{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A motivating example: descriptive epidemiological meta-regression of Parkinson's Disease\n",
    "========================================================================================\n",
    "\n",
    "The goal of this document it give a concise demonstration of the \n",
    "strengths and limitations of DisMod-MR, the descriptive\n",
    "epidemiological meta-regression tool developed for the Global Burden of Disease,\n",
    "Injuries, and Risk Factors 2010 (GBD2010) Study."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A systematic review of PD was conducted as part of the GBD 2010\n",
    "Study. The results of this\n",
    "review---data on the prevalence, incidence, and standardized mortality ratio of\n",
    "PD---needed to be combined to produce estimates of disease prevalence by\n",
    "region, age, sex, and year.  These prevalence estimates were combined\n",
    "with disability weights to measure years lived with disability (YLDs),\n",
    "which were then combined with estimates of years of life lost (YLLs)\n",
    "to produce estimates of the burden of PD quantified in disability-adjusted life-years (DALYs).\n",
    "\n",
    "PD is a neurodegenerative disorder that includes symptoms of motor\n",
    "dysfunction, such as tremors, rigidity, and akinesia, in the early\n",
    "stages of the disease.  As the disease develops, most patients also\n",
    "develop nonmotor symptoms, such as cognitive decline, dementia,\n",
    "autonomic failure, and disordered sleep-wake regulation.  The standard\n",
    "definition for PD diagnosis includes at least two of four cardinal\n",
    "signs---resting tremor, bradykinesia, rigidity, and postural abnormalities.\n",
    "There is no cure or treatments to slow the progression of the disease;\n",
    "however, motor symptoms and disability may be improved with\n",
    "symptomatic therapy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This document works with simulated data, so that the dataset is fully distributable.  This data is included for understanding how to use DisMod-MR, and is not intended for the study of the descriptive epidemiology of PD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, matplotlib.pyplot as plt, pandas as pd, pymc as mc\n",
    "import dismod_mr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "DisMod-MR uses the integrative systems modeling (ISM) approach to produce simultaneous\n",
    "estimates of disease incidence, prevalence, remission, and mortality. The hallmark of\n",
    "ISM is incorporating all available data.  In the case of Parkinson's Disease this\n",
    "consists of population level measurements of incidence, prevalence, standardized mortality rate (SMR),\n",
    "and cause-specific mortality rate (CSMR).\n",
    "\n",
    "I will begin with a look at a subset of this data, however.  Only that from females in the Europe, Western GBD region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kept 348 rows of data\n"
     ]
    }
   ],
   "source": [
    "model = dismod_mr.data.load('pd_sim_data')\n",
    "model.keep(areas=['europe_western'], sexes=['female', 'total'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the 348 rows of data, here is how the values breakdown by data type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>226.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m_all</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>csmr</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smr</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.762</td>\n",
       "      <td>0.672</td>\n",
       "      <td>1.115</td>\n",
       "      <td>1.276</td>\n",
       "      <td>1.569</td>\n",
       "      <td>2.100</td>\n",
       "      <td>2.864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count   mean    std    min    25%    50%    75%    max\n",
       "data_type                                                        \n",
       "p          226.0  0.005  0.008  0.000  0.000  0.003  0.007  0.052\n",
       "m_all       44.0  0.060  0.125  0.000  0.000  0.003  0.033  0.500\n",
       "csmr        39.0  0.000  0.000  0.000  0.000  0.000  0.000  0.001\n",
       "i           33.0  0.001  0.001  0.000  0.000  0.000  0.001  0.008\n",
       "smr          6.0  1.762  0.672  1.115  1.276  1.569  2.100  2.864"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = model.input_data.groupby('data_type')['value'].describe()\n",
    "np.round(summary,3).sort_values('count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More than half of the available data for this region is prevalence data.  I'll take a closer look\n",
    "at that now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7620067430983335"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_data('smr').value.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                count   mean    std    min    25%    50%    75%    max\n",
      "area                                                                  \n",
      "AUT               1.0  0.006    NaN  0.006  0.006  0.006  0.006  0.006\n",
      "ESP              43.0  0.006  0.007  0.000  0.001  0.006  0.009  0.037\n",
      "europe_western   13.0  0.007  0.006  0.000  0.001  0.006  0.013  0.017\n",
      "FRA              19.0  0.007  0.008  0.000  0.001  0.005  0.010  0.027\n",
      "DNK               6.0  0.004  0.003  0.001  0.001  0.004  0.005  0.009\n",
      "FIN               1.0  0.004    NaN  0.004  0.004  0.004  0.004  0.004\n",
      "NLD              20.0  0.009  0.015  0.000  0.000  0.004  0.011  0.052\n",
      "NOR               4.0  0.005  0.004  0.002  0.003  0.003  0.005  0.011\n",
      "SWE               6.0  0.003  0.003  0.000  0.001  0.003  0.004  0.008\n",
      "ITA              53.0  0.004  0.006  0.000  0.000  0.002  0.005  0.033\n",
      "GBR              36.0  0.004  0.006  0.000  0.000  0.001  0.005  0.022\n",
      "ISR               4.0  0.003  0.004  0.000  0.000  0.001  0.004  0.009\n",
      "DEU               9.0  0.004  0.009  0.000  0.000  0.000  0.006  0.026\n",
      "PRT              11.0  0.002  0.003  0.000  0.000  0.000  0.003  0.010\n"
     ]
    }
   ],
   "source": [
    "groups = model.get_data('p').groupby('area')\n",
    "print(np.round_(groups['value'].describe(),3).sort_values('50%', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the original dataset, there was a wide range in median values, which reflects a combination of country-to-country variation and compositional bias.  Simulating data has reduced this substantially, but there is still six-fold variation between ESP and GBR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = ['ESP', 'GBR']\n",
    "c = {}\n",
    "for i, c_i in enumerate(countries):\n",
    "    c[i] = groups.get_group(c_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = None\n",
    "plt.figure(figsize=(10,4))\n",
    "for i, c_i in enumerate(countries):\n",
    "    ax = plt.subplot(1,2,1+i, sharey=ax, sharex=ax)\n",
    "    dismod_mr.plot.data_bars(c[i])\n",
    "    plt.xlabel('Age (years)')\n",
    "    plt.ylabel('Prevalence (per 1)')\n",
    "    plt.title(c_i)\n",
    "plt.axis(ymin=-.001, xmin=-5, xmax=105)\n",
    "plt.subplots_adjust(wspace=.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A model for age-specific parameters when measurements have heterogeneous age groups\n",
    "-----------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DisMod-MR has four features that make it particularly suited for estimating age-specific prevalence of PD from this data:\n",
    "\n",
    "* Piecewise linear spline model for change in prevalence as a function of age\n",
    "* Age-standardizing model of age-group heterogeneity represents the heterogeneous age groups collected in systematic review\n",
    "* Country-level random effects for true variation in prevalence between countries\n",
    "* Negative binomial model of data, which provides data-driven estimation of non-sampling error in measurements\n",
    "  and elegantly handles measurements of 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will now fit the prevalence data with DisMod-MR's age-standardizing negative binomial random effect spline model and\n",
    "compare the estimates to the observed data.  Then I will use the results of the fit model to explore the four features listed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove fixed effects for this example, I will return to them below\n",
    "model.input_data = model.input_data.filter(regex='(?!x_)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: 5 rows of p data has invalid quantification of uncertainty.\n",
      "finding initial values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ihme/homes/abie/.local/lib/python3.6/site-packages/pandas/core/indexing.py:480: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: nan\n",
      "sampling from posterior\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ihme/homes/abie/.local/lib/python3.6/site-packages/numpy/lib/function_base.py:3250: RuntimeWarning: Invalid value encountered in median\n",
      "  r = func(a, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 59s, sys: 532 ms, total: 3min\n",
      "Wall time: 3min\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<pymc.NormalApproximation.MAP at 0x7f7466bab358>,\n",
       " <pymc.MCMC.MCMC at 0x7f7466b8f6d8>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.vars += dismod_mr.model.asr(model, 'p')\n",
    "%time dismod_mr.fit.asr(model, 'p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot age-specific prevalence estimates over data bars\n",
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "dismod_mr.plot.data_bars(model.get_data('p'), color='grey', label='Simulated PD Data')\n",
    "pred = dismod_mr.model.predict_for(model, model.parameters['p'], 'all', 'female', 2005,\n",
    "                                      'GBR', 'female', 2005, 1.,\n",
    "                                      model.vars['p'], 0., 1.)    # TODO: simplify this method!\n",
    "\n",
    "hpd = mc.utils.hpd(pred, .05)\n",
    "\n",
    "plt.plot(np.arange(101), pred.mean(axis=0), 'k-', linewidth=2, label='Posterior Mean')\n",
    "plt.plot(np.arange(101), hpd[0,:], 'k--', linewidth=1, label='95% HPD interval')\n",
    "plt.plot(np.arange(101), hpd[1,:], 'k--', linewidth=1)\n",
    "\n",
    "plt.xlabel('Age (years)')\n",
    "plt.ylabel('Prevalence (per 1)')\n",
    "plt.grid()\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.axis(ymin=-.001, xmin=-5, xmax=105);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_only = model  # store results for future comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This estimate shows the nonlinear increase in prevalence as a function of age, where the slope of the\n",
    "curve increases at age 60.  A nonlinear estimate like this is possible thanks to DisMod-MR's piecewise linear\n",
    "spline model.\n",
    "\n",
    "The age-standardizing model for heterogeneous age groups is also important for\n",
    "such settings; a naive approach, such as using the age interval midpoint, would result in under-estimating\n",
    "the prevalence for age groups that include both individuals older and younger than 60."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The exact age where the slope of the curve changes is _not_ entirely data driven in this example.  The knots\n",
    "in the piecewise linear spline model were chosen a priori, on the following grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 30, 45, 60, 80, 100]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.parameters['p']['parameter_age_mesh']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A sparse grid allows faster computation, but a dense grid allows more expressive age pattens.  Choosing\n",
    "the proper balance is one challenge of a DisMod-MR analysis.  This is especially true for sparse,\n",
    "noisy data, where too many knots allow the model to follow noisy idiosyncrasies of the data.  DisMod-MR\n",
    "allows for penalized spline regression to help with this choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The country-level random effects in this model capture country-to-country variation in PD prevalence.\n",
    "This variation is not visible in the graphic above, which shows the regional aggregation of country-level\n",
    "estimates (using a population weighted average that takes uncertainty into account).\n",
    "\n",
    "The country-level random effects take the form of intercept shifts in log-prevalence space, with values\n",
    "showing in the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(index=[alpha_i.__name__ for alpha_i in model.vars['p']['alpha']],\n",
    "                      columns=['mean', 'lb', 'ub'])\n",
    "for alpha_i in model.vars['p']['alpha']:\n",
    "    trace = alpha_i.trace()\n",
    "    hpd = mc.utils.hpd(trace, .05)\n",
    "    df.loc[alpha_i.__name__] = (np.mean(trace), hpd[0], hpd[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              mean     lb     ub\n",
      "alpha_p_GBR  0.033 -0.130  0.150\n",
      "alpha_p_AUT  0.010 -0.150  0.142\n",
      "alpha_p_NLD  0.006 -0.137  0.174\n",
      "alpha_p_PRT -0.001 -0.149  0.175\n",
      "alpha_p_ISR -0.004 -0.146  0.145\n",
      "alpha_p_NOR -0.004 -0.138  0.129\n",
      "alpha_p_FRA -0.005 -0.124  0.122\n",
      "alpha_p_SWE -0.011 -0.165  0.135\n",
      "alpha_p_DNK -0.014 -0.143  0.126\n",
      "alpha_p_DEU -0.019 -0.155  0.124\n",
      "alpha_p_ITA -0.019 -0.152  0.107\n",
      "alpha_p_ESP -0.031 -0.165  0.102\n",
      "alpha_p_FIN -0.033 -0.186  0.146\n"
     ]
    }
   ],
   "source": [
    "print(np.round(df.astype(float),3).sort_values('mean', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fourth feature of the model which I want to draw attention to here is the negative binomial model of data,\n",
    "which deals with measurements of zero prevalence in a principled way.  Prevalence studies are reporting transformations\n",
    "of count data, and count data can be zero.  In the case of prevalence of PD in 30- to 40-year-olds, it often _will_ be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_start</th>\n",
       "      <th>age_end</th>\n",
       "      <th>area</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>ITA</td>\n",
       "      <td>0.000467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>ITA</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>PRT</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>ITA</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>ITA</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>GBR</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>PRT</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>PRT</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>PRT</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>DEU</td>\n",
       "      <td>0.000167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>25</td>\n",
       "      <td>34</td>\n",
       "      <td>PRT</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>30</td>\n",
       "      <td>99</td>\n",
       "      <td>GBR</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>30</td>\n",
       "      <td>99</td>\n",
       "      <td>ITA</td>\n",
       "      <td>0.005370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>ITA</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>30</td>\n",
       "      <td>99</td>\n",
       "      <td>ITA</td>\n",
       "      <td>0.002525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age_start  age_end area     value\n",
       "394          0       54  ITA  0.000467\n",
       "371          0       49  ITA  0.000000\n",
       "559          0        4  PRT  0.000000\n",
       "444          0       34  ITA  0.000003\n",
       "276          0       49  ITA  0.000056\n",
       "187          0       29  GBR  0.000000\n",
       "563          5        9  PRT  0.000000\n",
       "574         10       14  PRT  0.000000\n",
       "575         15       24  PRT  0.000000\n",
       "19          20       59  DEU  0.000167\n",
       "558         25       34  PRT  0.000000\n",
       "161         30       99  GBR  0.000000\n",
       "438         30       99  ITA  0.005370\n",
       "422         30       39  ITA  0.000022\n",
       "375         30       99  ITA  0.002525"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_data('p').sort_values('age_start').filter(['age_start', 'age_end', 'area', 'value']).head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The negative binomial model has an appropriately skewed distribution, where prevalence measurements \n",
    "of zero are possible, but measurements of less than zero are not possible.  To demonstrate how this\n",
    "functions, the next figure shows the \"posterior predictive distribution\" for the measurements above,\n",
    "i.e. sample values that the model predicts would be found of the studies were conducted again under\n",
    "the same conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.vars['p']['p_pred'].trace()\n",
    "obs = np.array(model.vars['p']['p_obs'].value)\n",
    "ess = np.array(model.vars['p']['p_obs'].parents['n'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Posterior Predictive distribution')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "sorted_indices = obs.argsort().argsort()\n",
    "jitter = mc.rnormal(0, .1**-2, len(pred))\n",
    "\n",
    "for i,s_i in enumerate(sorted_indices):\n",
    "    plt.plot(s_i+jitter, pred[:, i], 'ko', mew=0, alpha=.25, zorder=-99)\n",
    "\n",
    "plt.errorbar(sorted_indices, obs, yerr=1.96*np.sqrt(obs*(1-obs)/ess), fmt='ks', mew=1, mec='white', ms=5)\n",
    "\n",
    "plt.xticks([])\n",
    "plt.xlabel('Measurement')\n",
    "plt.ylabel('Prevalence (%)\\n', ha='center')\n",
    "plt.yticks([0, .02, .04, .06, .08], [0, 2, 4, 6, 8])\n",
    "plt.axis([25.5,55.5,-.01,.1])\n",
    "plt.grid()\n",
    "plt.title('Posterior Predictive distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional features of DisMod-MR\n",
    "--------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Four additional features of DisMod-MR that are important for many settings are:\n",
    "\n",
    "* informative priors\n",
    "* fixed effects to cross-walk between different studies\n",
    "* fixed effects to predict out of sample\n",
    "* fixed effects to explain the level of variation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Informative priors are useful for modeling disease with less data available than PD, for example to include\n",
    "information that prevalence is zero for youngest ages, or than prevalence must be increasing as a function of\n",
    "age between certain ages.\n",
    "\n",
    "The informative priors are also key to the \"empirical Bayes\" approach to modeling age-specific differences between\n",
    "difference GBD regions.  In this setting, a model using all the world's data is used to produce estimates for each region,\n",
    "and these estimates are used as priors in region-specific models together with the data relevant to that region only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Cross-walk\" fixed effects can correct for biases introduced by multiple outcome measures.  For example, in the PD dataset,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = dismod_mr.data.load('pd_sim_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "crosswalks = list(model.input_data.filter(like='x_cv').columns)\n",
    "groups = model.get_data('p').groupby(crosswalks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x_cv_ascertainment', 'x_cv_diagnostic_criteria', 'x_cv_representative']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosswalks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x_cv_representative</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x_cv_ascertainment</th>\n",
       "      <th>x_cv_diagnostic_criteria</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.006</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.009</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>0.004</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "x_cv_representative                              0      1\n",
       "x_cv_ascertainment x_cv_diagnostic_criteria              \n",
       "0                  0                         0.006      -\n",
       "                   1                         0.009      -\n",
       "1                  0                         0.004      -\n",
       "                   1                         0.004  0.009"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(groups['value'].describe(),3).unstack()['mean'].fillna('-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Incorporating data on parameters other than prevalence\n",
    "------------------------------------------------------\n",
    "\n",
    "So far this example has focused on modeling the prevalence of PD from the\n",
    "prevalence data alone.  However, this represents about half of the available\n",
    "data.  There is also information on incidence, SMR, and CSMR, which has not\n",
    "yet been incorporated.\n",
    "\n",
    "DisMod-MR is capable of including all of the available data, using a compartmental\n",
    "model of disease moving through a population.  This model formalizes the observation\n",
    "that prevalent cases must once have been incident cases, and continue to be prevalent\n",
    "cases until remission or death.\n",
    "\n",
    "In this model, incidence, remission, and excess-mortality are age-standardizing negative binomial random effect spline models,\n",
    "while prevalence, SMR, CSMR, and other parameters come from the solution to a system of ordinary differential equations.\n",
    "\n",
    "The results of this model are smoother prevalence curves that take longer to calculate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "plt.subplot(2,2,1); dismod_mr.plot.data_bars(model.get_data('p')); plt.xlabel('Age (years)'); plt.ylabel('Prevalence')\n",
    "plt.subplot(2,2,2); dismod_mr.plot.data_bars(model.get_data('i')); plt.xlabel('Age (years)'); plt.ylabel('Incidence')\n",
    "plt.subplot(2,2,3); dismod_mr.plot.data_bars(model.get_data('csmr')); plt.xlabel('Age (years)'); plt.ylabel('Cause-specific mortality')\n",
    "plt.subplot(2,2,4); dismod_mr.plot.data_bars(model.get_data('smr')); plt.xlabel('Age (years)'); plt.ylabel('Standardized \\nmortality ratio');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'age_end', 'age_start', 'age_weights', 'area', 'data_type',\n",
       "       'effective_sample_size', 'lower_ci', 'sex', 'standard_error',\n",
       "       'upper_ci', 'value', 'x_cv_ascertainment', 'x_cv_diagnostic_criteria',\n",
       "       'x_cv_representative', 'year_end', 'year_start'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.input_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_i_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "WARNING: 5 rows of p data has invalid quantification of uncertainty.\n",
      "using stored FE for beta_X_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "fitting submodels\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ihme/homes/abie/.local/lib/python3.6/site-packages/pandas/core/indexing.py:480: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "fitting all stochs\n",
      "\n",
      "finding step covariances\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "sampling from posterior distribution\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/abie/.conda/envs/dismod_mr/lib/python3.6/site-packages/pymc/StepMethods.py:1282: UserWarning: \n",
      "Covariance was not positive definite and proposal_sd cannot be computed by \n",
      "Cholesky decomposition. The next jumps will be based on the last \n",
      "valid covariance matrix. This situation may have arisen because no \n",
      "jumps were accepted during the last `interval`. One solution is to \n",
      "increase the interval, or specify an initial covariance matrix with \n",
      "a smaller variance. For this simulation, each time a similar error \n",
      "occurs, proposal_sd will be reduced by a factor .9 to reduce the \n",
      "jumps and increase the likelihood of accepted jumps.\n",
      "  warnings.warn(adjustmentwarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 43min 20s, sys: 53min 23s, total: 1h 36min 44s\n",
      "Wall time: 30min 50s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<pymc.NormalApproximation.MAP at 0x7f7465467198>,\n",
       " <pymc.MCMC.MCMC at 0x7f746528ebe0>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.vars += dismod_mr.model.consistent(model)\n",
    "%time dismod_mr.fit.consistent(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/abie/.conda/envs/dismod_mr/lib/python3.6/site-packages/ipykernel_launcher.py:14: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "plt.subplot(2,2,1); dismod_mr.plot.data_bars(model.get_data('p')); plt.xlabel('Age (years)'); plt.ylabel('Prevalence')\n",
    "plt.subplot(2,2,2); dismod_mr.plot.data_bars(model.get_data('i')); plt.xlabel('Age (years)'); plt.ylabel('Incidence')\n",
    "plt.subplot(2,2,3); dismod_mr.plot.data_bars(model.get_data('csmr')); plt.xlabel('Age (years)'); plt.ylabel('Cause-specific mortality')\n",
    "plt.subplot(2,2,4); dismod_mr.plot.data_bars(model.get_data('smr')); plt.xlabel('Age (years)'); plt.ylabel('Standardized \\nmortality ratio')\n",
    "param_list = [dict(type='p', title='(a)', ylabel='Prevalence (%)', yticks=([0, .01, .02], [0, 1, 2]), axis=[30,101,-0.001,.025]),\n",
    "          dict(type='i', title='(b)', ylabel='Incidence \\n(per 1000 PY)', yticks=([0, .001,.002, .003, .004], [0, 1, 2, 3, 4]), axis=[30,104,-.0003,.0055]),\n",
    "          dict(type='pf', title='(c)', ylabel='Cause-specific mortality \\n(per 1000 PY)', yticks=([0, .001,.002], [0, 1, 2]), axis=[30,104,-.0002,.003]),\n",
    "          dict(type='smr', title='(d)', ylabel='Standardized \\nmortality ratio', yticks=([1, 2, 3,4, ], [1, 2,3, 4]), axis=[35,104,.3,4.5]),\n",
    "          ]\n",
    "\n",
    "for i, params in enumerate(param_list):\n",
    "    ax = plt.subplot(2,2,i+1)\n",
    "    if params['type'] == 'pf': dismod_mr.plot.data_bars(model.get_data('csmr'), color='grey')\n",
    "    else: dismod_mr.plot.data_bars(model.get_data(params['type']), color='grey')\n",
    "    \n",
    "    if params['type'] == 'smr': model.pred = dismod_mr.model.predict_for(model, model.parameters.get('smr', {}), 'all', 'female', 2005, \n",
    "                                                               'GBR', 'female', 2005, 1., model.vars['smr'], 0., 100.).T\n",
    "    else : model.pred = dismod_mr.model.predict_for(model, model.parameters[params['type']],\n",
    "                                                       'all', 'female', 2005, \n",
    "                                                       'GBR', 'female', 2005, 1., model.vars[params['type']], 0., 1.).T\n",
    "    \n",
    "    plt.plot(np.arange(101), model.pred.mean(axis=1), 'k-', linewidth=2, label='Posterior Mean')\n",
    "    hpd = mc.utils.hpd(model.pred.T, .05)\n",
    "    plt.plot(np.arange(101), hpd[0], 'k-', linewidth=1, label='95% HPD interval')\n",
    "    plt.plot(np.arange(101), hpd[1], 'k-', linewidth=1)\n",
    "\n",
    "    plt.xlabel('Age (years)')\n",
    "    plt.ylabel(params['ylabel']+'\\n\\n', ha='center')\n",
    "    plt.axis(params.get('axis', [-5,105,-.005,.06]))\n",
    "    plt.yticks(*params.get('yticks', ([0, .025, .05], [0, 2.5, 5])))\n",
    "    plt.title(params['title'])\n",
    "    plt.grid()\n",
    "    \n",
    "plt.subplots_adjust(hspace=.35, wspace=.35, top=.97)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_with = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most notable difference between the estimates from this model and from the model\n",
    "that used prevalence data only is that this model produces estimates of incidence and\n",
    "mortality in addition to prevalence.  In many cases, the model also produces estimates\n",
    "of the remission rate as well, but there is no remission of PD, so the estimates of zero\n",
    "are not very interesting in this example.  It is another place that informative priors are useful,\n",
    "however.\n",
    "\n",
    "There are also differences between the means and uncertainty intervals estimated by these methods,\n",
    "which show that the additional data is important.  Although the prevalence data alone predicts \n",
    "age-specific prevalence that peaks at 2%, when the incidence and mortality data is also included, the\n",
    "maximum prevalence is a bit lower, closer to 1.5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = dismod_mr.model.predict_for(p_only, model.parameters['p'],\n",
    "                                    'all', 'total', 'all', \n",
    "                                    'GBR', 'female', 2005, 1.,\n",
    "                                    p_only.vars['p'], 0., 1.)\n",
    "\n",
    "p2 = dismod_mr.model.predict_for(p_with, model.parameters['p'],\n",
    "                                    'all', 'total', 'all', \n",
    "                                    'GBR', 'female', 2005, 1.,\n",
    "                                    p_with.vars['p'], 0., 1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(p1.mean(axis=0), 'k--', linewidth=2, label='Only prevalence')\n",
    "plt.plot(p2.mean(axis=0), 'k-', linewidth=2, label='All available')\n",
    "\n",
    "plt.xlabel('Age (years)')\n",
    "plt.ylabel('Prevalence (%)\\n\\n', ha='center')\n",
    "plt.yticks([0, .01, .02], [0, 1, 2])\n",
    "plt.axis([30,101,-0.001,.025])\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplots_adjust(top=.97, bottom=.16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the data is so noisy, the differences between the mean estimates of these different models are not significant; the posterior distributions\n",
    "have considerable overlap.  At age 80, for example, the posterior distributions for age-80 prevalence are estimated as the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(100*p1[:,80], density=True, histtype='step', label='Only prevalence', linewidth=3, color=np.array([239., 138., 98., 256.])/256)\n",
    "plt.hist(100*p2[:,80], density=True, histtype='step', label='All available', linewidth=3, color=np.array([103, 169, 207, 256.])/256)\n",
    "plt.title('PD prevalence at age 80')\n",
    "plt.xlabel('Prevalence (%)\\n\\n', ha='center')\n",
    "plt.ylabel('Probability Density')\n",
    "plt.legend(loc='upper right')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplots_adjust(bottom=.16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion\n",
    "==========\n",
    "\n",
    "I hope that this example is a quick way to see the strengths and weaknesses of DisMod-MR.\n",
    "This model is particularly suited for estimating descriptive epidemiology of diseases\n",
    "with sparse, noisy data from multiple, incompatible sources.\n",
    "\n",
    "I am currently working to make it faster, as well as to improve the capabilities for modeling\n",
    "changes between regions over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Jul 23 14:29:20 PDT 2019\r\n"
     ]
    }
   ],
   "source": [
    "!date"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dismod_mr",
   "language": "python",
   "name": "dismod_mr"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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