notebooks/hidden_contacts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance comparison in the presence of unreported contacts\n",
    "\n",
    "This notebook reproduces Fig. 5. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import json\n",
    "from src import utils\n",
    "from src.utils import get_fig_dim\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set_theme(context='paper', style='ticks', font_scale=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the values of sensitivity s<sub>e</sub> and specificity s<sub>p</sub>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "se=0.8\n",
    "sp=0.98"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "name=\"hidden_contacts\"\n",
    "width_pt = 469\n",
    "palette = sns.color_palette('husl', 6)\n",
    "type_dict = {\n",
    "    'N' : int,\n",
    "    'untraced' : float, \n",
    "    'false_negatives' : int,\n",
    "    'false_positives' : int,\n",
    "    'k' : float,\n",
    "    'bench' : bool,\n",
    "    'lambda_1' : float,\n",
    "    'lambda_2' : float,\n",
    "    'method' : str,\n",
    "    'num_of_infected' : int,\n",
    "    'num_of_tests' : int,\n",
    "    'avg_group_size' : float,\n",
    "    'r' : float,\n",
    "    'score' : float,\n",
    "    'se' : float,\n",
    "    'sp' : float,\n",
    "    'd' : float,\n",
    "    'seed' : int\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading file 64/64\n"
     ]
    }
   ],
   "source": [
    "files = glob.glob(\"./outputs/{name}_*_se_{se}_sp_{sp}_*.json\".format(name=name, se=se, sp=sp))\n",
    "\n",
    "dicts = []\n",
    "for fl_id, fl in enumerate(files):\n",
    "    clear_output(wait=True)\n",
    "    print('Reading file ' + str(fl_id+1)+'/'+str(len(files)))\n",
    "    with open(fl,\"r\") as f:\n",
    "        js = json.load(f)\n",
    "        sizes = []\n",
    "        for group_id in js['groups']:\n",
    "            sizes.append(int(js['groups'][group_id]))\n",
    "\n",
    "        avg_group_size = np.mean(sizes)\n",
    "        \n",
    "        for seed in js['seeds']:\n",
    "            seed_js = js.copy()\n",
    "            seed_js.pop('seeds', None)\n",
    "            seed_js.pop('groups', None)    \n",
    "            seed_js = seed_js | js['seeds'][seed]\n",
    "            seed_js['seed']=seed\n",
    "            seed_js['avg_group_size'] = avg_group_size\n",
    "\n",
    "            dicts.append(seed_js)\n",
    "\n",
    "input_df = pd.DataFrame(dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df = input_df.astype(type_dict) # Fix types\n",
    "total_df = total_df.rename(columns={'avg_group_size' : 'Average pool size'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "perc_df=total_df.pivot(index=['lambda_1', 'lambda_2', 'se', 'sp', 'r', 'k', 'd', 'bench', 'seed', 'N', 'untraced'], columns='method', values='score').reset_index()\n",
    "perc_df=perc_df[perc_df['bench']==False]\n",
    "perc_df['Percentage'] = (perc_df['binomial'] - perc_df['negbin'])/perc_df['binomial']*100\n",
    "perc_df = perc_df[['N', 'untraced', 'Percentage']]\n",
    "perc_df['traced'] = (1-perc_df['untraced'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "infected_df=total_df[['lambda_1', 'lambda_2', 'se', 'sp', 'r', 'k', 'd', 'method', 'seed', 'N', 'untraced', 'bench', 'num_of_infected']]\n",
    "infected_df=infected_df[infected_df['bench']==False]\n",
    "infected_df = infected_df.rename(columns={'num_of_infected': 'Num of infected'})\n",
    "infected_df['traced'] = (1-infected_df['untraced'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 607.422x187.704 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.latexify(font_serif='Times New Roman', font_size=10, usetex=False)\n",
    "\n",
    "fig_height, fig_aspect = get_fig_dim(width_pt, fraction=0.65)\n",
    "fig, [ax1, ax2] = plt.subplots(nrows=1, ncols=2, figsize=(2*fig_height*fig_aspect,fig_height))\n",
    "\n",
    "# Subplot 1\n",
    "traced = sorted(perc_df['traced'].unique(), reverse=True)\n",
    "sns.pointplot(data=perc_df, x=\"N\", y=\"Percentage\", hue=\"traced\", palette=palette[0:4],\n",
    "            ci=95, join=False, dodge=.5, capsize=0.1, legend=False, hue_order=traced, ax=ax1)\n",
    "sns.despine()\n",
    "\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "labels = [x[:-2]+r'%' for x in labels]\n",
    "ax1.legend(handles=handles, labels=labels, title='Percentage of contacts\\nsuccessfully traced', ncol=2, loc='upper right')#, bbox_to_anchor=(0.45, 1))\n",
    "ax1.set_ylabel('Average percentage of tests saved')\n",
    "ax1.set_xlabel('Number of traced contacts')\n",
    "ax1.set_ylim([25,50])\n",
    "ax1.set_yticks([30, 40, 50])\n",
    "ax1.get_yaxis().set_major_formatter(matplotlib.ticker.PercentFormatter(decimals=0))\n",
    "\n",
    "ax1.text(x=0.01, y=1, s=r'A', size=15, transform = ax1.transAxes, horizontalalignment='center', verticalalignment='bottom', weight='bold')\n",
    "\n",
    "# Subplot 2\n",
    "traced = sorted(infected_df['traced'].unique(), reverse=True)\n",
    "sns.barplot(data=infected_df, x=\"N\", y=\"Num of infected\", hue=\"traced\", palette=palette[0:4],\n",
    "            ci=95, hue_order=traced, ax=ax2)\n",
    "sns.despine()\n",
    "\n",
    "handles, labels = ax2.get_legend_handles_labels()\n",
    "labels = [x[:-2]+r'%' for x in labels]\n",
    "ax2.legend(handles=handles, labels=labels, title='Percentage of contacts\\nsuccessfully traced', ncol=2, loc='upper left')#, bbox_to_anchor=(0.45, 1))\n",
    "ax2.set_ylabel('Number of infected contacts\\namong traced')\n",
    "ax2.set_xlabel('Number of traced contacts')\n",
    "ax2.set_ylim([0,4])\n",
    "\n",
    "ax2.text(x=0.01, y=1, s=r'B', size=15, transform = ax2.transAxes, horizontalalignment='center', verticalalignment='bottom', weight='bold')\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig('figures/fig5.tif', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e312f55c3c97e43ef328a86651ae8cf61ffd8a19c7888f1a2843b6f17cf5ecdd"
  },
  "kernelspec": {
   "display_name": "Python 3.9.1 64-bit ('env')",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  },
  "metadata": {
   "interpreter": {
    "hash": "c425b3d18afb7ef01beae38734bbc4c3f66a0926b3e7f0a803887fb6dc8ff96a"
   }
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}