_notebooks/2020-03-19-cases-and-deaths-per-million.ipynb

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   "source": [
    "# COVID-19 Deaths Per Capita\n",
    "> Comparing death rates adjusting for population size. \n",
    "\n",
    "- comments: true\n",
    "- author: Joao B. Duarte & Hamel Husain\n",
    "- categories: [growth, compare, interactive]\n",
    "- hide: false\n",
    "- image: images/covid-permillion-trajectories.png\n",
    "- permalink: /covid-compare-permillion/"
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    "#hide\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import altair as alt\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "chart_width = 550\n",
    "chart_height= 400"
   ]
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   "source": [
    "## Deaths Per Million Of Inhabitants"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
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   "source": [
    "Since reaching at least 1 death per million\n",
    "\n",
    "> Tip: Click (Shift+ for multiple) on countries in the legend to filter the visualization."
   ]
  },
  {
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   "execution_count": 2,
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   "source": [
    "#hide \n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\", \n",
    "                   error_bad_lines=False)\n",
    "data = data.drop(columns=[\"Lat\", \"Long\"])\n",
    "data = data.melt(id_vars= [\"Province/State\", \"Country/Region\"])\n",
    "data = pd.DataFrame(data.groupby(['Country/Region', \"variable\"]).sum())\n",
    "data.reset_index(inplace=True)  \n",
    "data = data.rename(columns={\"Country/Region\": \"location\", \"variable\": \"date\", \"value\": \"total_cases\"})\n",
    "data['date'] =pd.to_datetime(data.date)\n",
    "data = data.sort_values(by = \"date\")\n",
    "data.loc[data.location == \"US\",\"location\"] = \"United States\"\n",
    "data.loc[data.location == \"Korea, South\",\"location\"] = \"South Korea\"\n",
    "\n",
    "data_pwt = pd.read_stata(\"https://www.rug.nl/ggdc/docs/pwt91.dta\")\n",
    "\n",
    "filter1 = data_pwt[\"year\"] == 2017\n",
    "data_pop = data_pwt[filter1]\n",
    "data_pop = data_pop[[\"country\",\"pop\"]]\n",
    "data_pop.loc[data_pop.country == \"Republic of Korea\",\"country\"] = \"South Korea\"\n",
    "data_pop.loc[data_pop.country == \"Iran (Islamic Republic of)\",\"country\"] = \"Iran\"\n",
    "\n",
    "# per habitant\n",
    "data_pc = data.copy()\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\", \"South Korea\", \"Japan\", \n",
    "             \"Brazil\", \"Iran\", 'Netherlands', 'Belgium', 'Sweden', \n",
    "             'Switzerland', 'Norway', 'Denmark', 'Austria', 'Slovenia', 'Greece',\n",
    "             'Cyprus']\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "MIN_DEATHS = 10\n",
    "filter_min_dead = data_pc.total_cases < MIN_DEATHS\n",
    "data_pc = data_pc.drop(data_pc[filter_min_dead].index)\n",
    "\n",
    "# compute per habitant\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_cases\"] = data_pc.loc[data_pc.location == i,\"total_cases\"]/float(data_pop.loc[data_pop.country == i, \"pop\"])\n",
    "\n",
    "    # get each country time series\n",
    "filter1 = data_pc[\"total_cases\"] > 1\n",
    "for i in countries:\n",
    "    filter_country = data_pc[\"location\"]== i\n",
    "    data_countries_pc.append(data_pc[filter_country & filter1])"
   ]
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days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 649, \"location\": \"Austria\", \"date\": \"2020-03-24T00:00:00\", \"total_cases\": 3.205329032796181, \"n_days\": 2, \"log_cases\": 1.1648147474421182, \"trend_2days\": 0.6931471805599453, \"trend_4days\": 0.34657359027997264, \"trend_12days\": 0.11552453009332421, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 650, \"location\": \"Austria\", \"date\": \"2020-03-25T00:00:00\", \"total_cases\": 3.4342811065673366, \"n_days\": 3, \"log_cases\": 1.2338076189290694, \"trend_2days\": 1.0397207708399179, \"trend_4days\": 0.5198603854199589, \"trend_12days\": 0.17328679513998632, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 651, \"location\": \"Austria\", \"date\": \"2020-03-26T00:00:00\", \"total_cases\": 5.609325807393317, \"n_days\": 4, \"log_cases\": 1.7244305353775409, \"trend_2days\": 1.3862943611198906, \"trend_4days\": 0.6931471805599453, \"trend_12days\": 0.23104906018664842, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 652, \"location\": \"Austria\", \"date\": \"2020-03-27T00:00:00\", \"total_cases\": 6.639610139363518, \"n_days\": 5, \"log_cases\": 1.8930532478133335, \"trend_2days\": 1.7328679513998633, \"trend_4days\": 0.8664339756999316, \"trend_12days\": 0.28881132523331055, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 4342, \"location\": \"Greece\", \"date\": \"2020-03-21T00:00:00\", \"total_cases\": 1.164898259850776, \"n_days\": 0, \"log_cases\": 0.15263375260393272, \"trend_2days\": 0.0, \"trend_4days\": 0.0, \"trend_12days\": 0.0, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 4343, \"location\": \"Greece\", \"date\": \"2020-03-22T00:00:00\", \"total_cases\": 1.3441133767508953, \"n_days\": 1, \"log_cases\": 0.29573459624460596, \"trend_2days\": 0.34657359027997264, \"trend_4days\": 0.17328679513998632, \"trend_12days\": 0.057762265046662105, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 4344, \"location\": \"Greece\", \"date\": \"2020-03-23T00:00:00\", \"total_cases\": 1.5233284936510147, \"n_days\": 2, \"log_cases\": 0.42089773919861206, \"trend_2days\": 0.6931471805599453, \"trend_4days\": 0.34657359027997264, \"trend_12days\": 0.11552453009332421, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 4345, \"location\": \"Greece\", \"date\": \"2020-03-24T00:00:00\", \"total_cases\": 1.7921511690011938, \"n_days\": 3, \"log_cases\": 0.583416668696387, \"trend_2days\": 1.0397207708399179, \"trend_4days\": 0.5198603854199589, \"trend_12days\": 0.17328679513998632, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 4346, \"location\": \"Greece\", \"date\": \"2020-03-25T00:00:00\", \"total_cases\": 1.9713662859013132, \"n_days\": 4, \"log_cases\": 0.6787268485007119, \"trend_2days\": 1.3862943611198906, \"trend_4days\": 0.6931471805599453, \"trend_12days\": 0.23104906018664842, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 4347, \"location\": \"Greece\", \"date\": \"2020-03-26T00:00:00\", \"total_cases\": 2.329796519701552, \"n_days\": 5, \"log_cases\": 0.845780933163878, \"trend_2days\": 1.7328679513998633, \"trend_4days\": 0.8664339756999316, \"trend_12days\": 0.28881132523331055, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 4348, \"location\": \"Greece\", \"date\": \"2020-03-27T00:00:00\", \"total_cases\": 2.5090116366016715, \"n_days\": 6, \"log_cases\": 0.9198889053175999, \"trend_2days\": 2.0794415416798357, \"trend_4days\": 1.0397207708399179, \"trend_12days\": 0.34657359027997264, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}], \"data-cf5cfaa2f9f67721e9c0baa1e72af7ac\": [{\"label\": \"Doubles every 2 days\", \"x_coord\": 6, \"y_coord\": 4.0}, {\"label\": \"Doubles every 4 days\", \"x_coord\": 16, \"y_coord\": 3.5}, {\"label\": \"Doubles every 12 days\", \"x_coord\": 25, \"y_coord\": 1.8}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Stack data to get it to Altair dataframe format\n",
    "data_countries_pc2 = data_countries_pc.copy()\n",
    "for i in range(0,len(countries)):\n",
    "    data_countries_pc2[i] = data_countries_pc2[i].reset_index()\n",
    "    data_countries_pc2[i]['n_days'] = data_countries_pc2[i].index\n",
    "    data_countries_pc2[i]['log_cases'] = np.log(data_countries_pc2[i][\"total_cases\"])\n",
    "data_plot = data_countries_pc2[0]\n",
    "for i in range(1, len(countries)):    \n",
    "    data_plot = pd.concat([data_plot, data_countries_pc2[i]], axis=0)\n",
    "data_plot[\"trend_2days\"] = np.log(2)/2*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_4days\"] = np.log(2)/4*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_12days\"] = np.log(2)/12*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_2days_label\"] = \"Doubles every 2 days\"\n",
    "data_plot[\"trend_4days_label\"] = \"Doubles evey 4 days\"\n",
    "data_plot[\"trend_12days_label\"] = \"Doubles every 12 days\"\n",
    "\n",
    "\n",
    "# Plot it using Altair\n",
    "source = data_plot\n",
    "\n",
    "scales = alt.selection_interval(bind='scales')\n",
    "selection = alt.selection_multi(fields=['location'], bind='legend')\n",
    "\n",
    "base = alt.Chart(source, title = \"COVID-19 Deaths Per Million of Inhabitants\").encode(\n",
    "    x = alt.X('n_days:Q', title = \"Days passed since reaching 1 death per million\"),\n",
    "    y = alt.Y(\"log_cases:Q\",title = \"Log of deaths per million\"),\n",
    "    color = alt.Color('location:N', legend=alt.Legend(title=\"Country\", labelFontSize=15, titleFontSize=17),\n",
    "                     scale=alt.Scale(scheme='tableau20')),\n",
    "    opacity = alt.condition(selection, alt.value(1), alt.value(0.1))\n",
    ")\n",
    "\n",
    "lines = base.mark_line().add_selection(\n",
    "    scales\n",
    ").add_selection(\n",
    "    selection\n",
    ").properties(\n",
    "    width=chart_width,\n",
    "    height=chart_height\n",
    ")\n",
    "\n",
    "trend_2d = alt.Chart(source).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_2days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ").mark_line(color=\"grey\", strokeDash=[3,3])\n",
    "\n",
    "\n",
    "labels = pd.DataFrame([{'label': 'Doubles every 2 days', 'x_coord': 6, 'y_coord': 4},\n",
    "                       {'label': 'Doubles every 4 days', 'x_coord': 16, 'y_coord': 3.5},\n",
    "                       {'label': 'Doubles every 12 days', 'x_coord': 25, 'y_coord': 1.8},\n",
    "                      ])\n",
    "trend_label = (alt.Chart(labels)\n",
    "                    .mark_text(align='left', dx=-55, dy=-15, fontSize=12, color=\"grey\")\n",
    "                    .encode(x='x_coord:Q',\n",
    "                            y='y_coord:Q',\n",
    "                            text='label:N')\n",
    "                   )\n",
    "\n",
    "trend_4d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_4days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "trend_12d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_12days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "\n",
    "plot1= (\n",
    "(trend_2d + trend_4d + trend_12d + trend_label + lines)\n",
    ".configure_title(fontSize=20)\n",
    ".configure_axis(labelFontSize=15,titleFontSize=18)\n",
    ")\n",
    "plot1.save((\"../images/covid-permillion-trajectories.png\"))\n",
    "plot1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.011192,
     "end_time": "2020-03-28T20:35:32.203324",
     "exception": false,
     "start_time": "2020-03-28T20:35:32.192132",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Last Available Total Deaths By Country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "papermill": {
     "duration": 0.050436,
     "end_time": "2020-03-28T20:35:32.264909",
     "exception": false,
     "start_time": "2020-03-28T20:35:32.214473",
     "status": "completed"
    },
    "tags": []
   },
   "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>Deaths per Million</th>\n",
       "      <th>Log of Deaths per Million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>6.639610</td>\n",
       "      <td>1.893053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>25.285809</td>\n",
       "      <td>3.230243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2.338389</td>\n",
       "      <td>0.849462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>9.069423</td>\n",
       "      <td>2.204909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>29.705614</td>\n",
       "      <td>3.391336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>4.164930</td>\n",
       "      <td>1.426699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2.509012</td>\n",
       "      <td>0.919889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>29.299141</td>\n",
       "      <td>3.377558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>153.874919</td>\n",
       "      <td>5.036140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>32.108593</td>\n",
       "      <td>3.469124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>3.581268</td>\n",
       "      <td>1.275717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>7.357564</td>\n",
       "      <td>1.995729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2.726441</td>\n",
       "      <td>1.002997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>110.841879</td>\n",
       "      <td>4.708105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>10.594609</td>\n",
       "      <td>2.360345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>27.253407</td>\n",
       "      <td>3.305179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>11.498667</td>\n",
       "      <td>2.442231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>4.872720</td>\n",
       "      <td>1.583652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  Deaths per Million  Log of Deaths per Million\n",
       "Country                                                                 \n",
       "Austria        2020-03-27            6.639610                   1.893053\n",
       "Belgium        2020-03-27           25.285809                   3.230243\n",
       "China          2020-03-27            2.338389                   0.849462\n",
       "Denmark        2020-03-27            9.069423                   2.204909\n",
       "France         2020-03-27           29.705614                   3.391336\n",
       "Germany        2020-03-27            4.164930                   1.426699\n",
       "Greece         2020-03-27            2.509012                   0.919889\n",
       "Iran           2020-03-27           29.299141                   3.377558\n",
       "Italy          2020-03-27          153.874919                   5.036140\n",
       "Netherlands    2020-03-27           32.108593                   3.469124\n",
       "Norway         2020-03-27            3.581268                   1.275717\n",
       "Portugal       2020-03-27            7.357564                   1.995729\n",
       "South Korea    2020-03-27            2.726441                   1.002997\n",
       "Spain          2020-03-27          110.841879                   4.708105\n",
       "Sweden         2020-03-27           10.594609                   2.360345\n",
       "Switzerland    2020-03-27           27.253407                   3.305179\n",
       "United Kingdom 2020-03-27           11.498667                   2.442231\n",
       "United States  2020-03-27            4.872720                   1.583652"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "label = 'Deaths'\n",
    "temp = pd.concat([x.copy() for x in data_countries_pc]).loc[lambda x: x.date >= '3/1/2020']\n",
    "\n",
    "metric_name = f'{label} per Million'\n",
    "temp.columns = ['Country', 'date', metric_name]\n",
    "# temp.loc[:, 'month'] = temp.date.dt.strftime('%Y-%m')\n",
    "temp.loc[:, f'Log of {label} per Million'] = temp[f'{label} per Million'].apply(lambda x: np.log(x))\n",
    "\n",
    "temp.groupby('Country').last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "papermill": {
     "duration": 6.575277,
     "end_time": "2020-03-28T20:35:38.852079",
     "exception": false,
     "start_time": "2020-03-28T20:35:32.276802",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "# Get data and clean it\n",
    "\n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\", error_bad_lines=False)\n",
    "data = data.drop(columns=[\"Lat\", \"Long\"])\n",
    "data = data.melt(id_vars= [\"Province/State\", \"Country/Region\"])\n",
    "data = pd.DataFrame(data.groupby(['Country/Region', \"variable\"]).sum())\n",
    "data.reset_index(inplace=True)  \n",
    "data = data.rename(columns={\"Country/Region\": \"location\", \"variable\": \"date\", \"value\": \"total_cases\"})\n",
    "data['date'] =pd.to_datetime(data.date)\n",
    "data = data.sort_values(by = \"date\")\n",
    "data.loc[data.location == \"US\",\"location\"] = \"United States\"\n",
    "data.loc[data.location == \"Korea, South\",\"location\"] = \"South Korea\"\n",
    "\n",
    "# Population data (last year is 2017 which is what we use)\n",
    "data_pwt = pd.read_stata(\"https://www.rug.nl/ggdc/docs/pwt91.dta\")\n",
    "\n",
    "filter1 = data_pwt[\"year\"] == 2017\n",
    "data_pop = data_pwt[filter1]\n",
    "data_pop = data_pop[[\"country\",\"pop\"]]\n",
    "data_pop.loc[data_pop.country == \"Republic of Korea\",\"country\"] = \"South Korea\"\n",
    "data_pop.loc[data_pop.country == \"Iran (Islamic Republic of)\",\"country\"] = \"Iran\"\n",
    "\n",
    "# per habitant\n",
    "data_pc = data.copy()\n",
    "\n",
    "# I can add more countries if needed\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\",\"South Korea\", \"Japan\", \n",
    "             \"Brazil\",\"Iran\"]\n",
    "\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "# compute per habitant\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_cases\"] = data_pc.loc[data_pc.location == i,\"total_cases\"]/float(data_pop.loc[data_pop.country == i, \"pop\"])\n",
    "    \n",
    "# get each country time series\n",
    "filter1 = data_pc[\"total_cases\"] > 1\n",
    "\n",
    "for i in countries:\n",
    "    filter_country = data_pc[\"location\"]== i\n",
    "    data_countries_pc.append(data_pc[filter_country & filter1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.011905,
     "end_time": "2020-03-28T20:35:38.876070",
     "exception": false,
     "start_time": "2020-03-28T20:35:38.864165",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Appendix\n",
    "\n",
    "> Warning: The following chart, \"Cases Per Million of Habitants\" is biased depending on how widely a country administers tests. Please read with caution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.011454,
     "end_time": "2020-03-28T20:35:38.899000",
     "exception": false,
     "start_time": "2020-03-28T20:35:38.887546",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Cases Per Million of Habitants\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "papermill": {
     "duration": 0.246174,
     "end_time": "2020-03-28T20:35:39.161029",
     "exception": false,
     "start_time": "2020-03-28T20:35:38.914855",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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1.2130075659799042, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5196, \"location\": \"Iran\", \"date\": \"2020-03-18T00:00:00\", \"total_cases\": 213.90344447432636, \"n_days\": 22, \"log_cases\": 5.365524719157986, \"trend_2days\": 7.6246189861593985, \"trend_4days\": 3.8123094930796992, \"trend_12days\": 1.2707698310265663, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5197, \"location\": \"Iran\", \"date\": \"2020-03-19T00:00:00\", \"total_cases\": 226.7911239236752, \"n_days\": 23, \"log_cases\": 5.42402943494876, \"trend_2days\": 7.971192576439371, \"trend_4days\": 3.9855962882196856, \"trend_12days\": 1.3285320960732283, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5199, \"location\": \"Iran\", \"date\": \"2020-03-20T00:00:00\", \"total_cases\": 242.03209856884206, \"n_days\": 24, \"log_cases\": 5.489070356075174, \"trend_2days\": 8.317766166719343, \"trend_4days\": 4.1588830833596715, \"trend_12days\": 1.3862943611198906, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5200, \"location\": \"Iran\", \"date\": \"2020-03-21T00:00:00\", \"total_cases\": 253.9341046377436, \"n_days\": 25, \"log_cases\": 5.537074802800543, \"trend_2days\": 8.664339756999317, \"trend_4days\": 4.332169878499658, \"trend_12days\": 1.4440566261665526, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5201, \"location\": \"Iran\", \"date\": \"2020-03-22T00:00:00\", \"total_cases\": 266.6000075764918, \"n_days\": 26, \"log_cases\": 5.585749436163557, \"trend_2days\": 9.010913347279288, \"trend_4days\": 4.505456673639644, \"trend_12days\": 1.5018188912132147, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5202, \"location\": \"Iran\", \"date\": \"2020-03-23T00:00:00\", \"total_cases\": 283.9848218241316, \"n_days\": 27, \"log_cases\": 5.648920792451783, \"trend_2days\": 9.357486937559262, \"trend_4days\": 4.678743468779631, \"trend_12days\": 1.559581156259877, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5203, \"location\": \"Iran\", \"date\": \"2020-03-24T00:00:00\", \"total_cases\": 305.69427802848406, \"n_days\": 28, \"log_cases\": 5.7225855111177, \"trend_2days\": 9.704060527839234, \"trend_4days\": 4.852030263919617, \"trend_12days\": 1.617343421306539, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5204, \"location\": \"Iran\", \"date\": \"2020-03-25T00:00:00\", \"total_cases\": 332.87422149431916, \"n_days\": 29, \"log_cases\": 5.8077647053985615, \"trend_2days\": 10.050634118119207, \"trend_4days\": 5.025317059059604, \"trend_12days\": 1.675105686353201, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5205, \"location\": \"Iran\", \"date\": \"2020-03-26T00:00:00\", \"total_cases\": 362.3088928179276, \"n_days\": 30, \"log_cases\": 5.892497143053641, \"trend_2days\": 10.39720770839918, \"trend_4days\": 5.19860385419959, \"trend_12days\": 1.732867951399863, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5206, \"location\": \"Iran\", \"date\": \"2020-03-27T00:00:00\", \"total_cases\": 398.3598967077887, \"n_days\": 31, \"log_cases\": 5.987355859769773, \"trend_2days\": 10.743781298679153, \"trend_4days\": 5.371890649339576, \"trend_12days\": 1.7906302164465253, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}], \"data-2cf9e9c9dd4f97c8eda44cd9355d02e6\": [{\"label\": \"Doubles every 2 days\", \"x_coord\": 10, \"y_coord\": 6}, {\"label\": \"Doubles every 4 days\", \"x_coord\": 28, \"y_coord\": 6}, {\"label\": \"Doubles every 12 days\", \"x_coord\": 45, \"y_coord\": 3}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Stack data to get it to Altair dataframe format\n",
    "data_countries_pc2 = data_countries_pc.copy()\n",
    "for i in range(0,len(countries)):\n",
    "    data_countries_pc2[i] = data_countries_pc2[i].reset_index()\n",
    "    data_countries_pc2[i]['n_days'] = data_countries_pc2[i].index\n",
    "    data_countries_pc2[i]['log_cases'] = np.log(data_countries_pc2[i][\"total_cases\"])\n",
    "data_plot = data_countries_pc2[0]\n",
    "for i in range(1, len(countries)):    \n",
    "    data_plot = pd.concat([data_plot, data_countries_pc2[i]], axis=0)\n",
    "data_plot[\"trend_2days\"] = np.log(2)/2*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_4days\"] = np.log(2)/4*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_12days\"] = np.log(2)/12*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_2days_label\"] = \"Doubles every 2 days\"\n",
    "data_plot[\"trend_4days_label\"] = \"Doubles evey 4 days\"\n",
    "data_plot[\"trend_12days_label\"] = \"Doubles every 12 days\"\n",
    "\n",
    "\n",
    "# Plot it using Altair\n",
    "source = data_plot\n",
    "\n",
    "scales = alt.selection_interval(bind='scales')\n",
    "selection = alt.selection_multi(fields=['location'], bind='legend')\n",
    "\n",
    "base = alt.Chart(source, title = \"COVID-19 Confirmed Cases Per Million of Inhabitants\").encode(\n",
    "    x = alt.X('n_days:Q', title = \"Days passed since reaching 1 case per million\"),\n",
    "    y = alt.Y(\"log_cases:Q\",title = \"Log of confirmed cases per million\"),\n",
    "    color = alt.Color('location:N', legend=alt.Legend(title=\"Country\", labelFontSize=15, titleFontSize=17),\n",
    "                     scale=alt.Scale(scheme='tableau20')),\n",
    "    opacity = alt.condition(selection, alt.value(1), alt.value(0.1))\n",
    ").properties(\n",
    "    width=chart_width,\n",
    "    height=chart_height\n",
    ")\n",
    "\n",
    "lines = base.mark_line().add_selection(\n",
    "    scales\n",
    ").add_selection(\n",
    "    selection\n",
    ")\n",
    "\n",
    "trend_2d = alt.Chart(source).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_2days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ").mark_line( strokeDash=[3,3], color=\"grey\")\n",
    "\n",
    "labels = pd.DataFrame([{'label': 'Doubles every 2 days', 'x_coord': 10, 'y_coord': 6},\n",
    "                       {'label': 'Doubles every 4 days', 'x_coord': 28, 'y_coord': 6},\n",
    "                       {'label': 'Doubles every 12 days', 'x_coord': 45, 'y_coord': 3},\n",
    "                      ])\n",
    "trend_label = (alt.Chart(labels)\n",
    "                    .mark_text(align='left', dx=-55, dy=-15, fontSize=12, color=\"grey\")\n",
    "                    .encode(x='x_coord:Q',\n",
    "                            y='y_coord:Q',\n",
    "                            text='label:N')\n",
    "                   )\n",
    "\n",
    "\n",
    "trend_4d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_4days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "trend_12d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_12days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "(\n",
    "(trend_2d  + trend_4d + trend_12d + trend_label + lines)\n",
    ".configure_title(fontSize=20)\n",
    ".configure_axis(labelFontSize=15,titleFontSize=18)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "start_time": "2020-03-28T20:35:39.175622",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Last Available Cases Per Million By Country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "papermill": {
     "duration": 0.043474,
     "end_time": "2020-03-28T20:35:39.247335",
     "exception": false,
     "start_time": "2020-03-28T20:35:39.203861",
     "status": "completed"
    },
    "tags": []
   },
   "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>Cases per Million</th>\n",
       "      <th>Log of Cases per Million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>16.326762</td>\n",
       "      <td>2.792806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>58.102863</td>\n",
       "      <td>4.062215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>496.858755</td>\n",
       "      <td>6.208306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>619.515055</td>\n",
       "      <td>6.428937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>398.359897</td>\n",
       "      <td>5.987356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>1457.178970</td>\n",
       "      <td>7.284258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>11.515130</td>\n",
       "      <td>2.443662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>413.185300</td>\n",
       "      <td>6.023896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>128.222102</td>\n",
       "      <td>4.853764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>183.044235</td>\n",
       "      <td>5.209728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>1417.753496</td>\n",
       "      <td>7.256829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>222.796107</td>\n",
       "      <td>5.406257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>313.311857</td>\n",
       "      <td>5.747199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  Cases per Million  Log of Cases per Million\n",
       "Country                                                               \n",
       "Brazil         2020-03-27          16.326762                  2.792806\n",
       "China          2020-03-27          58.102863                  4.062215\n",
       "France         2020-03-27         496.858755                  6.208306\n",
       "Germany        2020-03-27         619.515055                  6.428937\n",
       "Iran           2020-03-27         398.359897                  5.987356\n",
       "Italy          2020-03-27        1457.178970                  7.284258\n",
       "Japan          2020-03-27          11.515130                  2.443662\n",
       "Portugal       2020-03-27         413.185300                  6.023896\n",
       "Singapore      2020-03-27         128.222102                  4.853764\n",
       "South Korea    2020-03-27         183.044235                  5.209728\n",
       "Spain          2020-03-27        1417.753496                  7.256829\n",
       "United Kingdom 2020-03-27         222.796107                  5.406257\n",
       "United States  2020-03-27         313.311857                  5.747199"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "label = 'Cases'\n",
    "temp = pd.concat([x.copy() for x in data_countries_pc]).loc[lambda x: x.date >= '3/1/2020']\n",
    "\n",
    "metric_name = f'{label} per Million'\n",
    "temp.columns = ['Country', 'date', metric_name]\n",
    "# temp.loc[:, 'month'] = temp.date.dt.strftime('%Y-%m')\n",
    "temp.loc[:, f'Log of {label} per Million'] = temp[f'{label} per Million'].apply(lambda x: np.log(x))\n",
    "\n",
    "temp.groupby('Country').last()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": "2020-03-28T20:35:39.276746",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This analysis was conducted by [Joao B. Duarte](https://www.jbduarte.com). Assitance with creating visualizations were provided by [Hamel Husain](https://twitter.com/HamelHusain). Relevant sources are listed below: \n",
    "\n",
    "\n",
    "1. [\"2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE\"](https://systems.jhu.edu/research/public-health/ncov/) [GitHub repository](https://github.com/CSSEGISandData/COVID-19). \n",
    "\n",
    "2. [Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), \"The Next Generation of the Penn World Table\" American Economic Review, 105(10), 3150-3182](https://www.rug.nl/ggdc/productivity/pwt/related-research)"
   ]
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