Simulation_Examples.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import itertools\n",
    "from testing_freq import *\n",
    "from utils import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Copenhagen Network Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>user_a</th>\n",
       "      <th>user_b</th>\n",
       "      <th>rssi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>-73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>18</td>\n",
       "      <td>-86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>-62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>43</td>\n",
       "      <td>-87</td>\n",
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       "      <td>63</td>\n",
       "      <td>-96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   timestamp  user_a  user_b  rssi\n",
       "0          0      20      19   -73\n",
       "1          0      46      18   -86\n",
       "2          0      48      47   -62\n",
       "3          0      68      43   -87\n",
       "4          0      89      63   -96"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bt = pd.read_csv('bt_data_clean.csv', header = 0, names = ['timestamp','user_a', 'user_b', 'rssi'])\n",
    "bt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Construct adjacency matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "execution_count": 4,
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   "source": [
    "time_step = 86400\n",
    "start = time.time()\n",
    "adj_mats, df = construct_adj_mat(bt, time_step = time_step, data_loops = 3, dist_thres = -75)\n",
    "end = time.time()\n",
    "end - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(112, 692, 692)"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "adj_mats.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up the simulation parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "myseed = 12\n",
    "\n",
    "disease_params = dict()\n",
    "disease_params['ext_inf'] = 0.02\n",
    "disease_params['asymp'] = 0.3\n",
    "disease_params['init_status'] = gen_init_status(n_nodes = adj_mats.shape[1], asymp = disease_params['asymp'], seed = myseed)\n",
    "disease_params['beta'] = gen_trans_prob_NPI(n_nodes = adj_mats.shape[1], base_beta = 0.007, p_sd = 0.2, p_fm = 0.2, seed = myseed)\n",
    "disease_params['sigma_a'] = 0.3\n",
    "disease_params['sigma_s'] = 0.3\n",
    "disease_params['gamma_a'] = 1/7\n",
    "disease_params['gamma_s'] = 1/10\n",
    "disease_params['n_time_day'] = 1\n",
    "\n",
    "test_params = dict()\n",
    "test_params['test_freq'] = 3\n",
    "test_params['spec'] = 0.99\n",
    "test_params['symp_test_delay'] = gen_symp_test_delay(n_nodes = adj_mats.shape[1], univ_delay = 3)\n",
    "test_params['nc_schd'] = 0.05\n",
    "test_params['nc_symp'] = 0.1\n",
    "test_params['time_dep'] = True\n",
    "test_params['time_dep_type'] = 'W'\n",
    "test_params['false_symp'] = 0.05\n",
    "\n",
    "quar_params = dict()\n",
    "quar_params['quar_delay'] = gen_quar_delay(n_nodes = adj_mats.shape[1], univ_delay = 1)\n",
    "quar_params['quar_comp'] = gen_quar_comp(n_nodes = adj_mats.shape[1], seed = myseed)\n",
    "quar_params['quar_len'] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<testing_freq.TestFreq at 0x1e401307188>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the simulation class\n",
    "testin = TestFreq(adj_mats, disease_params, test_params, quar_params)\n",
    "testin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run simulation without testing or isolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "(s_nodes_byt, e_nodes_byt, ia_nodes_byt, is_nodes_byt, r_nodes_byt) = testin.sim_spread(seed = myseed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "661"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_inf = list(set(np.concatenate(ia_nodes_byt + is_nodes_byt).ravel()))\n",
    "len(all_inf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run simulation with testing and isolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.8690245151519775"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = time.time()\n",
    "(ia_nodes_byt, is_nodes_byt, test_pos_schd_byt, test_pos_symp_byt, q_schd_byt, q_symp_byt) = testin.sim_spread_test(seed = myseed)\n",
    "end = time.time()\n",
    "end - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "631"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the total number infected\n",
    "all_inf = list(set(np.concatenate(ia_nodes_byt + is_nodes_byt).ravel()))\n",
    "len(all_inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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