYLNbr9Sj7z2azo9Go3+9jbxuPx5fBoHM4FNfrer2u\n3d3r0PFo4kH6PykdOnTcS8xms8Fg8ErEDhp8p9PZaDSQYcuyrCzLaDug0niKYDA4m82oc0JR\nFOxqjUYj/TCG6uv06dMgi5S9MQzzmv/9v+uxWO/sWazn1us1EjfADxB0oihKpVKBIWC5XAYC\nAWxjk8lkoVDgOC6ZTC6Xy+3tbTSPcRynZa7dbrfVaqVSKQyWTsjpHogmsRNwOBwul6vZbMZi\nsWKxaDAY/H5/o9GIxWKCIEynU6vVimtFCBkOh5Cp0QaOE9QcTuFOp6Od5NF7B7MLTZMGV0NU\nCsZ4NpsNoSr4Fih+oVBgGMbn8+EtIUkSDT1JpVKqqkJIx3Ec1c95vV6Px4PN+2Aw2NjY6Dsc\nrCSJlYogCFTVp0PHIwud2OnQoeP2KJVKZrM5EAicON7tduEtQDRxqVRiGIZ2UsG3iDIo+hCe\n5/v9PrZy+Ji3Wq34DKbJdoQQm83GcRxk+1qr7MZg4Pqrv/rOW96CNGPmGISQc+fOIegkm83W\najVJksAkeJ6/cuUKfvTGxsbh4SGGi3jIwcEBou+0CXaXLl2iRpDFYqGlcYPBYDQaPVgCOyAe\nj1cqlVQqRf0TUArC2hKLxVRVbbfbHMeJohiNRmVZpmWv7XZbO6FstVpY0VLmh2xC+l1M4/Cf\nyIgmx0M7QgjP8xzHybIMYler1TiOg/iPOjauX7+eyWTq9fpyuTx9+jQhpFgssiwbCoW021iE\nnvh8PpfLJctyy2xWCbF1u3a7Xd/G6tChEzsdOnTcHphm3Sqww95zMBiwLBsIBCaTCWTs+C4K\nWDc2NrRLsWAwiNYHook+uX79OtHMeAghg8HA4/G89NJLhBCz2Ux/9E99//uDbLaYTsNBiSEf\nKMXe3l4+n2dZlgadLBYLu92+ublJHQNYDVssFkyt0KC1s7ODUg2Iw1RVvXr16t7e3uHhoclk\nwliIvrBCoeByuW41kdz/oDHFsJXAGIvLWKlULly4gMUo/BPBYJBlWcrVptMpTT8hhJRKJS01\nxxeQx0EoiZYOfLdUKkG5aDQaseleLpfL5XI6nYqiaDAY+v2+x+PBrjwUCuG9cfXq1VgsxvN8\nsVjEMne9Xne7Xa1/ghCytbVVLpdFUdzc3Ox2u8RsXjgcgcUC+sh7cFV16LifoRM7HTp03B4/\nQmCXyWQqlYogCBzH4YPcZDLxPG8wGEwm062pEzs7O+VymfbGMgyTyWRqtZo2LI3neQjsoKKj\nVlnPYLD74ovffvpput6lMWwcx5nN5lwuh6CTUqmEtBRJkuLxOE7b3t4ul8tYrXa73UgkcnBw\nEIvF0CTmdDrhgc3n84vF4vTp08Ph0GQy3Rp08iCO6wgh8Xh8OBy63W4EwYTD4eFwCGtCqVRC\n9yvHceBekiQpitLr9Xiex23V9uS22204amkrK7bh8MOiV5ccE7v5fA7GDNetqqoIHMYDEXHn\ndDolSRqNRqFQCH8YtFotlmWRTYPwZJZlQfJqtRr9UwE2jlKplMlkEFsz9Xq9kwmrF4vp0KET\nOx06dNwWi8Wi2+3eSuwkSQLhQxMo1qZGo9FgMEiS5PV6IbCrVqvaR7ndbgRekOOJncVioem4\nOAih1d7eniAIDMPQLrKnnntuefbsjUjEbrcXi0W6hJVlGUwCcitJkmq1GujgYrFot9t45lQq\nhUdtbGzIsnz27NmDg4Pd3V1CiDbB7sqVK9vb24VCQVXV5XKpJXaqqoJD/Ctf4nuCYDAIqh2P\nx4vFImZj7Xbb4/H0ej3s02mNGyJsyuUy3b9rt6vz+RwT3G63S6d6WikkCB+d59FNOna4mO05\nnU76nLj75XI5GAziUaD+mUwml8sRQiKRiKIo8E8sl0vI+Mhx6Ekul4OL1u12T7xeW6+3WCw6\nnQ4Ntdah49GETux06NBxG5TLZZ7nQ6HQieOlUonGzCLBBNOa+XyOui0k2GkL4A0GQ7fb1e4x\n/X4/+B/tGGUYxmq1siy7WCxUVaX1Bs7h8NyVK73/+B8JIZlMZjgcghOAKMA/Ox6Ps9lstVpV\nFGUwGDgcjo2NDUjlOI4LhUL7+/v4FiEkEAj0+30qsMNUab1e7+/v7+3tvfzyy9CBaXvD6vX6\ner3Wdos9QGAYJhqN4jfFNjwQCIDnIVjO5/MpijIajQgh/X4fKSTJZJImDNOSD0VRwP9kWYaR\nlvzzzrdms2m32+lunfaPCYIAvZ3NZkOHmNFolCQJRPDg4MBgMHi9XvwgZCCPx+N+vw/+Daez\nzWbTTuMgs4OLVpblnt1u7/VGoxHllzp0PLK4PbEL/Utwj1+xDh067gHK5XIikbg13SOfz6OE\n1GQyRaNRfPBLkgSN1O7ubqlU8nq9WoFdNBqF+ZEe2d7eRlIdBcMwrVbL4/H84Ac/QGodnuHp\nZ5/txGIver14HlSdwoSrquqZM2cgovd4PBDYKYrC83wqlVosFrBbrtfrfr/v9/shEcM4yuv1\nDodDWqpx48YNg8GwtbVVr9dtVNxtHAAAIABJREFUNls8Htf2hhUKhVAopO0We7BAZXbtdnux\nWCCmGNS20WicOXOGENLv9zmOEwQBx6m+sN1uOxwOeu/A1xmGoTt0lmVpoOBqtcLDgVqthk2u\nyWQCU5ckSVVVQRCg5BMEwWq1gq6FQiF4LK5du7axseHxePL5POi1IAitVutEml02mx0Oh4PB\nAA2zA4fDMx4TQlwuly6z0/GI4/bELvvPwfN8u91ut9v44w+T/Ha7feHChfe85z33+BXr0KHj\nHuBHVMTCfyrLstlsxrjOaDQiC8Pn89EaMUBV1Z2dnXq9rq2oymQyvV4PYz8QOIfDAYEdPpVR\nYOUaj/cuXy78238LXggGoM1AwT4O/fQw5xJCxuMxrSNDkwEkfbPZLJvN7u/vYw5ULpftdvvG\nxgYh5PLly+fOnZtMJtjiPdBNYrciHo83m02/34/+NBhj4Ww9OjqC+ZQQAuYKSgeeRwhRFEU7\naq1UKrjp9G5izIZkY0IIHLX4e2CxWECYKEkSiOB6vZ5Op8vlEupMm81mtVqn0ylScvB+2N/f\nJ4Qg9MTlckG4SWV29JXAEosMmlarJUajrvHYajZTpqhDxyOL2xO7v9Xgc5/73Hg8fvrppy9f\nvtxsNl988cV6vX79+vW3vvWt169f/8hHPnKPX7EOHTruNtbrdbvdvpXYTSaTXq8H34MoiqIo\nImoYYRl2ux1qqhOfrGazmWVZGjCLFGLM+cixJAuzmbNnz2I5C97w1LPPdoJB/p3vnM/nVGBH\nqw54npdlGUEnqCuYTCY2m02WZUR7EEKy2ezh4SHUXYSQra2tZrNJiR0UY+PxuFKpnD9//oUX\nXiCEjMdjLY0TBKFWqz2gzgkA/RmtViuRSIDYYfFqMpmq1arH48ElBYGr1+vwVbjdblw0rTG2\n0WjArEqzoynPpidofzRVQ0LyqCjKbDbDWhZvHszwWq1WKBSCqhI+WZiaZVn2+XyyLENm12q1\ntD1msVgMmXw8z6vJJCtJ1tFIlmV9YqfjEcedNXaf/OQnbTbb17/+dfTPAKdPn/7a176mKMrH\nP/7xu/nydOjQ8WNAuVw2GAy0FZTi6OjIbrdzHLdarUwmU7vdRv3UarUSBCEajZZKpXg8js9m\nQgjLsjgtGAxSbVwkEsnlchj10WfG5hSJuIgpdo3H5y9d+s6b32yz21VV3dzcRK6KoiggIj6f\nj8YOQ7G3XC6dTmckEhmPx7DrxmKxXC5nNBorlQrHcZPJxOFwQEBCnROXL1/e2NiIRCJHR0cW\ni8VqtYbDYfrCSqUSy7I0ue1BBM/zwWCwUqmgNBadsI1GIxAIQHeIyEDaBhYMBpEwghukHbWu\nVitcPbqqpp2/hBCGYcAIqScGBmeIKbFDNxgMbrfbarUyDIOeMULIzZs3w+EwHrVeryVJ2tzc\nBEVDHVypVELzmFY/Fw6Hm80my7LpdHri8RCGsbTb4/F4Op1q24d16HjUcGdi9/zzzz/55JNw\nn2lht9vf9KY3Pffcc3fnhenQoePHBiQMYxmnRT6fxx7WaDTGYrF6vQ5TpMFgwLoTAjut1xUC\nO2qQVFX11KlTR0dH9AghhOM4jI5efPFFQggI5Zuee64TCFTPnYNBkjaKEkKw9kUZ/ObmJnJu\naWCH3W4nhMCAuVgsUAXbbrdDodDBwcGpU6cYhplMJqPRCGPCK1euXLhwQRCE8XhsNpszmYx2\nlVwoFJLJJLaEDy7i8Thkdr1eb7FYBIPBZrOZTCZFUVwsFiC4yIseDAawGNOe3FqtBvUbQANu\n8PZgGAYTOGA6nWIgCjQaDbBGk8nk8XhwsslkAuNHfh42rVar1eFw4AdRBSfeb4QQSZJ6vZ7P\n59NO48LhcKfTQVlFbzab2e2+2Wy5XPI8rw/tdDzKuDOxU1X1lf6RVCoV7T9pHTp0PBzQ5oBQ\nIHgCxM5gMLhcLlEU4WZA32soFMIERfuQdDrd7XYnkwk5XswFg0HM5+iRSCSyXq9TqVSlUiGE\nDIdD53h88dKlZ9/61kQymcvlaH6KegxCyO7uLhXYFYtFURR5nu/1epgSSZKUPH5sMBiUJAmB\ndnQPa7VaQRQGg8He3t7Vq1eRtfaQCewA+CcCgYDVasU2ttFoQF1XKBQuXLhACJnNZsisQScs\nVqWEEEEQaBcFTqPdrziizTeRZZkSO1VVV6sVhp2KouBNIgiCKIrYuvI873a7jUZjr9cjhFCH\nysHBATmW2UWjUTQCYxur3fJjyNfpdLLZ7Hw+H3s8EUHgOM7j8egyOx2PMu5M7B5//PHnn3/+\ny1/+8onjX/rSl77//e+/7nWvuzsvTIcOHT8erFarZrN5a7pHrVZbr9ebm5v4AjkmhBCWZUVR\nZFl2uVyeCJvAOWazeT6f40xo241GI53qEUIg29ra2oJyq9frvfH55zt+f/7UqUQiMZvNrFZr\ntVqlXBA/yGg0TqfTbDa7Wq1ardZ8Pt/Y2DCZTOPxGC/s1KlT+/v7qqpiMcfzvMlkAmGF45Jh\nmMuXL6dSKafTeeXKFYStaOV00+m01+s90AI7IJFIrFarfr+fTCax1mw0GqFQiGGYw8NDumjG\nZgZmiHw+b7FYYFLBNBRAZoqiKPSveizQKaEfDoeQQmrHq6IodjodzHdHo5EoiqvVCvViCDhc\nLpehUAjPiaiaTCbTbDbRRwyZ3Qn/hMVicbvdzWbT7XZvbGzM/X7XaMSyrNFo1Cd2Oh5l3JnY\nfepTn7Jare9973vf/e53f/7zn//617/+R3/0R+9617ve9773ORyO3/7t374Hr1KHDh33DKVS\nyWAwgGxpkc/nI5EIQukURYGZEaxusVj4fL5KpRKLxSixQy1Eq9WKRCJ08QrRGx5INXZQ3GO1\n53A4RFFshUJ/9fa3S7KMRV46nYbADqexLGs2m/P5vN/vd7lckMEhZQ1JKxj8oCbV6XSWy2Wb\nzVYul7e3t/FzMeqTZfn69evnz59XVRVLQ7/fr/UK5PN5m812a1vuAweHw+FyuWiaXSQSmUwm\nsKQ0m030hWB5TQh58cUXvV4vjLSYj4K6AaPRCBeEZg1qtZKEkCtXrmhldhBcgo6D/K3Xa7vd\nzrKsLMvUwry/vx8MBkHBMcCLRqNms7lQKMTjcUmSqtVqOByGhI7+LIweCSGZTGaysWFptzEO\nbDQa2r8cdOh4pHBnYnfmzJlvfOMb58+f//M///MPfvCD73znO3/5l3/5q1/96utf//pvfvOb\n29vb9+BV6tCh454BLZ+3Cuxo0AkChOv1OqwMqJ9Kp9OlUsnv99MNnSzL0WgUS1IcQfQJkoS1\ngrlGo7GxsXHlyhVy3Bb18sWL1XgcdVKqqmpXgZgA+f3+o6MjGnRisVhYloWADK/KarXOZjNB\nEEAK0+l0Pp/HlrDZbM7n80wmc3BwgM1juVxGFB8UXRRoEru1LfdBBJXZDYdDZIg0m81gMAj7\nAtwtsCQXi8VkMqkdXtKoYUKIqqoulwtFZPQIZnjkOI/w3Llz9Oe2222TyQSZHeSPRqPR6XSa\nTCaDwTCdTlFQe3h4GAqFVqsVODqihtEtht4zRVFg2TmxjcUfEplMpmOxWDsdnucxEYRkUIeO\nRxCvqnni6aeffumll77//e//yZ/8yac//elnnnnmpZdeeuGFF5544om7/fp06NBxj1EsFm/d\nwy4Wi2aziaATs9kcDAbn8zk2bqhnhcDuBB2Mx+OwKZDj5Dmz2XyCJ2Ees7u7WywWCSFQXxFC\nWJaF8QIFoPRRKCJDKzwV2K3Xa4/HM51OR6MRy7Kz2QwuV3Icz4a+Mqjljo6OQqGQw+G4cuXK\nqVOneJ6/dOkSuSXo5IFuErsViUSiUqn4fD673V6pVILBYKPRgE9iPp+D8iJ/eDwenz9/nhxf\nOkLIfD6n8zlCCOiXNsqOmktQyIbQY/ynKIo+nw9f40mo28ZsNnMc5/V6WZYFued5Hj8Uu1R0\ni8Gfa7Vay+VyOBw+Qew6nY4sy+l0euR2u0ajoM8ny7Ldbte3sToeWbzaSjGWZS9evPjYY4+9\n8Y1vfOtb33rhwoWH469YHTp0aDGfzzudzq3ErlAowKiIz0ue55FewTCMIAiYwOHjGedTPZzb\n7QYD4DjObrcXCgWDwaA1WEDXtb29DYEd7Xo3Go3JZHI6nVosFm0BPKZKNpvNYDDE43HUg4qi\n6PF48FSI2N3Z2blx4wYlhcPhMJvNQsKFPexyuczlcmAwKKdiGCaRSNAX1m63kZl8N67zvUc8\nHh8Oh/P5nG5jm80mGNjNmzdhpBBF0WAwyLKMKVq5XMbmmmEY7T663+/LsrxYLGixGIay9EPh\nxC1GOrEgCCCOiqIsl0vEFBsMBo7jJEmaTqewueAmgpRns9npdIqpME2z0zI29Il1Oh2e582n\nTrGyHJBljuOcTqfun9DxyOJVEbtms/mLv/iLLpfrzJkzTzzxxN/93d994xvf+Omf/ulr167d\n7denQ4eOe4lisWg2m7VBbkAul8NSslqtIrWOEIKA2fl8bjKZut1uNBqFI5UQwnGcxWJpNBrQ\naRFCZFmOx+NHR0eCIGgLRkulEhZ5qqra7fbVagV+gMRjVVWTySS8F/Rp0UNPg04wJpQkCSwE\nQjE0aGGOGAwGj46OMJRaLpf1ej2bzV65csVqtdIEELvdnkgktDb/QqFwQnL3QCMYDCKRmBK7\ner1ut9sNBkMul8N2lRzz7EKh4HQ6a7Wa2+3WqiHxBVRuiqLQ9wndw+KEF198EZUeeE6czzAM\npryEkOFwSAgRBIGmzKiqWq/XQ6EQWGChUCCEuFwur9ebz+eDweBqtarX64FAoF6vU/2cxWJx\nuVz4cyL4+OMqw1haLVmWFUXRJ3Y6Hlncmdh1Op2nnnrqC1/4Qjab/YVf+AUc9Hq9zz777FNP\nPZXP5+/yK9ShQ8e9Q6lUgl1Ue1BVVSSKDYdDSOCx/4JwihASi8XQwYrJDWIvkFdMBXaKoqRS\nKXggKFGw2+3jbndPUdp/+IfhZjMTjWo5BORTHo+HHjQajSzL2my2fD5PBXYw29ZqNYSqrFYr\no9E4m82wsRVFMRwOL5dLnJ/P53mej8ViV65cOX/+PMMwN2/eJIQIgnBr0MlD4IelQCEkYoqn\n0yk0iLPZzOl0go5jT4obevny5VgsNplMosd3BE1x9I2BhSltjKX9YxjUHR0dQWaHx9KCMp7n\nIZGUZZlSRvBsQsi1a9dCoRAyk+fzOd45CD2BzI4yfq1+DqNHQkh6d3dmtyuFAuzSvV4Pw10d\nOh41vCpXbC6X+83f/M3Lly9/6lOfwsGf+ImfeP755yeTie6K1aHjYUKhUEBsrxadTmc2myHo\nhOf5QCCASAtCiNPp5Hk+HA63Wi26fcPkJhKJ0O0bUm1FUQRvoCbZN9+48ZHPfOad//W/Xvj9\n3//gH/7hz/z7f/+Gv/s7oqoMw/j9foxtaI8FOZ7xuFyu6XQKHlYoFERRDAQCmOI4HI5+vx8M\nBm/cuEEIQY8Zxn7gIkdHR6iNbzQaIB9Xr15FZLHWOSFJUqVSeWgEdgDS7DY2NnABjUZjo9GI\nRCKYqCEIBsPRarV65swZRVEgjyOEjMdjunglx95kalBFpQS+xuL74sWL5HhiJ0kSLbdAcgrY\nudlsNpvNkiRhz1sqlUKh0Gg04nkePmVCyObmJoL3MNBttVoul+tETDGIXTgcnmxsGGq1QCCA\n0eCJfjMdOh4R3JnYfe1rX7t48eJv/MZvaDUThJDHHnvsscce+853vnPXXpsOHTruKcbj8XA4\nvFVVlsvlECyCzgm0ikFUJ0kShFksy9I9LP6/AnwLAjuWZd1uNwb8tLrg6eeeO/8//sezTz/d\nOzz8vz72sd/5L//lu29/+9PPPvu2v/5rs9lMBXaNRkM7QZQkyWQyBYNBsBMMeKxWK9hAIpEQ\nRTGTyRweHmLzaLFYSqUS9rAYPW5tbV27di0cDoMBdLtdp9Npt9v9fj/9KZVKBXTwrl7we4x4\nPN5sNkVRxDY2FAo1Go2trS1FUfr9PnKJQewWi8X29jbDMLi8hBCGYbBdBZbLJcdxnU4HTF1R\nFBBrjGMlSeJ5XvuRgUHder2GkhKbd5yGIgpCSL/fDwQCLMtiHYxI6nQ6TUe8VGan1c9FIpF2\nuy3LMsMwcjzuHo99Ph/DMHpMsY5HFncmdt1u9+zZs7e1Spw6dQrVzjp06HgIUCwWbTablt8A\nCDohhNRqNSzI6OptPB4rijKfz0EacD52o41Gg25RRVFEqpwgCMhDcY9GT33ve3/5b/7NlTe/\necQwhBDW43nh7Nn/973vfcMLL3jqdRQVwB6BJ2FZFq9tsVjg9RSLRQThdrtd7N3gnMhkMqPR\nCHYBDIF2dnbIPw86wZHDw0PUW93aJIZ2+bt6we8x0BLWaDSQTYMQOFyH69ev45IqimIwGBRF\nmUwmVqu11WphFKeqKl28EkIkSYIBluYdojWYXsNisej1evE1umLxBdblhJDRaCQIwmq1UlW1\n0+k4nU4kFXu9XjBI+CewNy+Xy263e7FYtFqtQCBwYmInyzKWs6adHfdohL869JhiHY8sXlWO\n3Q9/+EOaTUWhquqNGzfwR54OHToeAiDo5MRfcaIoVqvVTCYjimK73RYEYTKZSJJEs0tcLle9\nXvf7/XBUEELQNFqr1bAJxRMicISq5Xb39ycbG5ey2Ww2+9JLLxFCksmkqqqFTKYZiWxduYId\nLlZ4eAg4HMMw7XYbgrlCoaAoit/v73a7DMPwPI9OM4wJQSxsNls4HIaADEEnhJBWq4UMzhs3\nbqiqeiLohDwsTWIngDU6ZHbz+dxmszUaDZPJZDQaQZExV8PO+vr16+FwuN/vU6J/IhkO3A7F\nvoQQhNXReMJLly7RNDtcYXJcLAuHynw+NxqN2K1XKhX4cyGzA59uNBq49ZDZpVIpkE6WZbvd\nLt5aeLVOpxNbV9fenns06nQ6HMfBbHGXr6gOHfcj7kzs3vGOd9y8efPDH/4w/YcE/PEf//EP\nf/jDt73tbXfttenQoeOeAs6JEweLxSLDMMlkEmn+FosFzRPQPFmtVqjXT0g1oM0CFcC3ptMp\n6g3wae0ejRapFMMwp0+fxooW6bWEkI7fH1wsSqUSIWQwGFCiKUnSarUym808z6MFK5fLEUL8\nfj+GNNFotNlsbmxsQDaHDqt2u03bSxF0cnh4aLfbQ6GQJEkw1QqCoPVJLBaLdrv9MDknKCCz\nczqdHo9HEIT5fD6ZTDY2NiBkhNYNF/zo6Gh7e1uSJGp9RbIxfSqcpk2zwxHc7mq1+trXvpae\nrCiK2WxWFAXNwji4sbEBrd5oNMLV3t/fD4VCvV4PhmskIGYymVarFQqFWJZ1Op2TyYTjOK1+\njvonzLu7rvG43Wj4/X5YQ0Aodeh4pHBnYvfxj3/8DW94w2c/+9l0Ov0rv/IrhJDPfe5zr3/9\n6z/4wQ+ePXv2E5/4xN1/kTp06Ljr6Pf7k8nkVjaTy+VSqZTBYKhWq1ardWNjg2ZbUGMsx3F0\nD0ub4yORCI0pcblchUJBEIR/8jYajWQ2I4SAYTAMg9UbwzAWSeI9HhBBmDHxJBaLZTqdGgyG\nTCbDsixS2QghWOepqrq3t7dYLJLJJDZ39Xrd4/F0u90TQSegLAzDYODn8XhCoZDWGYDQPjqL\nepgAYoeWtl6vx/N8o9GIxWKLxUKSJNx9/A3fbrcxRUMvMB4O9RsAwwRmpYQQURTxBd4ei8UC\n3RL0fKjx1us13f/wPG80Gm02G8dxaJlDg22328XaFzK7cDhstVolScIzl0qlYDB4W/8ESaVY\nWbYMh2DtJ2oqdOh4RHBnYmc0Gr/97W9/+tOf5jjum9/8JiHkb/7mb/L5/K//+q8///zzWtWF\nDh06HlwUi0WXywWNmhZ0KVmr1bBKw8IO+zUQAq3ADiOZarVqs9nwSS/LciwWGwwGkiTRD/V6\nPB7I56MWy9WrVwkhLpcL6is7wyQKhfnZs4SQcDgM0kYIQWuZLMvr9ZoK7LB+xWc8VsOEkM3N\nzcViEY1GBUFwu90ejweDqFwuZzKZwuFwsVjEHvbg4ACU4tY97K0r6YcDiURitVr1ej3I7JDz\nB5ldtVrd29sjxxHQdDg6GAyoi1lL7MDRe70eDiI9DqNTnNBut6nfAp5ofE29tIvFQhAElmV5\nngcLX6/XMDjjfQhbNG49Uoin02m32w0Gg7f6JxRFIckkYVn3aIR3AqL47ubl1KHjfsSrCig2\nm80f/ehHERN17dq1fr/f6/U++clPav+R69Ch44HGbZvERqPRYDAAkarX68vlcr1eS5JENH1T\niBfRCuyi0Wi73V6v11BcgXJhYEPZUve1rx27XO/8X//r4PJlQsjm5qaqqqws//Sf/ZlgNB7u\n7RFCtFM0cDhCiCAIeD3Xr19XVTUWi63Xa5ZlvV7v/v4+IQTbN8yKFosF1QHncrlMJlMqldBA\nparq/v4+9n0niF2hUHj4BHaAw+FwuVyQ2a1WK6T7om9jf3/f4/EgWw7mmGKx6Pf7+/0+rjzD\nMJSTEULQybter6l/wmQy0ehgQsjBwYFWZrdcLjHkQ9sE/jBAC4UoipVKBe0X7Xbb6XRCH4l1\nPDmW2YXDYUEQIArE3BHfDYfDkiR1u11iMpFQyDMedzoddM7qEzsdjyDuTOw+9rGP0YYJh8Nx\n5swZreldhw4dDwHQi3orsTs6OnI6nT6fbzQaIfK31+uB2NlsNrfb7ff7tQl2gMfjwRaVEAKi\nQHPvKNx+/5d+7uc8xeIvfOpTP/XXf5391rd+8lvf+pU/+IN0Pv/Vf/fvSp0OIWQ8HtNHwXuL\nUloahwFDKyEEbbOVSsXhcNy8eROLY5PJRAV2NGP56OgonU5jBYlSLI7joNgDer3ebVfSDw3i\n8TjmqSgFgX+C5/lKpUIIcTgclEPfuHFjc3NTEIRgMEgIQfqJ9j5iF0//wod/ghACe8T+/v7j\njz+Ob4HJYT5qNBp5noeKzmazCYIgSVKj0YAm7wc/+EEoFAIvH4/H+IMhm83O53OIKT0ez2Kx\nWC6XUOARQmw2G/VPkFQqvF43Gg232z2fzyEMvSfXVYeO+wV3Jna/+7u/e+7cuccee+z3fu/3\naE6VDh06Hia02+3FYnGrc4IWPFSrVaPR6HK56EJNlmXUv94qsMMmFFItRVGwEaNdYcBgMBiF\nQgdf/vLLjz0W6nRCzzwTaTRunDv3+Q99yPj44/P5nOf5brdLpzIej6fX67Esi9fT7XZFUVQU\nBQ4JQsjrX//68XiMWrNgMDgYDNxut9VqRcYHDTqBwI4QcnBwYDabbTZbOp3GgAooFApY4N6F\ny3xfIJFIgMOhgmKxWIzHY7/f3+v1yHEkCi57pVLBltZoNDIMA8cMmDQFy7JUSYm9KjmW2UHD\nB4qGFbzRaFQUZbVa0b8EnE6nwWAwmUyiKKLUpFKphEKhfr+P24qRm8Ph8Pv9SMtD/6/NZnsl\nmV1UklarVTQaxXRZz+TS8ajhzsTuK1/5ynvf+97Dw8OPfOQjsVjs7W9/+zPPPIOQSR06dDwc\nQOrYiV5URVFKpRIV2IEGUVntaDRaLBYMw8TjcWpRRLospmWUxgUCAcSjUJbGcdxwOIzFYj/M\n5Z578snvfuxj/8+v/uqfvP/9P3zHO9YOBwRSgUAAYi+knOzs7EwmE1mWsYe9dOkSIcTr9Q4G\nA6PRaLVa5/O5oijRaBTByMhP3tnZoR7PUCiEMQ+o4f7+viiKi8XiUQg60QLxfrPZLJVKYVyH\nbSy20mC9uPLD4TAcDqPjlT6cmpeZY8DESggRBAGJJKBxsiyPRiMtRaZvAOqlhWfCarUiTdrt\ndq9Wq0Ag0Gq1sIgvl8s4E30hWOAOh8Nb0+wosQvM54QQl8ulKIrD4dC3sToeNdyZ2L373e/+\n0pe+1Ol0vvKVr7znPe959tlnf/7nfz4UCv3SL/3Sd7/7XX3KrUPHQ4Db7mErlYooijheq9WQ\nW4bPZhgjEJYRDodpKacsywgEns/n1PRANEs6QoiqquiVOnPmDD6bM5kMrRZYr9fL5ZIcdxWQ\n45aCaDSKLR5GSvA9IBsPLxImDDgw8L/UD0s0QSeBQMDtdo9GI4wDZ7OZlsYpilIulx9uYhcM\nBsGiUqmUJEkej6fRaGD1TPPk6O2Yz+e4XDQjGot4+k5QFGUwGGAbi3Gadi5bqVTOnz9P3wmC\nIGDmZzAYcNp0OgUdZFm2Vqvt7OwoioK3Afa/8EoTQjKZTK1Wc7lcs9nMZrOdcLxGIpFWq6Uo\nCkml+EaD47h2u80wjMFg0P0TOh41vCrzBCHEarW++93vfuaZZ8Dw3v72t3/pS196y1vecuuH\ngQ4dOh4sgM3c+m85n8/HYjG0ebbbbYy7oHlyOBxer9dgMPR6PcrYAKfTSQVPELAPh8MTdeww\nXrhcLrAEmmO8WCxoRJk2II1lWXC1dDoN0d5gMEA7Av7zda97XbFYNJvNKM+o1+t2u51lWfxS\nNOjk8PBQu4d1uVxut5sWJBBCarWaIAi3rqQfJjAME41GK5WKxWIJBoNGo7HZbMIAcXh4yHEc\nBHbYll6/fj2ZTK7Xaxw0GAy4EaBltFYEJIwQYrPZsKzHBhYyO5wMpg5pHV3RYi07m81Wq1Wt\nVnvNa17DMMzNmzdNJhPmx51OBz8F+dV4121sbAiC0Gw28f4hhEQiEUmSer0eSaVItep1uxuN\nhsPhEEVRn9jpeNTwaokdhdVqfe1rX/vEE0/A7gSthg4dOh5cNBqN9Xp9K5uBjRQnYMW2XC4x\npJckyWKxuN1ujuOo9JbOYILBID5xFUWxWq39fl9L/hiG6ff7Npvtxo0bhBC73X7z5k0o6w0G\nQyAQWC6XBoMBzRN4zkAggJUcjJMHBwfYtPb7faPRyHFcMpnsdruBQGAwGIRCIUR1bG1tgT0g\n6MTr9dbrdexh8/k8onFv9cNGIpGHPsUJaXaEkHQ6vV6vG40GbMvgQH6/H2SaHE86CSEYsiJM\nTitJxA2iFmns3OnxSqXVKiEwAAAgAElEQVSCG0QIoWV0kiRBjYexn8vlggmj3++bzWbI7ILB\nIIigJEnoqzUajXDvYg4HwkffezabzeFwNBoNkkoRUTzldM7n81AotFgser3eiXR9HToebrxa\nYqeq6tWrV3/rt37rNa95TSqV+tCHPnTz5s0PfOADf/mXf3lXX58OHTruNtAHTz+bgdls1mq1\naEUsipuocH4wGIiiaDQatQI7hmEgsOM4Dp/r8DDSj3aA47jlcpnNZlE4EY/HJ5MJHms0Gi0W\nC/rmwQPw8b+7u4ufAh724osvMgyD8AtZlsPh8GKxEEXR6/Wi4YAQMplMtHtY5GWYTKZYLCbL\ncrlcXiwW0+n0URPYAcgdFEUxlUoNBgPMYoPB4Hg8FkUxkUggyoQQ0mq1sPsGn8NBmF4JIVQ3\nSVXXGM0ajUYw+/l8vlqtMBOl0zVCiCzLSKUmhIDQw4XTaDRsNttisYD9BeRSK7MbjUaSJE0m\nk9ls5vF4QE+Bf5TZJZOEZc+7XIQQUMYTNRU6dDz0uDOx+9u//duPfvSj2Wx2b2/vE5/4RD6f\n/8AHPvD1r3+93W5/4Qtf+Jmf+Zl78Cp16NBx93DbBLt8Pm+1WlEnVavV8OmLT3So2ofD4Wq1\nikQidM2qKEowGFwul+Px+IQkS/uh7nQ6GYbZ2tpCKJrX66UnQ2CnqiqtroIMf2dnZz6fWywW\nKPdrtRqK6rHX29vbu379OiEEeSioChVFEaxUG3SSzWah5RJF0WAwgNnQF4bZ1UMcdEIBrtZo\nNFKplCzL6NhFtl+j0Th79iw5drbO53OHw2EymabTKW4TlccRjRkCgjZyLIjkeZ7Kr2u12vnz\n5/E1z/O4oaDvOAhSaDabzWZzrVbDXSOEtFot3O6DgwMcgX/CaDQOBgOn04kcbPpL/SOx43kS\nDnvGY5ZlEZdjtVp1mZ2ORwp3JnZPPvnkZz7zmV6v9/73v5/yuXe84x0nTO86dOh4ECHLMnT0\nJ47n8/nNzU18+tZqteVyuVgswOEcDgd1mJ4Q2CEdjS7IRFHEbE97DsYwtEY2/eu/fvHSJUII\nPI84Tndn2LsZjUZVVZE2NxqNVquVKIrj8RhxaBcvXjw4ODAYDI1GA6koNpstk8lgdNdoNObz\n+ebmZj6fh8CuUChgABmLxbRbV/TGgvQ83OB5PhAIVCoVnucjkYjVam00GtFolGGYarUKJyw9\nuVQqxWIxWBwIISaTCdHT+K6qqkajcTqd0jQ7nucxjcONrlQqFy9exLewz8WjqG96uVxKkiSK\noizLtVoNgdKdTgchLIQQOm8LBoN2ux1P4vP5oA2lr0TrnyCl0sbGBjy/qqrqMjsdjxTuTOze\n//73/8Vf/EWn0/niF794//C5+XxeqVQmk8mJzxUdOnT8i1CtVhVFSSaT2oOqqhYKBcxOxuPx\ndDoVRXE2m4GiybLsdDp5ntcK7IAT8RZYyVF/KzAYDHw+3/7+PsMwXq/Xu79PGMZsNjMM4/F4\nRFFkWRayKjCDSCRy9epVOCQIIf/wD/9ACDGZTDDeulwug8HQbDY9Hs9sNkPoriAI2j1sOBzG\nfBFr1kKhIMuyJEm3CuxSqZR2a/wQQyuzUxSl0WhEIhFMNxmGwagMxOvGjRvwrECwiDA5yvwY\nhkH4MEgY0bSH4UoeHBxYLBbcSjrcXa/XNPSOUkP4J2DjQOo13kuz2QwPZBgGf2zwPM/zfL/f\nXywWeKsQQsLhsCiK/X6fpFKkXE6lUvP53Ofz4Wnv0WXVoeM+wJ2J3Re/+MWf/dmf/bHzOVVV\nX3rppQ9/+MPZbNZut9vt9mQy6XK5bDZbNpv90Ic+dPny5R/vK9Sh40FEsViMRCIn/oHTOF9C\nSK1WMxqNtNAd9QPIHkskElqBndPprNfr9G8tk8nkcDgMBoP2ry/856lTp5A2kgyFnJPJYGMj\nEAiYTCar1cqyLBLIkJGGkw8ODhiGwetBsQTGNqIobm9vo5YKGkHsfNfrNZJ1CSHYwB4eHiYS\nCYvFslqtGo3GarWaTCaPpsAOQP+EqqrpdHo2mzUaDavVarFYkAYXCoVwGqwMsVhMURTM2DBM\n1XaNgMZRqrder8G9sHXtdruyLENmhzw83FZ0y+IhFosFGj5BEGazmdlsXq/XGxsbVPdJV66b\nm5uTyQR/ZqxWK6vVSr/lcDjsdvs/+idKJSyUnU6nJEnz+ZzWVOjQ8dDjzsTus5/97J/+6Z/e\ng5fyIyAIwvve977XvOY1/+2//bd+v3/q1Km3ve1t73rXu972tredOXNmOBz+/u///oULFz7w\ngQ9opTw6dOi4I15JYIe1Fzl2TlitVkjrsLtEVnAkEqE7U1VVUd9O6yKgmRNFUZt1gif5/9j7\n8iBHzvL8ry/dtzTS6NZcO97TZ3YXXzgGQ5xgwuEikFDELo5UAFMURQKGcOVnOwmuCoTYSQq7\nElwUCUe4g4vLuDDYXvCx9np3djTSSBodo5E0o/vu6/fHw3w0WuNdg3ft3e3njy1NT0+rR907\n39Pv+7zPY7FYfiXDz+cZVa17PLCjG41GiqJAyYe1nxCya9cuxJUyDAMBHyFkMBgYDAZVVQ8e\nPJhKpRRFGY/HgiCg+xYOh3Hyg8FgfX0dDnbow+ZyOWTXGgwGGnJKCGm1WvV6/XwQ2AHxeHw4\nHG5ubkajUYZhRqNRo9GYnp7GC0zCYmq1Xq+HQiG40KE4ZzQatcROlmXYFNMtqqqCq+EIlUoF\nbnao9eL20DbBRVHkOA7TObRoZzQaW60W3kgrs8NNUi6XYamtdWb49fxELheJRHDD4FB60U7H\n+YOTE7sPf/jD73vf+87AqTwH7rjjjq9+9asHDx782c9+VqvVHnvssR/+8Idf//rXf/jDHz72\n2GPVavUXv/jFdddd98UvfvHOO+98cU9Vh46zCOPxuFQqnUjs0uk01bAXi0VFUaCCIoQ4HA7E\ndiE6VvtTgiAEg0FaGhkOh71eb6IWKEkSz/OYhzUajYNnnhkbDH27HZ5ksDihI5aIgoUbGZzS\nYIwiSVK322VZ1mQyud3uY8eOoXvr8/kGgwHDMNo+LAqBm5ub1OjEbDabzWaqIARWV1ftdrvP\n53tBPtiXPux2u9PphB1JOByGQjEWi7EsWygUIHQjhODTHo/HXq8XGW5ku8CmPZogCO12m3I1\n5I+R7W5sPp/fu3cvPm2M40CBR++N4XCIhqwgCHR+ot/vl8tlZNfmcjnsabPZAoGA2WyWZdnv\n98uyPEHsflWxKxQ4QhwOR7lc5jhOEARdZqfj/MHJid3b3/72ra2thx9++AyczW/DfffdF41G\nH3zwwSuvvBL9IC04jtu/f//999+/b9++//zP/3xRzlCHjrMR+XwemWDajdAkgQbJslwul/v9\nfrfbRfEDbTXwM63ADi1aLMOqqqKRynHchItKv99HVinDMIFAwFQqNTwej9eLPHgcB9bEMDoJ\nh8Owu9u3bx8h5PDhw4QQq9UKIR3OvFgs2my20WiEKt1wOJwwOkmn0y6XCyKwTCYzHo+Hw+FE\ncU47LHKeAN1YQgjScsvlMh2Cttls2r+0x44dSyQSdBKZ8jYKjMF6PB58yfM87boSQrLZLEah\nyTZrV1UVQa7U6BgZsqPRqFAo4LK22+3NzU1cNQzW4OBzc3McxxmNRqPR2Gg0tra2aOIZ5ifU\neJyIIimV4NLncDjG47FesdNx/uDkxO4zn/nMJz/5yTe/+c333ntvMpnc2tpq/ibOwFmWSqWD\nBw9OSLAnwPP8VVddpRsm69Bx6shms9FodOJhKZ1Ow6COEFIul2VZRq8NK2u9XjcYDEajccLB\nzuFwVKtV5FKg14m6jjZXGgK72dlZjFXa7XZPo1H3eKanp81ms9VqRdQs2c4hVVV1z549jz/+\nOCFk79694/F4fX0dgfTItLj00ksJIa1WC7UfBFFMTU1B0UWNTlZWViC5Q791PB4PBgOtnE5V\nVZqKe/4ADJts2xQXi8VgMKgoSjabJYS4XC7sxjBMKpXCsDDY9olZ4dDA0BsJPsaCIID/4YC4\nspVKBd1VSZIQUocfMRqNmM7Z3Nx0uVwMw0CEh9Q4tPix59zcHE6g1+shZY7K7H41P2GzEZYl\nudz8/DzDMAjDwJ18uj5KHTpeSjg5sfP7/f/6r/+6vr7+zne+84ILLvD5fO7fxBk4y3A4fOjQ\noYlUognIsvzII4+cD1YFOnS8UHhWgV0qlZqbm8Pqi+INzEEIIWazWVXV4XCIWCqtwA5ZT+Vy\nGUs1x3FIi9L+twX9wgoNVZanXq97PMgHGw6H2hIOFditr6+jbZdKpbAdHV6WZRcXFyuViqIo\no9HIYrHUajWe52m5DkYn8Xh8bW2N9mGNRqPVanU6ndq/XahKnm8BidFotNFodLvdSCSCVqzd\nbjeZTJubm+PxGDEPhBCWZWFTTK/OaDTCFDM9FJg6HVDFPWCz2WjmWKPRgOmJNn9MW80Fe8P8\n7MbGhsfjYRjGaDTSgywvL2NP9IvBREOhkNbNzuFw2Gy29c1NEgxCZgftJiFEluVKpXL6Pkwd\nOl46mGxrnogbb7zxDJzHc+Pmm2/+xCc+cc0119x5550HDx6cKDDIsvzkk09+9KMfPXz48P/7\nf//vxTpJHTrOLgyHw42Njeuvv167EVWuV77ylfgSI7G0sgKXE0yVTjTjWJYNhUI0JKDf72Oc\nYmNjgzIAURRtNtva2hrLsjabrVqteur1lb17QQeREE+rQWi3QeAFEnb8+HGyTRokSUKTDuPw\n/X4f+acnGp3AOAN+LplMBi4tE33YTCZDh0XOHwQCAaPRCEVdKBTK5/OYk8hms6VSaefOnU8+\n+SQhRFGUXq/ndrtNJtNwOBQEAddRm9OlqirP891uFyZ22EjHqFVVzefzu3fv/ulPf6qq6vz8\nPGZvkUuL/TE/YbfbOY4rFoszMzNbW1uIA4ZQL51OX3311ThsLBbL5XKDwSAYDKZSKW2jZnp6\nulwu70skSC7ndDrNZjOEmwaDAUTwjH28OnS8WDg5sfuP//iPM3Aez41bb711aWnpK1/5ylVX\nXeVyuRYWFtxut91u73a7jUZjdXUV/3Xf8pa3fOhDH3qxT1aHjrMD2WwWwnntxlKp1O/36eRE\noVCQJAkGFtgSi8UeeeQRFOegtcK3tra2AoEAVnH0SRE7QStwGKvcuXPn008/Dcu6brPpbLWY\nhQVwOCjoaZKYoiiRSCSVShFC4vG4LMsrKyuEEOwpiiL8LNbW1mCZizey2WwQipFtoxMUINEF\nzmazo9HoxIhYCOxO42f9kgTKrvl8fufOnfPz84VCAW52xWKxWCwePHiQaELDSqVSJBLJ5XLo\ny+PzxGeOi2U0Gvv9vtPppNbTWnOTQqEAlSQhBGJNQgioPI6Dih2tE+/bt++JJ54QRbFUKtls\ntn6/X61W6ZnjbHmeNxgM7XYbPosYpg6FQrlcjszMkFyOEBKLxZLJpNFoVBRFn5/QcZ7gVLNi\nCSHj8XhpaenRRx+ldgZnDIIg/M///M8TTzxxyy23uN3uZ555BlOxP/jBD44cOeJwOG655ZYn\nnnjiS1/6Eg0x1KFDx3Mjl8uhq6XdmEqlgsEgaledTqfdbvf7fdj5EkIQ6oAQiPX1dbq0u1yu\nRqNBDcNNJhOE9rAmIdsBEmQ7ZhQdVVejwcqyPDNjMBgsFgvP81odraqqu3fvPnr0qKqqi4uL\nmUwGExvj8RiHuuyyy3BKCLmq1Wocx+3atYuK9NfX1+fn59PpNIxONjY2+v0+Jny1SRuiKBYK\nhfNNYAdobYpRVwsGgwgjEQTBZDLhgjIMc/To0XA4DNkcwzD9fp9yfdRu8S/trqL4CnsUQkg6\nnWYYBmOz2WwWkXFww9G2dLvdbr/fh+MJ7q5qtYqQCcTNYbe5uTlU+Pr9PigmlXuiRgsrO/yC\ngiBYLBZc5dP/ierQ8eLjlIhduVy+6aabnE7n7t27L7/88ocffvj+++9/1atedfTo0dN9fhQM\nw1xyySWf+9znMplMv99vtVq5XK7VavX7/Uwm87nPfe6SSy45rybadOj4PfHbBHaQo5HtSFYY\njBFCsMyDXcViMW0nzul02mw2qmHieR6tUq2vJMdxDMOUSiWWZRFZ4Wk0ZJ5PdrvD4XAwGNA1\nng5d7tq1a21tDdbEy8vLRqNRFEVVVTmOs9ls6AyOx2NJklwuF0gb7cNCTseybKfTQQES6bco\n6WnVXWgfU0nZeYVoNFoul0VRDIVCHMflcjlK7FRVpWI4juNgUwzFpKIonU6HPkVT4R3ZtiAm\n2x1zu92OF61WazAY4Oo0Gg182qqqWiwWWibgOK7b7ULEiQRbsq3Jw/2Aki0hxO/3WyyW8Xhc\nKBRisZjFYqHd2GAwOB6Pu14viF00GpUkCVXGRqNBqaEOHecwTk7sqtXq1Vdffd99983Pz//5\nn/85Nnq93oceeujqq6+GH9UZBmwUdBqnQ8fvjF6vV6vVJohdt9stl8taYocAeBTSbDZbKBQq\nlUrU006LQCBApw57vR4tz9Blezwe+/3+arWKEVpJkjz1etPr7fR6kGdhfgI/Auq2vr4uSZLB\nYOB5PplMDodDjuNYlh2Px+BqTz31FIwzQD0FQaDZaOjAptPpUCiEecxMJoO/GycK7FDXeQE/\n3rMFmDbDeEogEGg0Gk6nE4y5Xq9rq5j1eh2zCGS7u0ozxNB8R4Fta2tLm8mmJdDFYhHzE4qi\noC2LgAr6yeP+cTgcJpOpVCqhaEcIcblcKBNCZAnMzMzIsry1tRWNRsFEsd3pdFoslprVSgoF\nIknBYBDkHiepd2N1nA84ObG7/fbb0+n03//93z/99NO33347Nh48ePCRRx5pt9t33HHHaT7D\nX0GPFNOh4wVEJpMxmUw0OQpIpVIWi4UKzFG2EUUR6yLHcYlEolAowPNM+2RVq9UmoghUVYXy\nlYq0VFVFNUiW5V8lw9brw0jEaDR6vV4wAMoCFUUJh8PpdJplWa/XWygU0PtDUKmqqvv378cJ\ngxZgUHdxcZHGj2YyGQROgKdKklQoFHq9XrfbPdHB7vzswxJCDAaD3+9HuWt+fh5UKRgMGgyG\nQqGAjw4fO0YcPB4Pz/P4kFFRA3AzIAsEXobYTu1vCCH5fJ4KOukzgFa+CWpos9nw7vF4HByx\nXC5DToeRC2BxcRH9fWgu19bW6M0TDAZLgkAkiZRKPM8HAgE06zmO04mdjvMBJyd23/72ty++\n+OK/+7u/m9DiXHLJJZdccsmDDz542s7t19AjxXToeGGBPuxE2Rt9WGxEalOv1xNFEbRpa2tr\nenq63+//ygZ2ex11Op39fp8mSqFQZzKZMN9KnYoJIa1WCwdvNBqqqnrq9dbUlCzLyIOnQ5Tg\ngnv37kVWWDgcPn78uN1uZ1mWYRjkE2BCAqZoPM/j3WleAoxOMCAJgd3a2hqqPizLag2Ze71e\ntVo9DycnKKjMDn3STCYTDAaRAEGnYdAJPXbsWCQSwSAC9G30INgBawQ1KGEYhhqdYtqaZVkQ\n8aeeegplYMjs6HEgvpQkqVgsIqCW5/l8Pm+xWBiGwQMG9pybm8MobqfTQVuWGt2FQqGMJMHK\njhASjUbhZiyKom50quN8wMmJXa1W27Nnz7P2PXfu3HlmnIH0SDEdOl5YZLNZ7QABIURRFFS5\n8OXGxgZarvCuw3IO1pVIJLRaJZfL5fV66RIODhcMBmmhjhACQoZ8J5RYCCHeRiMvCLIsNxoN\nGKERQgRBwAvQR0LI4uJiMplUFAWlPkmSUPiB+7Esy263W5ZllmXpMC9GQDBQCQqYyWSsVisq\n/Vq/JCSMTVQuzysgf0JV1enpaY7j4BED+Rqa5tiN47iVlRWQLbQ1tV1XXDLcFRDbgREiUASl\nuI2NDVmWcTnW19dBuOFdR0+GYRjM69RqNVgTq6raarWmpqbgV0z9dCwWi9PpRKTE7OyswWDQ\nyuzWNzdJKARiB2kgZHalUukMT/7p0HHmcXJit3v37scff/xEz25VVZeWlugj8mnFaY0UO378\n+BPPCf0hT8c5hlar1Ww2JwR2a2tr4/GY1q4KhQJypZxOJyEELVpMwk78NRBF0ev1UgKH1R2x\nYBSyLIdCIbjfWSwWjuMYVXU1Gltut8/nQzUFVR9MO9pstlwuh+qOxWKBjy4OrigKpFrJZBIn\ng55gIpGgaq10Or2wsJBKpXbs2IGH0kwmoyiKKIonCuzOtySxCcTj8eFwuLm5CQ8aBIuJolir\n1UajEbV8R/89EonQZDlZlrXpFPSA9XodrA63BM0ZU1W1XC4jLEQURdipEELMZrOWIGLQFRVi\nUHYM4mC2RjuxNz8/ryhKsViMx+OKomiJ3Wg0kiIRSuxEUcSPD4fDRqNxmj5JHTpeIjg5sXvN\na15z/Pjx97///dohOELIvffe+/jjj1933XWn7dx+jdMXKba6urp79+7LnhNnTEeoQ8eZAcpX\nE4H3qVQqFovR8kmxWDQajTC2IITwPJ9IJDKZDMuyWoEdfEZovASYFso5RLPeq6oK+iXLsiiK\nsiw7Wi1Oklo+H2xpyXbVB0eIRCJINjOZTMlk0u1204FZhmHgYLe0tISN6PBiI9k2OpmdnaUF\nyH6/X6lUer1ev9+fkNNls9nzVmAH2O12p9OJv5zxeLzf77tcLlz6UqmEuhohRFXVbrcbCATg\n8AwWTokduqKEEJ7nZVmGXQ7t6dP3yufzdGyZbp9wPCGEuN1uq9WKbixIHrR6RqNR+xd+bm4O\nDNJms0mShOAynJXFYun6fGRtDV/a7XaLxYIOsi6z03HO4+TE7tZbb33Zy1521113zczM/PVf\n/zUh5O677z5w4MC73vWuPXv2fPzjHz/9J3kaI8Xm5uYQH/kc+Od//uff7/R16HhpIZvNnlim\n0hqdEEKKxSLqHDCwgEUFGmRagZ3dbpckCbOuZLsP6/f7tcs5voW8L4ZhUMzz1OsKy25ZrY1G\ngxqeUT3+rl27YFzn9XqXl5etVishxGq1Mgzj8XiojS3LsiaTCR4ZlILA6GQ8HsuyjPpcJpPh\neV4QBKvV6vf76YlVq9V2u32+JYmdCHRjCSF79+5FbG4gEEBUF6aMkdwKu7hgMAjaR/kWoG1x\ngqkD7Xabvk6lUgaDAffDL3/5S1zWfr8/oY1GsBiIHcxx8CxhMploahkhJB6PY6YHw7zdbpe+\n1/T0dN3hQMWOEBIOh202G4yUaTNXh45zFScndoIg/OQnP7nzzjs5jvv+979PCPnxj3+8urr6\n0Y9+9JFHHtHKI04fbr755kKhcM011/z85z8/cTxCluXHHnvs+uuvP3z48M033/x8D263293P\nCe3Evg4d5wByudwEm2k2m5ubm5TY9Xq9ZrPZ6XRQgIHbMDjczMyMVmDncDimp6cHgwHWdTCz\nCekVIcRut6OuZjKZsI+/3W663VaXS5KkXq8HAR9d9Q0Gw2g0Go/HU1NToF9o7cmyTEs+7XZb\nURR0iqenp+n/U2p0kkgk8F5wsDObzRN0NpPJeL1eWnY6bxGLxVAJi8ViDMMcO3YMtnaFQsHp\ndIJzg6kfPXoUVTSw8EqlQqee0aDH32ct4et2u7RFDokbAuKQC4IfmejGwIu4UCiEw2EM1sAZ\nG+9C1ZyQ2UmStLa2Nj8/D7M9fCsUCm0YjZTYoRsLqV9ue6MOHecqTsmg2GQyffCDHywWi+12\n++jRo1tbW5ubm7fddpv2sey04tZbb/2zP/uzQ4cOXXXVVVNTU/v373/1q1994403/tEf/dGB\nAwcCgcD+/ft/9KMf6ZFiOnScFJubm51OZ4LYraysOBwO6kyGvCZCiNfrJYRYrVbMIpDtyUeK\n4XCIFZcQwnEcVnQ6IQtgLYeUnmVZTEFGRqO6x2Oz2Twej7qNwWDAMIzFYikUCjgZnufdbne7\n3WYYBrXDAwcOEELK5TJU/OATe/fupe+FDiwEdtiYyWTG4/FoNDrR6OR8noeliEajVMVos9kK\nhQLIOngYbdkbDIa1tbVwOAxmj4odqndkuy6L20NbpVNVFXcRIUQUxUajgesyGAyuvfZa/KDN\nZtMS7na7PR6Ph8MhLAzRu7fb7XjfI0eO0D3RRl9bW5ubm5uQ2eVZFlZ2+AWbzSamsxuNhm6e\noOPcxvOIFCOE2O323bt3u1yubDZLHcbPAPRIMR06XihkMhmn0zlRptLOGZBta2KO40CbDAZD\nIpFYWVlhWVZbpIHCna7ioFl2u12rmsCe/X4fHrb9fh9bHNVq3ePpdruiKIJEsiyLycdYLJZO\np1G929jYQPPUarWyLGuxWPA8idUdcQKEkF27duHtYHSCiDMUIHGGg8FggtjJspzP589zgR0Q\nCASMRiO6scFgsNFoYP4AQxX0GQDSyWg0CmqOq6yV2eHGAOHTutxR8kcIWVtbw/yEFmD8dJ/B\nYGCxWEwm0/r6+vT0NMgiHhuMRmM6naY/ODMzw7LscDhEFZAa5geDwZrVCis7QkgoFGJZFuU9\nRVEwba1Dx7mKUyJ2Dz300M0337y0tEQI2drauuyyy2ZnZ51O5/vf//4Tp2VPE/RIMR06XhDk\ncrmJMhWaWVqBXaFQAM1C2Guj0YjH4+Vy2eVyaSNibTYbx3FU9oRuHYTzRFPCAf0SBIFlWaPR\niB83FYvtqSnE0aJ1C2cNhmHi8XilUhkOh0ajsVQq4bAmk0lRFBoskc1mUekBlaT0AkYnxWJx\namqKLvZms9lkMvl8Pmregd9xIjT2vAXDMOFwGMRucXERziCIWC0UCtREBo6DBoPB6XTCi5j8\nZgUXLVq81n7U2irAysoKLQE+9thjaKD3ej1o+OhuHo/HZDIhNNZoNJLtOWu73V6tVuluiURC\nURQ8b3i93nq9TunmaHpaZVmSzeLEAoEAvUl0mZ2OcxsnJ3bf//73r7nmmi984Qt4Lv/Upz51\n+PDhV7ziFRdddNG//Mu/3Hfffaf/JCcBd6V4PG4ymZaWlp566qmJiV0dOnQ8K1RVXVtbm+jD\nZrNZRVEoxRFFsTCjDQMAACAASURBVFQqYTGG+QjGVLvdbjgc1v5fg0scGlugAoQQrSctlmqY\nVkCrB55ns1rdjYYYiyEtCosx6nbo2Npstk6nYzab0RkkhLRaLRo4QQjZ2tpSVRUa3927d9NT\ngtHJysqKtg9rNBoFQTjR6ISSBh1UZre4uEgIOXr0KOYnQK0IIQzDoFB37NixcDiMT57nee3l\nxp2Aiw7RJL4FiSRe42pCVHfkyJGFhQWkm0w0WyDrxPwE6nnopQqCAPEldrPZbA6HAxaMOPNi\nsYiz9YfDo+3EWEJIJBKhZYhUKnU6PkMdOl4iOKVIMbPZ/NOf/nT//v2Konz1q1+97LLLfvzj\nHz/88MMej+fee+89A2dJCNnY2HjPe97ztre9DV/2er0PfehDdrt9z549F198sc1me+tb36oX\n2HXoeG5sbGz0+/2JMlUqlaJzBoQQLPC0vWUymYLBYLVaVVV1QuSOTFi8FgRBkiSO4yCE0oaK\ngrHB7hi8MG4wCONx3eNhWZaWfDqdDkp6pVJpdnYWlsVzc3PD4dBkMqFjizPvdrvgEKjiXHTR\nRTgCjE5isVixWEQBUlGUXC7X6/UGg8FE11UX2GkRjUbL5bIoilarVRCE1dXVYDDIMEyhUDCZ\nTCaTiV5QalNMCDEajTR/gt4J6KFrq3Tj8ZgKsmE6A7LY6XQOHDiAH8TNRoHwt0ql4vf7MfeA\nVDFc92PHjtE95+bmWJbN5XK4vnQ2IhQKtT0eLbGrVquCIPA8f2Z89XXoeLFwcmJ37NixG264\n4eqrr2ZZdmlpqVKpvOUtbyGEGI3GK6644sw8+mQymX379v3bv/0bGkOqqr71rW/99Kc/7fV6\n3/KWt7zjHe/Ys2fPl770pQMHDmhFuzp06JhANpv1+XwTY0+ocmn38Xg8mFElhBiNxkQiAWPY\niamIdrvdarXAzKijGNZprZF4u90GI6SpEszqqsowOYbpdDp4F6QIoNmazWbRz22326gMud1u\nlmVpPgQEdizL9vt9QRACgQD9RYxGY6/XMxgMyA0rlUrj8Rjux7SNSwgZDoflclkX2FHAKGp9\nfZ0Q4vP5KpXK9PR0r9fb3NwcDAY0PhjxISjfEkJEUWRZdsIbAUMtNEoEwBXE60KhgGEXVVUd\nDge1u9N2dVGghY0ipnN4nuc4Do375eVluicmedvttt/vZ1mWLkm/ktltd12j0SieVRRFGQ6H\nZ1IjrkPHGcbJiZ0sy7T58qMf/YgQcs011+BLr9erjQs8ffjQhz5Uq9Xuvffeb37zm4SQBx54\n4Fvf+tYNN9yQTqf/+7//+5577jl8+PBnP/vZfD5/Znz1dOg4S3GiwK5Wq9E5AyCbzWKpRpet\n0+kkEom1tTWbzYaFnxCiqqrVarVYLPAcIdtS+vF4TDtx2FMQBMy6chwHQRXHcXwu13G7BZtN\nFEW01VDCYVnW7XaPx2PwBpoTBc63b98+HBOLN+iplpyl0+n5+Xn8C5aQyWRsNpvVakXIKd0z\nk8kYDAaaSa/DYDD4/X582jMzM7Ag6fV6PM+XSqX5+Xlaset0OsFgEC111NLoBwsqRmdOtc8P\nqObi9dLSEvWsefrpp0H6O52Otns7Ho9dLpfNZisWi6FQCMaEo9FoNBohHoMeOZFIoMe6vr7u\n9/trtRq+DAaDW3a7tD1pgaN5vV7cmXqekI5zGCcndgsLCz/96U8xvHbvvfeGQiE0Psbj8cMP\nPxyLxU7/SZKHHnrola985dvf/nb8sT506BAh5NOf/jR1rmIY5n3vex96xGfgfHToOBsBP4gT\n+7Ber5fmPqGU1ev1UHTBOg07DL/fT8ddYUqCNE+yPSFBttNCyW862DEMMxqNEO1KCHG73Z5G\noxMIINkdCy0qKKimRKPRSqXC8/yOHTs2NjY4jkOOBZLECCFY1/GOVHUHoxM42GnNisEnJujs\nysoKBipfqM/2HEAsFoMADhLMZrPJcZzb7S4UCtFoFJ82GHa1Wg0EAggRxn1CD4LrDidqhmG0\nfXZK+NbW1oxGI751+PBhVFJHoxEM8+ihXC6XIAgQ+SHrArcKvIjpngiWIISsrKxccMEFiqKg\n0+p2u3s+n7odR0EICYfDUPIxDHP8+PHT9DHq0PGi4+R/19797nc3m809e/YsLi4uLS3ddNNN\nLMv+5Cc/ufzyy1Op1I033ngGzrLX69FRO0IINLy0OwAwDDM3N6c/h+nQ8duAvqS2I0lOCJzI\n5XI8zzcaDbvdjmIMIuElSbJYLNp1t9frgfkRQqDBMhgMdOHEPlj14XLMsiyKcxaLxb21VbHZ\ner0eSoMWiwXZFYIgrK+vLywsNBoNTGwgkJRhGKfTiYOjckO2NXmUp8LoxGQyDYdDDHKOx+NS\nqdTpdPr9vpbYqaqaSqVo0UgHgPwJVVXxpzWVSoG9FYtF6O3INm87duxYJBJBD91kMmkNq0HX\nMDM7HA7pndDtdinJa7VakiRBVFev12kW3ITMDiwf8xN4C+wgCAKmJeieiUSCZdlsNov5iZWV\nFUIIwzDc3By/sUG2CWU0GoXMDt7LL/Tnp0PHSwUnJ3Y33XTTpz71qW63m8/nX/va1956662E\nkIceeuiJJ554zWte88EPfvD0nyS5+OKLH3zwQVp+h0Ppo48+qt1nOBw++uijVEatQ4eOCWSz\nWW1CAyFkNBrl8/mJPuzU1JSiKBA2mUymRCLx1FNPEULgEkz37Pf7GFQkhNCRRjx00aYbRPQm\nk4lhGGpysbGx4Ws2Gx7PcDhEFefSSy9FdkUoFKrVarOzs8gbwDgUCCUtwmHZxqxuIBCgp0SN\nTqLRKPgiLFEURREEQdt1zefzg8FA+1vrIITE43EY11ksFovFUiqVgsEgRlNZlkVVDOQ7l8vB\nphgzDfRyk23mh2YoUt3odq/XS/cslUpzc3MItKB8juM4rddxo9Ho9Xq9Xs9qtdJZDYZhIOvU\nzk/E43GWZev1+tTUFM/zVGZn2bWLkWVSLOLLSCRSqVR8Pp+iKNDwnb4PU4eOFxEnJ3Ysy378\n4x+v1Wr9fv/b3/42Kmdve9vbstnsd77zHeT5nG585CMfabVaV1111Xe+853xePyqV73qj//4\nj9/97ndjvSGEVKvVv/iLv8jn89ddd90ZOB8dOs5GZLPZCaOT1dVVnue1NbxsNmswGFiWhcqt\n1+vF4/HV1VWDwaAVNplMJrfbjQYr2Z5OpSYUtOlmNpvRzKW5ogaDYTweu+v1UTRKfwS1HEEQ\n4G8CY7xYLJZMJgkhtVqNEHLw4EEcE4aa+EN02WWX0VN6VqMTu91utVonuq7JZDIWi+lRgROw\n2+0OhwNNj3A4LIqi2WzudDrj8bhareLOgYpua2srEomgXouK7ESIHG3OanWNBoOBTl4fPXp0\n9+7dIHClUgkPBp1Oh7ocE0K63S7P80ajsVareb1eQRCGw6Gqqo1Gw2azaZsz8XhckiRVVTc2\nNgKBADW6c194ocqytBsbDocZhkGQsaqq+mysjnMVpyoxUVX12LFjX/va1+6+++7vfe97hJBE\nInHGDIGvv/76//qv/9rY2PjTP/1Tv99/8ODBXq+XyWQuvvjiubm53bt3RyKRb3zjG69+9av/\n5m/+5syckg4dZxckSSoWiycK7GZnZ+mq3Ov1arVas9mkejhJkmKxWLVa9fl8qMYBZrMZVTHo\nqGBpe2JyNI6M9RgT6x6Px9LrGQaDqsOBVd9gMKysrECJ3+v1kAZGCNm7dy9trRoMBqoChLss\nJrroOAWMTqanp2u1mpbYybIsSdKEwC6ZTKJnp2MCVGYXi8WQ5dVqtZxOZ7FYpI8EoiiORiOb\nzWaxWDiOw5d0Mpr+i+oaz/N0Prrf7+MxAIGtwWAQ2w8fPox5536/T1kdIURRFL/fj/mJcDhs\nMBja7TboHeIx6J5erxc0/ciRI4uLi5IkQZQZjMU6dvtgW04Hm2KagPfMM8+c3k9Th44XCadE\n7H7yk59cfPHFl1xyyZve9Kb3vve9r3nNa+bm5m644QZtMfx046abbiqXy5///Of37dtXKpV+\n9rOfYXsul9va2nrd6173ox/96P7775/w2dKhQwdQKBSQ1kW3qKq6urqq7UjCy7fVamF4UBCE\n6elpiKWokxnQ7Xb7/T7U7lgpqYRO25hDH40QEggE0JUbjUb+dlslpGw2gwFcfPHFqJ0gP2Bh\nYQHUDeMUU1NTDMPQ06aMczQa2e12ShpgdNLpdJxOJ0JmO53O5uZmt9sdDofaydlarVav13Vi\n96yIRqOohIVCIVmWNzc3MadcKBQmJoiPHz8eiUTw9xaknHIykDyw9vF4TK9Rp9NBJU9V1Xq9\nDl86QkilUqEXSJtXQQgBXSsUCnDOg+ySEIJ2Kq3MMQyTSCR4ns9ms+D6KOu63e6W291fWqIH\njEQim5ubMMqmjnc6dJxjODmxO3LkyJ/8yZ8cOXLkhhtuuOuuu77+9a9/7nOfu+666/7v//7v\n2muvLW7LF84A7Hb7O9/5zoceeqhcLo/H4/X1dbzY2Nj46le/+spXvlKfcdOh47chm81OBC2U\ny+VOp3OiwI4QAokFHOzQD0VCPHZDdxWNM7K9kCNbgmgmJ7DSa5d5hmGazWZ4MOg5nWNBwDIP\nEslxnNfrRXUNmRNwzkO1j6aL4g8OOncTgRPz8/MrKyuUsa2urmL00uFw0BB6Qsjy8vLU1BSt\n/+nQIhaLNRqNbrcbDAZVVc3n81NTUyaTqVAoUIUclHArKyvhcJheXG3SK8py6I1SOxtCiCiK\nJpMJPwJahkKdoiiUz9Enc+zWarW63S4arJifwIVDvXZCZoceMeLO6PzEOBQSNdmykUikWCzi\nIGjx69Bx7uHkTOijH/3ocDj8yle+8p3vfOc973nPG97whltuueWHP/zh5z//+Wq1+uEPf/gM\nnOWJ4DguGAxOT09rywM6dOj4bThRYJdKpaanp7VmY5g2oG3T4XCYSCSOHz/OMEy9XteGTHi9\nXlmWtU4lsDUhGoEdFml8F5Z4KPvZq9WWz4dcUY7jsAabTCaDwRCLxSCoR04AlmqGYShde+KJ\nJ8i2+/HLXvYybITRyczMjDbxNpPJWK1Ws9k84UKcTCb1edjfhkAgYDQaC4WC2Wx2uVySJDmd\nTlEU6/X6YDDw+/2EEFmWOY5bX1+HTTFuGBodS7ZldnRUljoaEkJcLhft1x85coRSc2oX3Ol0\n8AIPCa1WC+xfURSU2aDmzOfzBoNhdXWVHjkej8OGGlYsvy7mzcywGjVeNBrt9/uhUEhRFFEU\nz4wPqw4dZxgnJ3aPP/74tdde+6Y3vWli+zvf+c5LL7304YcfPj0npkOHjhcMqHCfSOy05bpW\nq9VsNhuNBg0AlWU5FouVSiWHw4ECDPZEZhQVVNGE0IkxQ1EUjUajLMsOhwNM0WazsSxrWV+v\nOZ1owy0uLq6trTEMMxgMOp3O/Pz8oUOHMB6rqqrT6VQUBd1YHDObzRJCRqORIAi0zAOjE1SS\nICJUVTWbzY5Go+FwqBXYdTqdUqmk92F/GxiGCYfDkNmFQiFQ82azCT85UGRVVWVZbrfbaM6q\nqoqa3ASxo0m+COrFtxRFwXVnGGZ1dRXpIISQZDKJqwkva7Jd9x2Px1ar1eFwlEql6elpg8FQ\nqVQMBkOz2QwEAtqSm9/vx9k+9dRTi4uLv/a43rHDrImadLvdVquVPszoMjsd5yROaSp2QnBN\ngRjHF/iMdOjQ8UIjl8uxLAt9EoBpAy2xW11dtVqtnU4nFAohJwACu16vd6KDXbfbRbGczpZi\nHdVW0CVJwuwqjfxqt9tWq9XbbNbdblRi9uzZI4oitHrtdnthYeHYsWMsy4Jb4OC0rqOqKt5l\nQiyYSqVCoVA+n5+dnUUxr1qtdrvdXq8nSZL2z9fKyorNZpuwwNShRSwWozI75ExsbW1NT09P\nyOxkWW42mz6fD9cOCwEdoaDuhtgTgxSEkFarhZqcqqpbW1v0xkDCL4p/5m3xJeDz+YxGI9zs\neJ5vt9uBQECSpJmZGcx2YDcIMZFyCyNreCY49u2zt1otTRReJBJBoQ4N5dP4UerQ8SLh5MTu\niiuueOihh04kcN1u9+c///mVV155ek5Mhw4dLxhyuVw0GtXmt6ZSKZPJpKV6uVwOgZ5Ynk0m\nE2JbCSG9Xk+71rIsOxgMaMuVaOiddh/U4ch2V47juNFoZDAY3FtbDa8XlsVYWd1ut9vtdjgc\nU1NT9XrdYDBA2I7CIfU0qVQqME4jhFxxxRXa32V+fj6VSmkDJ2w2G2Jktd7my8vLi4uLZ2yc\n/2xENBotl8uiKIZCocFgUKvVGIZxuVygVtgH3Ovo0aM0qA2Xm7qZaN3sJEmSZRlb4HSNG0yS\npHa7jeKZoii0lY9AC7L9kDAajcbjMWglci+QbwbWqA2NhcyuXq9bLBYqs7Pv3csoyua2MRYh\nJBKJlEolqEg3NMU8HTrOGZyc2H384x/f3Ny88cYbtYKGZDL5ute9rl6v/+3f/u3pPD0dOnS8\nAHhWgd38/Lw2IiKbzaLfihlVCOywOmqJHc/zLpeLbDfL8MhHLSSoIS0W736/bzAYIHhCY65f\nLpt7vV4wSAgJhUKpVIphmOFwyLLswsLC5uamoihOpxMEDtEUlDX+4he/wAuGYeiv0+/3y+Wy\nx+PRDoJgvNdgMGgFduPxOJfL6QK75wbY2/r6Orrhsiw7nU4kxlosFjp8g6lSRIMQQjDiOqF4\npso5bdCc3W6nMrunnnpK66ICNd5EKl2z2ex0Or1ez+Fw0J4vOrksy2qTweLxOIhjrVajMjsm\nFlNZtn3kiPYXrFQq0WhUVdV+v6818dGh49zAsxO7V2rw/ve/3+/3f+9731tYWJibm7vyyivn\n5uZ27tz5wAMPXHbZZffdd98ZPmMdOnQ8LwwGg0qloiV2mDbQ9mHhDNJoNKxWK1ZWtDtzuZwg\nCLTiQgjhOA6NWm1VBhI6LWhmVCwWw3rMMIzJZJpqNgkhNYeDEHL55ZeDMnY6nUajMT8/f+TI\nEbJNCp1OJ8Mw2pNMp9M4snamFUYnzWYzGAyi/CPLcj6f7/V6g8FAS+zS6fRzCEt0AAaDwe/3\n5/N5o9Ho8XicTqfRaByNRqIoVioV0L7RaKSqaq1WC4fDaKmLosjzPO4ceqvIsgwOx7IsLeYZ\njUY8BoCcUQUk8i0IIXSgATwPJthGoxF9fI7jcrkcpjd8Pt/6+jp9u2AwiHc5fPjw4uLiaDTq\n9XpEEEY+3yiZpL8gbIodDgdoKI2p0KHjnMGzE7unfhNbW1uICW+1WsvLy61Wy+PxeL3e48eP\nf/nLXz7DZ6xDh47nhWw2KwiCVlhWKBQm3N2y2azD4YDxLCHEYrFA/9RqtSZsh0ejEbU+Qf1G\n2DYu0RZsqP8FXdG73a7ZbJ7udvtWa5fjGIZBlIXP58Obzs7OQsy+ubmJ2ViynR8I0AoQdT8h\nGqMT2ofN5/OyLKMKqJXiJZPJ+fl5bT9ax7OC2hSHQiGj0Tgej2u1mtvtpjbF1JLQ4/Hg+iIU\nGLOr2ukHyuHoXURtigkh1WqV3paVSoVOyWA6B9tVVUWbvlgshkIhFIAdDsdwOFxcXBRFkY5Q\nsCwLvcGEzE6ORpXt8AlCiCAIfr+fuvMsaVzudOg4N/DsxG7z+eAMn7EOHTqeF3K5HFLS6ZZU\nKhUOh7XCOBA7su03YTabE4kE3E/gPUv3xKArnYQlhKAzS6kYIYTjOEVRkCSLxi7MTYbDoaVc\nbvp8hBCn07m0tET18nNzc4PBADGgOE673eY4ji785XKZuuJdcskl2IjSI2Rh2j6sw+EwmUxa\nWaGqqqlUSp+HPRVEo9FCoaCqajAYHI1GrVZrc3MT07J0fgLF0WQyGQ6HMY+MW4I25TEljY1w\nSMH2wWAACxuM01JnO1VVHQ4H5mdpwxdfwugEIj+GYfr9fjweB+EjhMDvEIjH4xzHbW5ums1m\no9EIC0Z+ft5aq2mLypFIBKO+hBBtNJkOHecGnp+jb6vVOnbsGJIcdejQcVYgk8lM9B9XVla0\nLU5VVXO5HJwm0AhrNpuzs7OYYKC5n4QQlmVBBzH5KIoiZFhk244YuymKYrFYZFn2+/0IdzKZ\nTDzPD4dD5+Zmw+tVVfXyyy/Ht+r1ervd3rlz5/LyMsuyHMcJggD6CANb4Je//CVO1WQy0YW/\nVCqhAmSxWCgFhPqKYRhtSXJtbW04HGp/ax2/DfF4fDgcbm5uhkKhVqsFWo9gMfohY9YhmUxi\nWFVRFPRbtc8PDMNgipkQQsdXoZCjV/CZZ56Zmpqidw7eC/cVPQ4GnMvl8vT0NDSdoHTQCUzM\nT2DAolwu+/1+PFQYFxfdrZY26TgajeJ3wRnqMjsd5xhOidi1Wq2Pfexjfr/f5XLt2bPH6/V6\nvd6PfOQj9P+qDh06XpqAXYVWYNdut6vVKm1cEkLK5fJwOGy1WiBtaK7F4/HV1VVFUbQGsxh0\nRfkNYBim0WgQTeAEXqOMRws8o9HIbDY7HA5PvV5zOBiGQQnH6/WCIO7YsQNCeFmW4V5GNPOw\nZFsLhaFIujGdTgeDwbW1tR07duAEBoPBxsZGp9MZDAZaB7tkMhmPx09Ms9VxIux2u8PhyOfz\noHE+n89kMuFCj8djh8OhqirkdEhxpTVdk8lE2+WAJEm4nWRZpk15u91O52+SyeTMzAx+fGtr\nC08R9FkClGs4HHa7XdhZg/Ch/JZOp0Oh0ObmJmVmKB8SQp555pkdO3aMx+NWq0XicU+7vb6+\nTs8qEon0ej2fzwdfZfSddeg4Z3ByYtfr9Q4cOHDbbbdxHPf617/+3e9+9xvf+Eaj0fgP//AP\nBw8e1J27deh4KSObzZrNZmoYRghJpVI2m027JZvNejweSZLA4RwORywWkyQJziNkW8aO4hwy\nvsi2oo72c7XETlVVPPVhAaa1vfF47KnX6x6PyWQ6fPgwIcRsNtvt9pmZGayviqKg8oc/LHv3\n7qUHpN5jl19+ufZ3mZub0w6CZDIZJNObTCZtwS+ZTOp92FMHZHYGg8Hj8aDr2uv1DAZDsVic\nnZ3FvAusB5HiyjAMngHG4zG1IybbDVZCCMMw1BZYFEVK8qrVKgYyECCLn6U9dwDSPYfDUa1W\nPR4Px3Fra2sGg6Hf7y8sLKiqinBhQgjHcZFIhOO4VCqF0NgjR46QRMLaaFQ06Zdut9tisQiC\ngE6xtuanQ8c5gJMTu0984hPJZPLDH/5wLpf7xje+cffdd//v//5vNpv94Ac/uLy8/MlPfvL0\nn6QOHTp+R8DoRMu6EDih3QLyR7adJiRJmpubg6cxanJUDo/dZFkG/VJVlXoX08IewzAsy/b7\nfYvFglqIIAjoeUmtlq3brXs8u3fvXl9fh+vYYDDYuXNnMpkEU7TZbDBAsdvtVJiVzWZpLi2l\nazA6sVqtsizTrmsmk3E6nRDt0d+xWq02Gg2d2J06otEotSkmhAwGg1KpFA6HUaIjhIiiaLPZ\nFEUZDAao4RkMBjRe4VaD40B7hxuJEr7RaASHQsguMeOM24xmjtH5CagkrVarxWKBzE4QhHq9\n7vF46J05IbPjeb5er5vNZkEQVlZWSCLBKEpHY4zCMEwkEhkOh1AR6IOxOs4xnJzYPfDAA/v2\n7bvjjju08eFGo/Gf/umfdu/e/eMf//h0np4OHTp+L2Bygn4py3I2m9VKzWAO0ul0wKJYlm02\nmyB2MDqhtRP0T5ltoE5zotEJIcTv96uqmkgk0FNjWdZkMlksFk+9TlS17vEsLCwoiuJyuXq9\n3mg0WlxcPH78OFZ0URTxggZOkO3xRhyZ0rV0Om0ymRqNRjwepxWgTCajKIosy9o+7PLyst/v\nhzBLx6kgFos1Go1utwuZHc/zkNxp5yfA+Z555plYLEYzJ+gdQg/V7XZB+ql0B4lktK+K0iDZ\nLtRhoVEURVsYhkEdfXfcPKqqrq+vWywWrccq7HVUVc3n836/f2NjQ41ECMcZ1tdpEC3ZdrOD\n6Umz2dS6bevQcbbj5MRuZWXlwgsvPNGrnWXZiy++WI9k0aHjJYtGo4ExCLoll8tJkqTdUiwW\nJUlCRBj8vaxWayAQyGazWCCp5zAEdoIgaAdgqUcdPSDP89hIu7TD4RAH9zabQ5NpbLejD+vx\neKxWaywWMxgMiHbF9AaOpjU6oTWVCdXd3NycNnCiXq83m81WqzUejycEdrov8fNCIBAwGo2F\nQgEitnA4jHAIuMeBbIGNZbPZSCTCsux4PEb4hJY/EUIkSaLyR8q/BUGATyFkdnQmA+JIsq2u\noy9Go1G73W632y6XazgcwhORELK6uppIJLrdLn3AiEajeLtkMrljxw5JkjZbLRIOezsd7fwE\niF0wGASPLGoatTp0nO04ObGbmZl5VgmCqqrLy8sTdvY6dOh46SCbzdrtdq/XS7ekUql4PK6t\nvmezWa/XC3WU1nnkRCcjVMKw0FKxPMoq+BKvRVFsNpsQQtFv9fv9fr8/1WrVPZ5oNIqksk6n\nI8vyBRdckEqlWJYdjUbggsPhkOZbEEIGgwGNNNy5cydeqKq6uro6PT1dr9e1AjtU+zweD/3x\nTqezvr6u92GfFxiGgb8JGt80Exa5Dh6PBwbFZFskR+1vFEUZDoeUwAF0VIJeFJ7nEepFtoNi\n8brf74Pfayes8e9oNDKZTOPxmCYLE0KGw2EwGCSE0KIdLBsZhkmn07joS0tLJJGISNLE/AQh\nxG63o9YI72sdOs4NnJzYXX311Y899thnPvMZ7fy5qqqf/exnH3/88auuuup0np4OHTp+dzxr\nktiE5Uc2m9UK1VutFvqwcJoFLcPiSkdK6bpLXYixA167XC44lYAachzHcRwGaR21Wt3jufTS\nSzFcWa1Wh8Phzp07l5aWEokEZFgsy8qyrA2xpeU6i8VCq4ClUmk4HIqi6PP5aBBFJpOx2Wwm\nk0lrdJJMJu12O5Z/HaeOWCyWz+cNBoPP5+N5XpbljY0Nr9dbKBQwP7G1tWU2m0ejkc/nw51g\nsVjQfPd6Nluc+wAAIABJREFUvdr1QpIk3AAosxFCxuMxzQeDzA43FTxTMBONKi/ZTiIRBMHl\ncpVKpWAwCFs7s9nM8zwqghMyO0EQGo2GxWLhOC6ZTJJEYqrX01bsBEGAzQrGeLXRZDp0nO04\nObG7/fbbo9HoBz7wgQsvvPCWW2657bbbbrnllgsvvPADH/hANBq9/fbbz8BZ6tCh4/kC7nRa\nYre1taWtbxFCRFEslUq9Xg9rp8Vi6ff7cLAzm80wJyPbKyv0dtqJRRrrSba5Hcdx4H/I4sR2\nk8mEEo57a6vu8UCVPz09jeKK1WpNpVJo5I1GI1QTr7jiCnpkxFEQQrRVNxid5HI52ofF7zsc\nDkej0YTAbnFx8UQxiY7nBmyfRVEMhUK9Xo/n+UKhAO9i0G5JklBpW11dDQQCmJ+g8y70A0cH\nX2tTQgiRZVlbEu73+xjTUVXVaDQiiYQC8xPwOKTzE+PxGAEShULB6XTm83lKJePxONje2tra\n1NRUtVpVEwlno6Gt2OEX7HQ60AXW63VdZqfjnMHJiZ3b7T506NA73vGOpaWlu+6662Mf+9hd\nd921tLT0jne849ChQ7oeWYeOlyZqtVq329VOTqRSKZfL5fP56BZ0S3u9HmiZy+Wanp622WyZ\nTAZ5oFRLB7MSLNtUdaftw9L8AGjkYW5HCFEURRTFfr9vMBg89fooEkF1BMOSO3fuRBcMfb3B\nYICRW1pygwoer2ngBH6X2dnZQqFAeer6+vpgMOh0Ooqi0N96PB7ncjldYPc7AOxtfX09GAyW\ny+VAINBoNLSDsYQQv99PCFleXo7H45hlhjWJdqQGDVzcM61WSzsbi2cAhmEymQzGq1HAw11H\nE4rx5Xg8Ho1GOB9JkgRB8Pl88DpZWFiQJIkW5GKxGA4F/aUsy3W73VypdLtdapiMX7BUKk1N\nTaEMXCqVTvdHqkPHmcEpGRSHQqF77rmn3+8nk8kHH3wwmUz2+/177rlHmz6pQ4eOlxSy2azb\n7aaqJkKIds4AyOVytI+JtXB+fn5ra2trawslNNRRiCZqAjszDIM6ilYORQiB0YnNZkNqBdge\nHPLU4dDRbnv270dobKFQGI1GF1xwwdLS0tzcXK/XQ59uPB5DVo8DVqtV9OxYlqV8AkYngiAI\ngkDlWaurq06nk+f5cDhMW36pVIrjuIngDR2nAoPB4Pf7YVMMj2u422BIFlcfjde1tbVwOIzB\nFzwM0KtJ7w1YHKuqSp8reJ6ndYFSqUSHHkRRrNfr2vuKCvjq9bqiKAaDAewft6gsy7hh6CSf\n0WiETWM6nUbtdo1huI0NI8tOzE/0ej2/348isT4IqOOcwbMTu1tvvfWXv/yl1nGeEGIwGHbs\n2HHNNdfs2LFjQhurQ4eOlxom+rCiKKK2od0nk8nQ8psgCJVKZW5ubmVlRdsLw/qKYViqugML\nJJqVmxDCMAyW7VgsNmF0Qghxbm0xqjqKRAghiURCluWpqSmPx5NKpaCj53keFcELL7yQHpMK\n7ILB4ITRydbW1sLCAu0LZzIZk8kkCMLEPOzCwgLtD+p4XoBN8fT0NMuyeEJoNpuYlgVzgidO\np9OBTTHP83a7HQsHRna0ckzU6ijnBkUjhCDHwufzUeM6SZKsVutElgnZtjtutVpms1kUxVwu\n53a7WZbt9XoMw2h1colEAg7GIJTHBwMiy7OCoO3Gejwe2BRDUaDbFOs4Z/DsxK7dbr/+9a+P\nxWLvfe97H3jgAT1KT4eOswsnCuxWV1cZhtHWrobDIdK3CCEMw8DTPxqNrqysICRAy4dkWYb+\nnS63Ew1Z7A8+R8csEDgxGo14nvfU66LB8OT6OiGE53lBEHbt2rW6uiqK4sbGBiFkNBrhHV/2\nspfR911aWsKLCbY3NzeXTqcpTxVFsVgsdrtdbZKYoiipVEqfh/2dAUUdz/M+n284HLIsm8lk\n0I3Fh1yv10OhEKp0ZrNZlmUMypDtEi/Z5mSj0QgUnDZDVVXVNkbpXYQ7jQ7S4sdBCjmOs9ls\n6AWrqtrpdOLxuKIomUwmEAjUajWaRRaPx3GctbU1n8+XlSTC83FV1VbsMPkriiI0po1GQ5fZ\n6Tg38OzE7u677y4UCl//+tdtNtt73vOeQCDwl3/5l9/61rf0ADEdOs4KIP51QmA3OzuLRRfA\n6CuCPlVVtVqtMzMzkiTl83mMCqLhhZ1p3QW+xPQg2hqeJEntdptlWRq+iXkIURQVRfHU671g\nsN5oCIKAPuzOnTuPHz8OqRz6vEgUpW4slPORE4xOPB7PYDCgubHo/ELjTydq19bWxuPxRJFS\nx6kjHo8Ph0NYE29sbHg8nkqlArYXjUYJIZIk7dq1ixDyzDPPRCIRGFbjDmm1WtpqLsLoCCH1\neh03oaqqW1tbtOBarVZpDViWZdrJ1R4ErsV4d8zPokJcqVTwDIPbgGwTO9wqs7OzCsuKfn9w\nNJqYn4hEIltbW0aj0Wq1qqqqu9npODfwWzV2LMseOHDgH//xH5eXlx9++OHFxcXbb799amrq\nDW94wxe/+EWqjNahQ8dLENls1u/3azuqq6urJxqdoAeKtROBE6lUCs017Z6gbjRMFk4o5DcF\ndgzDuN1uSZICgQDYGMYYzWYzKjqeRkOMx1VVnZmZaTabDodjamoqmUw6HA7UewghsizH43H6\nvplMBi8sFgv9XWB0MhqNIpEIdT/JZDJut1sQhJmZGVpohKif9v50PF/Y7XaHwwGZ3fr6ejwe\n73a7kUikXC7TYDdcuJWVlUQigU8et0Sn05n45FF1QwseW1RVRW2YZdn19XXqbAfOhxdUeEe2\nR3NarRbsVKxWK6SWDMMYjUZYq9Kz8ng8yFnBHdXxer3dbqfT0ZYJo9HoxsZGMBjE3UXLwzp0\nnNU4peGJnTt3fuQjH3nssceWl5f/8A//8Atf+EIoFLruuuv+/d//feIBSIcOHS8FTDjYVSqV\nVqtF61t0H7Su4BzbaDTm5+dTqVQwGKQLKp17nTAiPlFgB0E9wzCQZBFCFEVBKBnWe0+9vmGz\nEUJcLhfP83v27IE7CZZw2N0RQq655hp6zNXVVbyFtp2KM8xms9pBEDSateO0hJCVlRV9Hvb3\nBGR2wWCwXq/j/pFlWVGUVqtFMxs4jtvc3MRoKs/zND5Y64xNtgWXhBDY1GGLw+EghCiK0u/3\nA4EAbjCDwdBsNpEaPHGn9Xo9DE+gQpxKpTDD12g0BEHQ+gyjPk0t9Cpms6Va5Xme1oAJIWjp\nOp1ONGF1m2Id5wZOidhRRKPRW2655YEHHiiVSm9961t/8IMf6G0OHTpeakD8q7YPu7Ky4vf7\ntROyvV6vVqtBda6qKuZn3W53Op3GIqetvoCi0VWWSpEmTGhhcqGtiAyHQ4wuEkJ8zWZeEFiW\n3djYkCQJfdh4PA43EwjYWZbVztonk0m82LdvH92YTqdjsVi1WqXErtvt1mq1TqejTRLb2Nho\nNpsTU8A6ni+i0Wg+nwdZNxgMGD71+Xxge6qqZjIZr9c7Ho89Ho/dbkeoK4pzPM9rva9FUcS0\nBA2NRWuVdvaNRiMdo1ZVlc5rYwfckBCDVioVu93e6/VEUQQ5S6VS0Wi02+3SbhLkd2azGbXA\nqsWiZDKBQEBbjMDkL8Mw+Klms0mVozp0nL14fsSOwuPxQHUH9ykdOnS8dFAqlSRJmhDYnTgP\nazKZtJPv8/PzhUJhMBjgPzUEdnRNhTsx2U4IJRp1HWAwGGAvTG3nsBGGFKyiOJrNLZcLFSCL\nxRIKhZaXlx0OB0w0YHWm9cWs1+tgigzDaCOnyuUyKj0wUcPvYjQah8OhzWajbhrJZDIQCGi5\nrI7fAbFYrNFoDIdDv99fLpetViskbsVicX5+HpQI8seVlZVdu3ZBVQnq32w2tTeYoigg/c1m\nkxK+RqNBHw9arRZ4FX6KNt+xA33SMBgMxWIxGo0qijI9PY336vV6EN7RbLFEIoEQPMjs2m63\nvLoKTz7tLwjTE0mSoBmAs6MOHWc1np3YTZ8ytM4COnToeCkgm80Gg0GqcBoOh6VS6USBHZ1R\nMJlMpVIJArtwOIz+rFY8h+4bFl263E6MUEAgRY1OGIZBPhiO42o2GUmqezyQ2O/evbtYLPZ6\nPRRvKFm89NJL6THT6TTeYmpqivIAGJ1Uq1Vtc3Z1ddXlchmNxrm5OXpWyWRS78P+/ggEAvA3\nCQaDMAeu1+uRSKRQKIBty7K8e/duQsiRI0cuuOAC5IbhKrRarQljLHU7TZjK7Hq9Hsp7DMNU\nq1WtahNCzwmZHSGk2+0iXpZhGK/Xm0qlEPkKKSc1PbHZbE6nczQaIYWs5XYL1WpoaurE+QmU\n9HADHzly5PR8kDp0nDk8O7Gb/00YDIZKpVKpVDAfznEcvrzoootuvPHGM3zGOnToeG5MCOzS\n6bQgCBhj1O7T7/d/1ST1+SRJmpmZWVlZwUpMDcaws7ahBrtgsl1WoTsMBgOGYVCiI5o8eBRa\nprtdmec7TicqNLt27VpaWopEIkiCGgwGWN3/4A/+QHvaOIGLLrqIbkylUjMzM7lcjvJUdAMV\nRVEUhT5ndjqdcrmsG538/sDf/EKhEAqFyuXywsKCLMt2u73T6dDJlc3NTYZhCoVCPB6HoA2N\ne4ZhpqamtFyN4zg8b2gfDyCzw23gdrupzK5WqxmNRu1cNo7TarVEUUSBTZZl5GEoilIuly0W\nSz6fp3fm7Owsy7LIsd2y2xlFCStKu93G0DeAil0gEIDKk87V6tBx9uLZid3PNbj77rtbrdbL\nX/7yp59+ulwuP/HEE6VS6dixY9dee+2xY8c+8IEPnOEz1qFDx3MAjm5aYgfXNy05azQazWaT\nTho6HI5IJDIcDqvVKkYZJpJVOY7Dj3Mcp10UyTb5k2W52+2qqlqpVOgqDtsL1PkSklR3u/3T\n0+l0muf5aDS6vLyMGokgCKPRCA4X1I1FlmW6xMJQg2wbnYAH0EYzktPq9booipTYLS8v2+12\nOrmp4/dBLBbDYGyj0cDjQalUMpvNGxsb4Hb4tPGcEI/Hx+PxeDzGJA3uHK33IaZoqRJOVVXq\nesiyrM1mo8HE6P+S33zAwB3lcrkGgwHLsqVSyev14pbIZDKzs7OSJFHXEszD2my2arUqBYMK\nywqlEs/z2m6s1+s1m80mkwnBeu12W5fZ6TjbcXKN3W233Wa1Wr/73e9q9cu7du369re/rSjK\nrbfeejpPT4cOHc8P8JCjojSQoRP7sCaTiVrT1ev1ubm5ZDLpdDoha6NGr6qqwusOy63b7cZP\nUQaGNRsUzev10mR3jEDSkUZHrVb3eHbu3IkYsY2NjVar1Ww2CSGoykiSpDU6WVtbA+8UBIG6\nYMDoBBbENHI0k8kg7WBqaorWgZaXly+44IIJeqrjd0M0Gi2Xy/CvhlNgOp1GGS8YDBJC8vk8\nAscajcZFF12kqirLsjDH6XQ6lCehdIcHg3a7Ta8OrRyrJ+QRT2SRcxyHO8rpdJZKpUAg0Gq1\nZmdnK5UKy7KyLHu9Xq3MDvMTiqJks9lIItFxOhtPPgmxID0mSpKyLLdaLZyhni2m42zHyYnd\nI488ctVVV8FtSAubzXbllVf+7Gc/Oz0npkOHjt8F2Ww2HA5T3lMqlfr9/olGJyinQaVEk8QC\ngQBWZbLN2JjtfFhI1Cf07BTY2ev10tWaYZjhcEgPwudyDY8HW/bu3bu0tBQKhUqlkqIo4/HY\nYDCwLPuKV7yCHnB1dRWnoe0gP6vRSSaTsVqtBoOBGp2MRqO1tTVdYPdCAYbPlUoFlMjtdlcq\nFcjs8Jm32+0DBw4QQh5//PGFhQWGYcxmMy59o9GgMjvcM2jlS5JESVur1aLN00ajgfsNNyct\nD+NodLfBYFAsFjGrYbfb8/k8er7j8VhRFOpm53K5rFZrp9PZ3Nz0+/0tt7t//PiJ8xPRaLTZ\nbML0h2GYZ5555nR9lDp0nBGcnNg9hx93Pp+n64cOHTpeCpgQ2IEMTWS/ZrNZFOEURfH5fEaj\ncWpqam1tDeW6CYGdNlgMO5AT8sSgcx+NRtTuDjtA+e7z+Zy12jAcPn78OMuyMzMzx48fdzgc\nHMdhMUb6ExXUE0JWVlawiu/fv59uTKfTwWCw3W7TAqQsy2tra6PRaDweU2KXSqV4ntfW/3T8\nPoAnCGR2sCkWRdFut29sbOBDVhTF4XAwDJNMJo1Go9vthscN9G3a2RdCCMyEieYhQZIkOuhD\nY+VQKq7VatrHDErs2u12o9GIx+MoE5pMJp/Phxvb7XZXq1W45xBCEokE7fbWHQ6Sy2EKRPsL\nwnLZ7/cjOlY71q1Dx9mIkxO7/fv3P/LII1/72tcmtn/lK185dOiQVuysQ4eOFxej0Wh9fX2C\n2E30YWFfh+EGQghMfTOZDMMwMDqhfVhCCLgXnt+MRiOapxPrNM/zcCGmhRCMRtI+rNNudzWb\nnv37m83m7Ozs5uZmo9FA5wt9WFEUae+YENLtdmlLl9YaYXRCCJmenoamihBSLBZFUYR9Bj1C\nMplcWFjQ8lEdvycgswMlSiQSKMeqqkrz6I4fP24ymXB7LC4uws0Okjgq0ASQGEEIwc6EEFTd\n8BoyO9qZ7XQ6gUCAbFM6WgAejUYGg6Hf75tMpkwmMzc3h0JgpVLBwATNLJmZmWFZ1ul01uv1\njsfjqNdZlm21Wtp4TDjhuVwu6pyih8bqOKtxcmJ3++23WyyWN73pTW984xvvueee7373u5//\n/Off8IY3vPnNb7bb7XfccccZOEsdOnScChD/SsNSu90uJhm1+2SzWSjkOI6zWq35fB59WCiN\nyLZVGNmut8FhjhAyPT2N9ZX6U4C6QUEfCATowCzLsvQ1IWS8uspLkjwzQwi56KKLjh8/jqae\nKIqSJIHbXX/99XT/VCqFJdxisVB+lkqlTCbTxsbGRB8Wc5TRaBTnrChKOp3W52FfWCAfNhgM\ntlotl8uFSWS/318sFi0WC2p1kUhEluV+v48iq9VqxZ3T6XQoT8JlRWis1siafgtKAFxK2BRT\nY0L6Xbz2eDzFYjGRSLTb7dnZ2Xw+j2cJpL6mUinsRmV2mUyGnZ11t1rNZlP7EEIIQcWa5/l2\nu437Vjc90XFW4+TEbvfu3ffff/+FF174jW98413vetdrX/vav/qrv/rmN7954MCB73//+7qx\nuw4dLx3kcrloNKolQ7AC1u6TzWZhPizL8vT0dLfbnZ2dTafTWtJG+19YkvEtGEyQbb0daB/D\nMDA6oe5lDMNgKcVxjEajsLamsOzhrS2GYRYWFpaWlpxOJ/pxkiQh9Em7ftMkMa1NZjqdTiQS\n6+vrE0liJpNJEAS6Zy6XG4/HE5pCHb8n4vE4nGt4nu/3+zzP5/P5SCRSLBZxd5XL5Ze//OWq\nqv785z+HpyAdhj1RZgflnKIotFDX6XRofRfj1WR7BhZ3F9FMatNB7GKxiFkNzPfgFoJagBI7\nKA2azWa/32dmZmytVj6dnpifIIREIpHBYNDv941GI8dxx44dO70fqA4dpxOnlDzx8pe//Mkn\nnzx06NAXv/jFO++888tf/vKTTz756KOPXn755af7/HT8f/beNEayszwbfs5S+15da9fSXb1v\nnhXPeLyMDcEYCydCgAhBSQSKABGJQBBSYkEIQoYg8QOFFxSCHSQEJCRR4LVMjAMOjjEejz3j\nWXuttburq2vf9+Wc8/240s97qDHM5Iu73QPn+jGqPn2q6lTP0/3c576vRYGCW8eNBDuQ2ekR\n8JCoJZjNZnM6nc1ms16vZ7NZsle0oaeCMSt9OvxjiYxghxfs9/ugOtEdlz4LwVC2YrHt8aRy\nOZ/PV6vVCoUC5rA4rd/vyxUS8nYLKPlkT9ur0+l0Oh2tUzudzu7ubr1e73a7cqOT8fHxofh5\nBf9LmEwms9m8s7ODksjr9YJml0wm8ZNvtVqjo6Msy66urhJCfD5fu91G6c+yLMSqRFacoWdM\nnfCoqTUhpN/vo20mSRLHcaVSSa7UoTPZRqORSqUmJibQLwwEAni1aDTqdrtbrRad5gcCASSe\ntd1uRpLqq6uvWdhls1mtVmu1WvV6vZKBruC2xq1GirEse/z48RMnTtxzzz1vectbjh07plgJ\nKFBwqNBqtbLZLC3sMH4amsOm02nsmizL8jxfKBSmpqaQJEsDJyirnZLnCCFarRYpEUN6KTTq\n9Ho9dSaTh8xKktTtdu3lsjgxIYri8ePHV1ZWRkZGcrkcSFo3zmFTqRQd49KZMoxO6vX6zMwM\n/cuTSCQ4jqvX62q1mlZ7kUhE0cPuB5AFB5tiv9+vUqn6/X6z2YTjiSRJ+XzebDajYXbkyBE0\nYtF143key4Zqa1CEyT0RaZHH8zxdQpIkVSoV5MLJyzuQ/Pr9frFYtFqtW1tbs7OzuVyOENJo\nNMbGxjiOi0ajOBn3NjabbZdlRZa1VatqtXqodEPOrNvtRlwKXHX274epQMG+4pYKu3Q6/YEP\nfMBisSwuLt59990vvvji008//ba3vW15eXm/r0+BAgW3CKSE0RJna2ur1+sNhf5Riy9CiNvt\nhmNFOByGbHAoDVYQBJj7E0JGR0fxgDrYAdhuqdEJiHdo+2F7rlarjkolYzAwDIPACbPZjNpR\nEARs/3I9LCXYUYUE2dP2bm9vy+tUxM/zPI+2DSEknU5XKpWhWlbB64JAIACb4t3dXY/HAzNq\nvV4PRzroJ+bn5yVJSqfTd9xxB8uyOp1OEATQ7KgiR970lRd2KpWKdnw1Gg0KQWTZQT8hB2o+\nm82GJdFoNCYnJyuVCma7iDaW0+ywjLd3d5s2m6fTabfblUpFXrrBpliv13e7XegqLl26tG8/\nSwUK9hc3L+xyudzZs2e//e1vT01Nvf/978fBkZGRn//852fPnpXvEwoUKHgDkUgkoFjEl5FI\nJBgMUlt/AJMyQgj2S4Zh7HZ7Op1G4ASNbKLn0IM0WBZOFvQFu90uqHi0GSNPGjCZTIPBwN1o\nbKtUDoej0+lks1nMYXHaYDAYyjpbW1vDS8l5cpFIxOFw9Pt96mlCCInH43gReubGxobX60WD\nR8Hri2AwWC6XrVZrrVazWCyDwWBraws2xQaDgWGYaDT6wAMPEEKef/55CFGpZqJSqdBGL/5z\nUTxBPY3jvV4P9xWwKcZ6w13EYDCQLzmqn1Cr1eFwGFoNRFDAGy+TyXAct7W1hfLR5XKpVKpS\nqSSKYtvttlQqOOFGm2IwCvr9vkqlopmzChTcdrglVWw0Gv385z9/9erVL3zhCzh41113nTt3\nrlarKapYBQoOCVDY0S9vNDoRBAHjKmyTLMuOjY3B4BcSRcSLYTvErJbqMOjoCm08PIZRGcZw\nOCKKIo0H+G+yHSGmXC5nNh85cmRlZQXGE2jVoDX41re+lV5hp9Oh1CgaEdtqtTKZjCiKY2Nj\ntL6sVqulUgnBaLQrubGxoehh9wlut1uj0bTbbZ7n4TYyGAwsFgvVTyDaFRUVIWRycpK2xDiO\nGxkZwVqi1Rse0P/QTqcjiiIODgYDeCJC5UPpm2gNUppds9nc3Nw0mUw8zy8vL8/MzKAviNRa\nURThSMcwDOwPR0dHm06nNpMpFAq4n5F/QL/fX61WB4MBCIX4TVGg4HbEzQu7J5988vjx45/5\nzGfkXkSEkBMnTpw4ceK5557bt2tToEDBraJer5dKJUqwq1QqhUJhqLDb2tqiYlWz2ZxIJCYn\nJyORCFpcNBwMmyjmWThfrVaDOwUvCfqngOd5URQtFos8NgoMOezijUbD2m6z7XbJbj9+/Pja\n2prFYrFarZDNDgYDjuPkol3KiyKE+Hw+PIDRyY16WL1e32q1zGYz+jTVajWTySiF3T4BPa2d\nnR2Px5NOp91ut8FgEAQhm82i59rtdrvdrsvlQol28uRJQgiNoOA4DjUcNRlGr44Wf4hCwbKB\nyhVlHMMwlUpF7r9DH7RaLZZlE4mEy+VKpVIzMzO5XI5l2X6/73K5eJ6nAyVQM202W9FoNJdK\nWq1Wr9ffWNjlcjmr1WoymXQ6Xb/fp/cYChTcXrh5YZfP55eWll5TKjE/Pw8lnQIFCt5YoPGG\n0HRCSDgcNpvN9EuA0oZEUQwGg8ViMRQKxeNxuaNYoVDAhgqtK4o8r9dLxRBwM8HJGJ/pdDqq\neYSSkRCiUqmwPc9ynMQw4tiYIAipVKpWq6lUKvikCIJA5RHA6uoq3shsNtPyMRqNBgKBUqk0\nVNhZrVa1Wk2LV2Td3sjHUvB6QU6z83q9KpUKJsM0HCyZTELIfPXqVY/Hw3Ec2reg2Q3JEeQJ\nY4BGo0GeGFp3WAC4B5CzMOnTsYwhl+l2u3a7HbJWQki/3+/3+xsbGzgZUbaiKKZUKlulYrfb\nBUG4MX9CFEW73U72ys3Lly+/fj88BQoODrfkY3fx4kW5wQEgSdLq6ur8/Pz+XJgCBQr+B7iR\nYHejx2QikZC7kJhMplqtJooiSHU4iC2N2QMeUzkFjX8FOI4TRbHRaNC/D+jhEUJ6vR7ofSct\nlqrFMrW4uLq6ajKZkDlBCNFoNJIkvfnNb6avBttbvD69eBidaDSakZERbLo4uLm5iS1fbnQy\nOzurqPX3D8FgENFbKOw6nU4ymXS5XOVyGT/29fX1I0eOEEJeeeUVhmHcbjct5mq12lAWCCx1\niMzvmv6LJYSWHmh2ENji/CF5bDgcPn78OCHk8uXLk5OTuG1IJpNarbZQKOCmxePxsCybz+eb\nLpexVlOJYqlUKpfL8lpTo9E4HA4EWhSLRZ1OR+UXChTcXrh5YffII4+sra194hOfoL+HwBNP\nPHHx4sUHH3xw365NgQIFt4rNzU06hwWxfWgO22q1Wq0WOmoqlSqfz09NTUUiEbvdTosh2u5S\nq9W0sJMkCb0NlHG0jQd2HcuyCIrFmVQPK4pit9tlWbazslK22++66y7kwyKgAmdyHCdPdM3l\ncjRtlhZ2Ozs7nU6nUqnI69R0Oo3dVxRF0Aq73e729rZidLKvQHuV47hGo2E2m9vttiiKVqsV\n+glQHdzSAAAgAElEQVRCyObmJsMwOp0OnMuFhQVBEFCQsSxrt9tRpVGaHdnzSsTrowFMG3W0\n1GMYplqt0noOx/GsfD5fr9cbjYZGo1lbW5udnQUhL5/PT0xMcByHbDGWZV0uV6FQMC4tMZJE\nkkmMcTOZzNAH7Ha7iKaw2+2FQoE2pxUouI1w88Lu0UcfPXPmzNe+9rVQKPTRj36UEPL1r3/9\n9OnTH/7wh5eWlj772c/u/0UqUKDg16FUKlWrVVrYJRIJWvFQnDt3Dg9EURwdHaUEOwhasX3K\n9zn0w0B7gtYB/iNIjyV7lhNWq1XubIddGRkVvV7P6XT2VlerTqdarU4mk/V6HUQr6GEpiw6g\nil0ic7CLRqNerzeVSg0Zndhstn6/73a74X8WDod5npcHzip43aFWq10uV6PRUKlUvV6P53nk\nuSWTSdwSlEolQRCCwaAgCO12G3xHqnulbna0WkInmOqsRVEUBAEHeZ5HOw23CjRYlgIOi7Va\nzeVyRSKR0dHRfD4/OTk5GAzUajVkQAzDUJrd9PS0KIqG2VmR47hk0mKxGAyGIZpdIBAoFAoc\nx1mtVpBNIQRRoOD2ws0LO5VK9bOf/ezLX/4yx3HPPPMMIeTZZ5+NxWKf/vSnz507N2SmoECB\ngoNHPB43mUx0UhmJREKhEJ2fAqurq9T3dWxsrNvtGo3GarWKLRNzWLp9UuaTPKwTPXu5TgIv\nCJ0sy7JUD9vv9/GX4b777tOn09zMzNramk6nq9VqGNvh2u677z75Fa6srGCDNxgM9A8LtB0q\nlUpetIFQqFar5UYnMzMzQ8M+Ba87gsHg9va2x+PJZDJutxtLqN1uezweQogoijs7O7/zO79D\nCHn++ecR50VT6Wq12tDYB/cGcp21nMFJ3XZw40GZfPQ09I8dDkc4HF5cXBRFMZPJBAIBuNmV\ny+XBYBCJROTuOSLD1MzmkXodzos36ifq9brD4dBqtUporILbF7dkUKzVaj/1qU/t7OzUarXl\n5eVisVgoFB577DGa9KdAgYI3EJubm3Ij4huNTjqdDkZUdMA6OjqaTCbR1cA5gUAAWyA6KzgO\n21gcHyLYqVSqwWBQqVTQ2KMbLcuy3W4Xu7IoivZSafTsWfgS+3w+JIFiQic3pUOKAB7Tcg1G\nJ8h+pUXbYDBIJpPdbpfa2omiGIvFFD3sASAQCCB/AjQ72BQbjUb0TZHu5XQ6GYaBD1wwGKRJ\nJ41GQ155I02OPsYDnudxPspB2r1jGEalUtGn0zEuISSXy6VSKXR/r127Njs72263WZZNpVJW\nq7XdbkPh5/P5cLA+MuJqtxF6O6SfQElnMpkEQUin0waDAZNcBQpuL9y8sCsUCvQ2y2QyLS4u\n0sZAo9EolUr7eHUKFCi4GaAkoHPYfD5fqVTk7r5kr/EAsaHVao3FYpOTkxsbG7THRggpFAq0\nsKOGw4IgoMcmr/AIIQzDwMzsRrGCxWIhhPR6PavVuvJf/6XpdIzHjm1tbdXrdXorOBgMwGen\nz4KAEaUnJd5FIhGdTpdOp+V16tbWliRJxWKRZVkYbSQSiV6vJy8TFewTxsbGOp2O0WhMpVIe\nj4eqYovFIlYC6jmLxQJ/nLm5ORgiYu3ZbDZw4yCgxmtyHEdpdqjn6KLC4J5lWVEUm82mXDaB\n73IcVy6XtVptLpfTarWRSGR2drbVakHTA+NDTGNBm0un09LYmLlUqtVqgiCUSiV5ExGWLoSQ\nSqXSbDZHR0drtRq8lBUouI1w88LO6XR+//vff81vffGLX1TukhUoeGORy+WazSZl1EUiEbmA\nFLh8+TKNcpqamtrd3fX5fKlUinLS7Xb77u4utlukuFLaE9oqlGBH9qZjHMcZjUa6T8vFFqjY\nTp482VleJgyz3utBbAhjMLRh4Ishv0I8kCSJTl2RJNZqteR1KpLEJEkaHx/H9ayvr4dCIcrl\nUrB/gHnvYDBotVqo3rxeL8dxqVQK+olKpdLtdhcXFwkh29vb4+Pj8KwmhEiSxPM8/puGPK5p\n6w6LB/0/3GCQvWqv0WigJUwvBn1fQRACgQByVprNJsMwIyMjeAWWZQeDATVHDIVCvV6Pm5jQ\n53K5XM7j8QzxSgkhfr+/0Wh0Oh2tVotfIiVdScFtB/5XfeO73/0ufXzu3LmhgEhCSLfb/dGP\nfiQP+1OgQMHBI5FI2O129MnIaxmdtFqtbDaLLZAQ4nQ6UWapVCpKmJucnLxw4QLZ22hBe+J5\nXqfTQfSKvgU1/dfpdEjVlPuQYSZbrVZBrg8Gg/l8fuB0LicSZrNZr9cjCQD79JCCNZlMYiNX\nq9XYUOF+MjY25vP5UDQAcD/hOA4tOkmSwuHwEF1Pwf4hGAzCMbjX69E2WKFQGB8fh7fI1tbW\nvffe++KLL/785z//wz/8Q4PBQLeJWq12o5sdTR4DaCFI9midqOfgjChvsMFqkezNjt761rdG\nIhGs/+XlZYZh8vk88id6vZ5arZ6bm7tw4ULX63WUSoQQh8NRKBTS6bRcZuT3+1944QW9Xo+B\nLCFkeXn5jjvueP1/jgoU7Bt+ZWH3R3/0R/Tx448//vjjj7/mae9+97tf/4tSoEDBLUNOsIPr\nx2uKEjALU6lU6XQ6FAphyol5GSGEmuyrVCraPtFqtZTJjv2YMtkHg4EoinSXpeYUIyMjhUKh\n3+8bDIbz5897KhV2ZiaRSKjV6kAgsLm5iZNdLpdc25HNZumbBgIBvBSMTorFonxbbTab2WzW\narUOBgN86nQ6XavVbjTtU7BPCAQC58+fh37C6XSqVKpCocCyrNvtTiQShJBXXnllZmaG5/md\nnR1CyMTExMrKCpZfs9nkeV7umHOjnwiywggh4APAOgdVnVar7ff7tG8nSZLFYqnX61hycEZc\nXV194IEHzp8/D/6fy+XK5/Nw/8HS2iQkVK9btFp0CnGRFH6/H0IiQgjogxj9K/6ICm4j/MrC\n7qmnnsKD3/3d3/34xz8uz3OkMBgM99xzz35dmgIFCm4GODI88sgj+DIWi/E8LzeHI4Rcu3aN\nbp+BQCAWi913333PPvssbXjYbDb00sieHhYz2V6vh26KyWSq1+vYhrHDoXQTRZEaGqPPB1KU\nJEnz8/PLy8vHW62K18txXLfbhTYChSPypiief/55mh9F2yfRaNTtdmcyGXnRFo/HNRpNtVrV\n6XTI1djY2BgdHcWkWMEBIBgM/vjHP56YmEin016vF+mxCGwlhDAMg7UEH2PU32tra5iKguJZ\nqVSoVBaviQYwlcEiIhbrQafTNZtNlUoFj0M5OY8QYjKZer1eu932+XyZTEar1SaTSa/Xq9Vq\nO51Ov98HTy4Wi01PT6tUKrPZHCsU3iJJpkoFnsnJZFL+6bRa7cjIiFarrVQqlUplfn7++vXr\n+Xx+KMRFgYLDjF9Z2NGt4qGHHnrHO96hGBErUHAIsbu72+l05AQ7+LLSE+r1eiqVwnYoSdLU\n1FQ0GlWr1bRDRgiZnZ09f/48HsubKHTMKs8GIITAJ4xlWSp4RF4Thl94hTvvvPPixYueRmNT\nqzWZTDabDe0cjLcWFhbou0uSFI1GtVptu92WJAl6CHwWk8nUarXke2o8Hnc4HOl0empqiqYd\ngNGl4GDgdrs1Go1Kpdrd3UVvzO/3dzqdTCaj1+t7vV6/36/X66dPn/7hD3946dKl2dlZlHRk\nT75DPVAA+ObIW3ccx2m1WrT36I0ELK+JLDGWENJsNtE2RgRFKBTa2NjY3NycnJyMx+OtVqvZ\nbHa7XUqzCwaDy+WyxPOqVCppNLpcrnQ63e12cUMCIL+uXC4LguB2u5eXl2OxmFLYKbiNcHPx\nxDPPPPPggw+mUqlnn32WHvyHf/iHq1ev7ueFKVCg4OZIJBKIYyd7FdKQ0cnKygo1EMZAdmRk\nJJ1O6/V6KkrFHBY0JmpZotfr6RPpaAxfwuik0WjQZE+qwECpp1arL168SAgxZjJRQprNpsfj\nQUkniqLNZjMajfQK4/F4v9/HdxmGGR0dxTtmMplOp4P4dvnJYAFiDlutVnO5nCLhOkhAOgqG\npclkKpVKgUCg3W7v7OwEAgH8P7788ssYoF+8eNFisVitVmpQ8pqhsUP+dqIoQv1A9mh2WHs6\nnY76YwOVSkWr1TIMUygU8vm82+3mOA7aWASf5PN5hmGKxSIEvAsLCxLLDjweW7Xa6/Xgg32j\nfgJOy+CtYrz7uv8YFSjYP9y8sOv3+5/85Cf9fv+nPvUpevCv//qvjx079qd/+qc3ZsgqUKDg\nwJBIJKjRSTqdbjQaQ4UdWOSo1RwOB4xOwuFwu92mY1bKIsIRDFWNRiOdeaFTQjfUdrstT4Ii\ne2pZGJgh9GJjY8Ot1TLFYslu7/f75XKZ7MVAHTt2TH6FL7zwAm3+ud1uVJPRaFSn02UyGfnH\nKRQKtVqtVCpRgt36+rrFYqFJaAoOBoFAIJvNovHGsixsijudzsTEBBbStWvXGIbRarWYv4dC\nIThOI0+CqqfJXlTJYDCQl2ugAeA4vZ1AFB4Nu6M0O4/Ho1arO52OxWIZDAb9fn9jY2NqagrG\nipVKxe/3q9VqONLhl6VqswVEUavVYrGB+kkBm2Kr1WowGPL5vMViwUx5/3+uChS8Prh5Yff3\nf//3X/nKV06dOvW5z32OHvzud7/79re//e/+7u+eeOKJfbw6BQoU/GoIgpBMJuVzWI/HI7cN\nr1QqqVQKZZkoirOzs9vb206ns1Qq0cnX/Px8v9+Xb6t43O12sZmhu0Yd77AT6/V6qn5gGAak\nqFKphHPOnj1bq9WO6vWEkH4wODU1Bc+IG+ewcBtGqIA8OjYSiTidTkmSaNlK9gI2ms2mzWYD\nqW59fV3Jhz14BIPBTCbj8Xiy2azD4ej3+2q1Gu00QgjP8/V6XRAEeJ0Ui8Xx8XGsMfxrsVjo\njBXlF9mzP6SArAfLCSsNOlxEmcmnsZjSEkKMRmM2m9VqtY1Go1qtBoNB+OcZjUaO47ACNRqN\nwWDIGwzeTocQUq1WeZ4fMjRxOp0ajcZisTAMs7OzMzk5qWSLKbi9cEuF3djY2PPPP//Od76T\nHnzggQeeeuqpmZmZb37zm/t5eQoUKPiV2NnZGQwG8mLoxnadwWCgI1e32w25AzhSOEg9xlG6\nYb80m83U4o7mhuFbOp2O5/lut0vbeDjNarVi7MVxHChNi1pty2AoiyJcbQkhkiQZjUaaUYYr\nFEWR2tKCYAejE8xb5UG0sVgMDreQU7Tb7e3tbaWwO3hggmkymZA/kclkgsGgTqfb3d21WCxY\nMNeuXbv33nsJIefOnQuFQlSpg+YxajWsKPw7lIBHCNHpdBDl0ITZTqeD1SLXT+TzedwwVKvV\nRCLh9/t1Ol04HJ6dncXT4bESi8XwLJ/Pl9PrLeVyp9PZ2tpyOp25XE7+vpg1syxbr9eLxSI+\nrOJmp+A2ws0Lu3g8fv/9999o/snz/JkzZ8Lh8P5cmAIFCm6CRCIxOjqK303kI71mYUfVhbu7\nu4FAIB6Pi6KIUZdOp6OtCHlG59jYGB14DTnvC4IAtSyqMWpH7Pf7cb7X671+/bpWq+2trpbs\ndlEU4YQHdtSQJdj58+d5nq9Wq4SQfr+Pwo4ancg/jiiKW1tbyInHHDYSiWg0GnmGrIKDgVqt\nhphgd3fX4/HACq7X6yEBBRX/+fPnfT4fQsYQZIxpLMdx9XqdFvpkr7AbskRFYw8PsNKwYi0W\ni7xdh/y60dFRlUqFVzAajQzDgGaHBmE+n0e3L5VKEULm5ubKFguXTKK3Nzo6Su9SKPx+f6vV\ngmwWUiEkoyhQcFvg5oUdwgFf81tbW1ter/f1viQFChTcEuQEu0gkotVq0V0ACoVCNptF4w0z\nzVgsNj4+vr29TWev09PTctN/sjdppe0TmgpAeyTU3oK+EfolvV4P59xzzz2lUmlycrJ57Vrd\n7YZbLNmzJZufn6dPbLfbuVyONvCQakD25rC1Wk1e2KVSqV6vl8vlaObYxsbG9PS0PJdMwYEh\nGAwiocFkMqGtBZfgqakpqHByuVy/37dYLM1mUxCEUChE82SHqij8D8LThB6EfhYP5GJYmhhL\nv0v2ssXgP9fpdFqt1s7OjlartVqt6PMFAgGdToeu29zcXMVqZbPZqUCA53nQAIbqtkAgUCgU\neJ63Wq2FQsFqtZZKJdyfKFBw+HHzv4n33nvvf/3Xf/3gBz8YOv7kk08+//zzZ86c2Z8LU6BA\nwa9Dv99PpVLywo46gADLy8sjIyN0E11YWMjlcmib6XQ6FGG0TUKHYpIkOZ3ORCKBE9BlYVkW\nJ/A8r9FoeJ5HtUefZTab4WbCMEyr1ZIk6eTJk1wikTebp6enEUjAMIxGo5GXnhcvXpQkCRpJ\nrVZLZ8rRaNRgMLjdbjnvKhaLORyOXq/n9Xo1Go0gCNFoVNHDvlEIBALpdFqn01FVgU6nQ8gE\nDE0IIRsbG3CiiUQiNJcC51ssFlqRU3NEp9Mpfwv0cVG64WSNRoNpLMhzZG8FFotFtADb7fbu\n7i7P8yqVKhqNzs/PU4mPKIoo7HQ6XdvtZiRpTq+XJKlSqbAsu76+Ln9rv98vCAK6jKlUampq\niud5yC8UKDj8uHlh9zd/8zejo6Pvfve73/a2tz322GNPPPHEl770pUceeeSd73ynw+H40pe+\ndABXqUCBgiFghIrZJTatG41ORkZGsPNxHNfr9fR6PbLFUO2pVKohSjjs6GZmZijxDqBTWoyl\nOp0OHaVhx0UKJyHEbrdfunSJ47jBYGAtFCoOB8hVKCjn5+flpeerr74Kt2GMffFZGo1GOp1u\nNptDYRLxeNxgMPA8j4+ZSCSQe/v6/DQV/A8xNjbW7Xbtdns+n7fb7ZlMZmxsDF0xu92O6u3C\nhQunTp0ihJw/fx4jWrnDIvpz8oar3EyOENJsNjUajZyBh1AKuUcx2njVatVut2PIW6vVPB6P\n2WxGtthgMOA4rlwudzodjPgJIfrpaZHjxgkRBCEej9tsNkxpKWBTrNfrEU0RDAYlSVJodgpu\nF9y8sHM6nS+88ML73//+n/70p3/1V3/1oQ996NFHH/33f//3d7zjHc8//7wyilWg4A3B5uam\n3+/H7rizs9PtdpGdCmQyGeQsYedzuVzxeHxqaioSiVAa+9jYGOWh41+VSsUwTLvdpqntQ1Mz\nuPlrtVr6LNR8cMWTJOnOO+/c3d31+/2RK1eMjYb5xAlE0GJcK5/DlkqlarUKqhb6dijsYrGY\nTqfL5/Pywg4EqU6nIwgCPub6+nooFLqRca/gYIC5OYibXq83nU5PTk42m81EIjExMYH6aWdn\nB5PT3d1dg8HgdDqtVishBHw4aoJI9pYfvZ2g3LuRkRGy54BNCOn1ejBipK6HRNZmxmIwm81q\ntRoN3UAgoFarYXpiMpk4jkNfeXJmpmo2c8mk2WwulUper7derw+Fm8GTr1qttlotu90uCEIk\nEpGLNhQoOLS4JXpKKBT63ve+l8/nz5079/3vf/+5557b2dn50Y9+JP8zrUCBgoPEEMHO7/dT\nT1dCyMrKis/nAztWkqTFxcV4PG61WrvdLp3DUi0qngL7Oq/Xu7q6Cg0sjsjfVJIktVpNpVR4\nrl6vD4fDGKjB/e7YsWO5l14ikuQ7e5am0LIsK/cuefnllwkh4C0ZjUabzYa7xEgkMjIyotPp\nfD6f/MPCbJbjuNHRUUmSIpGIood9YxEMBgeDAdVPzM7OwhwR/496vV6SpLW1Nbfb3e/3a7Xa\n+Pg4ai/ob+QvhYVUr9extOiahPkwtbXDatTr9ZQYQPZKQxD+8FzYnXS73VQqhdsAQRBcLhel\n2R09erRitRYvXZqenkb5KEnSjW52lUql0+no9fpSqWSz2ZCusa8/UgUKXhe8dmH3zDPPPPPM\nM+C+VPbA8/z8/PxDDz107Ngxg8FAjx/sBStQoIB0Op10Ok3rpHA4fOMc1u/3U/7T6Ohoq9WC\n7gFNOI7jEH+OMSjZcxTz+XzYRFHDEdkuq1KptFotMidwBAXi3NwcfGhNJtPly5cJIVqt1pBO\nd7TagdWKliHDMAjrpFcIxW6lUoEN3smTJ7GFx+NxSZKw49KT4/G4y+UCB59l2d3d3SFphYKD\nRyAQKJfL3W7XZDLl83nU4njM8zzc5q5du4Zc4MuXL4dCIawT1Gdms5nOYSVJAm2OmjKSvT6u\ntAcc5Hm+0WjI/bSxTnK5HIrCer0O2x2HwxEOh2GayLIs+AMo7EwmU81u725soOzDb8TKyor8\n08GmWK/XWyyWVCo1MTGh0WiUaayC2wKvXdg9/PDDDz/8cDabJYTYboaDvWAFChSQzc1NnufR\n06rVarlcTl7l7OzslMtlShs3Go3JZNLj8cRiscFggAQwTJcIIXTX1Gg0LMsWi0U5EYrI9LAg\n50mSJA8DIITY7Xbs0IuLi5ubmyMjI/F43FWrdfx+tOVAxZM32JLJZKvV8ng8ZC9mAHEUYEEV\nCoWhoi0Wi0H5iOMbGxs+nw8SWgVvFILBIBK9BEEQRTGbzc7NzWEG6nK5UC1tb2+jVrt69Sps\nitGEQzUmzyDGEhrSOCN6GI9RKapUqmq1ynEcZLCY0kLNEwgEtFotpBXIjaDOjizLlsvlfr9f\nqVQw8O2Pjmr2bo0SiYROp8OUlsLlcmk0GqvVilug8fFxQRCUwk7BbQH+NY/iHgv36x/5yEcO\n9IoUKFBwMyQSibGxMdRVkUjEaDTKY7VWVlbGxsboRjU5ORmLxfx+/8WLF7VabafTYRgGMgj6\nFIZhEJ0JPSzHcTfOy7rdLhoh+Xye7BHsNBrN9vY2xmGBQOD8+fNLS0uvvvrqm3M5fm4OTUFC\niCiK8lrt/PnzhBD0bzQazdTUFOJuMYeFWwo9uVarFYtFNPZoktiQH56Cg4fb7YalSD6ft9ls\n6XR6fn7+P//zP5vN5l133QV1KiFkY2NDq9VWKhWNRuNyuSC+4TgONihk784Bi213d1f+FoPB\nwO12Z7NZZg8wUMRBQohOp2s2m/ROAw+0Wi1adLlcrt1uu93uTCYDqly73Y5Go6dOndLOzZmf\nf55lWbvdnsvlxsbGNjc3u90uFXAgtlgQhFarVS6XA4EAUlLk5yhQcDjx2oUdAryBb3zjGwd1\nMQoUKLglJBKJo0eP4jHUf3RwKUnSysrKPffc85Of/ARHjh079p3vfAf56HR6lU6n5S+IzdVu\nt2ezWepIN0QVV6lUcjkF3nFubg4zLI1GA88Iu93ebDZHymXhzBns3CzLBgIBSgEED91ms5XL\nZRSUb3rTm/AtRMSOjY3JHdFjsZhery8UCnq9fmRkpFwu5/N5hWD3hgMJDd1ul+on3vSmN42M\njNTrdTRT9Xp9q9VaXl4eHx9fX19H9w61viAIcp9h+oK1Ws1oNNJZPyHEYrEgGQJiC+qWAn23\n/N4jk8ngBqPZbHa7XUEQLBZLNBpdWloCN85oNA4Gg1gsdurUKd899xi//OWVa9dmZ2fPnTvn\ncDi2tra2trbkkp1AILC+vl4ul0VRbDQaIyMjtVotGo3Cw0WBgkMLxdtTgYLbDM1mM5/PY4ok\nCEIikZA3w7a3txuNBqxZCSEcx0HlmslkBEGg8a/yQpAQAne6ZDKJZ2GeJX9TDL8Gg8GQTevY\n2Biqt8nJyXA4rNPp0um0RqNx1Wobe30UnufldVg4HO73+2gxSpJksVjgYAejkxvJc/F43Ol0\nsiyLTXd9fd1msw15nil4QxAIBED3hH6CEALruJ2dHbS1oK7Af+iFCxdCoRAspvF0OkzHEY1G\nI4rikIUNmAO0CsRyRRqKKIrUXhteJ9CJ47goim63OxwOQ+THsmyj0YBuVxAE2/HjDCHbL76I\nRQV5+NCk1e/3I1XZbDbv7OzgfmPI8U6BgkOI1y7sPP8THPAVK1DwW45EIqHVaqE93NzcHAwG\ncrXp8vJyKBSiaTFerzcejweDwVQqRc1BOI6j5g7YL7vdrtfrLZfLg8FAXtLJ679Op4OAMvpd\nnucxliWEzM7Odrvd2dnZ1dXVQbOpKxbX90yMe72e3En4lVdeIYRglMZx3KlTp/AuaNdVq1V5\n10SSpEQigaEz5rMbGxuKL/EhQTAYLJfLvV7PYDDkcjlBEObm5lqtViwWc7lckN8ZjUZYDcfj\n8fHx8cFgoNFoQI+j9w9YVHhMY1EIIaIoFgoFamtC679cLgdrHoZhaHMXNwk00dhgMGg0mkQi\nYTKZDAYD7O4QNbuzs0N8PpHj2mtrfr+f4zhMeyORiPzTQX5ksViMRmMqlRofH+92u5FIZEgq\nrkDBYcNrF3ZTvwy1Wp3NZrH0fT4ffg2y2eyxY8fe8573HPAVK1DwW45EIjE+Po4NLBKJjI2N\nUdKPKIpra2tLS0vXr1/HkaNHj8ZiMbPZLJ98VSoVeX0GfrpWq8W0dDAYUMkhPQ0RT/IkMULI\n1NTU+vo6OHk7OzsMw3g8nlqtNtrpEFHMWyzwTHG5XFRl1e12t7a23G439LCSJNGZcjQatVqt\nNpsN7mVANpttNpuFQkEUxVAo1G63k8mkMoc9JECOCCQLgiDkcrnR0VGTyYT8VkglCCErKysW\ni6XT6QwGA4/HAzc7Qkir1ZK7xyEHJZlMyvU61WoVi4eGjEFtA/NtyLTJntK2UCiAKoCACrzg\n5ubm9PS0IAiCIDgcDpPJFIvFCMd1nE7N7i7LsqCNmkymcrksb0jrdLqRkRGDwSCK4s7OzsTE\nRL/f7/f7Q7beChQcNrx2YfcLGb7+9a9Xq9X777//6tWr6XT61VdfTaVSKysrb3nLW1ZWVj75\nyU8e8BUrUPBbjiEHO/ngMpFItNvtyclJuj/5/f5yudxsNkGPg4nJEPtbpVKp1eqtrS3qBCb3\nocAD2JKVy2X6LPjV4Yjf719ZWUFTRKPRHNHrBa22YTRCxiivw1BxgkQvSdLMzAyqSRidDAaD\noW5cPB632+21Ws3hcBgMhnA4rNFoYGWs4A2HWq12uVwGg6FYLFoslnQ6zTDM7OwsJMxkj7lH\nwBQAACAASURBVGZXLBbxX7ayshIKhbCoUAvKk8Fw71Gv12nlh4MulwsnoHrr9/ssy1osFpR6\nVDnBsiyih9ELRGLs+Ph4OBymNw9Y2NFolBDCTExYKpXNzc3Z2VkIMqiDMYXf75ckqdlsgg/q\n9/vNZrMyjVVwyHFzjt1jjz1mMBieeuqpI0eO0IMLCwtPPvmkKIqPPvrofl6eAgUKfgnVarVc\nLqOwKxaLpVJJPrhcXl6emprKZDLYO81mczKZtFgsW1tbkD5QaSF9CkLZwXgTBAEuEoiIJTIr\nCgzI5Fx1juNgPEEIWVhYaLVa4Mh3u91JSSrabBiedTodeWGHIAo6KaZh0zA6Gfo4hBD4KrMs\ni4JvY2NjZmZmyBRDwRuIQCAgSRL0E9AozM3N9Xq9eDyOJFlw3fBfduXKlfHxcaihsbSghqb3\nD3DPDgaD8regQ1jasUNRSPaafJjhQsrt9/tBOVCpVL1ez+l0hsNhSMjBw6vX65lMptls6ubn\nbdXq9evXl5aW8LIqlWooEBZudrVajef5VCo1Ozvb6/XQpd7fH6sCBf8L3Pzv47lz5+67776h\nEQwhxGg03nvvvS+88ML+XJgCBQpeA4lEwmAwOBwOQkgkErFarXRwKQjCxsbG0tLSpUuXcGRm\nZiYajXq93m63y/M8pdPJSUKozMB8IntNkaEkMbDR3W63fD8LBoPhcBgZFfV6neM4dAdNJpMh\nkynabIQQTG8pE7dareZyuUAgAGNzo9FIe2/QyfI8L9/UB4MBvFQkSZqYmICRmEKwO1QIBoP1\nej2dTtOkE0S9ZbNZj8fTaDQYhlGpVCBKZrPZYDAIlxzKDZCX6UirQ7lGAW6c/EyWZXd2dtRq\nNcuyer2edvIIIa1WC41nURTVajXHcbVaLZ/Pe71etPF4nsf1kLExe622vb3tdDp5nkdYxVBh\nFwgE6vW6SqVCnuz8/Hyr1UJpuK8/VQUK/je4eWEnSRI1oxrC9va23EpegQIF+w3MYSnBTt7f\nikaj/X5/ZmaGhiOdOHFia2sL1RiY7AzDDPn6siwLKSs8JliW1Wq1NE8WT+E4DsZg9Agh5O67\n78YbjYyMXL9+Hb53arV6aWmpdulSyWYjhOh0urm5Ocrtu3LlCsMwdrsdX1LZBD6LWq2enJyU\n2yMnk0lBEMDuDQaD8XicZsUqOCQYGxsD88xut2cymVqtBv0yy7IjIyOiKBoMhlqt1mw2odQu\nFAperxedAmRL4HXgP1ytVhmGoW52WISFQgGNPbA/CSEqlarT6Xi9XkmSTCYTXUUMw6RSKRD7\nBEHo9/sYzkYikWPHjuHVrFar0WiMxWJkfNxWreIew+12V6tVSZLq9ToNwSMym2K1Wr2zs2O3\n2x0OhzKNVXDIcfPC7tSpU+fOnfvXf/3XoeP//M//fP78+TvvvHN/LkyBAgWvAUqwA4lbTrBb\nWVmZmZkRRRH9NqSH9Xq9dDpNqyWO4yiRDkd6vZ7VajWZTFSoiNadXA+LJHXK20OvBRFkhJCF\nhYVqtYrts9frLS0tcYlE2W5nGKbVasnnsJcuXWIYBtxzhmGofV2tVstkMtVq9cbACYgrfT4f\nz/Pr6+sTExNU26vgMMBkMpnNZo1G0+v1TCYThKVzc3O08abRaBAUi/n+lStXQqEQGr1YRei3\n4YggCCzLttttCCawCHu93ujoKA2+I4T0+32e58HUrFQqVFfB83yr1ZqcnFSr1Th/c3NzZmYm\nHA5j3sowDMrQaDQqjY3pKhXS6eRyOcxYrVarXq+Xm54wDOP1elUq1WAwSKVSkiRRSsAB/5wV\nKLh13Lyw+8IXvqDX69/73ve++93vfvzxx5966qlvfvOb73rXu973vveZTKYvfvGLB3CVChQo\nIIQUCoV6vY7CLhaLMQxDszX7/T7msFeuXMERn88Xi8W8Xm+tVqOd9cFgQKNgyV66TL1ex34J\nQQNad5Cs4jTkgcqZeS6XCwx0QKVSgTtlNpvtZrO5UinZ7RzHqVQqeNQRQhDwOjk5CR66z+ej\nTL6rV68ajcZ2u32jg53JZALBTpKkcDis6GEPIYLBoFqtzmQy09PTKOympqYYhonH4waDAesN\nARWEkI2NjVAohPYYVheWgVymQwgB2YBCq9WCSId/MZ3vdDqIudPr9VjAqBFVKhU1QAbNDgaN\nSKcFza7RaBRNJiJJ1lrt6tWrSLTTaDQ3ZosFAgFkkYEDOjc3V6/Xs9msXEikQMGhws0Lu8XF\nxaeffvro0aM/+MEPPvzhD//e7/3eRz7ykR/+8IenT59+5plnhpjOChQo2D8kEgmLxYJmRiQS\nmZiYoJ5z4XCYEDI9PX316lUcOX78ODIbiIwzJ/f1pez1VquF5grLsgaDgQY9UdI6NkU5we6+\n++7b2NiQJEmv16+trYHexPP80aNHr/zf/8sKQnlkxGg0Tk9P02bh5cuXGYaxWCx48bNnz9JX\nu3btmsPh8Pl8mLgB7XYbSVCiKE5MTKRSqWazqfzBOYRA3Nbu7u7MzAykzWq1emxsrNvtulyu\nRqPBcVy5XBYEgef5er3ucDjkymvaG6ZdN0JIrVaTv0W9XqdrDEdAuIRg1m6349YF5d3u7i71\n2UY6hUajicViU1NTMD3RarUmkynSahG12tVqwetOrVZ3Oh04fstNWGBT3O12DQZDKpXCKjUY\nDErTTsGhxS2Jy+6///5Lly6dP3/+O9/5zpe//OXvf//7ly5deumll+6+++79vj4FChRQbG5u\nol0nSVI0Gh2aw87NzcExGDvl+Ph4JpMpl8sISieEQKNKZO4SvV5Pp9NRkzmoYuVBT0iVJYSg\nzUaPIzmKEDI3N5fP57Va7dbW1mAwWFxczPziFwLP18xm+RxWFMXr16/zPI85l0qlohe/vb1d\nLBZvLNri8bhKpcrlcmq12uPxbGxs+Hw+o9H4Ov9MFfyvEQwGUYWPj49LkgTm5dLSEu4TQIND\n/gSac4lEQl7EI7xYkiQUbbjHoL7EQKlUQulG28Ycx7VarampKfj4yI/n8/lgMMiyLIy4cQsU\nDodPnz6Nc/R6vUajiSUSJBAIiiJIdS6Xq9Vq1Wq1fr+fSqXoWwcCAUEQ9Hq9yWSCWePMzIxK\npVIKOwWHFrfqGsCy7PHjx0+cOHHPPfe85S1vOXbs2FDMnwIFCvYV2DJR2GWz2VqtRsOXer1e\nJBJZXFxEXCa20u3tbY1GUy6XaZODYRiMWdGQAMe8VCrhIEq6RqMB3wf6pmQvl4xeic1mi8Vi\n6K8YjUa1Wj0+Pp7P59FN1CSTJZuNV6sHgwEVOsRisV6vNz8/jwGWfKJ65cqVQCCQz+dvLOw8\nHk+/35+YmGAYZn19XdHDHk643W61Wt3v96vV6tjYGKaxs7OzoigiX0SlUnW73VAohCiwa9eu\noQSEUpsmhqE4g7GOIAhyK4Z6ve7z+XCcHtRoNKgOC4WCnHkpSZLH44H7CcMwyWQS8nCoX0H9\nbLVaW1tb0thYUJL6/X6tVpudnUUQ2cjIiFwbq9Pp7Ha70WiE4wkhZG5urlarbW1t4eMoUHDY\ncEuFXTqd/sAHPmCxWBYXF+++++4XX3zx6aefftvb3ra8vLzf1/er0Gw2t7e3a7Wa4iek4LcE\n2Wy21WqhsItEIi6Xi/q4rq+v8zw/OTn50ksvEUIkSVpcXMSgChlKOM3pdMpjNwVBMBgMarWa\nutOhvzL0O9Xr9Twej/zgmTNnwuGwKIoqlSoWi1FfiWPHjl24cMFWKpXtdovFEgqFaNzT5cuX\nyR6bihDy5je/GQ/6/f7q6qrZbLbZbPDSo4jH4xqNBhLLUqlUKBQUgt3hBJx71Wo1prFoZen1\nerfbXSgUTCYTmrvwxCGEbG9vj4+PwzQb60o+SCV76wQsAgD3KlDO4kUw2E0kElB5ezweKoYl\nhBSLRRSI6Oc5HI5Op7OzswMRRqfTwYi/PjLiarUIIdevXz9x4gQhRKvVWiyWG93sIOnIZrO9\nXg8UCLVaPRRBpkDBIcHNC7tcLnf27Nlvf/vbU1NT73//+3FwZGTk5z//+dmzZ4dSk/cPkiRd\nunTpE5/4xNTUlNFoNBqNY2NjFovFYDBMTU19/OMfp9QiBQp+I5FIJBCIRG4InFheXl5YWOA4\n7r8t9RkGBDsMueh+KXf5hzxQnhImCAJKQNgR4yC1vsOX2Ibn5ubgWBYIBFKplCAImUxGEITF\nxcVLly7ZS6WS3d5sNmkd1uv1wuGwVqsFEdBkMtHh7+rqKiEkm80eOXJkaPRWqVRgYBYKhdbX\n1+E08fr/WBW8HggEAizLorCrVqvQScDT3m63dzodtVodDocXFhZ4nodpIjpqQ6+Dog3/woiE\not1uowNH28mdTicej2May7IsfTV06eCqg5NTqdTo6GgkEjly5AhOAwMhp9PxOzso0fR6PYzx\nYJInt3L0+/2NRgPN72g0ynHc5OSkQrNTcGhxS6rYaDT6+c9//urVq1/4whdw8K677jp37lyt\nVjsYVWyv13vf+9538uTJv/3bvy0Wi/Pz8w8++OC73vWuBx98cHFxsVwuf/WrXz127Ngf//Ef\ny4V7ChT8JoEanbTb7VQqRQu7drsdj8cXFxcHgwEGpkiPwFaErHRCCMuypVKJvppOp4OxcLFY\nxOyVOtXJR2DYF+kWyzCM0Wjc3t7Gl16vV61W+3y+XC5nt9utVmupVLKXy1WHo9Vq0dHq6uqq\nJEl33HEH5rDHjx+nr3/lypWpqal8Pn/HHXfIPyz0sEjwtFqtGxsbSrvuMCMYDMLd12q1IuyB\nELKwsCBJEqox/G8uLCzgT/Tq6qrf78fixD0GNagjhGAd4raEvkW5XMZ9BS3g8GB0dJQQksvl\n6HFIZaempniexy3K6uoqTE+OHz+O10QQxRbDkM1Nh8MBw2G3293tduv1OsuydJETQgKBACgK\no6Oja2trhJC5ublGowHnyP3+2SpQ8D/FzQu7J5988vjx45/5zGeGYnxOnDhx4sSJ5557bt+u\n7f/hi1/84r/8y7/cddddL7zwQj6fv3Dhwk9+8pN/+7d/+8lPfnLhwoVcLvfyyy8/+OCD0HYc\nwPUoUHDAEEURAyxCSDQaValUNLNhbW1No9GMj49fvXoVO5/P54tGozabDZZdOOh0OuUtDcgm\nrFYrHcvSOamcTicIgtlslu9eJ06cCIfD6JGkUimO4wwGA8uyx44du3z5MiNJ1nJZCIVGR0ep\nE/KlS5dEUaRUv7vuugsPKpXK1tYWz/Ojo6M0PwOIxWKYzM7MzLTb7WQyqRDsDjP8fj/DMMVi\nESIJzCitVqvZbM7n89T4kOM4DEzX1tbGx8fxJdYn/sVKo+uNjvIJIeVy2el0ogrEEZZlbTbb\n7u6uWq1uNpujo6Og3NEgClov4qqy2Wy9XrdYLKDZVavVbY4j6fSU39/tdjudzszMjCAI0F7I\np7EgEZpMJpPJFA6HB4PB9PT0YDCQJGnIG0WBgsOAmxd2+Xx+aWnpNaUS8/Pz4MbuN7797W8H\nAoHnnnvu3nvvpdsDBcdxp06devrpp48cOfKtb33rAK5HgYIDxu7ubrfbpYXd1NQUvdFaXl5e\nXFxkWZYmiZ0+fToWi2GeRblx8hszENXRO6GbLmZPYMGTvSEsCjv5r/+pU6cwgbLb7dvb2+12\nu1wui6K4uLh44cIFc7XKDwY5s5k22Or1ejKZNJlMmLo6nU5aQV65csVut29tbQ216yATwZtO\nTU1tbGxotVq/3/96/kAVvK5Qq9UgfV69enVmZgYLgxAyPz/f7XbNZjOavlevXoVRcKlU8vv9\nnU4HoSZkb/xKrXZutKEWRRHqB7K3mMEfRRQsIWQoUmVzcxPBZejb6XQ6k8kUjUYnJyeRNtvp\ndDoeD5GkIxYLIWRtbQ1udoIgOBwOeWHHMMzo6KhOp4O6IpFI6HS6YDBoMpmUaayCQ4hb8rG7\nePHijWQISZJWV1fn5+f358J+CalU6q677pLfvd0Inufvu+8+ef9cgYLfGCQSCY/Ho9frJUmK\nxWJ0DttsNre2tpaWliRJovmVY2Nj29vb9Xqd5sMSQuRBSZjDCoIA136VSsVxHOo8xE7AIQXb\np3yAq9VqEamJpHae5y0WC1KbjEZjoVCwl0oiyyYZhjbYrl27xnHc0aNHsbWfOnUKxyVJunbt\nGrxqFxcX5R82lUp1Oh3wtMbHxzc2NmZnZ4cmBgoOG2BTfP36dZ/Pp9VqQb9GqWQwGAaDgV6v\nj8fjR48exfmdTofn+WazKTeNI4RgPoulK28eE0Jo+5nuBeVyudFoQB5eqVRoyCzLspVKZXp6\nmnr9YOgfDofRMGYYxmAwcIGAqFLZazWO49bX141GI34X1Gp1LpeDWhzw+/39fn93d3diYgJ5\nYnNzc51OB26O+/HzVKDg/zdu/rfykUceWVtb+8QnPkGTIoEnnnji4sWLDz744L5d2/+Dz+c7\nf/78UDD5EARBOHfunHJbr+A3EpRgl0qlYN+F4ysrK0ajESIGbJBGo3F3d5dhmG63C+sTQojV\napWPUzudjlartdvt6Iv0+30awQQBI/oiHMfxPC/fXOfn59GiYBimVqtpNBq73Q4vJGjkR8rl\nhsNhcThcLheecvnyZbCs8CWt4TY3N6vVaq/XC4VCclYfISQej7tcrkqlgg5NLBZT5rCHH9PT\n08ViURAE+MZhGuvxeDQaTaPRAM2u0Wggf4wQsry87Pf76f0DsiKo6LXX691YMJXLZdx+UC61\nVqs1m82tVothmGw263Q6oaXFS0Gjg64EAvfi8bjNZlOpVJIkDQaDgShWzWayuWm1WhFQ63a7\nkWCLMpS+td/vr9fr1Wo1GAyur6+Lojg3N9dut1ut1q/KUleg4I3CzQu7Rx999MyZM1/72tdC\nodBHP/pRQsjXv/7106dPf/jDH15aWvrsZz+7/xdJPvjBDyaTyQceeOAXv/jFjfIIQRAuXLjw\n8MMPX758+YMf/OABXI8CBQcJQRCSySQ1OvF6vdTcdWVlZXFxkWEYmiS2sLAQi8Vg5Et7ITgf\nux3P84IgdLvdZrPZ7/ehtOh0OpIk0SEpqsDBYOBwOIYCJ1ZWVvCCiUSi2WzWajXMYV955RVC\niK1UqjqddA6bzWaLxaLD4YBoPRAIyOewk5OTkUhkaA5LCAEHn2GYubm5eDwuSRL1w1NwaDEx\nMaHVal0uF6axkUgEKycUCuFuAa2BSCSC5RGPxycnJzmOA+mNEAIhxVDpJufelEolzFupQQ/e\nIhwOu91uQRC8Xi+qRhxPJpNgGnAcVyqVJiYmMOX3eDygmZZKpbLF0lxdHRsbazQavV4P9nvJ\nZHJ8fHyosOv3+zabrdPpdDqd7e1ti8XidruVaayCQ4ibF3YqlepnP/vZl7/8ZY7jnnnmGULI\ns88+G4vFPv3pT587d47+md5XPProo7//+79//vz5++67z+l0njp16qGHHnrPe97z9re//fTp\n0263+9SpUz/96U//4A/+4C/+4i8O4HoUKDhIIOkyGAySXzY6qdVqyWQSpCWMhwghp0+fjkaj\nvV6PZkhIklQsFsnexqnRaBiG0ev1KOZ4nkdtRwgBHxzTKDxRzsFQqVQajQYvNTo6Cmf/Uqnk\ncrm0Wm0ulyOEOKrVjNEon8PyPL+0tISEqJMnT+J4t9tdW1tzu939fn+IztHv93d2dtC/mZyc\nXF9fn5iYoA4XCg4tWJadm5uTJCkSifh8PvjGEULuvPNOQojBYKhWqwzDLC8vI7Ko1+tZrVaE\nneD/l2VZWtjhS5Zl5YNasN+wONHn63a7tVotnU6jWAS7gJ6/s7MDjRFep1QqwT/5yJEjaA1K\nktR2u9tra/glSiQSeJ1CoTBU2BkMBpvN5nQ619fXx8bG6DRWkiToZBUoODy4JdqKVqv91Kc+\ntbOzU6vVlpeXi8VioVB47LHHhgYo+weVSvVP//RPr7766sc+9jGbzXb9+nWoYv/jP/7j2rVr\nZrP5Yx/72Kuvvvq9731P2QAU/OZhfX09EAhgpJVOp6mNyPLystVq9Xq91Wq12WyiM8HzfKFQ\naLVatCZDCCYeY8zE8zxSlQgh3W4XeyR1nQAnHdunPOk8FAqFw2FqnmIymWAsd+LEibW1tf/W\n3lardZcLu6kkSVevXh0MBtQ/j3byVlZWeJ4vlUozMzO4DArIJjKZDMdxPp+PNngUHH4sLi6m\n02mDwRCNRv1+P0xPQqEQ7gFYljUajTs7Ow6HA3+oS6USaHZDvolgCOAptGFMQ1CwMnEOy7Ja\nrVar1aLFsLW1RRc2asRQKMRxHF7/hRdegOkJmsSCIKhUqo7bTTY3g8EgwzArKysjIyM8z4ui\naLFY6vW6nJkKm+JCoeD3++HgMzs7W6/X4Z59UD9jBQpujpsXdl/72tf+8R//EY9NJtPi4iKM\nHw8YDMOcOHHiq1/9ajweh1IdHJ1WqxWPx7/61a+eOHFCSTlT8JsHSZJWVlbQUYCNqtfrxbdw\nnGEYDIMkSXK73ZFIhFLLsRfKnURUKpUoiv1+v1gsgr1O9gavN3bfTSaTnJl39uzZ69evo8kX\nj8fb7TbqxYWFBcxhiSSZcjnD0aOoFBOJRKvV8vv9Fy5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Oh8COYRgMoioUCo1Gc3p6KooiGgOQXdPr9dI0m9Ql1mKxSDNhoiiiQ4Y+QA6P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\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ytrbW1NEiWWeE3Y7XaZTBYIBE5PT0H0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\nQuwAACbEDgBgQuwAACbEDgBgQuwAACYCwmeVaN8yvfgAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(y, type=\"n\", xlim=c(1,18), \n",
    "   ylab=\"Predicted/observed\", xlab=\"Week\")\n",
    "for(i in 1:100){\n",
    "     sim=sim.cb(S0=floor(coef(fit)[\"S0\"]), \n",
    "     beta=coef(fit)[\"beta\"], I0=11)\n",
    "     lines(sim$I, col=grey(.5))\n",
    "}\n",
    "points(y, type=\"b\", col=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further examples\n",
    "----------------\n",
    "\n",
    "### Influenza A/H1N1 1977\n",
    "\n",
    "The `flu` dataset in the `epimdr`-package represents the number of children confined to bed each day during a 1978 outbreak of the reemerging influenza A/H1N1 strain in a boarding school in North England (fig. \\[fig:flu\\]). This subtype of influenza had been absent from human circulation after the A/H2N2 pandemic of 1957 but reemerge (presumably from some laboratory freezer) in 1977. The school had 763 boys of which 512 boys were confined to bed sometime during the outbreak. None of the boys would have had previous exposure to A/H1N1.\n",
    "\n",
    "The typical time of illness was 5-7 days. Since the data is number confined to bed each day, the data is not incidence but (a proxy for) *prevalence*. The data looks pretty log-linear for the first 5 days. Family studies have been used to estimate the serial interval for flu between 2 and 4 days . Volunteer studies shows the mean infectious period around 5 days ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>day</th><th scope=col>cases</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td> 9 </td><td>192</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>10 </td><td>126</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>11 </td><td> 70</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>12 </td><td> 28</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>13 </td><td> 12</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>14 </td><td>  5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & day & cases\\\\\n",
       "\\hline\n",
       "\t9 &  9  & 192\\\\\n",
       "\t10 & 10  & 126\\\\\n",
       "\t11 & 11  &  70\\\\\n",
       "\t12 & 12  &  28\\\\\n",
       "\t13 & 13  &  12\\\\\n",
       "\t14 & 14  &   5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | day | cases |\n",
       "|---|---|---|\n",
       "| 9 |  9  | 192 |\n",
       "| 10 | 10  | 126 |\n",
       "| 11 | 11  |  70 |\n",
       "| 12 | 12  |  28 |\n",
       "| 13 | 13  |  12 |\n",
       "| 14 | 14  |   5 |\n",
       "\n"
      ],
      "text/plain": [
       "   day cases\n",
       "9   9  192  \n",
       "10 10  126  \n",
       "11 11   70  \n",
       "12 12   28  \n",
       "13 13   12  \n",
       "14 14    5  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8q51ZAms3vPe0OazR7kN5QKIfW9mXoCMG0J/RFwqyGlX8XK38DaN2f1zvEbSoGQ\njkUtJp4AzHPXfpR6BMuvI52fXokxVuphrr9JSh8S7i2mlefKUl+fncfFT05vPM5nmDz0IdXF\nvcV0cjrufeIJOJxrt3beS0t4/p65R4GQfsa9xfQykuexrmBYDum7Br6zv7ts5DaSR4GQBuPe\nYnrZ5iI+Am41pA1R7KaXFryQzMru5TcUfUhz6F+YgIA8wPX3eAJnNaQu7GPf42zWj9NEXuQh\nAQTGakjlr8t5cmU1LvP4ISTQjOWrCOWeq9qrPJd5/BASaMZqSD1rnPc9nqnYg9NEXggJNGM1\npD8SbvQeKN56fRTP35xHSKAZKyFd71WLuZJaJ7nY1fa49veu4U1KN7vvANHqYMGB+5qVbjJ8\nF9HqVkIqdTGOU5GF9H3cVTMWPNWo9Fqa5SF4a0o3emrBjKviltEsj/sjFXSq7CjvZVwybk+i\nPnULAnQh6Xbv2ZHukWVPkayPkC5aN+e3/1OKLyBZH4I2v3iK7/FChVkk6yOkgob0zXnSfgLJ\n+hC0h5JznvQZSrI+Qirojtz7zd/0H5L1IWj33ZTzZNAAkvURUkGPNfE/uqu+SrI+BO2VqjkX\nKW1Mc58XhFTQtrBFvsfZMeS/ugyBORjjv9vvwjCed3MwDyFd5LGoqTsytj0S9grN8hC8V8Ie\n2Z6xY2rUYzTLI6SLven9xfmkeUSrgwXzkrI/dZXeJFrdckjz+1yfg9tMtKcI7f4R1+HS1L4f\nd5OtbTWkNxiLtdGZDQDBsRrSFbHLBNxECCGBZqyGFHkPv1nyISTQjNWQKg/nN0s+hASasRrS\npEpH+Q2TByFB0J44SbGq1ZAyBtZ+f/uJk178hqIL6YY3aNYFbtzVSW7gZzWk+HiWi99QZCEd\nxX3M9fdyqVSCVa2GdHc+fkORhfRG6UySdYGj8+VfJFgVZzYU1GUwybLA1ePVCG6AgJAKOIN7\nudjBieIE95RFSAV8Uuw8xbLA2dgGAk4SKIKVkKpfjONURCH160mxKvC2N+IL6WtaCYldjONU\nNCGll6C+yQ7wcUc76UviW7t8X4WfIFgV+NscskL2kggp33DcFMkuunSXvSJCyuOujN+LtYvl\nIZslr4iQ8vzq4nmvNCDVeojkBRFSngdbyF8TBPk0cr/cBRFSnsunyl8TBHHXfVDugggp13a2\nRfqaIMxbcXKvAY6Qck2tJX1JECf9sqelroeQcrV4SPqSINAz5aWe74WQcux3/Sp7SRDpdAmp\nv6SJkHK8XFH+iY4g0oTaWRJXQ0g5Ogi5igvQORy9UOJq8kNK3ZNS5N/98kM6FfG15BVBtLub\nSVxMakjuNaOrxzLGoquPWm+4ofyQ3o9Pk7wiiLYj9Ad5i8kMKa0XYwlNk7slNy3JWH+jXweW\nH1KPfpIXBPF6dpa3lsyQJrGWy/35ZK5MZlMMtpQe0oXin8hdECRY51onbS2ZIVWrkn9kP6NB\nDYMtpYe0OPK03AVBhutvl7aUzJDCC/4m94gIgy2lhzS4i9z1QIqvw6Td50Xuv0gX8p5nNk4y\n2FJ2SFnlcYVVW2oyStZKMkOanP8z0qpkZnTPXNkh/RiCe8ba0kcxIi5NXxiZIaX3ZiyhWYfu\nHZuXYqxvusGWskO6r43U5UCWzBqyLgQu+XWkkYlRjLGoxJFrDF+UlR1S0gypy4E00i4ELv3M\nBnfK7sLPbEhfMC/PELkhbWA7ZC4H8ki7ELjkkE5vyL37y4Fdl7xrT62kPKXlhvRoQ5mrgUyy\nLgQuNaStbRlzdfNfYqSF0V4kf2vXaJLM1UCmE8XkXAhcZkj741mrPuVYJd+xfYVC2i3xBXCQ\nbYycC4HLDGkge9fjyRrD2nh/T0ShkGZWxa8i2ZekC4HLDKnG1d4/s3qwNz1KhXTNGImLgWxy\nLgQuM6SYAb6Hg8XLnlQppGNhy+QtBtLJuRC4zJDq1/PfWPIldnOWQiG9VYrgBm8gj5QLgcsM\naTwbdMj76O7ExqaqE9LNg+StBQSkXAhcZkip9Rmrti37ydGWrES8KiGdjfmvtLWARCsJFwKX\n+jpS2sx2FXxHms9NrGB4YzKZIS2IOSttLSAh40LgVFcRyty51OC9MkPqL/1WOiCZjAuBO/5y\nXOkl35W1FFCRcCFwx4f0TfhxWUsBFQkXAnd8SPe2l7US0BF/IXCnh+Su8pKklYCQ+AuBOz2k\nVa6/Ja0ElIRfCNzpIU2QeVlbICP8QuBOD+mKJyUtBLREXwjc4SH9yTbJWQiIib4QuMNDeqqm\nnHWAnOALgTs8pKvGy1kHyMnsRUgAABUmSURBVAm+ELizQzoU8ouUdUABYi8E7uyQXi0n8+6I\nQOoroRcCd3ZIN9wjZRlQgtALgTs6pNORX8pYBtQg9ELgjg7pQ9zv0kmEXgjc0SH17itjFVCF\nyAuBOzmkC3EfS1gFlCHyQuBODul/kSkSVgF1CLwQuJNDGiLxptegAoEXAndwSFnlZ4tfBJQi\n7kLgDg7pJ9zv0nHEXQjcwSGNay1+DVCMsAuBOzikWtPFrwGKEXYhcOeG9AfbJnwNUI6oC4E7\nN6TH6gtfAtQj6kLgzg3pyonClwAFCboQuGND2utaI3oJUJGgC4E7NqTnK+N+l44k6ELgjg2p\nnchfTgGFibkQuFNDOh5mdDcMsDExFwJ3akhzSuJ+l04l5ELgTg3p1gGCFwBlCbkQuENDOhe7\nSOwCoDARFwJ3aEiLYsT9riSoTsSFwB0a0oCuYvcPShNwIXBnhpRZeo7Q/YPaBFwI3JkhLQ0V\neGEmUB//C4E7M6RR1wndPaiO/4XAnRlS4gtCdw/K434hcEeGtJrtErl7UB/3C4E7MqSJV4rc\nO+iA94XAHRlSvcdF7h10wPtC4E4M6S/2h8C9gxYyW/G9eIMTQ5peXeDOwZmcGFLrcQJ3Ds7k\nwJAOh/4kbufgUA4MaXa5THE7B4dyYEidh4rbNziV80I6E/U/YfsGnaS90KFKo4GcLiblvJDm\nFhPwi8agn5PNyo57Z+ZNYa9x2ZvzQurbW9iuQSe31T3ifXg7lMu/SY4LKT3hI1G7Bp0cDFnm\nf9J5II/dOS6kL8JPito16OR/MTkXbnihHo/dOS6ku28QtWfQyoJSOU/e4HKii9NCyqrA52dL\n0N061z7/k9EdeOzOaSH9EnJA0J5BL+46/tcTd8e9xWN3TgvpgasE7Rh082PUwI0ZJ+df1p7L\niS5OC6n2U4J2DNpZ0YRFsKix57jszGEhbWJbxewYdHRw6doLnHblsJCeuELMfsHpHBZSs4fF\n7Beczlkh7XP9JmS/4HjOCunFSrjfJQjhrJCuv1fIbkFrHxzjsBNHhXQy/FsRuwW9NX6Aw07k\nh5S6J6XI768EhfRuQrqI3YLe3oxLsb4TqSG514yuHssYi64+ar3hhoJC6tZfxF5BcxcqzLS+\nE5khpfViLKFpcrfkpiUZ6290M2QxIZ2LXSBgr6C9x6tavzO3zJAmsZbL/RNnrkxmUwy2FBPS\nf6Nxv0soxPHYjy3vQ2ZI1arkXy0ho0ENgy3FhDToZgE7BRsY1tTyLmSGFN6zwBsjIgy2FBJS\nZhku58uD/WwPWW51F3L/Rco/QzCzcZLBlkJCWhZ6hP9OwRZuudXqHmSGNDn/Z6RVyczo1ipC\nQhpzLf99gj38GLLF4h5khpTem7GEZh26d2xeirG+Ri/pCAkpkcNBTrCpFsMt7kDy60gjE6MY\nY1GJI9cYvigrIqS1bCf3fYJdzLV63zHpZza4U3YXfmbDieFD87QRENKkxtx3CbaRmfSEtR2o\nc67d8WFiQ2owmfsuwT5mlrV2JWt1QipIwLd2u9gG3rsEGzmdYO3FEceENKMa7z2Crdxfz9Lv\nqjkmpDb38d4j2Mq+iC+tfLjMkOIvZrAl/5COhv7IeY9gM/2SrXy0zJBeb8pYtYZ5DLbkH9Ib\nZXG/SzC0wbXOwkdL/dYuoyNbZGpD/iF1uYvzDsF22g2w8MFyf0ZaTBXSmajFfHcI9rMkfG/w\nHyw3pP2xn5najntI83C/SyiK+4qHgv9ghxy169ez6G3A6WaXCP7LzhkhpZf4gOv+wJYulH8x\n6I91RkhfhZ/guj+wp0cTgz6264yQhnG5KRvY3ZHooC+P44iQ3JVf4bk7sK2hrYL9SEeE9KvL\nwnFNcJBtIb8E+ZGOCOnBFjz3BjbWpUeQH+iIkDpO57k3sLHvQ/8K7gMdEdJ53MwFTGo+MriP\nc0RIAGZ9EBPcTV4QEkABGZdNDerjEBJAQdMrpgXzYQgJoKCU+HeC+TDbh3Rsu/VbdoCT3Fc/\nmGNT9g4pa0YVxiJvCfKIJjjS3vBvgvgoW4fk7pvw3O/7vmif8AePvYFD9LkhiA+ydUjzo373\nPmR1xZkNYN5qVxCXQLR1SDcN8T9uZ1bvNQBO0vbOwD/G1iHVnJ3zJMHcpSIAvP4beSDgj7F1\nSHVyf3uimLlLRQB4ues8EvDH2Dqk3r39j6tde3jsDpzi1ZIB37bb1iF9H+q7Cu3ZVp157A0c\n41zpgH8T1NYheSaGDZ///fO1k/Zx2Rs4xsSaWQF+hL1D8ixuXzL8ivtP8tkZOMbhqE8D/Aib\nh5QN1/yGwA1uE+AH2D8kgMBtDfk1sA9ASACF6NQ7sO0REkAhvg3dEdD2CAmgMI3HBrS5rUN6\nYxePvYAjvVs8oIO9dg5pX9gyDnsBZ0qv8nQgm9s5pMnVcRkuCNq0SoFcvMHGIWVVDeivFICL\npMS9H8DWNg5pScRh6zsB5xrVIIDvaGwc0k19rO8DHGxX2FLzG9s3pL2hAfzfAPBPPW40v619\nQ3oUhxrAmlWuTaa3tW1ImVVxCwqwqPUQ05vaNqTPcKgBrFoYedDsprYNqUtfHoOAo2XVmGR2\nU7uGtDf0ex6DgLO9VOacyS3tGtKk2jjUAJadLTXL5JY2DSnzshl8JgFnm1DL5MUbbBrSfyOO\n8JkEnO1Q1GJzG9o0pBtv4zMION3Aa81tZ8+Q/g79gdMk4HB/uFaa2s6eIU3EoQbgpIO5b25s\nGVJGpWd5TQJO91WYqetd2zKkTyNxqAF4aTjOzFa2DKnT7bwGAXg77pSJrewY0t+hP3KbBBwv\nrYKZHxTsGNIjl+NQA/DzZOX0ojeyYUgZlWbymwTgRLG5RW9kw5AWRh3nNwmAZ0TTorexYUg3\n9Oc3CIDHs9PE6/v2C2lP6E8cJwHweLreXOQm9gvp4To41AB8/eTaXNQmtgspo+LzPCcByNby\nnqK2sF1IC6JxqAF4mxd9tIgtbBdSxwEc5wDwyaz+WBFb2C2knSGB/+8BKMpzZc8bb2C3kB6q\nw3UQAJ/UUm8Yb2CzkDIqvsB3EgCf8bWNL95gs5DmR5/gOwmAz/6Izw3fb7OQkgfyHQQgR//r\nDd9tr5B2hvzCeRIAv99da43eba+QHqzPeRCAXNffbfReW4WUXuEl3pMA5PjzN6P32iqkeTjU\nAERsFVL7QbwHATDHTiHtCPmV+yQAptgppPENuA8CYA5FSKdXbcsw3iKokNLKvRzcPAAm7Vq8\neFfh75EZ0qyHvX9u78AYixyTYrRlUCF9HHMyuLkATNnWhhUvztpsK+x9MkNq7f3AgyVZrf6D\n6rP6Fwy2DCqk6wcHOReAGbvLdt7o8WzsXLawaxhLD2kgm5zp8bifZpMMtgwmpL9CzN02ACA4\n/a72/USS0bpfIe+UHlKNur5LKrjrNTPYMpiQ7sehBhApI3aR/8mi2EJ+xJceUkzOXTL6xVzy\n3j21kvKUYamB7jyt7CtBjgVgxgG21f9kCzv4z/dKD6lRS/8b7S675L3p783KM46lBbrzubGG\nhy8ALEphq/xPVrHT/3yv3JDue/XbKa4F3udLWB+DLX8OPKR2dwU5FYA5dSf6HyfWLeSdMkPq\nE8m8ins8Z7qHFNtisGXgIf3lWhXkVADmvB3zvfdhafScQt4p9QXZrL+/f+OhXq08noOszSaj\nDQMPaVzDYIcCMGlcWNdp07qGFnrjMZpThC7sNX5/wCGllX0t+GkAzFl2V4sWdxV+HXA1z7UL\nOKQPi+FQA1CySUjXDhEzCIA59ghpq2u1oEkATLFHSP/BoQagZYuQ0srMEjUJgCm2COkDHGoA\nYrYIqa3hhZIAxLNDSDjUAOTsENJYEzedBhDKBiFdKDNb3CQAptggpPeKFXJWO4BUNgipTZE3\nygUQTf+QtrjWCJwEwBT9QxpjdPEHADm0D+l8ySJu7gkggfYhvVschxqAnvYhXT1M5CAA5uge\n0mbjGxICyKF7SKNaCB0EwBzNQzpX8k2xkwCYonlI78QHfElWAAE0D6nVCLGDAJijd0ibGQ41\ngBL0DmlkS8GDAJijdUjnSrwlehIAU7QO6W0cagBFaB3SVfeKHgTAHJ1D+p2tEz4JgCk6h3Rv\nK+GDAJijcUjnShR2nxoAChqH9FbCWeGDAJijcUgtRwmfA8AkfUPawP4QPgeASfqGNLy18DEA\nzNI2pHMJ7wgfA8AsbUN6E4caQCHahtR8tPApAEzTNaQNbKPwKQBM0zWkYVcLHwLAPE1DSo17\nT/gQAOZpGtLrONQAStE0pGZjhM8AEAA9Q1qPQw2gFj1Duqet8BEAAqFlSGfi3hc+AkAgtAxp\ndqnzwkcACISWITW9T/gEAAHRMaQjYVuETwAQEB1D8hwTPgBAYLQMCUA1CAmAA4QEwAFCAuBA\nr5CyVs+ZszpL+PIAgdIqpDX1WLVqrN4a4esDBEinkLbG33bQ4zl4W/w24QMABEankLp1cHsf\nsjp0Fz4AQGA0Cik9aon/yeKodOETAAREo5AOsJxv6bayA8InAAiIRiGdYSv8T35x4UZ9oBiN\nQvI0ud//OK6J8AEAAqNTSJ9ELPQ+LIyYL3wAgMDoFJJnamjb++9vGzpV+PoAAdIqJM+G8Z07\nj98gfHmAQOkVEoCiEBIABwgJgAOEBMABQgLgACEBcICQADhASAAcICQADhASAAcICYADhATA\nAUIC4AAhAXCAkAA4QEgAHCAkAA7UDOk3BqCZ3wL+Mhcfkmf9al4eKv+eaLGjRa/Qq6boFd5l\nD4teonNj0Su8zKaJXqLtDf/2hbY+8K9yCSHxM7um8CVKLBS9wpSrRK+QxX4QvcS4LqJXOMw2\niV5i4ECOO0NIF0NIpiCkSyGkiyEkUxDSpRDSxRCSKQjpUgjpYgjJFIR0KYR0MYRkCkK6FEK6\nGEIyBSFdCiFdDCGZgpAuhZAuhpBMQUiXQkgXQ0imIKRLaRXSnLrClyi3RPQKz1wjegV3+ArR\nS0zoJnqFE64/RS8xdCjHnWkVUvrfwpfYnSl6hXPib5q70y16hdNHRK/g2SF8hRMnOO5Mq5AA\nVIWQADhASAAcICQADhASAAcICYADhATAAUIC4AAhAXCAkAA4QEgAHCAkAA4QEgAHCAmAA4QE\nwIFGIZ0d3yCm5iDRv80zjy0Wuv+v2hYr33unwAVSJ9SLqTfhrLD9vx7vf0x/PCki6bF0kUuI\n+4znruDF5zOuT0hp9VndO1qx+G1CVzlSWmxIc1j8LdezsoeELZB2Javfrz67UtS9dDKa+b8G\n3X1Z5R6VWB8Bv0OYu4S4z3juCl6cPuP6hDSTDcj0eN5h1whdpRcTGtLp2KTsv2BfZyOErfA8\nG5blybqbvShk7wf+dwPzfw2uYS3Oe843Z2vFLSHqM56/ghenz7g+IbVjB70PrVynBS4yn9UT\nGtJs9mn2n1k39Re2Qk/mvdbBNtZbyN5jGcv5GhzJlmf/uZyNEbeEqM94/goefp9xfUKqUM33\n0IdtELfG0TLJTwsNqU286NsXdmC7sv/cxToK2ftnixZV838NJiVkZP+ZkVBD3BKiPuP5K3D8\njOsT0jrft8pZ5Vwnxa3Rp9ju6UJDKn9lxueTnvhO4MVJnmYPZf85gT0taoGGvq9Bd1RT31tN\nY4UtIfIz3jA3JG6fcX1C8skawwReCGohe9UjNKTMkGtu9N5ZsWuqsCWy7mHXjWnHRmSJWsD/\nNZjCOvjeSmYC/qc0LHBMTcxnPHcFfp9xvUI62JNV2its78fKtcsSG9IBxhI/P7W5C3tA2BLu\n2aHZpYa/LewfPf/X4G7W3fdWN7ZH1BI+gj7jOStw/IzrFJL75Th29S5x++8Xs8MjNqSDjK3L\nfjhbIULYj0qTWNcNqRtuZY+LWiD3XyT/D2HJLEXUEh6Bn/GcFTh+xjUK6VhnVvYNgddv/JK9\n4BEcUmZIku+xL9soaIWj4Zd7XyNNqx15TNAKuT8jNfe91TRGwD99uSGJ+4z7V+D5GdcnpHMt\nWReBxxm8L1vkelXYGuWu8D3c5fuHSYSf2ZCcFX4RtELOV3liKe9PYZmlqotbQuBn3L8Cz8+4\nPiFNZGOE/QDt8/Vgr2YsefD3wtboEX44+093o9ALghbYzzr7Hjux/YJWyPkqv5etyv5zJRsl\nbgmBn3H/Cjw/49qElFmxhLhDXQWIPfz9Det+3nv2wW2iFnDXc3nn/6+rvqgVGuae2dAh05PR\nQcg/rf4lRH7GCx4XdNi3djtZfAs/saetig0pqwOr2qcZu+ygsBXWxbCr+1/FYteLWiDna9Dd\nmzW5txHrJ24JkZ9xB4e0NO/72V1C1xEbkufco62LXTHylMAV/r6zdnTtweJeJMj9GkybXC26\n9TQhZ3/7lxD5GXdwSAAqQ0gAHCAkAA4QEgAHCAmAA4QEwAFCAuAAIQFwgJAAOEBIABwgJAAO\nEBIABwgJgAOEBMABQgLgACEBcICQADhASAAcICQADhASAAcICYADhATAAUIC4AAhAXCAkAA4\nQEgAHCAkAA4QEgAHCAmAA4QEwAFCAuAAIQFwgJAAOEBIGlnhu39d6GV3/UU9CVwKIWlkBavS\nvXv3NsVY1BfUo8AlEJJGVvjvfZw11RV3gnoWuBhC0siK3JuIT2b/RzsJXAohaSQvpGMxJdwe\nz7oelSMqdV3j8TzPPvD+15fZW5TTORtC0kheSJ5r2RHPn/Ghne6ox+L3evaxbt7/2DoqhXA4\nh0NIGskPqT/71TORzc9+NoO9k51QdKrHs4v1oRzO4RCSRvJD+g9b5Pn29YzsZ1+xmR7Pc96m\nprD/UQ7ncAhJIwX/RVrlfTi36rl63pD2sr4eT72yGYSzOR1C0kjBn5GOek7dVzc0pEEnb0ie\nVsUvbGCjKWdzOoSkkbyQjseWcntuYUOWpGb/N29IM9ni8Ww16XAOh5A0UuB1pEc9ZyK6e59+\n5AvpbzbgsjpuwtEcDyFpJPfMhqdc8Sc9J1ib7HL+rs2meP/bVaH+RyCCkDSSc65dcf+5du1Z\nUp+O4V3CyszIfuNZxvZQj+doCEkjOWd/VxnsO/v76F2V4tq97Z5R9v7sN7awa4mHcziEZA+z\n2JvUIzgbQrKF9LpRp6hncDaEZAfdLmf3Uc/gcAjJDloUu/MC9QwOh5AAOEBIABwgJAAOEBIA\nBwgJgAOEBMABQgLgACEBcICQADhASAAcICQADhASAAcICYADhATAAUIC4AAhAXCAkAA4QEgA\nHCAkAA4QEgAHCAmAA4QEwAFCAuAAIQFwgJAAOEBIABwgJAAO/h99xvfwAJpouQAAAABJRU5E\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(flu)\n",
    "plot(flu$day, flu$cases, type=\"b\", xlab=\"Day\", \n",
    "     ylab=\"In bed\", log=\"y\")\n",
    "tail(flu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The “fast-and-furious\" estimate of $R_0$ is thus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3.17188439700297</li>\n",
       "\t<li>4.25782659550445</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3.17188439700297\n",
       "\\item 4.25782659550445\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3.17188439700297\n",
       "2. 4.25782659550445\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3.171884 4.257827"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit=lm(log(cases)~day, subset=day<=5, \n",
    "     data=flu)\n",
    "r=fit$coef[\"day\"]\n",
    "V=c(2,3)\n",
    "V*r+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is higher than most estimates of $R_0$ of pandemic flu (which typically lies in the 1.5-2.5 interval). However, contact rates within a boarding school is likely to be higher than average across human populations as a whole.\n",
    "\n",
    "### Ebola Sierra Leone 2014-15\n",
    "\n",
    "The CDC’s record for the 2014-15 ebola outbreak in Sierra Leone is in the `ebola`-dataset. The serial interval for ebola is estimated at around 15 days with an incubation period of 11 days. The mean time to hospitalization is 5 days and mean time to death or dismissal was 5 and 11 days, respectively . The data is the back-calculated incidence as the difference of the cumulative cases reported by the [](http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/cumulative-cases-graphs.html). Because of the complexities of reporting and revisions of case-load through time, this lead to some negative numbers for certain dates. These were set to zero as a crude fix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>date</th><th scope=col>day</th><th scope=col>cum_cases</th><th scope=col>cases</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>98</th><td>7/8/15 </td><td>468    </td><td>13945  </td><td>34     </td></tr>\n",
       "\t<tr><th scope=row>99</th><td>7/15/15</td><td>475    </td><td>13982  </td><td>37     </td></tr>\n",
       "\t<tr><th scope=row>100</th><td>7/22/15</td><td>482    </td><td>14001  </td><td>19     </td></tr>\n",
       "\t<tr><th scope=row>101</th><td>7/29/15</td><td>489    </td><td>14061  </td><td>60     </td></tr>\n",
       "\t<tr><th scope=row>102</th><td>8/5/15 </td><td>496    </td><td>14089  </td><td>28     </td></tr>\n",
       "\t<tr><th scope=row>103</th><td>8/12/15</td><td>503    </td><td>14122  </td><td>33     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & date & day & cum\\_cases & cases\\\\\n",
       "\\hline\n",
       "\t98 & 7/8/15  & 468     & 13945   & 34     \\\\\n",
       "\t99 & 7/15/15 & 475     & 13982   & 37     \\\\\n",
       "\t100 & 7/22/15 & 482     & 14001   & 19     \\\\\n",
       "\t101 & 7/29/15 & 489     & 14061   & 60     \\\\\n",
       "\t102 & 8/5/15  & 496     & 14089   & 28     \\\\\n",
       "\t103 & 8/12/15 & 503     & 14122   & 33     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | date | day | cum_cases | cases |\n",
       "|---|---|---|---|---|\n",
       "| 98 | 7/8/15  | 468     | 13945   | 34      |\n",
       "| 99 | 7/15/15 | 475     | 13982   | 37      |\n",
       "| 100 | 7/22/15 | 482     | 14001   | 19      |\n",
       "| 101 | 7/29/15 | 489     | 14061   | 60      |\n",
       "| 102 | 8/5/15  | 496     | 14089   | 28      |\n",
       "| 103 | 8/12/15 | 503     | 14122   | 33      |\n",
       "\n"
      ],
      "text/plain": [
       "    date    day cum_cases cases\n",
       "98  7/8/15  468 13945     34   \n",
       "99  7/15/15 475 13982     37   \n",
       "100 7/22/15 482 14001     19   \n",
       "101 7/29/15 489 14061     60   \n",
       "102 8/5/15  496 14089     28   \n",
       "103 8/12/15 503 14122     33   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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8Xy8vJUVhWzEOwA2Iz5VbELFtD27RQb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\n/wdnsCOmtbhBIQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(ebola)\n",
    "par(mar = c(5,5,2,5))\n",
    "plot(ebola$day, ebola$cases, type=\"b\", xlab=\"Week\", \n",
    "     ylab=\"Incidence\")\n",
    "par(new=T)\n",
    "plot(ebola$day, ebola$cum_cases, type=\"l\", col=\"red\",\n",
    "      axes=FALSE, xlab=NA, ylab=NA, log=\"y\")\n",
    "axis(side = 4)\n",
    "mtext(side = 4, line = 4, \"Cumulative incidence\")\n",
    "legend(\"right\", legend=c(\"Cases\", \"Cumulative\"),\n",
    "     lty=c(1,1), pch=c(1,NA), col=c(\"black\", \"red\"))\n",
    "tail(ebola)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first use the regression method with Lipsitch’s correction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>day:</strong> 1.69881135562533"
      ],
      "text/latex": [
       "\\textbf{day:} 1.69881135562533"
      ],
      "text/markdown": [
       "**day:** 1.69881135562533"
      ],
      "text/plain": [
       "     day \n",
       "1.698811 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit=lm(log(cum_cases)~day, subset=day<100, \n",
    "   data=ebola)\n",
    "r=fit$coef[\"day\"]\n",
    "V=15\n",
    "f=.5\n",
    "V*r+1+f*(1-f)*(V*r)^2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We next aggregate the data in two-week increments roughly corresponding to the serial interval so we can apply the removal method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "14721"
      ],
      "text/latex": [
       "14721"
      ],
      "text/markdown": [
       "14721"
      ],
      "text/plain": [
       "[1] 14721"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Maximum likelihood estimation\n",
       "\n",
       "Call:\n",
       "mle2(minuslogl = llik.cb, start = list(S0 = 20000, beta = 2), \n",
       "    method = \"Nelder-Mead\", data = list(I = cases))\n",
       "\n",
       "Coefficients:\n",
       "       Estimate Std. Error    z value     Pr(z)    \n",
       "S0   2.7731e+04 2.5949e-07 1.0687e+11 < 2.2e-16 ***\n",
       "beta 1.4237e+00 1.1783e-02 1.2083e+02 < 2.2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "-2 log L: 5546.683 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data aggregation\n",
    "cases=sapply(split(ebola$cases, \n",
    "   floor((ebola$day-.1)/14)), sum)\n",
    "sum(cases)\n",
    "\n",
    "#Removal MLE\n",
    "fit = mle2(llik.cb, start = list(S0 = 20000, beta = 2), \n",
    "   method = \"Nelder-Mead\",data = list(I = cases))\n",
    "summary(fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the difference in magnitude of the estimates of $S_0$ (in the ten thusands) and $R_0$ (around 1.4), the numerical method used to calculate confidence intervals struggles, so we suggest starting standard errors for the `confint`-function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 1.42370820032527), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 27730.6415590325), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 27730.6415590325), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 27730.6415590325), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 27730.6415590325), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>2.5 %</th><th scope=col>97.5 %</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>S0</th><td>26393.579452</td><td>29287.725327</td></tr>\n",
       "\t<tr><th scope=row>beta</th><td>    1.384683</td><td>    1.463184</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & 2.5 \\% & 97.5 \\%\\\\\n",
       "\\hline\n",
       "\tS0 & 26393.579452 & 29287.725327\\\\\n",
       "\tbeta &     1.384683 &     1.463184\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 2.5 % | 97.5 % |\n",
       "|---|---|---|\n",
       "| S0 | 26393.579452 | 29287.725327 |\n",
       "| beta |     1.384683 |     1.463184 |\n",
       "\n"
      ],
      "text/plain": [
       "     2.5 %        97.5 %      \n",
       "S0   26393.579452 29287.725327\n",
       "beta     1.384683     1.463184"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confint(fit, std.err = c(100, 0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The removal and Lipsitch methods provides comparable estimates that are somewhat lower than those concluded by more elaborate analyses by the WHO team for the Sierra Leone outbreak .\n",
    "\n",
    "### Ebola DRC 1995\n",
    "\n",
    "| Disease     | Serial Interval | Location    | Year |\n",
    "|:------------|:----------------|:------------|:----:|\n",
    "| Ebola       | 14d             | DRC         | 1995 |\n",
    "|             |                 | Uganda      | 2000 |\n",
    "| SARS        | 5d              | Hong Kong   | 2003 |\n",
    "|             |                 | Singapore   |      |\n",
    "| Hog cholera | 7d              | Netherlands | 1997 |\n",
    "| FMD         | 21d             | UK          | 2000 |\n",
    "\n",
    "The `ferrari`-dataset holds the incidence data for a number of outbreaks – Ebola DRC ’95, Ebola Uganda ’00, SARS Hong Kong ’03, SARS Singapore ’03, Hog Cholera Netherlands ’97 and Foot-and-mouth UK ’00 – aggregated by disease-specific serial intervals . Let’s explore the 1995 DRC ebola outbreak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Eboladeaths00'</li>\n",
       "\t<li>'Ebolacases00'</li>\n",
       "\t<li>'Ebolacases95'</li>\n",
       "\t<li>'FMDfarms'</li>\n",
       "\t<li>'HogCholera'</li>\n",
       "\t<li>'SarsHk'</li>\n",
       "\t<li>'SarsSing'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Eboladeaths00'\n",
       "\\item 'Ebolacases00'\n",
       "\\item 'Ebolacases95'\n",
       "\\item 'FMDfarms'\n",
       "\\item 'HogCholera'\n",
       "\\item 'SarsHk'\n",
       "\\item 'SarsSing'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Eboladeaths00'\n",
       "2. 'Ebolacases00'\n",
       "3. 'Ebolacases95'\n",
       "4. 'FMDfarms'\n",
       "5. 'HogCholera'\n",
       "6. 'SarsHk'\n",
       "7. 'SarsSing'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Eboladeaths00\" \"Ebolacases00\"  \"Ebolacases95\"  \"FMDfarms\"     \n",
       "[5] \"HogCholera\"    \"SarsHk\"        \"SarsSing\"     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>4</li>\n",
       "\t<li>6</li>\n",
       "\t<li>5</li>\n",
       "\t<li>18</li>\n",
       "\t<li>36</li>\n",
       "\t<li>99</li>\n",
       "\t<li>40</li>\n",
       "\t<li>17</li>\n",
       "\t<li>4</li>\n",
       "\t<li>1</li>\n",
       "\t<li>&lt;NA&gt;</li>\n",
       "\t<li>&lt;NA&gt;</li>\n",
       "\t<li>&lt;NA&gt;</li>\n",
       "\t<li>&lt;NA&gt;</li>\n",
       "\t<li>&lt;NA&gt;</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 4\n",
       "\\item 6\n",
       "\\item 5\n",
       "\\item 18\n",
       "\\item 36\n",
       "\\item 99\n",
       "\\item 40\n",
       "\\item 17\n",
       "\\item 4\n",
       "\\item 1\n",
       "\\item <NA>\n",
       "\\item <NA>\n",
       "\\item <NA>\n",
       "\\item <NA>\n",
       "\\item <NA>\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 4\n",
       "2. 6\n",
       "3. 5\n",
       "4. 18\n",
       "5. 36\n",
       "6. 99\n",
       "7. 40\n",
       "8. 17\n",
       "9. 4\n",
       "10. 1\n",
       "11. &lt;NA&gt;\n",
       "12. &lt;NA&gt;\n",
       "13. &lt;NA&gt;\n",
       "14. &lt;NA&gt;\n",
       "15. &lt;NA&gt;\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  4  6  5 18 36 99 40 17  4  1 NA NA NA NA NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "230"
      ],
      "text/latex": [
       "230"
      ],
      "text/markdown": [
       "230"
      ],
      "text/plain": [
       "[1] 230"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names(ferrari)\n",
    "ferrari$Ebolacases95\n",
    "sum(ferrari$Ebolacases95, na.rm = TRUE)\n",
    "y = c(na.omit(ferrari$Ebolacases95))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of initial susceptibles must be larger than the summed incidence so we make an initial guess of 300."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”Warning message in mle2(llik.cb, method = \"Nelder-Mead\", start = list(S0 = 300, :\n",
      "“couldn't invert Hessian”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "mle2(minuslogl = llik.cb, start = list(S0 = 300, beta = 2), method = \"Nelder-Mead\", \n",
       "    data = list(I = y))\n",
       "\n",
       "Coefficients:\n",
       "        S0       beta \n",
       "241.118108   3.181465 \n",
       "\n",
       "Log-likelihood: -48.3 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(beta = 3.18146475276021), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 241.118108367497), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in optim(par = c(S0 = 241.118108367497), fn = function (p) :\n",
      "“one-dimensional optimization by Nelder-Mead is unreliable:\n",
      "use \"Brent\" or optimize() directly”Warning message in dbinom(I[-1], S, p, log = TRUE):\n",
      "“NaNs produced”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>2.5 %</th><th scope=col>97.5 %</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>S0</th><td>233.973778</td><td>254.051292</td></tr>\n",
       "\t<tr><th scope=row>beta</th><td>  2.692505</td><td>  3.718357</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & 2.5 \\% & 97.5 \\%\\\\\n",
       "\\hline\n",
       "\tS0 & 233.973778 & 254.051292\\\\\n",
       "\tbeta &   2.692505 &   3.718357\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 2.5 % | 97.5 % |\n",
       "|---|---|---|\n",
       "| S0 | 233.973778 | 254.051292 |\n",
       "| beta |   2.692505 |   3.718357 |\n",
       "\n"
      ],
      "text/plain": [
       "     2.5 %      97.5 %    \n",
       "S0   233.973778 254.051292\n",
       "beta   2.692505   3.718357"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit = mle2(llik.cb, method = \"Nelder-Mead\", \n",
    "    start = list(S0 = 300, beta = 2), \n",
    "    data = list(I = y))\n",
    "fit\n",
    "confint(fit, std.err = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimated $R_0$ is 3.2. It thus appears that the Ebola outbreak in DRC in 1995 was more explosive than in Sierra Leone in 2014. This could be due to aggregation across a larger geographic area of the latter and/or the more intensive public health interventions. We will revisit on the DRC outbreak using the ‘next-generation matrix’ method.\n",
    "\n",
    "### Contact tracing\n",
    "\n",
    "The directional transmission graph of gonorrhoea among 89 individuals is in the `gonnet`-data set. The initial cluster of 17 cases all frequented the same bar, each infected between 0 and 7 other partners with 2.17 as the average. We can use the `statnet`-package to visualize the chains of transmission:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: statnet\n",
      "Loading required package: tergm\n",
      "Loading required package: ergm\n",
      "Loading required package: network\n",
      "network: Classes for Relational Data\n",
      "Version 1.13.0.1 created on 2015-08-31.\n",
      "copyright (c) 2005, Carter T. Butts, University of California-Irvine\n",
      "                    Mark S. Handcock, University of California -- Los Angeles\n",
      "                    David R. Hunter, Penn State University\n",
      "                    Martina Morris, University of Washington\n",
      "                    Skye Bender-deMoll, University of Washington\n",
      " For citation information, type citation(\"network\").\n",
      " Type help(\"network-package\") to get started.\n",
      "\n",
      "\n",
      "ergm: version 3.9.4, created on 2018-08-15\n",
      "Copyright (c) 2018, Mark S. Handcock, University of California -- Los Angeles\n",
      "                    David R. Hunter, Penn State University\n",
      "                    Carter T. Butts, University of California -- Irvine\n",
      "                    Steven M. Goodreau, University of Washington\n",
      "                    Pavel N. Krivitsky, University of Wollongong\n",
      "                    Martina Morris, University of Washington\n",
      "                    with contributions from\n",
      "                    Li Wang\n",
      "                    Kirk Li, University of Washington\n",
      "                    Skye Bender-deMoll, University of Washington\n",
      "Based on \"statnet\" project software (statnet.org).\n",
      "For license and citation information see statnet.org/attribution\n",
      "or type citation(\"ergm\").\n",
      "\n",
      "NOTE: Versions before 3.6.1 had a bug in the implementation of the bd()\n",
      "constriant which distorted the sampled distribution somewhat. In\n",
      "addition, Sampson's Monks datasets had mislabeled vertices. See the\n",
      "NEWS and the documentation for more details.\n",
      "\n",
      "Loading required package: networkDynamic\n",
      "\n",
      "networkDynamic: version 0.9.0, created on 2016-01-12\n",
      "Copyright (c) 2016, Carter T. Butts, University of California -- Irvine\n",
      "                    Ayn Leslie-Cook, University of Washington\n",
      "                    Pavel N. Krivitsky, University of Wollongong\n",
      "                    Skye Bender-deMoll, University of Washington\n",
      "                    with contributions from\n",
      "                    Zack Almquist, University of California -- Irvine\n",
      "                    David R. Hunter, Penn State University\n",
      "                    Li Wang\n",
      "                    Kirk Li, University of Washington\n",
      "                    Steven M. Goodreau, University of Washington\n",
      "                    Jeffrey Horner\n",
      "                    Martina Morris, University of Washington\n",
      "Based on \"statnet\" project software (statnet.org).\n",
      "For license and citation information see statnet.org/attribution\n",
      "or type citation(\"networkDynamic\").\n",
      "\n",
      "\n",
      "tergm: version 3.5.2, created on 2018-08-18\n",
      "Copyright (c) 2018, Pavel N. Krivitsky, University of Wollongong\n",
      "                    Mark S. Handcock, University of California -- Los Angeles\n",
      "                    with contributions from\n",
      "                    David R. Hunter, Penn State University\n",
      "                    Steven M. Goodreau, University of Washington\n",
      "                    Martina Morris, University of Washington\n",
      "                    Nicole Bohme Carnegie, New York University\n",
      "                    Carter T. Butts, University of California -- Irvine\n",
      "                    Ayn Leslie-Cook, University of Washington\n",
      "                    Skye Bender-deMoll\n",
      "                    Li Wang\n",
      "                    Kirk Li, University of Washington\n",
      "Based on \"statnet\" project software (statnet.org).\n",
      "For license and citation information see statnet.org/attribution\n",
      "or type citation(\"tergm\").\n",
      "\n",
      "Loading required package: ergm.count\n",
      "Loading required package: statnet.common\n",
      "\n",
      "Attaching package: ‘statnet.common’\n",
      "\n",
      "The following objects are masked from ‘package:ergm’:\n",
      "\n",
      "    colMeans.mcmc.list, sweep.mcmc.list\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    order\n",
      "\n",
      "\n",
      "ergm.count: version 3.3.0, created on 2018-08-25\n",
      "Copyright (c) 2018, Pavel N. Krivitsky, University of Wollongong\n",
      "                    with contributions from\n",
      "                    Mark S. Handcock, University of California -- Los Angeles\n",
      "                    David R. Hunter, Penn State University\n",
      "Based on \"statnet\" project software (statnet.org).\n",
      "For license and citation information see statnet.org/attribution\n",
      "or type citation(\"ergm.count\").\n",
      "\n",
      "NOTE: The form of the term ‘CMP’ has been changed in version 3.2 of\n",
      "‘ergm.count’. See the news or help('CMP') for more information.\n",
      "\n",
      "Loading required package: sna\n",
      "sna: Tools for Social Network Analysis\n",
      "Version 2.4 created on 2016-07-23.\n",
      "copyright (c) 2005, Carter T. Butts, University of California-Irvine\n",
      " For citation information, type citation(\"sna\").\n",
      " Type help(package=\"sna\") to get started.\n",
      "\n",
      "Loading required package: tsna\n",
      "\n",
      "statnet: version 2018.10, created on 2018-10-17\n",
      "Copyright (c) 2018, Mark S. Handcock, University of California -- Los Angeles\n",
      "                    David R. Hunter, Penn State University\n",
      "                    Carter T. Butts, University of California -- Irvine\n",
      "                    Steven M. Goodreau, University of Washington\n",
      "                    Pavel N. Krivitsky, University of Wollongong\n",
      "                    Skye Bender-deMoll\n",
      "                    Martina Morris, University of Washington\n",
      "Based on \"statnet\" project software (statnet.org).\n",
      "For license and citation information see statnet.org/attribution\n",
      "or type citation(\"statnet\").\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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m/58uXLli0r8qVevXrt2rWrcePGiq2obFJSUry9vaOiorS1tS0tLbt3766pWdzu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\nKkGxAwAAUAmKHQAAgEr8Hw8McWwFXHfUAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "require(statnet)\n",
    "data(gonnet)\n",
    "nwt = network(gonnet, directed = TRUE)\n",
    "plot(nwt, vertex.col = c(0, rep(1, 17), rep(2, 71)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The subsequent infections, in turn, infected between 0 and 6 partners with an average of 0.62. The drop is (i) due to the sexual network being depleted of suscpetibles, and (ii) because infection across heterogenous networks will differentially infect individuals according to their number of contacts . Epidemics across social networks is the topic of chapter \\[chap:c11\\] and we will revisit on this network therein.\n",
    "\n",
    "### Advanced: The next-generation matrix\n",
    "\n",
    "For epidemics that are not simple linear chains, it is less straight-forward to calculate $R_0$ from parameterized models using the ‘logical method’. The [](https://en.wikipedia.org/wiki/Next-generation_matrix) is the general approach that work for all compartmental models of any complexity . It is done in a sequence of steps:\n",
    "\n",
    "1.  Identify all $n$ infected compartments,\n",
    "\n",
    "2.  Construct a $n \\times 1$ matrix, $\\vec{F}$, that contains expressions for all *completely new* infections entering each infected compartment,\n",
    "\n",
    "3.  Construct a $n \\times 1$ matrix, $\\vec{V^-}$, that contains expressions for all losses out of each infected compartment,\n",
    "\n",
    "4.  Construct a $n \\times 1$ matrix, $\\vec{V^+}$, that contains expressions for all gains into each infected compartment that does *not* represent *new* infections but transfers among infectious classes,\n",
    "\n",
    "5.  Construct a $n \\times 1$ matrix, $\\vec{V}= \\vec{V^-} - \\vec{V^+}$,\n",
    "\n",
    "6.  Generate two $n \\times n$ Jaccobian matrices, $\\vec{f}$ and $\\vec{v}$ that are the partial derivatives of $\\vec{F}$ and $\\vec{V}$ with respect to the $n$ infectious state variables,\n",
    "\n",
    "7.  Evaluate the matrices at the disease free equilibrium (dfe) finally\n",
    "\n",
    "8.  $R_0$ is the greatest eigenvalue of $\\vec{f} \\vec{v}^{-1}|_{dfe}$.\n",
    "\n",
    "#### SEIR\n",
    "\n",
    "This is quite an elaborate scheme, so we will try it out first for the SEIR model for which we already know the answer. Unfortunately, `R` can not do vectorized *symbolic* calculations, so we need to do this, one matrix element at a time. In chapter 1, we discussed how to use `expression` to do symbolic calculations in `R`. The `quote`-function is an alternative way to define mathematical expressions; `substitute` allows some simple additional manipulations.\n",
    "\n",
    "Step 1: Infected classes are $E$ and $I$, let us label them 1 and 2.\n",
    "\n",
    "Step 2: All new infections: $dE/dt = \\beta S I / N$, $dI/dt =0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "F1 = quote(beta * S * I/N)\n",
    "F2 = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: All losses $dE/dt= (\\mu+\\sigma) E$, $dI/dt=(\\mu + \\alpha + \\gamma) I$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vm1 = quote(mu * E + sigma * E)\n",
    "Vm2 = quote(mu * I + alpha * I + gamma * I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4 : All gained transfers $dE/dt= 0$, $dI/dt=(\\sigma) E$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vp1 = 0\n",
    "Vp2 = quote(sigma * E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 5: Subtract Vp from Vm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "V1 = substitute(a - b, list(a = Vm1, b = Vp1))\n",
    "V2 = substitute(a - b, list(a = Vm2, b = Vp2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 6: Generate the partial derivatives for the two Jacobians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "f11 = D(F1, \"E\"); f12 = D(F1, \"I\")\n",
    "f21 = D(F2, \"E\"); f22 = D(F2, \"I\")\n",
    "\n",
    "v11 = D(V1, \"E\"); v12 = D(V1, \"I\")\n",
    "v21 = D(V2, \"E\"); v22 = D(V2, \"I\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 7: Assuming N=1, the disease free equilibrium (dfe) is $S=1, E=0, I=0, R=0$. We also need values for other parameters. Assuming a weekly time-step and something chickenpox-like we may use $\\mu=0$, $\\alpha=0$, $\\beta=5$, $\\gamma=.8$, $\\sigma=1.2$, and $N=1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "paras = list(S = 1, E = 0, I = 0, R = 0, mu = 0, \n",
    "   alpha = 0, beta = 5, gamma = .8, sigma = 1.2, N = 1)\n",
    "f = with(paras,\n",
    "   matrix(c(eval(f11),eval(f12),eval(f21),\n",
    "   eval(f22)), nrow = 2, byrow = TRUE))\n",
    "v=with(paras,\n",
    "   matrix(c(eval(v11),eval(v12),eval(v21),\n",
    "   eval(v22)), nrow=2, byrow=TRUE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 8: Calculate the largest eigenvalue of $f \\times \\mbox{inverse}(v)$. Note that the function for inverting matrices in `R` is `solve`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
       "6.25"
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       "6.25"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "max(eigen(f %*% solve(v))$values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us check that the next-generation method and the ‘flow’ method are in agreement recalling that for the SEIR-flow $R_0 =  \\frac{\\sigma}{\\sigma +\\mu} \\frac{\\beta}{\\gamma+\\mu+\\alpha}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "6.25"
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      "text/latex": [
       "6.25"
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       "6.25"
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      "text/plain": [
       "[1] 6.25"
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     "metadata": {},
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   ],
   "source": [
    "with(paras,\n",
    "sigma/(sigma+mu)*beta/(gamma+mu+alpha))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SEIHFR\n",
    "\n",
    "| Parameter    | Meaning                        |  Value|\n",
    "|:-------------|:-------------------------------|------:|\n",
    "| $N$          | Population size                |       |\n",
    "| $1/\\sigma$   | Incubation period              |     7d|\n",
    "| $1/\\gamma_h$ | Onset to hospitalization       |     5d|\n",
    "| $1/\\gamma_f$ | Onset to death                 |   9.6d|\n",
    "| $1/\\gamma_r$ | Onset to recovery              |    10d|\n",
    "| $1/\\eta_f$   | Hospitalization to death       |   4.6d|\n",
    "| $1/\\eta_r$   | Hospitalization to recovery    |     5d|\n",
    "| $1/\\chi$     | Death to burial                |     2d|\n",
    "| $\\Theta$     | Proportion hospitalized        |    80%|\n",
    "| $\\Lambda$    | Case fatality ratio            |    81%|\n",
    "| $\\beta_i$    | Transmission rate in community |  0.588|\n",
    "| $\\beta_h$    | Transmission rate in hospital  |  0.794|\n",
    "| $\\beta_f$    | Transmission rate at funeral   |  7.653|\n",
    "\n",
    "The SEIHFR model forms the foundation for many of the recent Ebola models. The model has 5 compartments corresponding to Susceptible, Exposed, Infectious in community, Infectious in hospital, Dead but not yet buried, and removed (either buried or immune). The model is more complex than previous compartmental models and can not be represented by a simple linear chain. The parameterization used here is motivated by the original formulation of , but the notation conforms to the other sections of this book; Each infectious compartment contributes to the force of infection through their individual $\\beta$s. There are two branching-points in the flow: The hospitalization of a fraction $\\Theta$ of the infectious cases after an average time of $1/\\gamma_h$ days following onset of symptoms, and the death of a fraction $\\Lambda$ of the $I$- and $H$-class after an average time of $1/\\gamma_f$ days and $1/\\eta_f$ days, respectively. For the 1995 DRC outbreak, assumed that hospitalization affected transmission rates but not duration of infection or probability of dying. Model parameters are given above, and the model equations are:\n",
    "\n",
    "$$\\begin{aligned}\n",
    "\\frac{dS}{dt}&=&-(\\beta_i I + \\beta_h H +\\beta_f F) S / N\\\\\n",
    "\\frac{dE}{dt}&=& (\\beta_i I + \\beta_h H +\\beta_f F) S / N - \\sigma E\\\\\n",
    "\\frac{dI}{dt}&=&\\sigma E - \\Theta \\gamma_h I -(1-\\Theta)(1-\\Lambda)\\gamma_r I - (1-\\Theta) \\Lambda \\gamma_f I\\\\\n",
    "\\frac{dH}{dt}&=&\\Theta \\gamma_h I - \\Lambda \\eta_f H - (1-\\Lambda) \\eta_r H\\\\\n",
    "\\frac{dF}{dt}&=& (1-\\Theta)(1-\\Lambda)\\gamma_r I+ \\Lambda \\eta_f H - \\chi F\\\\\n",
    "\\frac{dR}{dt}&=&(1-\\Theta)(1-\\Lambda)\\gamma_r I + (1-\\Lambda) \\eta_r H+\\chi F\n",
    "\\end{aligned}$$\n",
    "\n",
    "There are 4 infected compartments ($E$, $I$, $H$ and $F$), thus $\\vec{F}$, $\\vec{V^-}$, and $\\vec{V^+}$ will be $4 \\times 1$ matrices, and $\\vec{f}$ and $\\vec{v}$ will be $4 \\times 4$ matrices.\n",
    "\n",
    "Step 1: Infected classes are $E$, $I$, $H$ and $F$, let us label them F1 - 4.\n",
    "\n",
    "Step 2: All new infections $dE/dt = \\beta S I / N$, $dI/dt =0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "F1 = expression(betai * S * I / N + betah* S * H / N +\n",
    "   betaf * S * F / N)\n",
    "F2=0\n",
    "F3=0\n",
    "F4=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: All losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vm1 = quote(sigma * E)\n",
    "Vm2 = quote(Theta * gammah * I + (1 - Theta) * (1-\n",
    "   Lambda) * gammar * I + (1 - Theta) * Lambda * \n",
    "   gammaf * I)\n",
    "Vm3 = quote(Lambda * etaf * H + (1 - Lambda) * etar * H)\n",
    "Vm4 = quote(chi * F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4: All gained transfers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vp1 = 0\n",
    "Vp2 = quote(sigma * E)\n",
    "Vp3 = quote(Theta * gammah * I)\n",
    "Vp4 = quote((1 - Theta) * (1 - Lambda) * gammar * I+ \n",
    "   Lambda * etaf * H)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 5: Subtract Vp from Vm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "V1 = substitute(a - b, list(a = Vm1, b = Vp1))\n",
    "V2 = substitute(a - b, list(a = Vm2, b = Vp2))\n",
    "V3 = substitute(a - b, list(a = Vm3, b = Vp3))\n",
    "V4 = substitute(a - b, list(a = Vm4, b = Vp4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 6: Generate the partial derivatives for the two Jacobians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "f11 = D(F1, \"E\"); f12 = D(F1, \"I\"); f13 = D(F1, \"H\") \n",
    "     f14 = D(F1, \"F\")\n",
    "f21 = D(F2, \"E\"); f22 = D(F2, \"I\"); f23 = D(F2, \"H\") \n",
    "     f24 = D(F2, \"F\")\n",
    "f31 = D(F3, \"E\"); f32 = D(F3, \"I\"); f33 = D(F3, \"H\") \n",
    "     f34 = D(F3, \"F\")\n",
    "f41 = D(F4, \"E\"); f42 = D(F4, \"I\"); f43 = D(F4, \"H\") \n",
    "     f44 = D(F4, \"F\")\n",
    "\n",
    "v11 = D(V1, \"E\"); v12 = D(V1, \"I\"); v13 = D(V1, \"H\")\n",
    "     v14 = D(V1, \"F\")\n",
    "v21 = D(V2, \"E\"); v22 = D(V2, \"I\"); v23 = D(V2, \"H\")\n",
    "     v24 = D(V2, \"F\")\n",
    "v31 = D(V3, \"E\"); v32 = D(V3, \"I\"); v33 = D(V3, \"H\")\n",
    "     v34 = D(V3, \"F\")\n",
    "v41 = D(V4, \"E\"); v42 = D(V4, \"I\"); v43 = D(V4, \"H\")\n",
    "     v44 = D(V4, \"F\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 7: Disease free equilibrium: the dfe is $S=1, E=0, I=0, H=0, F=0, R=0$. We also need values for other parameters. We use the estimates from the DRC 1995 outbreak scaled as weekly rates from tables and appendices of ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "gammah = 1/5 * 7\n",
    "gammaf = 1/9.6 * 7\n",
    "gammar = 1/10 * 7\n",
    "chi = 1/2 * 7\n",
    "etaf = 1/4.6 * 7\n",
    "etar = 1/5 * 7\n",
    "paras = list(S = 1,E = 0, I = 0, H = 0, F = 0,R = 0,\n",
    "     sigma = 1/7*7, Theta = 0.81, Lambda = 0.81, betai = 0.588, \n",
    "     betah = 0.794, betaf = 7.653, N = 1, gammah = gammah,\n",
    "     gammaf = gammaf, gammar = gammar, etaf = etaf, \n",
    "     etar = etar, chi = chi)\n",
    "\n",
    "f = with(paras, \n",
    "matrix(c(eval(f11), eval(f12), eval(f13), eval(f14),\n",
    "     eval(f21), eval(f22), eval(f23), eval(f24),\n",
    "     eval(f31), eval(f32), eval(f33), eval(f34),\n",
    "     eval(f41), eval(f42), eval(f43), eval(f44)),\n",
    "     nrow = 4, byrow = T))\n",
    "\n",
    "v = with(paras, \n",
    "matrix(c(eval(v11), eval(v12), eval(v13), eval(v14),\n",
    "     eval(v21), eval(v22), eval(v23), eval(v24),\n",
    "     eval(v31), eval(v32), eval(v33), eval(v34),\n",
    "     eval(v41), eval(v42), eval(v43), eval(v44)),\n",
    "     nrow = 4, byrow = T))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 8: Calculate the largest eigenvalue of $f \\times \\mbox{inverse}(v)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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    "max(eigen(f %*% solve(v))$values)"
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