Figure2-Spatial analysis and plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from itertools import chain\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.lines import Line2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class='info-circle alert alert-block alert-info'>Covid-19 Case Data</div>\n",
    "- 2020-01-23 to 2020-04-01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cumulative cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 72)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0123-0401\n",
    "case_df = pd.read_csv('Data/city_cumulative_cases.csv')\n",
    "\n",
    "case_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# city coding: \n",
    "# cities outside of Hubei: 0\n",
    "# cities in Hubei (not Wuhan): 1\n",
    "# city of Wuhan: 2\n",
    "\n",
    "hubei_cities = ['武汉市', '黄石市', '十堰市', '宜昌市', '襄阳市', '鄂州市', '荆门市', '孝感市', \n",
    "                '荆州市', '黄冈市', '咸宁市', '随州市', '恩施土家族苗族自治州', '仙桃市', '潜江市', '天门市', '神农架林区']\n",
    "\n",
    "case_df['hubei_city'] = case_df['city_cn'].map(\n",
    "        lambda x: 0 if x not in hubei_cities else (2 if x=='武汉市' else 1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city_cn</th>\n",
       "      <th>city_id</th>\n",
       "      <th>2020-01-23</th>\n",
       "      <th>2020-01-24</th>\n",
       "      <th>2020-01-25</th>\n",
       "      <th>2020-01-26</th>\n",
       "      <th>2020-01-27</th>\n",
       "      <th>2020-01-28</th>\n",
       "      <th>2020-01-29</th>\n",
       "      <th>2020-01-30</th>\n",
       "      <th>2020-01-31</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <th>2020-02-02</th>\n",
       "      <th>2020-02-03</th>\n",
       "      <th>2020-02-04</th>\n",
       "      <th>2020-02-05</th>\n",
       "      <th>2020-02-06</th>\n",
       "      <th>2020-02-07</th>\n",
       "      <th>2020-02-08</th>\n",
       "      <th>2020-02-09</th>\n",
       "      <th>2020-02-10</th>\n",
       "      <th>2020-02-11</th>\n",
       "      <th>2020-02-12</th>\n",
       "      <th>2020-02-13</th>\n",
       "      <th>2020-02-14</th>\n",
       "      <th>2020-02-15</th>\n",
       "      <th>2020-02-16</th>\n",
       "      <th>2020-02-17</th>\n",
       "      <th>2020-02-18</th>\n",
       "      <th>2020-02-19</th>\n",
       "      <th>2020-02-20</th>\n",
       "      <th>2020-02-21</th>\n",
       "      <th>2020-02-22</th>\n",
       "      <th>2020-02-23</th>\n",
       "      <th>2020-02-24</th>\n",
       "      <th>2020-02-25</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <th>2020-02-27</th>\n",
       "      <th>2020-02-28</th>\n",
       "      <th>2020-02-29</th>\n",
       "      <th>2020-03-01</th>\n",
       "      <th>2020-03-02</th>\n",
       "      <th>2020-03-03</th>\n",
       "      <th>2020-03-04</th>\n",
       "      <th>2020-03-05</th>\n",
       "      <th>2020-03-06</th>\n",
       "      <th>2020-03-07</th>\n",
       "      <th>2020-03-08</th>\n",
       "      <th>2020-03-09</th>\n",
       "      <th>2020-03-10</th>\n",
       "      <th>2020-03-11</th>\n",
       "      <th>2020-03-12</th>\n",
       "      <th>2020-03-13</th>\n",
       "      <th>2020-03-14</th>\n",
       "      <th>2020-03-15</th>\n",
       "      <th>2020-03-16</th>\n",
       "      <th>2020-03-17</th>\n",
       "      <th>2020-03-18</th>\n",
       "      <th>2020-03-19</th>\n",
       "      <th>2020-03-20</th>\n",
       "      <th>2020-03-21</th>\n",
       "      <th>2020-03-22</th>\n",
       "      <th>2020-03-23</th>\n",
       "      <th>2020-03-24</th>\n",
       "      <th>2020-03-25</th>\n",
       "      <th>2020-03-26</th>\n",
       "      <th>2020-03-27</th>\n",
       "      <th>2020-03-28</th>\n",
       "      <th>2020-03-29</th>\n",
       "      <th>2020-03-30</th>\n",
       "      <th>2020-03-31</th>\n",
       "      <th>2020-04-01</th>\n",
       "      <th>hubei_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>26</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>67</td>\n",
       "      <td>80</td>\n",
       "      <td>96</td>\n",
       "      <td>112</td>\n",
       "      <td>132</td>\n",
       "      <td>156</td>\n",
       "      <td>183</td>\n",
       "      <td>212</td>\n",
       "      <td>228</td>\n",
       "      <td>253</td>\n",
       "      <td>274</td>\n",
       "      <td>297</td>\n",
       "      <td>315</td>\n",
       "      <td>326</td>\n",
       "      <td>337</td>\n",
       "      <td>342</td>\n",
       "      <td>352</td>\n",
       "      <td>366</td>\n",
       "      <td>372</td>\n",
       "      <td>375</td>\n",
       "      <td>380</td>\n",
       "      <td>381</td>\n",
       "      <td>387</td>\n",
       "      <td>393</td>\n",
       "      <td>395</td>\n",
       "      <td>396</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>411</td>\n",
       "      <td>413</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>417</td>\n",
       "      <td>418</td>\n",
       "      <td>422</td>\n",
       "      <td>426</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "      <td>429</td>\n",
       "      <td>435</td>\n",
       "      <td>435</td>\n",
       "      <td>436</td>\n",
       "      <td>437</td>\n",
       "      <td>442</td>\n",
       "      <td>446</td>\n",
       "      <td>455</td>\n",
       "      <td>458</td>\n",
       "      <td>479</td>\n",
       "      <td>485</td>\n",
       "      <td>499</td>\n",
       "      <td>512</td>\n",
       "      <td>522</td>\n",
       "      <td>554</td>\n",
       "      <td>559</td>\n",
       "      <td>565</td>\n",
       "      <td>569</td>\n",
       "      <td>572</td>\n",
       "      <td>576</td>\n",
       "      <td>577</td>\n",
       "      <td>580</td>\n",
       "      <td>580</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>120000</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>41</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "      <td>67</td>\n",
       "      <td>69</td>\n",
       "      <td>79</td>\n",
       "      <td>81</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>95</td>\n",
       "      <td>107</td>\n",
       "      <td>113</td>\n",
       "      <td>119</td>\n",
       "      <td>120</td>\n",
       "      <td>122</td>\n",
       "      <td>125</td>\n",
       "      <td>127</td>\n",
       "      <td>128</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>133</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>141</td>\n",
       "      <td>142</td>\n",
       "      <td>145</td>\n",
       "      <td>147</td>\n",
       "      <td>152</td>\n",
       "      <td>156</td>\n",
       "      <td>163</td>\n",
       "      <td>166</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>130100</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>130200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>130300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_cn  city_id  2020-01-23  2020-01-24  2020-01-25  2020-01-26  \\\n",
       "0     北京市   110000          26          36          51          67   \n",
       "1     天津市   120000           0           8          10          14   \n",
       "2    石家庄市   130100           1           4           5           7   \n",
       "3     唐山市   130200           0           0           0           0   \n",
       "4    秦皇岛市   130300           0           0           0           0   \n",
       "\n",
       "   2020-01-27  2020-01-28  2020-01-29  2020-01-30  2020-01-31  2020-02-01  \\\n",
       "0          80          96         112         132         156         183   \n",
       "1          23          24          27          31          32          41   \n",
       "2           9          10          11          11          12          13   \n",
       "3           1           3           3           7           8          11   \n",
       "4           0           0           0           0           1           1   \n",
       "\n",
       "   2020-02-02  2020-02-03  2020-02-04  2020-02-05  2020-02-06  2020-02-07  \\\n",
       "0         212         228         253         274         297         315   \n",
       "1          48          60          67          69          79          81   \n",
       "2          16          21          21          24          24          24   \n",
       "3          11          11          12          17          19          23   \n",
       "4           2           2           3           3           5           7   \n",
       "\n",
       "   2020-02-08  2020-02-09  2020-02-10  2020-02-11  2020-02-12  2020-02-13  \\\n",
       "0         326         337         342         352         366         372   \n",
       "1          88          91          95         107         113         119   \n",
       "2          24          25          27          27          27          27   \n",
       "3          27          28          31          33          35          43   \n",
       "4           7           7           9           9           9           9   \n",
       "\n",
       "   2020-02-14  2020-02-15  2020-02-16  2020-02-17  2020-02-18  2020-02-19  \\\n",
       "0         375         380         381         387         393         395   \n",
       "1         120         122         125         127         128         130   \n",
       "2          27          28          28          28          28          28   \n",
       "3          46          51          51          51          55          56   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-02-20  2020-02-21  2020-02-22  2020-02-23  2020-02-24  2020-02-25  \\\n",
       "0         396         399         399         399         400         400   \n",
       "1         131         133         135         135         135         135   \n",
       "2          28          29          29          29          29          29   \n",
       "3          57          57          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-02-26  2020-02-27  2020-02-28  2020-02-29  2020-03-01  2020-03-02  \\\n",
       "0         410         410         411         413         414         414   \n",
       "1         135         136         136         136         136         136   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-03  2020-03-04  2020-03-05  2020-03-06  2020-03-07  2020-03-08  \\\n",
       "0         417         418         422         426         428         428   \n",
       "1         136         136         136         136         136         136   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-09  2020-03-10  2020-03-11  2020-03-12  2020-03-13  2020-03-14  \\\n",
       "0         429         435         435         436         437         442   \n",
       "1         136         136         136         136         136         136   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-15  2020-03-16  2020-03-17  2020-03-18  2020-03-19  2020-03-20  \\\n",
       "0         446         455         458         479         485         499   \n",
       "1         136         136         136         136         137         137   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-21  2020-03-22  2020-03-23  2020-03-24  2020-03-25  2020-03-26  \\\n",
       "0         512         522         554         559         565         569   \n",
       "1         137         141         142         145         147         152   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-27  2020-03-28  2020-03-29  2020-03-30  2020-03-31  2020-04-01  \\\n",
       "0         572         576         577         580         580         582   \n",
       "1         156         163         166         174         174         176   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   hubei_city  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### daily cumulative case plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x158.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 2.2))\n",
    "\n",
    "ax.patch.set_alpha(0.015)\n",
    "ax.set_facecolor('#2b3679')\n",
    "\n",
    "dates = [str(d.date()) for d in pd.date_range('2020-01-24', '2020-03-15')]\n",
    "\n",
    "width = 0.9\n",
    "\n",
    "# 1. cities not in Hubei\n",
    "# ---------------------\n",
    "plot_df = case_df.copy()\n",
    "# plot_df = plot_df[~plot_df['city_cn'].isin(hubei_cities)]\n",
    "plot_df = plot_df[plot_df['hubei_city']==0]\n",
    "plot_df = plot_df.loc[:, dates]\n",
    "plot_df = plot_df.sum(axis=0).reset_index().rename(columns={'index': 'date', 0:'count'})\n",
    "\n",
    "x1 = plot_df['date']\n",
    "y1 = plot_df['count']\n",
    "ax.bar(x1, y1,\n",
    "                width=width,\n",
    "                edgecolor='w',\n",
    "                color='#1f77b4', \n",
    "                alpha=.8,\n",
    "                )\n",
    "\n",
    "\n",
    "# 2. cities in Hubei excluding Wuhan\n",
    "# ---------------------\n",
    "plot_df = case_df.copy()\n",
    "plot_df = plot_df[plot_df['hubei_city']==1]\n",
    "plot_df = plot_df.loc[:, dates]\n",
    "plot_df = plot_df.sum(axis=0).reset_index().rename(columns={'index': 'date', 0:'count'})\n",
    "\n",
    "x2 = plot_df['date']\n",
    "y2 = plot_df['count']\n",
    "ax.bar(x2, y2, bottom=y1,\n",
    "                width=width,\n",
    "                edgecolor='w',\n",
    "                color='#6baed6', \n",
    "                alpha=.8,\n",
    "                )\n",
    "\n",
    "\n",
    "# 3. city of Wuhan\n",
    "# ---------------------\n",
    "plot_df = case_df.copy()\n",
    "plot_df = plot_df[plot_df['hubei_city']==2]\n",
    "plot_df = plot_df.loc[:, dates]\n",
    "plot_df = plot_df.sum(axis=0).reset_index().rename(columns={'index': 'date', 0:'count'})\n",
    "\n",
    "x3 = plot_df['date']\n",
    "y3 = plot_df['count']\n",
    "ax.bar(x3, y3, bottom=y1+y2,\n",
    "                width=width,\n",
    "                edgecolor='w',\n",
    "#                 linewidth=0.1,\n",
    "                color='#999999', \n",
    "                alpha=.8,\n",
    "                )\n",
    "\n",
    "\n",
    "\n",
    "N = len(x1)\n",
    "\n",
    "every_nth = 1\n",
    "locs = range(0, N, every_nth)\n",
    "ax.xaxis.set_minor_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "every_nth = 5\n",
    "locs = range(0, N, every_nth)\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "# ticks = list(x)\n",
    "ticks = list(x1.str[-5:])\n",
    "# ticks.insert(0, '')\n",
    "ticks.append('03-16')\n",
    "# string date\n",
    "plt.xticks(list(range(0, N+1, every_nth)), ticks[::every_nth], \n",
    "           rotation='0', \n",
    "           horizontalalignment='center',\n",
    "           # va = 'center',\n",
    "           position=(0, 0)\n",
    "           # rotation_mode=\"anchor\"\n",
    "          )\n",
    "\n",
    "locs = range(0, 100000, 20000)\n",
    "ax.yaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "ticks = [f'{int(i/1000)}K' for i in locs[1:]]\n",
    "ticks.insert(0, '0 ')\n",
    "plt.yticks(locs, ticks)\n",
    "\n",
    "\n",
    "border_width = 1.35\n",
    "ax.spines['bottom'].set_linewidth(border_width)\n",
    "ax.spines['left'].set_linewidth(border_width)\n",
    "ax.spines['top'].set_linewidth(border_width)\n",
    "ax.spines['right'].set_linewidth(border_width)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "\n",
    "# # custom ticks\n",
    "ax.tick_params(axis='x', which='major', top=False, right=False, pad=3, labelsize=12, length=5, width=1.5, direction='out')\n",
    "ax.tick_params(axis='y', which='major', top=False, right=False, pad=3, labelsize=12, length=5, width=1.2, direction='out')\n",
    "ax.tick_params(axis='x', which='minor', top=False, right=False, length=4,  width=.6, direction='out')\n",
    "ax.tick_params(axis='y', which='minor', top=False, right=False, length=4,  width=.6, direction='out')\n",
    "\n",
    "\n",
    "# # plt.xlim(-1)\n",
    "ax.set_xlim(left=-1, right=52)\n",
    "\n",
    "other_cities = Line2D([0], [0], color='#1f77b4', marker='o',markersize=13, linestyle='None', markeredgecolor='w')\n",
    "hubei = Line2D([0], [0], color='#6baed6', marker='o',markersize=13, linestyle='None', markeredgecolor='w')\n",
    "wuhan = Line2D([0], [0], color='#999999', marker='o',markersize=13, linestyle='None', markeredgecolor='w')\n",
    "\n",
    "# ax.set_xlabel('Date', fontsize=13)\n",
    "ax.set_ylabel('Confirmed cases', fontsize=13)\n",
    "\n",
    "\n",
    "real_legend = ax.legend((other_cities, hubei, wuhan), \n",
    "                        ('Cities outside Hubei','Cities in Hubei\\Wuhan', 'Wuhan'), \n",
    "                        numpoints=1, fontsize=11, frameon=False, loc='best')\n",
    "\n",
    "out_dir = 'Outputs'\n",
    "# plt.savefig(os.path.join(out_dir, 'fig2-cumulative_cases.pdf'), pad_inches=0.01, bbox_inches='tight')\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### daily new cases\n",
    "* there may exist negative values in one day as cases may be overestimated in previous days in some areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 72)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = [str(d.date()) for d in pd.date_range('2020-01-23', '2020-04-01')]\n",
    "daily_case_df = case_df[dates].diff(axis=1)\n",
    "\n",
    "daily_case_df['city_cn'] = case_df['city_cn']\n",
    "\n",
    "hubei_cities = ['武汉市', '黄石市', '十堰市', '宜昌市', '襄阳市', '鄂州市', '荆门市', '孝感市', \n",
    "                '荆州市', '黄冈市', '咸宁市', '随州市', '恩施土家族苗族自治州', '仙桃市', '潜江市', '天门市', '神农架林区']\n",
    "daily_case_df['hubei_city'] = daily_case_df['city_cn'].map(\n",
    "    lambda x: 0 if x not in hubei_cities else (2 if x=='武汉市' else 1) )\n",
    "daily_case_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>2020-01-23</th>\n",
       "      <th>2020-01-24</th>\n",
       "      <th>2020-01-25</th>\n",
       "      <th>2020-01-26</th>\n",
       "      <th>2020-01-27</th>\n",
       "      <th>2020-01-28</th>\n",
       "      <th>2020-01-29</th>\n",
       "      <th>2020-01-30</th>\n",
       "      <th>2020-01-31</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <th>2020-02-02</th>\n",
       "      <th>2020-02-03</th>\n",
       "      <th>2020-02-04</th>\n",
       "      <th>2020-02-05</th>\n",
       "      <th>2020-02-06</th>\n",
       "      <th>2020-02-07</th>\n",
       "      <th>2020-02-08</th>\n",
       "      <th>2020-02-09</th>\n",
       "      <th>2020-02-10</th>\n",
       "      <th>2020-02-11</th>\n",
       "      <th>2020-02-12</th>\n",
       "      <th>2020-02-13</th>\n",
       "      <th>2020-02-14</th>\n",
       "      <th>2020-02-15</th>\n",
       "      <th>2020-02-16</th>\n",
       "      <th>2020-02-17</th>\n",
       "      <th>2020-02-18</th>\n",
       "      <th>2020-02-19</th>\n",
       "      <th>2020-02-20</th>\n",
       "      <th>2020-02-21</th>\n",
       "      <th>2020-02-22</th>\n",
       "      <th>2020-02-23</th>\n",
       "      <th>2020-02-24</th>\n",
       "      <th>2020-02-25</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <th>2020-02-27</th>\n",
       "      <th>2020-02-28</th>\n",
       "      <th>2020-02-29</th>\n",
       "      <th>2020-03-01</th>\n",
       "      <th>2020-03-02</th>\n",
       "      <th>2020-03-03</th>\n",
       "      <th>2020-03-04</th>\n",
       "      <th>2020-03-05</th>\n",
       "      <th>2020-03-06</th>\n",
       "      <th>2020-03-07</th>\n",
       "      <th>2020-03-08</th>\n",
       "      <th>2020-03-09</th>\n",
       "      <th>2020-03-10</th>\n",
       "      <th>2020-03-11</th>\n",
       "      <th>2020-03-12</th>\n",
       "      <th>2020-03-13</th>\n",
       "      <th>2020-03-14</th>\n",
       "      <th>2020-03-15</th>\n",
       "      <th>2020-03-16</th>\n",
       "      <th>2020-03-17</th>\n",
       "      <th>2020-03-18</th>\n",
       "      <th>2020-03-19</th>\n",
       "      <th>2020-03-20</th>\n",
       "      <th>2020-03-21</th>\n",
       "      <th>2020-03-22</th>\n",
       "      <th>2020-03-23</th>\n",
       "      <th>2020-03-24</th>\n",
       "      <th>2020-03-25</th>\n",
       "      <th>2020-03-26</th>\n",
       "      <th>2020-03-27</th>\n",
       "      <th>2020-03-28</th>\n",
       "      <th>2020-03-29</th>\n",
       "      <th>2020-03-30</th>\n",
       "      <th>2020-03-31</th>\n",
       "      <th>2020-04-01</th>\n",
       "      <th>city_cn</th>\n",
       "      <th>hubei_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>北京市</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>天津市</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>石家庄市</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>唐山市</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2020-01-23  2020-01-24  2020-01-25  2020-01-26  2020-01-27  2020-01-28  \\\n",
       "0         NaN        10.0        15.0        16.0        13.0        16.0   \n",
       "1         NaN         8.0         2.0         4.0         9.0         1.0   \n",
       "2         NaN         3.0         1.0         2.0         2.0         1.0   \n",
       "3         NaN         0.0         0.0         0.0         1.0         2.0   \n",
       "4         NaN         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   2020-01-29  2020-01-30  2020-01-31  2020-02-01  2020-02-02  2020-02-03  \\\n",
       "0        16.0        20.0        24.0        27.0        29.0        16.0   \n",
       "1         3.0         4.0         1.0         9.0         7.0        12.0   \n",
       "2         1.0         0.0         1.0         1.0         3.0         5.0   \n",
       "3         0.0         4.0         1.0         3.0         0.0         0.0   \n",
       "4         0.0         0.0         1.0         0.0         1.0         0.0   \n",
       "\n",
       "   2020-02-04  2020-02-05  2020-02-06  2020-02-07  2020-02-08  2020-02-09  \\\n",
       "0        25.0        21.0        23.0        18.0        11.0        11.0   \n",
       "1         7.0         2.0        10.0         2.0         7.0         3.0   \n",
       "2         0.0         3.0         0.0         0.0         0.0         1.0   \n",
       "3         1.0         5.0         2.0         4.0         4.0         1.0   \n",
       "4         1.0         0.0         2.0         2.0         0.0         0.0   \n",
       "\n",
       "   2020-02-10  2020-02-11  2020-02-12  2020-02-13  2020-02-14  2020-02-15  \\\n",
       "0         5.0        10.0        14.0         6.0         3.0         5.0   \n",
       "1         4.0        12.0         6.0         6.0         1.0         2.0   \n",
       "2         2.0         0.0         0.0         0.0         0.0         1.0   \n",
       "3         3.0         2.0         2.0         8.0         3.0         5.0   \n",
       "4         2.0         0.0         0.0         0.0         1.0         0.0   \n",
       "\n",
       "   2020-02-16  2020-02-17  2020-02-18  2020-02-19  2020-02-20  2020-02-21  \\\n",
       "0         1.0         6.0         6.0         2.0         1.0         3.0   \n",
       "1         3.0         2.0         1.0         2.0         1.0         2.0   \n",
       "2         0.0         0.0         0.0         0.0         0.0         1.0   \n",
       "3         0.0         0.0         4.0         1.0         1.0         0.0   \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   2020-02-22  2020-02-23  2020-02-24  2020-02-25  2020-02-26  2020-02-27  \\\n",
       "0         0.0         0.0         1.0         0.0        10.0         0.0   \n",
       "1         2.0         0.0         0.0         0.0         0.0         1.0   \n",
       "2         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "3         1.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   2020-02-28  2020-02-29  2020-03-01  2020-03-02  2020-03-03  2020-03-04  \\\n",
       "0         1.0         2.0         1.0         0.0         3.0         1.0   \n",
       "1         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "2         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "3         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   2020-03-05  2020-03-06  2020-03-07  2020-03-08  2020-03-09  2020-03-10  \\\n",
       "0         4.0         4.0         2.0         0.0         1.0         6.0   \n",
       "1         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "2         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "3         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   2020-03-11  2020-03-12  2020-03-13  2020-03-14  2020-03-15  2020-03-16  \\\n",
       "0         0.0         1.0         1.0         5.0         4.0         9.0   \n",
       "1         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "2         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "3         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   2020-03-17  2020-03-18  2020-03-19  2020-03-20  2020-03-21  2020-03-22  \\\n",
       "0         3.0        21.0         6.0        14.0        13.0        10.0   \n",
       "1         0.0         0.0         1.0         0.0         0.0         4.0   \n",
       "2         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "3         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   2020-03-23  2020-03-24  2020-03-25  2020-03-26  2020-03-27  2020-03-28  \\\n",
       "0        32.0         5.0         6.0         4.0         3.0         4.0   \n",
       "1         1.0         3.0         2.0         5.0         4.0         7.0   \n",
       "2         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "3         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   2020-03-29  2020-03-30  2020-03-31  2020-04-01 city_cn  hubei_city  \n",
       "0         1.0         3.0         0.0         2.0     北京市           0  \n",
       "1         3.0         8.0         0.0         2.0     天津市           0  \n",
       "2         0.0         0.0         0.0         0.0    石家庄市           0  \n",
       "3         0.0         0.0         0.0         0.0     唐山市           0  \n",
       "4         0.0         0.0         0.0         0.0    秦皇岛市           0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_case_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class='info-circle alert alert-block alert-info'>Mobility Data</div>\n",
    "* distance: spatial distance to Wuhan\n",
    "* local_flow: human flow from Wuhan\n",
    "* pagerank: PageRank centrality of a city in the mobility network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_df = pd.read_csv('Data/mobility_data.csv')\n",
    "mobility_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>city_cn</th>\n",
       "      <th>city</th>\n",
       "      <th>city_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>local_flow</th>\n",
       "      <th>pagerank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>110000</td>\n",
       "      <td>1050.640158</td>\n",
       "      <td>184.353501</td>\n",
       "      <td>0.015574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>Tianjin</td>\n",
       "      <td>120000</td>\n",
       "      <td>978.840171</td>\n",
       "      <td>30.484535</td>\n",
       "      <td>0.005574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>Shijiazhuang</td>\n",
       "      <td>130100</td>\n",
       "      <td>826.627664</td>\n",
       "      <td>30.892180</td>\n",
       "      <td>0.005191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>Tangshan</td>\n",
       "      <td>130200</td>\n",
       "      <td>1062.805689</td>\n",
       "      <td>9.256908</td>\n",
       "      <td>0.002692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>Qinhuangdao</td>\n",
       "      <td>130300</td>\n",
       "      <td>1142.576314</td>\n",
       "      <td>4.230623</td>\n",
       "      <td>0.001597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_cn          city  city_id     distance  local_flow  pagerank\n",
       "0     北京市       Beijing   110000  1050.640158  184.353501  0.015574\n",
       "1     天津市       Tianjin   120000   978.840171   30.484535  0.005574\n",
       "2    石家庄市  Shijiazhuang   130100   826.627664   30.892180  0.005191\n",
       "3     唐山市      Tangshan   130200  1062.805689    9.256908  0.002692\n",
       "4    秦皇岛市   Qinhuangdao   130300  1142.576314    4.230623  0.001597"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intra-city Avtivity Data: measured by Baidu Migration\n",
    "* mean_intensity: the mean/daily activity intensity from 2020-01-01 to 2020-01-27\n",
    "* intensity_2020: the aggregated activity intensity from 2020-01-01 to 2020-03-15\n",
    "* intensity_2019: the aggregated activity intensity in 2019 (corresponding to 2020 in Lunar calendar)\n",
    "* intensity_reduction: intensity_2019 - intensity_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intensity_df = pd.read_csv('Data/intra_city_activity_intensity.csv')\n",
    "intensity_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>city_cn</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>intensity_2019</th>\n",
       "      <th>intensity_2020</th>\n",
       "      <th>intensity_reduction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>5.677559</td>\n",
       "      <td>331.9212</td>\n",
       "      <td>241.5429</td>\n",
       "      <td>90.3783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>5.610923</td>\n",
       "      <td>356.6680</td>\n",
       "      <td>258.5126</td>\n",
       "      <td>98.1554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>5.479823</td>\n",
       "      <td>346.9245</td>\n",
       "      <td>260.6993</td>\n",
       "      <td>86.2252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>5.583632</td>\n",
       "      <td>355.8822</td>\n",
       "      <td>288.3937</td>\n",
       "      <td>67.4885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>5.384295</td>\n",
       "      <td>333.1633</td>\n",
       "      <td>280.1461</td>\n",
       "      <td>53.0172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_cn  mean_intensity  intensity_2019  intensity_2020  intensity_reduction\n",
       "0     北京市        5.677559        331.9212        241.5429              90.3783\n",
       "1     天津市        5.610923        356.6680        258.5126              98.1554\n",
       "2    石家庄市        5.479823        346.9245        260.6993              86.2252\n",
       "3     唐山市        5.583632        355.8822        288.3937              67.4885\n",
       "4    秦皇岛市        5.384295        333.1633        280.1461              53.0172"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intensity_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class='info-circle alert alert-block alert-info'>City Info</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 9)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_info_df = pd.read_csv('Data/city_info.csv')\n",
    "city_info_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_info_df['city_tier1'] = city_info_df['tier'].map(lambda x: 1 if x in ['Tier 1', 'new Tier 1'] else 0)\n",
    "city_info_df['city_tier2'] = city_info_df['tier'].map(lambda x: 1 if x in ['Tier 2'] else 0)\n",
    "city_info_df['city_tier3'] = city_info_df['tier'].map(lambda x: 1 if x in ['Tier 3'] else 0)\n",
    "city_info_df['city_tier4'] = city_info_df['tier'].map(lambda x: 1 if x in ['Tier 4'] else 0)\n",
    "city_info_df['city_tier5'] = city_info_df['tier'].map(lambda x: 1 if x not in ['Tier 1', 'new Tier 1', 'Tier 2', 'Tier 3', 'Tier 4'] else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>city_cn</th>\n",
       "      <th>city</th>\n",
       "      <th>city_id</th>\n",
       "      <th>province_cn</th>\n",
       "      <th>province</th>\n",
       "      <th>tier</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>city_tier1</th>\n",
       "      <th>city_tier2</th>\n",
       "      <th>city_tier3</th>\n",
       "      <th>city_tier4</th>\n",
       "      <th>city_tier5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>39.910925</td>\n",
       "      <td>116.413384</td>\n",
       "      <td>2153.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>Tianjin</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>Tianjin</td>\n",
       "      <td>new Tier 1</td>\n",
       "      <td>39.093668</td>\n",
       "      <td>117.209523</td>\n",
       "      <td>1561.83</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>Shijiazhuang</td>\n",
       "      <td>130100</td>\n",
       "      <td>河北省</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>38.048312</td>\n",
       "      <td>114.521532</td>\n",
       "      <td>1039.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>Tangshan</td>\n",
       "      <td>130200</td>\n",
       "      <td>河北省</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>39.636584</td>\n",
       "      <td>118.186459</td>\n",
       "      <td>796.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>Qinhuangdao</td>\n",
       "      <td>130300</td>\n",
       "      <td>河北省</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>39.941748</td>\n",
       "      <td>119.608531</td>\n",
       "      <td>314.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_cn          city  city_id province_cn province        tier        lat  \\\n",
       "0     北京市       Beijing   110000         北京市  Beijing      Tier 1  39.910925   \n",
       "1     天津市       Tianjin   120000         天津市  Tianjin  new Tier 1  39.093668   \n",
       "2    石家庄市  Shijiazhuang   130100         河北省    Hebei      Tier 2  38.048312   \n",
       "3     唐山市      Tangshan   130200         河北省    Hebei      Tier 3  39.636584   \n",
       "4    秦皇岛市   Qinhuangdao   130300         河北省    Hebei      Tier 3  39.941748   \n",
       "\n",
       "          lng  population  city_tier1  city_tier2  city_tier3  city_tier4  \\\n",
       "0  116.413384     2153.60           1           0           0           0   \n",
       "1  117.209523     1561.83           1           0           0           0   \n",
       "2  114.521532     1039.42           0           1           0           0   \n",
       "3  118.186459      796.40           0           0           1           0   \n",
       "4  119.608531      314.63           0           0           1           0   \n",
       "\n",
       "   city_tier5  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_info_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class=\"alert alert-block alert-danger\">Merge the Data</div>\n",
    "* use 'city_cn' (Chinese name) or 'city_id' to merge the data. \n",
    "* don't use 'city' (English name) alone as some cities share one English name. For example, the English names of '苏州市' and '宿州市' are both 'Suzhou'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df = pd.merge(city_info_df, \n",
    "                    mobility_df.drop(['city', 'city_id'], axis=1),\n",
    "                    on='city_cn'\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df = pd.merge(covid_df, intensity_df, on='city_cn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df = pd.merge(covid_df, case_df.drop(['city_id'], axis=1), \n",
    "                    on='city_cn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 92)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>city_cn</th>\n",
       "      <th>city</th>\n",
       "      <th>city_id</th>\n",
       "      <th>province_cn</th>\n",
       "      <th>province</th>\n",
       "      <th>tier</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>city_tier1</th>\n",
       "      <th>city_tier2</th>\n",
       "      <th>city_tier3</th>\n",
       "      <th>city_tier4</th>\n",
       "      <th>city_tier5</th>\n",
       "      <th>distance</th>\n",
       "      <th>local_flow</th>\n",
       "      <th>pagerank</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>intensity_2019</th>\n",
       "      <th>intensity_2020</th>\n",
       "      <th>intensity_reduction</th>\n",
       "      <th>2020-01-23</th>\n",
       "      <th>2020-01-24</th>\n",
       "      <th>2020-01-25</th>\n",
       "      <th>2020-01-26</th>\n",
       "      <th>2020-01-27</th>\n",
       "      <th>2020-01-28</th>\n",
       "      <th>2020-01-29</th>\n",
       "      <th>2020-01-30</th>\n",
       "      <th>2020-01-31</th>\n",
       "      <th>2020-02-01</th>\n",
       "      <th>2020-02-02</th>\n",
       "      <th>2020-02-03</th>\n",
       "      <th>2020-02-04</th>\n",
       "      <th>2020-02-05</th>\n",
       "      <th>2020-02-06</th>\n",
       "      <th>2020-02-07</th>\n",
       "      <th>2020-02-08</th>\n",
       "      <th>2020-02-09</th>\n",
       "      <th>2020-02-10</th>\n",
       "      <th>2020-02-11</th>\n",
       "      <th>2020-02-12</th>\n",
       "      <th>2020-02-13</th>\n",
       "      <th>2020-02-14</th>\n",
       "      <th>2020-02-15</th>\n",
       "      <th>2020-02-16</th>\n",
       "      <th>2020-02-17</th>\n",
       "      <th>2020-02-18</th>\n",
       "      <th>2020-02-19</th>\n",
       "      <th>2020-02-20</th>\n",
       "      <th>2020-02-21</th>\n",
       "      <th>2020-02-22</th>\n",
       "      <th>2020-02-23</th>\n",
       "      <th>2020-02-24</th>\n",
       "      <th>2020-02-25</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <th>2020-02-27</th>\n",
       "      <th>2020-02-28</th>\n",
       "      <th>2020-02-29</th>\n",
       "      <th>2020-03-01</th>\n",
       "      <th>2020-03-02</th>\n",
       "      <th>2020-03-03</th>\n",
       "      <th>2020-03-04</th>\n",
       "      <th>2020-03-05</th>\n",
       "      <th>2020-03-06</th>\n",
       "      <th>2020-03-07</th>\n",
       "      <th>2020-03-08</th>\n",
       "      <th>2020-03-09</th>\n",
       "      <th>2020-03-10</th>\n",
       "      <th>2020-03-11</th>\n",
       "      <th>2020-03-12</th>\n",
       "      <th>2020-03-13</th>\n",
       "      <th>2020-03-14</th>\n",
       "      <th>2020-03-15</th>\n",
       "      <th>2020-03-16</th>\n",
       "      <th>2020-03-17</th>\n",
       "      <th>2020-03-18</th>\n",
       "      <th>2020-03-19</th>\n",
       "      <th>2020-03-20</th>\n",
       "      <th>2020-03-21</th>\n",
       "      <th>2020-03-22</th>\n",
       "      <th>2020-03-23</th>\n",
       "      <th>2020-03-24</th>\n",
       "      <th>2020-03-25</th>\n",
       "      <th>2020-03-26</th>\n",
       "      <th>2020-03-27</th>\n",
       "      <th>2020-03-28</th>\n",
       "      <th>2020-03-29</th>\n",
       "      <th>2020-03-30</th>\n",
       "      <th>2020-03-31</th>\n",
       "      <th>2020-04-01</th>\n",
       "      <th>hubei_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>39.910925</td>\n",
       "      <td>116.413384</td>\n",
       "      <td>2153.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1050.640158</td>\n",
       "      <td>184.353501</td>\n",
       "      <td>0.015574</td>\n",
       "      <td>5.677559</td>\n",
       "      <td>331.9212</td>\n",
       "      <td>241.5429</td>\n",
       "      <td>90.3783</td>\n",
       "      <td>26</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>67</td>\n",
       "      <td>80</td>\n",
       "      <td>96</td>\n",
       "      <td>112</td>\n",
       "      <td>132</td>\n",
       "      <td>156</td>\n",
       "      <td>183</td>\n",
       "      <td>212</td>\n",
       "      <td>228</td>\n",
       "      <td>253</td>\n",
       "      <td>274</td>\n",
       "      <td>297</td>\n",
       "      <td>315</td>\n",
       "      <td>326</td>\n",
       "      <td>337</td>\n",
       "      <td>342</td>\n",
       "      <td>352</td>\n",
       "      <td>366</td>\n",
       "      <td>372</td>\n",
       "      <td>375</td>\n",
       "      <td>380</td>\n",
       "      <td>381</td>\n",
       "      <td>387</td>\n",
       "      <td>393</td>\n",
       "      <td>395</td>\n",
       "      <td>396</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>411</td>\n",
       "      <td>413</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>417</td>\n",
       "      <td>418</td>\n",
       "      <td>422</td>\n",
       "      <td>426</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "      <td>429</td>\n",
       "      <td>435</td>\n",
       "      <td>435</td>\n",
       "      <td>436</td>\n",
       "      <td>437</td>\n",
       "      <td>442</td>\n",
       "      <td>446</td>\n",
       "      <td>455</td>\n",
       "      <td>458</td>\n",
       "      <td>479</td>\n",
       "      <td>485</td>\n",
       "      <td>499</td>\n",
       "      <td>512</td>\n",
       "      <td>522</td>\n",
       "      <td>554</td>\n",
       "      <td>559</td>\n",
       "      <td>565</td>\n",
       "      <td>569</td>\n",
       "      <td>572</td>\n",
       "      <td>576</td>\n",
       "      <td>577</td>\n",
       "      <td>580</td>\n",
       "      <td>580</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>Tianjin</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>Tianjin</td>\n",
       "      <td>new Tier 1</td>\n",
       "      <td>39.093668</td>\n",
       "      <td>117.209523</td>\n",
       "      <td>1561.83</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>978.840171</td>\n",
       "      <td>30.484535</td>\n",
       "      <td>0.005574</td>\n",
       "      <td>5.610923</td>\n",
       "      <td>356.6680</td>\n",
       "      <td>258.5126</td>\n",
       "      <td>98.1554</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>41</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "      <td>67</td>\n",
       "      <td>69</td>\n",
       "      <td>79</td>\n",
       "      <td>81</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>95</td>\n",
       "      <td>107</td>\n",
       "      <td>113</td>\n",
       "      <td>119</td>\n",
       "      <td>120</td>\n",
       "      <td>122</td>\n",
       "      <td>125</td>\n",
       "      <td>127</td>\n",
       "      <td>128</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>133</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>141</td>\n",
       "      <td>142</td>\n",
       "      <td>145</td>\n",
       "      <td>147</td>\n",
       "      <td>152</td>\n",
       "      <td>156</td>\n",
       "      <td>163</td>\n",
       "      <td>166</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>Shijiazhuang</td>\n",
       "      <td>130100</td>\n",
       "      <td>河北省</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>38.048312</td>\n",
       "      <td>114.521532</td>\n",
       "      <td>1039.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>826.627664</td>\n",
       "      <td>30.892180</td>\n",
       "      <td>0.005191</td>\n",
       "      <td>5.479823</td>\n",
       "      <td>346.9245</td>\n",
       "      <td>260.6993</td>\n",
       "      <td>86.2252</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>Tangshan</td>\n",
       "      <td>130200</td>\n",
       "      <td>河北省</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>39.636584</td>\n",
       "      <td>118.186459</td>\n",
       "      <td>796.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1062.805689</td>\n",
       "      <td>9.256908</td>\n",
       "      <td>0.002692</td>\n",
       "      <td>5.583632</td>\n",
       "      <td>355.8822</td>\n",
       "      <td>288.3937</td>\n",
       "      <td>67.4885</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>Qinhuangdao</td>\n",
       "      <td>130300</td>\n",
       "      <td>河北省</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>39.941748</td>\n",
       "      <td>119.608531</td>\n",
       "      <td>314.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1142.576314</td>\n",
       "      <td>4.230623</td>\n",
       "      <td>0.001597</td>\n",
       "      <td>5.384295</td>\n",
       "      <td>333.1633</td>\n",
       "      <td>280.1461</td>\n",
       "      <td>53.0172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_cn          city  city_id province_cn province        tier        lat  \\\n",
       "0     北京市       Beijing   110000         北京市  Beijing      Tier 1  39.910925   \n",
       "1     天津市       Tianjin   120000         天津市  Tianjin  new Tier 1  39.093668   \n",
       "2    石家庄市  Shijiazhuang   130100         河北省    Hebei      Tier 2  38.048312   \n",
       "3     唐山市      Tangshan   130200         河北省    Hebei      Tier 3  39.636584   \n",
       "4    秦皇岛市   Qinhuangdao   130300         河北省    Hebei      Tier 3  39.941748   \n",
       "\n",
       "          lng  population  city_tier1  city_tier2  city_tier3  city_tier4  \\\n",
       "0  116.413384     2153.60           1           0           0           0   \n",
       "1  117.209523     1561.83           1           0           0           0   \n",
       "2  114.521532     1039.42           0           1           0           0   \n",
       "3  118.186459      796.40           0           0           1           0   \n",
       "4  119.608531      314.63           0           0           1           0   \n",
       "\n",
       "   city_tier5     distance  local_flow  pagerank  mean_intensity  \\\n",
       "0           0  1050.640158  184.353501  0.015574        5.677559   \n",
       "1           0   978.840171   30.484535  0.005574        5.610923   \n",
       "2           0   826.627664   30.892180  0.005191        5.479823   \n",
       "3           0  1062.805689    9.256908  0.002692        5.583632   \n",
       "4           0  1142.576314    4.230623  0.001597        5.384295   \n",
       "\n",
       "   intensity_2019  intensity_2020  intensity_reduction  2020-01-23  \\\n",
       "0        331.9212        241.5429              90.3783          26   \n",
       "1        356.6680        258.5126              98.1554           0   \n",
       "2        346.9245        260.6993              86.2252           1   \n",
       "3        355.8822        288.3937              67.4885           0   \n",
       "4        333.1633        280.1461              53.0172           0   \n",
       "\n",
       "   2020-01-24  2020-01-25  2020-01-26  2020-01-27  2020-01-28  2020-01-29  \\\n",
       "0          36          51          67          80          96         112   \n",
       "1           8          10          14          23          24          27   \n",
       "2           4           5           7           9          10          11   \n",
       "3           0           0           0           1           3           3   \n",
       "4           0           0           0           0           0           0   \n",
       "\n",
       "   2020-01-30  2020-01-31  2020-02-01  2020-02-02  2020-02-03  2020-02-04  \\\n",
       "0         132         156         183         212         228         253   \n",
       "1          31          32          41          48          60          67   \n",
       "2          11          12          13          16          21          21   \n",
       "3           7           8          11          11          11          12   \n",
       "4           0           1           1           2           2           3   \n",
       "\n",
       "   2020-02-05  2020-02-06  2020-02-07  2020-02-08  2020-02-09  2020-02-10  \\\n",
       "0         274         297         315         326         337         342   \n",
       "1          69          79          81          88          91          95   \n",
       "2          24          24          24          24          25          27   \n",
       "3          17          19          23          27          28          31   \n",
       "4           3           5           7           7           7           9   \n",
       "\n",
       "   2020-02-11  2020-02-12  2020-02-13  2020-02-14  2020-02-15  2020-02-16  \\\n",
       "0         352         366         372         375         380         381   \n",
       "1         107         113         119         120         122         125   \n",
       "2          27          27          27          27          28          28   \n",
       "3          33          35          43          46          51          51   \n",
       "4           9           9           9          10          10          10   \n",
       "\n",
       "   2020-02-17  2020-02-18  2020-02-19  2020-02-20  2020-02-21  2020-02-22  \\\n",
       "0         387         393         395         396         399         399   \n",
       "1         127         128         130         131         133         135   \n",
       "2          28          28          28          28          29          29   \n",
       "3          51          55          56          57          57          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-02-23  2020-02-24  2020-02-25  2020-02-26  2020-02-27  2020-02-28  \\\n",
       "0         399         400         400         410         410         411   \n",
       "1         135         135         135         135         136         136   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-02-29  2020-03-01  2020-03-02  2020-03-03  2020-03-04  2020-03-05  \\\n",
       "0         413         414         414         417         418         422   \n",
       "1         136         136         136         136         136         136   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-06  2020-03-07  2020-03-08  2020-03-09  2020-03-10  2020-03-11  \\\n",
       "0         426         428         428         429         435         435   \n",
       "1         136         136         136         136         136         136   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-12  2020-03-13  2020-03-14  2020-03-15  2020-03-16  2020-03-17  \\\n",
       "0         436         437         442         446         455         458   \n",
       "1         136         136         136         136         136         136   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-18  2020-03-19  2020-03-20  2020-03-21  2020-03-22  2020-03-23  \\\n",
       "0         479         485         499         512         522         554   \n",
       "1         136         137         137         137         141         142   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-24  2020-03-25  2020-03-26  2020-03-27  2020-03-28  2020-03-29  \\\n",
       "0         559         565         569         572         576         577   \n",
       "1         145         147         152         156         163         166   \n",
       "2          29          29          29          29          29          29   \n",
       "3          58          58          58          58          58          58   \n",
       "4          10          10          10          10          10          10   \n",
       "\n",
       "   2020-03-30  2020-03-31  2020-04-01  hubei_city  \n",
       "0         580         580         582           0  \n",
       "1         174         174         176           0  \n",
       "2          29          29          29           0  \n",
       "3          58          58          58           0  \n",
       "4          10          10          10           0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class=\"alert alert-block alert-info\">Combined data are stored in the following file:</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = [str(d.date()) for d in pd.date_range('2020-01-23', '2020-04-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df = covid_df.copy()\n",
    "# rename date\n",
    "for date in dates:\n",
    "    new_name = 'date_' + ''.join(date[-5:].split('-'))\n",
    "    combined_df.rename(columns = {date: new_name}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combined_df.to_csv('Data/combined_data.csv', encoding='utf-8_sig', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class=\"alert alert-block alert-success\"> 1. Regression Analysis </div>\n",
    "#### Human flow from Wuhan explains the spatial distribution of COVID-19:  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OLS Regression\n",
    "log_cumulative_cases ~ log_local_flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_df = covid_df.copy()\n",
    "\n",
    "# excluding Wuhan\n",
    "reg_df = reg_df[reg_df['city_cn']!='武汉市']\n",
    "\n",
    "\n",
    "# human flow from Wuhan is the sole predictor\n",
    "iv = 'local_flow'\n",
    "reg_df['log_{}'.format(iv)] = reg_df[iv].map(lambda x: np.log(x+1))\n",
    "iv = 'log_{}'.format(iv)\n",
    "\n",
    "\n",
    "results = []\n",
    "\n",
    "dates = [str(d.date()) for d in pd.date_range('2020-01-24', '2020-03-15')]\n",
    "\n",
    "for date in dates:\n",
    "\n",
    "    reg_df['cumulative_cases'] = reg_df[date]\n",
    "\n",
    "    dv = 'cumulative_cases'\n",
    "    reg_df['log_{}'.format(dv)] = reg_df[dv].map(lambda x: np.log(x+1))\n",
    "\n",
    "    dv = 'log_{}'.format(dv)\n",
    "\n",
    "\n",
    "    formula = '{0} ~ {1}'.format(dv, iv)\n",
    "\n",
    "    model = smf.ols(formula, data=reg_df).fit()\n",
    "\n",
    "    R2 = model.rsquared\n",
    "    R2_adj = model.rsquared_adj\n",
    "    \n",
    "    dic = OrderedDict()\n",
    "    dic['date'] = date\n",
    "    dic['R2'] = R2\n",
    "    dic['R2_adj'] = R2_adj\n",
    "    \n",
    "    results.append(dic)\n",
    "    \n",
    "#     print(f'[date]: {date}, [R^2]: {R2:.5f}, [adjusted R^2]: {R2_adj:.5f}')\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The regression is expresed as: log_cumulative_cases ~ log_local_flow\n"
     ]
    }
   ],
   "source": [
    "print('The regression is expresed as:', formula)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>date</th>\n",
       "      <th>R2</th>\n",
       "      <th>R2_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0.433859</td>\n",
       "      <td>0.432299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0.586411</td>\n",
       "      <td>0.585272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.696139</td>\n",
       "      <td>0.695302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>0.726518</td>\n",
       "      <td>0.725765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0.743971</td>\n",
       "      <td>0.743266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date        R2    R2_adj\n",
       "0  2020-01-24  0.433859  0.432299\n",
       "1  2020-01-25  0.586411  0.585272\n",
       "2  2020-01-26  0.696139  0.695302\n",
       "3  2020-01-27  0.726518  0.725765\n",
       "4  2020-01-28  0.743971  0.743266"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2_df = pd.DataFrame(results)\n",
    "R2_df.head()\n",
    "# R2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x158.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = R2_df.copy()\n",
    "\n",
    "fig, ax  = plt.subplots(figsize=(8, 2.2))\n",
    "\n",
    "\n",
    "ax.patch.set_alpha(0.015)\n",
    "ax.set_facecolor('#2b3679')\n",
    "\n",
    "\n",
    "# =================\n",
    "x = plot_df['date']\n",
    "y = plot_df['R2']\n",
    "\n",
    "\n",
    "## lollipop plot\n",
    "ax.vlines(x, ymin=0, ymax=y, \n",
    "#           linewidth=1.5,\n",
    "          linewidth=1,\n",
    "          color='grey', \n",
    "          alpha=0.9,\n",
    "          zorder=30,\n",
    "#           linestyle='-',\n",
    "          linestyle=(0, (4, 2)),\n",
    "         )\n",
    "\n",
    "ax.plot(x, y, \"o\", \n",
    "        # color='#e5001d', \n",
    "        markeredgecolor='k', \n",
    "        markeredgewidth=.75,\n",
    "#         markerfacecolor='#ff9896',\n",
    "#         markerfacecolor='#46ac88',\n",
    "        markerfacecolor='#74c476',\n",
    "        alpha=0.9, \n",
    "#         markersize=8.5,\n",
    "        markersize=6.5,\n",
    "        clip_on =False,\n",
    "        zorder=30,\n",
    "       )\n",
    "\n",
    "\n",
    "N = len(x)\n",
    "\n",
    "every_nth = 1\n",
    "locs = range(0, N, every_nth)\n",
    "ax.xaxis.set_minor_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "every_nth = 5\n",
    "locs = range(0, N, every_nth)\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "\n",
    "locs = range(0, 10, 1)\n",
    "locs = [i/10.0 for i in locs]\n",
    "ax.yaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "\n",
    "ticks = list(x)\n",
    "ticks = list(x.str[-5:])\n",
    "# ticks.insert(0, '')\n",
    "\n",
    "plt.xticks(list(range(0, N+1, every_nth)), ticks[::every_nth], \n",
    "           rotation='0', \n",
    "           horizontalalignment='center',\n",
    "           # va = 'center',\n",
    "           position=(0, 0),\n",
    "           # rotation_mode=\"anchor\",\n",
    "          )\n",
    "\n",
    "\n",
    "\n",
    "ticks = ['0.0', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9']\n",
    "plt.yticks([0.25, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], ticks, rotation='0')\n",
    "\n",
    "\n",
    "# ax.set_xlabel('Date', fontsize=13)\n",
    "# ax.set_ylabel('$R^2$', fontsize=13, labelpad=10, rotation=0)\n",
    "ax.set_ylabel('$R^2$', fontsize=13)\n",
    "\n",
    "\n",
    "border_width = 1.35\n",
    "ax.spines['bottom'].set_linewidth(border_width)\n",
    "ax.spines['left'].set_linewidth(border_width)\n",
    "ax.spines['top'].set_linewidth(border_width)\n",
    "ax.spines['right'].set_linewidth(border_width)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "\n",
    "for k, spine in ax.spines.items():  #ax.spines is a dictionary\n",
    "    spine.set_zorder(100)\n",
    "\n",
    "\n",
    "# # custom ticks\n",
    "ax.tick_params(axis='x', which='major', top=False, right=False, pad=3, labelsize=12, length=5, width=1.5, direction='out')\n",
    "ax.tick_params(axis='y', which='major', top=False, right=False, pad=3, labelsize=12, length=5, width=1.2, direction='out')\n",
    "ax.tick_params(axis='x', which='minor', top=False, right=False, length=4,  width=.6, direction='out')\n",
    "ax.tick_params(axis='y', which='minor', top=False, right=False, length=4,  width=.6, direction='out')\n",
    "\n",
    "\n",
    "ax.set_xlim(-1, list(x).index('2020-03-15')+1)\n",
    "ax.set_ylim(0.25, .9)\n",
    "\n",
    "\n",
    "# plt.tight_layout(pad=0.1)\n",
    "out_dir = 'Outputs'\n",
    "# plt.savefig(os.path.join(out_dir, 'fig2-cumulative_cases_R2_by_date.pdf'), pad_inches=0.02, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class=\"alert alert-block alert-success\"> 2. Regression Analysis </div>\n",
    "#### Spatial Spread of COVID-19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* OLS Regression\n",
    "* Negative Binomial Regression\n",
    "\n",
    "Regressions are performed in both Python and R. This tutorial presents Python implimentations. See 'Figure2-spatial analysis by R.ipynb' for R implimentations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OLS Regression\n",
    "log_cumulative_cases ~ log_local_flow + log_pagerank + log_population + log_distance + mean_intensity + city_tier1 + city_tier2 + city_tier3 + city_tier4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Regression Model: \n",
      "- - - - - - - - - - - - - - - - - - - - \n",
      "log_cumulative_cases ~ log_local_flow + log_pagerank + log_population + log_distance + mean_intensity + city_tier1 + city_tier2 + city_tier3 + city_tier4\n",
      "- - - - - - - - - - - - - - - - - - - - \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>log_cumulative_cases</td> <th>  R-squared:         </th> <td>   0.832</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>         <th>  Adj. R-squared:    </th> <td>   0.828</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>    <th>  F-statistic:       </th> <td>   194.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Thu, 25 Nov 2021</td>   <th>  Prob (F-statistic):</th> <td>2.90e-131</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>16:45:45</td>       <th>  Log-Likelihood:    </th> <td> -365.35</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td>   364</td>        <th>  AIC:               </th> <td>   750.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td>   354</td>        <th>  BIC:               </th> <td>   789.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>     9</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>   -1.2712</td> <td>    1.457</td> <td>   -0.873</td> <td> 0.384</td> <td>   -4.137</td> <td>    1.594</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_local_flow</th> <td>    0.8187</td> <td>    0.061</td> <td>   13.440</td> <td> 0.000</td> <td>    0.699</td> <td>    0.938</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_pagerank</th>   <td>   -0.2281</td> <td>    0.112</td> <td>   -2.042</td> <td> 0.042</td> <td>   -0.448</td> <td>   -0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_population</th> <td>    0.2410</td> <td>    0.063</td> <td>    3.811</td> <td> 0.000</td> <td>    0.117</td> <td>    0.365</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_distance</th>   <td>   -0.1721</td> <td>    0.100</td> <td>   -1.716</td> <td> 0.087</td> <td>   -0.369</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mean_intensity</th> <td>    0.0901</td> <td>    0.055</td> <td>    1.642</td> <td> 0.102</td> <td>   -0.018</td> <td>    0.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_tier1</th>     <td>    0.5803</td> <td>    0.226</td> <td>    2.570</td> <td> 0.011</td> <td>    0.136</td> <td>    1.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_tier2</th>     <td>    0.4786</td> <td>    0.164</td> <td>    2.921</td> <td> 0.004</td> <td>    0.156</td> <td>    0.801</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_tier3</th>     <td>    0.2048</td> <td>    0.125</td> <td>    1.635</td> <td> 0.103</td> <td>   -0.042</td> <td>    0.451</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_tier4</th>     <td>    0.0057</td> <td>    0.107</td> <td>    0.053</td> <td> 0.958</td> <td>   -0.205</td> <td>    0.217</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>22.382</td> <th>  Durbin-Watson:     </th> <td>   1.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  41.966</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.362</td> <th>  Prob(JB):          </th> <td>7.71e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.497</td> <th>  Cond. No.          </th> <td>    504.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             OLS Regression Results                             \n",
       "================================================================================\n",
       "Dep. Variable:     log_cumulative_cases   R-squared:                       0.832\n",
       "Model:                              OLS   Adj. R-squared:                  0.828\n",
       "Method:                   Least Squares   F-statistic:                     194.7\n",
       "Date:                  Thu, 25 Nov 2021   Prob (F-statistic):          2.90e-131\n",
       "Time:                          16:45:45   Log-Likelihood:                -365.35\n",
       "No. Observations:                   364   AIC:                             750.7\n",
       "Df Residuals:                       354   BIC:                             789.7\n",
       "Df Model:                             9                                         \n",
       "Covariance Type:              nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         -1.2712      1.457     -0.873      0.384      -4.137       1.594\n",
       "log_local_flow     0.8187      0.061     13.440      0.000       0.699       0.938\n",
       "log_pagerank      -0.2281      0.112     -2.042      0.042      -0.448      -0.008\n",
       "log_population     0.2410      0.063      3.811      0.000       0.117       0.365\n",
       "log_distance      -0.1721      0.100     -1.716      0.087      -0.369       0.025\n",
       "mean_intensity     0.0901      0.055      1.642      0.102      -0.018       0.198\n",
       "city_tier1         0.5803      0.226      2.570      0.011       0.136       1.024\n",
       "city_tier2         0.4786      0.164      2.921      0.004       0.156       0.801\n",
       "city_tier3         0.2048      0.125      1.635      0.103      -0.042       0.451\n",
       "city_tier4         0.0057      0.107      0.053      0.958      -0.205       0.217\n",
       "==============================================================================\n",
       "Omnibus:                       22.382   Durbin-Watson:                   1.624\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.966\n",
       "Skew:                           0.362   Prob(JB):                     7.71e-10\n",
       "Kurtosis:                       4.497   Cond. No.                         504.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_df = covid_df.copy()\n",
    "\n",
    "# excluding Wuhan\n",
    "reg_df = reg_df[reg_df['city_cn']!='武汉市']\n",
    "\n",
    "\n",
    "cols = [\n",
    "        'local_flow',\n",
    "        'pagerank',\n",
    "        'population',\n",
    "        'distance',\n",
    "       ]\n",
    "# ----------------------------\n",
    "# log-transform for highly skewed distributions\n",
    "for col in cols:\n",
    "    if col in ['pagerank', 'population']:\n",
    "        reg_df['log_{}'.format(col)] = reg_df[col].map(lambda x: np.log(x))\n",
    "    else:\n",
    "        reg_df['log_{}'.format(col)] = reg_df[col].map(lambda x: np.log(x+1))\n",
    "# ----------------------------\n",
    "ivs = ['log_{}'.format(col) for col in cols]\n",
    "\n",
    "\n",
    "ivs = ivs + [\n",
    "            'mean_intensity',\n",
    "            'city_tier1',\n",
    "            'city_tier2',\n",
    "            'city_tier3',\n",
    "            'city_tier4',\n",
    "            ]\n",
    "\n",
    "reg_df['cumulative_cases'] = reg_df['2020-02-09']\n",
    "# reg_df['cumulative_cases'] = reg_df['2020-03-01']\n",
    "# reg_df['cumulative_cases'] = reg_df['2020-04-01']\n",
    "\n",
    "dv = 'cumulative_cases'\n",
    "reg_df['log_{}'.format(dv)] = reg_df[dv].map(lambda x: np.log(x+1))\n",
    "dv = 'log_{}'.format(dv)\n",
    "\n",
    "formula = '{0} ~ {1}'.format(dv, ' + '.join(ivs))\n",
    "\n",
    "ols_model = smf.ols(formula, data=reg_df).fit()\n",
    "# print('\\n\\n\\n- - - - - - - - - - - -\\n{}\\n'.format(formula))\n",
    "\n",
    "print('\\nRegression Model: ')\n",
    "s = '- ' * 20\n",
    "print(f'{s}\\n{formula}')\n",
    "print(s+'\\n')\n",
    "\n",
    "ols_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>index</th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>t</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-1.2712</td>\n",
       "      <td>1.457</td>\n",
       "      <td>-0.873</td>\n",
       "      <td>0.384</td>\n",
       "      <td>-4.137</td>\n",
       "      <td>1.594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log_local_flow</td>\n",
       "      <td>0.8187</td>\n",
       "      <td>0.061</td>\n",
       "      <td>13.440</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>log_pagerank</td>\n",
       "      <td>-0.2281</td>\n",
       "      <td>0.112</td>\n",
       "      <td>-2.042</td>\n",
       "      <td>0.042</td>\n",
       "      <td>-0.448</td>\n",
       "      <td>-0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>log_population</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.063</td>\n",
       "      <td>3.811</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>log_distance</td>\n",
       "      <td>-0.1721</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-1.716</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.369</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mean_intensity</td>\n",
       "      <td>0.0901</td>\n",
       "      <td>0.055</td>\n",
       "      <td>1.642</td>\n",
       "      <td>0.102</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>city_tier1</td>\n",
       "      <td>0.5803</td>\n",
       "      <td>0.226</td>\n",
       "      <td>2.570</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.136</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>city_tier2</td>\n",
       "      <td>0.4786</td>\n",
       "      <td>0.164</td>\n",
       "      <td>2.921</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>city_tier3</td>\n",
       "      <td>0.2048</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.635</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>city_tier4</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.958</td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            index    coef  std err       t  P>|t|  [0.025  0.975]\n",
       "0       Intercept -1.2712    1.457  -0.873  0.384  -4.137   1.594\n",
       "1  log_local_flow  0.8187    0.061  13.440  0.000   0.699   0.938\n",
       "2    log_pagerank -0.2281    0.112  -2.042  0.042  -0.448  -0.008\n",
       "3  log_population  0.2410    0.063   3.811  0.000   0.117   0.365\n",
       "4    log_distance -0.1721    0.100  -1.716  0.087  -0.369   0.025\n",
       "5  mean_intensity  0.0901    0.055   1.642  0.102  -0.018   0.198\n",
       "6      city_tier1  0.5803    0.226   2.570  0.011   0.136   1.024\n",
       "7      city_tier2  0.4786    0.164   2.921  0.004   0.156   0.801\n",
       "8      city_tier3  0.2048    0.125   1.635  0.103  -0.042   0.451\n",
       "9      city_tier4  0.0057    0.107   0.053  0.958  -0.205   0.217"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ols regression results\n",
    "\n",
    "results_as_html = ols_model.summary().tables[1].as_html()\n",
    "\n",
    "ols_result_df = pd.read_html(results_as_html, header=0, index_col=0)[0]\n",
    "ols_result_df = ols_result_df.reset_index()\n",
    "\n",
    "ols_result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Negative Binomial Regression\n",
    "cumulative_cases ~ exp(log_local_flow + log_pagerank + log_population + log_distance + mean_intensity + city_tier1 + city_tier2 + city_tier3 + city_tier4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Regression Model: \n",
      "- - - - - - - - - - - - - - - - - - - - \n",
      "cumulative_cases ~ log_local_flow + log_pagerank + log_population + log_distance + mean_intensity + city_tier1 + city_tier2 + city_tier3 + city_tier4\n",
      "- - - - - - - - - - - - - - - - - - - - \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>cumulative_cases</td> <th>  No. Observations:  </th>  <td>   364</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   354</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>    <td>NegativeBinomial</td> <th>  Df Model:          </th>  <td>     9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>log</td>       <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1357.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 25 Nov 2021</td> <th>  Deviance:          </th> <td>  433.10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:45:52</td>     <th>  Pearson chi2:      </th>  <td>  657.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>10</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>   -3.6390</td> <td>    1.633</td> <td>   -2.229</td> <td> 0.026</td> <td>   -6.839</td> <td>   -0.439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_local_flow</th> <td>    0.8335</td> <td>    0.066</td> <td>   12.692</td> <td> 0.000</td> <td>    0.705</td> <td>    0.962</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_pagerank</th>   <td>   -0.3972</td> <td>    0.122</td> <td>   -3.259</td> <td> 0.001</td> <td>   -0.636</td> <td>   -0.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_population</th> <td>    0.4125</td> <td>    0.077</td> <td>    5.369</td> <td> 0.000</td> <td>    0.262</td> <td>    0.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_distance</th>   <td>   -0.1418</td> <td>    0.112</td> <td>   -1.269</td> <td> 0.204</td> <td>   -0.361</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mean_intensity</th> <td>    0.1604</td> <td>    0.068</td> <td>    2.355</td> <td> 0.019</td> <td>    0.027</td> <td>    0.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_tier1</th>     <td>    0.3396</td> <td>    0.240</td> <td>    1.417</td> <td> 0.156</td> <td>   -0.130</td> <td>    0.809</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_tier2</th>     <td>    0.3806</td> <td>    0.174</td> <td>    2.189</td> <td> 0.029</td> <td>    0.040</td> <td>    0.721</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_tier3</th>     <td>    0.0018</td> <td>    0.136</td> <td>    0.013</td> <td> 0.990</td> <td>   -0.266</td> <td>    0.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_tier4</th>     <td>   -0.1567</td> <td>    0.118</td> <td>   -1.323</td> <td> 0.186</td> <td>   -0.389</td> <td>    0.075</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:       cumulative_cases   No. Observations:                  364\n",
       "Model:                            GLM   Df Residuals:                      354\n",
       "Model Family:        NegativeBinomial   Df Model:                            9\n",
       "Link Function:                    log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1357.2\n",
       "Date:                Thu, 25 Nov 2021   Deviance:                       433.10\n",
       "Time:                        16:45:52   Pearson chi2:                     657.\n",
       "No. Iterations:                    10                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         -3.6390      1.633     -2.229      0.026      -6.839      -0.439\n",
       "log_local_flow     0.8335      0.066     12.692      0.000       0.705       0.962\n",
       "log_pagerank      -0.3972      0.122     -3.259      0.001      -0.636      -0.158\n",
       "log_population     0.4125      0.077      5.369      0.000       0.262       0.563\n",
       "log_distance      -0.1418      0.112     -1.269      0.204      -0.361       0.077\n",
       "mean_intensity     0.1604      0.068      2.355      0.019       0.027       0.294\n",
       "city_tier1         0.3396      0.240      1.417      0.156      -0.130       0.809\n",
       "city_tier2         0.3806      0.174      2.189      0.029       0.040       0.721\n",
       "city_tier3         0.0018      0.136      0.013      0.990      -0.266       0.269\n",
       "city_tier4        -0.1567      0.118     -1.323      0.186      -0.389       0.075\n",
       "==================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_df = covid_df.copy()\n",
    "\n",
    "# excluding Wuhan\n",
    "reg_df = reg_df[reg_df['city_cn']!='武汉市']\n",
    "\n",
    "\n",
    "cols = [\n",
    "        'local_flow',\n",
    "        'pagerank',\n",
    "        'population',\n",
    "        'distance',\n",
    "       ]\n",
    "# ----------------------------\n",
    "# log-transform for highly skewed distributions\n",
    "for col in cols:\n",
    "    if col in ['pagerank', 'population']:\n",
    "        reg_df['log_{}'.format(col)] = reg_df[col].map(lambda x: np.log(x))\n",
    "    else:\n",
    "        reg_df['log_{}'.format(col)] = reg_df[col].map(lambda x: np.log(x+1))\n",
    "# ----------------------------\n",
    "ivs = ['log_{}'.format(col) for col in cols]\n",
    "\n",
    "\n",
    "ivs = ivs + [\n",
    "            'mean_intensity',\n",
    "            'city_tier1',\n",
    "            'city_tier2',\n",
    "            'city_tier3',\n",
    "            'city_tier4',\n",
    "            ]\n",
    "\n",
    "reg_df['cumulative_cases'] = reg_df['2020-02-09']\n",
    "# reg_df['cumulative_cases'] = reg_df['2020-03-01']\n",
    "# reg_df['cumulative_cases'] = reg_df['2020-04-01']\n",
    "\n",
    "dv = 'cumulative_cases'\n",
    "\n",
    "formula = '{0} ~ {1}'.format(dv, ' + '.join(ivs))\n",
    "\n",
    "# α- dispersion parameter: can be obtained from R:MASS analysis\n",
    "# alpha = 0.444531\n",
    "alpha = 0.444530992433666\n",
    "\n",
    "nb_model = smf.glm(formula, data=reg_df, \n",
    "                        family = sm.families.NegativeBinomial(alpha=alpha)).fit()\n",
    "\n",
    "# nb_model = smf.negativebinomial(formula, data=reg_df).fit()\n",
    "\n",
    "print('\\nRegression Model: ')\n",
    "s = '- ' * 20\n",
    "print(f'{s}\\n{formula}')\n",
    "print(s+'\\n')\n",
    "\n",
    "nb_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>index</th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-3.6390</td>\n",
       "      <td>1.633</td>\n",
       "      <td>-2.229</td>\n",
       "      <td>0.026</td>\n",
       "      <td>-6.839</td>\n",
       "      <td>-0.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log_local_flow</td>\n",
       "      <td>0.8335</td>\n",
       "      <td>0.066</td>\n",
       "      <td>12.692</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>log_pagerank</td>\n",
       "      <td>-0.3972</td>\n",
       "      <td>0.122</td>\n",
       "      <td>-3.259</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.636</td>\n",
       "      <td>-0.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>log_population</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0.077</td>\n",
       "      <td>5.369</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>log_distance</td>\n",
       "      <td>-0.1418</td>\n",
       "      <td>0.112</td>\n",
       "      <td>-1.269</td>\n",
       "      <td>0.204</td>\n",
       "      <td>-0.361</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mean_intensity</td>\n",
       "      <td>0.1604</td>\n",
       "      <td>0.068</td>\n",
       "      <td>2.355</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>city_tier1</td>\n",
       "      <td>0.3396</td>\n",
       "      <td>0.240</td>\n",
       "      <td>1.417</td>\n",
       "      <td>0.156</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>city_tier2</td>\n",
       "      <td>0.3806</td>\n",
       "      <td>0.174</td>\n",
       "      <td>2.189</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>city_tier3</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.990</td>\n",
       "      <td>-0.266</td>\n",
       "      <td>0.269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>city_tier4</td>\n",
       "      <td>-0.1567</td>\n",
       "      <td>0.118</td>\n",
       "      <td>-1.323</td>\n",
       "      <td>0.186</td>\n",
       "      <td>-0.389</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            index    coef  std err       z  P>|z|  [0.025  0.975]\n",
       "0       Intercept -3.6390    1.633  -2.229  0.026  -6.839  -0.439\n",
       "1  log_local_flow  0.8335    0.066  12.692  0.000   0.705   0.962\n",
       "2    log_pagerank -0.3972    0.122  -3.259  0.001  -0.636  -0.158\n",
       "3  log_population  0.4125    0.077   5.369  0.000   0.262   0.563\n",
       "4    log_distance -0.1418    0.112  -1.269  0.204  -0.361   0.077\n",
       "5  mean_intensity  0.1604    0.068   2.355  0.019   0.027   0.294\n",
       "6      city_tier1  0.3396    0.240   1.417  0.156  -0.130   0.809\n",
       "7      city_tier2  0.3806    0.174   2.189  0.029   0.040   0.721\n",
       "8      city_tier3  0.0018    0.136   0.013  0.990  -0.266   0.269\n",
       "9      city_tier4 -0.1567    0.118  -1.323  0.186  -0.389   0.075"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# negative Binomial regression results\n",
    "\n",
    "results_as_html = nb_model.summary().tables[1].as_html()\n",
    "\n",
    "nb_result_df = pd.read_html(results_as_html, header=0, index_col=0)[0]\n",
    "nb_result_df = nb_result_df.reset_index()\n",
    "\n",
    "nb_result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Negative Binomial Regression: predicted vs confirmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_sample_df = reg_df[ivs].copy()\n",
    "\n",
    "reg_df['raw_predicted'] = nb_model.predict(out_sample_df)\n",
    "reg_df['predicted'] = reg_df['raw_predicted'].map(np.round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(364, 5)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df= reg_df[['city_cn', 'cumulative_cases', \n",
    "                 'raw_predicted', 'predicted', 'hubei_city']].dropna()\n",
    "pred_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>city_cn</th>\n",
       "      <th>cumulative_cases</th>\n",
       "      <th>raw_predicted</th>\n",
       "      <th>predicted</th>\n",
       "      <th>hubei_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>337</td>\n",
       "      <td>329.053697</td>\n",
       "      <td>329.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>91</td>\n",
       "      <td>98.847292</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>25</td>\n",
       "      <td>90.798560</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>28</td>\n",
       "      <td>27.557619</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>7</td>\n",
       "      <td>12.645066</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_cn  cumulative_cases  raw_predicted  predicted  hubei_city\n",
       "0     北京市               337     329.053697      329.0           0\n",
       "1     天津市                91      98.847292       99.0           0\n",
       "2    石家庄市                25      90.798560       91.0           0\n",
       "3     唐山市                28      27.557619       28.0           0\n",
       "4    秦皇岛市                 7      12.645066       13.0           0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "\n",
    "ax.patch.set_alpha(0.015)\n",
    "ax.set_facecolor('#2b3679')\n",
    "\n",
    "plot_df = pred_df.copy()\n",
    "\n",
    "# 非湖北城市\n",
    "# --------------------------\n",
    "x = plot_df[plot_df['hubei_city'] == 0]['predicted']\n",
    "y = plot_df[plot_df['hubei_city'] == 0]['cumulative_cases']\n",
    "\n",
    "# prevent values with 0\n",
    "x=x+1\n",
    "y=y+1\n",
    "\n",
    "ax.plot(x, y, 'o',\n",
    "        markersize=7,\n",
    "        markeredgecolor='w',\n",
    "        markerfacecolor='royalblue',\n",
    "        markeredgewidth=1,\n",
    "        alpha = 0.5,\n",
    "        zorder=10,\n",
    "       )\n",
    "\n",
    "\n",
    "# 湖北城市\n",
    "# --------------------------\n",
    "x = plot_df[plot_df['hubei_city'] == 1]['predicted']\n",
    "y = plot_df[plot_df['hubei_city'] == 1]['cumulative_cases']\n",
    "\n",
    "# prevent values with 0\n",
    "x=x+1\n",
    "y=y+1\n",
    "\n",
    "ax.plot(x, y, 'o',\n",
    "        markersize=7.5,\n",
    "        markeredgecolor='w',\n",
    "        markerfacecolor='C3',\n",
    "        markeredgewidth=1,\n",
    "        alpha = 0.5,\n",
    "        zorder=10,\n",
    "       )\n",
    "\n",
    "\n",
    "# perfect prediction line\n",
    "# --------------------------\n",
    "x = np.linspace(1, 2500, 250)\n",
    "ax.plot(x, x, '--', color='orange', zorder=11,)\n",
    "\n",
    "\n",
    "# specifying appearance\n",
    "# --------------------------\n",
    "ax.set_xlabel('Estimated cases: $(N+1)$', fontsize=12)\n",
    "ax.set_ylabel('Confirmed cases: $(N+1)$', fontsize=12)\n",
    "\n",
    "\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "# ax.set_xlim(0.5, 2500)\n",
    "\n",
    "\n",
    "n = 3\n",
    "loc_base = range(1, 10)\n",
    "\n",
    "minor_locs = [np.array(range(1, 10))*(10**i) for i in range(n+1)]\n",
    "minor_locs = list(chain(*minor_locs))\n",
    "ax.xaxis.set_minor_locator(ticker.FixedLocator(minor_locs))\n",
    "ax.yaxis.set_minor_locator(ticker.FixedLocator(minor_locs))\n",
    "\n",
    "major_locs = [10**i for i in range(n+1)]\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(major_locs))\n",
    "ax.yaxis.set_major_locator(ticker.FixedLocator(major_locs))\n",
    "\n",
    "# do not show minor labels (duplicated labels)\n",
    "ax.xaxis.set_minor_formatter(ticker.NullFormatter())\n",
    "ax.yaxis.set_minor_formatter(ticker.NullFormatter())\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', top=True, right=True, length=5, width=1, labelsize=12, pad=4, direction='in')\n",
    "ax.tick_params(axis='both', which='minor', top=True, right=True, length=3, width=.6, labelsize=12, pad=15, direction='in')\n",
    "\n",
    "border_width = 1.5\n",
    "ax.spines['bottom'].set_linewidth(border_width)\n",
    "ax.spines['left'].set_linewidth(border_width)\n",
    "ax.spines['top'].set_linewidth(border_width)\n",
    "ax.spines['right'].set_linewidth(border_width)\n",
    "\n",
    "\n",
    "other = Line2D([0], [0], color='royalblue', alpha=0.75, marker='o',markersize=12, linestyle='None', markeredgecolor='w')\n",
    "hubei = Line2D([0], [0], color='C3', alpha=0.75, marker='o',markersize=12, linestyle='None', markeredgecolor='w')\n",
    "\n",
    "real_legend = ax.legend((other, hubei), \n",
    "                        ('Cities outside Hubei','Cities in Hubei\\Wuhan'), \n",
    "                        numpoints=1, fontsize=10, frameon=False, loc='best')\n",
    "\n",
    "\n",
    "out_dir = 'Outputs'\n",
    "# plt.savefig(os.path.join(out_dir, 'figS2-nb_predicted_vs_reported_cases.pdf'), \n",
    "#             pad_inches=0.05, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Forest plot of the regression results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv_dic = {\n",
    "    'Intercept': 'Intercept',\n",
    "    'log_local_flow': 'Human flow from\\n Wuhan (log)',\n",
    "    'log_pagerank': 'Pagerank (log)',\n",
    "    'log_population': 'Population (log)',\n",
    "    'log_distance': 'Distance to\\n Wuhan (log)',\n",
    "    'mean_intensity': 'Intra-city activity',\n",
    "    'city_tier1': 'First-tier city',\n",
    "    'city_tier2': 'Second-tier city',\n",
    "    'city_tier3': 'Third-tier city',\n",
    "    'city_tier4': 'Fourth-tier city',\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def marker_color_func(p, coef):\n",
    "    'marker color based on p-value and coefficient'\n",
    "    \n",
    "    p = float(p)\n",
    "    coef = float(coef)\n",
    "    \n",
    "    if p<0.05 and coef>0:\n",
    "        mc = '#fdae6b'\n",
    "    elif p<0.05 and coef<0:\n",
    "        mc = '#9ecae1'\n",
    "    else:\n",
    "        mc = '#dddddd'\n",
    "        \n",
    "    return mc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2.5, 5))\n",
    "# fig, ax = plt.subplots(figsize=(2.5, 6))\n",
    "\n",
    "\n",
    "ax.patch.set_alpha(0.01)\n",
    "ax.set_facecolor('#2b3679')\n",
    "\n",
    "space_adjust = 0.13\n",
    "marker_size = 6.5\n",
    "line_width = .8\n",
    "\n",
    "\n",
    "# filter iv\n",
    "focused_ivs = ['log_local_flow', \n",
    "               'log_pagerank', \n",
    "               'log_population', \n",
    "               'log_distance', \n",
    "               'mean_intensity', \n",
    "               'city_tier1', 'city_tier2', 'city_tier3', 'city_tier4']\n",
    "\n",
    "\n",
    "# ----------------\n",
    "# nb model\n",
    "plot_df = nb_result_df.copy()\n",
    "plot_df = plot_df[plot_df['index']!='Intercept']\n",
    "\n",
    "plot_df = plot_df[plot_df['index'].isin(focused_ivs)]\n",
    "\n",
    "plot_df = plot_df[::-1]\n",
    "plot_df = plot_df.reset_index(drop=True)\n",
    "\n",
    "\n",
    "x = plot_df['index']\n",
    "y = plot_df['coef']\n",
    "# yerr = plot_df['std err']*1.96\n",
    "ps = plot_df['P>|z|']\n",
    "\n",
    "# ax.plot(\n",
    "#         y, x, 'o',\n",
    "#         markersize=6,\n",
    "#         markeredgecolor='k', \n",
    "#         markeredgewidth=1,\n",
    "#         markerfacecolor='C2',\n",
    "#         alpha=.5,\n",
    "#         )\n",
    "\n",
    "ivs = plot_df['index'].tolist()\n",
    "\n",
    "for i in range(len(x)):\n",
    "    p = plot_df['P>|z|'].iloc[i]\n",
    "    coef = plot_df['coef'].iloc[i]\n",
    "    index = plot_df['index'].iloc[i]\n",
    "    \n",
    "    mc = marker_color_func(p=p, coef=coef)\n",
    "            \n",
    "    ax.plot(\n",
    "            coef, ivs.index(index)-space_adjust, 's',\n",
    "            markersize=marker_size,\n",
    "            markeredgecolor='k', \n",
    "            markeredgewidth=.5,\n",
    "            markerfacecolor=mc,\n",
    "            # alpha=.9,\n",
    "            )\n",
    "\n",
    "\n",
    "\n",
    "for i in range(len(plot_df)):\n",
    "    row = plot_df.iloc[i]\n",
    "    ax.hlines(y=i-space_adjust, xmin=row['[0.025'], xmax=row['0.975]'], linestyles='-', \n",
    "              linewidth=line_width, color='k')\n",
    "\n",
    "\n",
    "    \n",
    "# ----------------\n",
    "# ols model\n",
    "plot_df = ols_result_df.copy()\n",
    "plot_df = plot_df[plot_df['index']!='Intercept']\n",
    "\n",
    "plot_df = plot_df[plot_df['index'].isin(focused_ivs)]\n",
    "\n",
    "plot_df = plot_df[::-1]\n",
    "plot_df = plot_df.reset_index(drop=True)\n",
    "\n",
    "    \n",
    "for i in range(len(x)):\n",
    "    p = plot_df['P>|t|'].iloc[i]\n",
    "    coef = plot_df['coef'].iloc[i]\n",
    "    index = plot_df['index'].iloc[i]\n",
    "    \n",
    "    mc = marker_color_func(p=p, coef=coef)\n",
    "            \n",
    "    ax.plot(\n",
    "            coef, ivs.index(index)+space_adjust, 'o',\n",
    "            markersize=marker_size,\n",
    "            markeredgecolor='k', \n",
    "            markeredgewidth=.5,\n",
    "            markerfacecolor=mc,\n",
    "            # alpha=.9,\n",
    "            )\n",
    "\n",
    "\n",
    "for i in range(len(plot_df)):\n",
    "    row = plot_df.iloc[i]\n",
    "    ax.hlines(y=i+space_adjust, xmin=row['[0.025'], xmax=row['0.975]'], linestyles='-', \n",
    "              linewidth=line_width, color='k')\n",
    "# ----------------\n",
    "    \n",
    "\n",
    "line_style = (0, (3, 2))\n",
    "ax.vlines(x=0, ymin=-1, ymax=len(x), linestyles=line_style, \n",
    "          linewidth=.5, color='#555555')\n",
    "\n",
    "\n",
    "\n",
    "locs = np.arange(-1, 4, 0.5)\n",
    "ax.xaxis.set_minor_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "locs = np.arange(-1, 4, 0.5)\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "\n",
    "# ticks = ivs\n",
    "# ticks = [iv_dic[iv] for iv in ivs]\n",
    "ticks = [iv_dic[iv] for iv in plot_df['index']]\n",
    "N=len(ticks)\n",
    "every_nth=1\n",
    "plt.yticks(list(range(0, N+1, every_nth)), ticks[::every_nth], \n",
    "           rotation='0', \n",
    "           horizontalalignment='right',\n",
    "           # va = 'center',\n",
    "           position=(0, 0)\n",
    "           # rotation_mode=\"anchor\"\n",
    "          )\n",
    "\n",
    "border_width = 1.35\n",
    "ax.spines['bottom'].set_linewidth(border_width)\n",
    "ax.spines['left'].set_linewidth(border_width)\n",
    "ax.spines['top'].set_linewidth(border_width)\n",
    "ax.spines['right'].set_linewidth(border_width)\n",
    "\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "# ax.spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "# # custom ticks\n",
    "ax.tick_params(axis='x', which='major', top=False, right=False, pad=2, labelsize=11, length=4, width=1.2, direction='out')\n",
    "ax.tick_params(axis='y', which='major', top=False, right=False, pad=7.5, labelsize=11, length=0, width=1.2, direction='out')\n",
    "ax.tick_params(axis='both', which='minor', top=False, right=False, length=3,  width=.8, direction='out')\n",
    "\n",
    "ax.set_xlabel('Coefficient', fontsize=11, labelpad=1)\n",
    "\n",
    "nb_ml = Line2D([0], [0], color='k',  linestyle='-', linewidth=line_width,\n",
    "                       marker='s',markersize=marker_size, markerfacecolor='w', markeredgewidth=1.1, markeredgecolor='k')\n",
    "ols_ml = Line2D([0], [0], color='k',  linestyle='-', linewidth=line_width,\n",
    "                       marker='o',markersize=marker_size, markerfacecolor='w', markeredgewidth=1.1, markeredgecolor='k')\n",
    "\n",
    "real_legend = ax.legend((ols_ml, nb_ml), ('OLS model', 'Negative Binomial model'), \n",
    "#                         ncol=2,\n",
    "                        frameon=False,\n",
    "                        numpoints=1, fontsize=9,  \n",
    "                        labelspacing=0.15,\n",
    "#                         loc='best', \n",
    "                        loc='center',\n",
    "                        bbox_to_anchor=(0.5, 1.045))\n",
    "\n",
    "plt.ylim(-.5, len(plot_df)-0.5)\n",
    "\n",
    "\n",
    "# plt.tight_layout(pad=0.1)\n",
    "out_dir = 'Outputs'\n",
    "# plt.savefig(os.path.join(out_dir, 'fig2-regression_results.pdf'), pad_inches=.01, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}