_notebooks/2020-03-19-estimating_infected.ipynb

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   "source": [
    "# Estimating The Infected Population From Deaths\n",
    "> Estimating the number of infected people by country based on the number of deaths and case fatality rate. \n",
    "\n",
    "- comments: true\n",
    "- author: Joao B. Duarte\n",
    "- categories: [growth, compare, interactive, estimation]\n",
    "- hide: false\n",
    "- image: images/covid-estimate-infections.png\n",
    "- permalink: /covid-infected/\n",
    "- toc: true"
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   "source": [
    "> Note: This dashboard contains the results of a predictive model. The author has tried to make it as accurate as possible. But the COVID-19 situation is changing quickly, and these models inevitably include some level of speculation."
   ]
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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",
    "from datetime import timedelta, datetime, date\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "chart_width = 550\n",
    "chart_height= 400"
   ]
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    "#hide\n",
    "def plot(data, type1, levels):\n",
    "    data_countries_pc2 = data.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",
    "        if type1 == \"scatter\":\n",
    "            data_countries_pc2[i]['cases'] = data_countries_pc2[i][\"total_cases\"]\n",
    "        data_countries_pc2[i]['infected'] = data_countries_pc2[i][\"total_infected\"]\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",
    "    \n",
    "    if type1 == \"scatter\":\n",
    "        data_plot[\"45_line\"] = data_plot[\"cases\"]\n",
    "\n",
    "    # Plot it using Altair\n",
    "    source = data_plot\n",
    "    \n",
    "    if levels == True:\n",
    "        ylabel = \"Total\"\n",
    "    else :\n",
    "        ylabel = \"Per Million\"\n",
    "\n",
    "    scales = alt.selection_interval(bind='scales')\n",
    "    selection = alt.selection_multi(fields=['location'], bind='legend')\n",
    "\n",
    "    if type1 == \"line\": \n",
    "        base = alt.Chart(source, title =  \"Estimated Infected Population By Country\").encode(\n",
    "            x = alt.X('n_days:Q', title = \"Days since outbreak\"),\n",
    "            y = alt.Y(\"infected:Q\",title = ylabel),\n",
    "            color = alt.Color('location:N', legend=alt.Legend(title=\"Country\", labelFontSize=15, titleFontSize=17),\n",
    "                             scale=alt.Scale(scheme='tableau20'))\n",
    "        )\n",
    "        \n",
    "        shades = base.mark_area().encode(\n",
    "            x='n_days:Q',\n",
    "            y='total_infected_lower:Q',\n",
    "            y2='total_infected_upper:Q',\n",
    "            opacity = alt.condition(selection, alt.value(0.2), alt.value(0.05))\n",
    "        )\n",
    "    \n",
    "        lines = base.mark_line().encode(\n",
    "            opacity = alt.condition(selection, alt.value(1), alt.value(0.1))\n",
    "        ).add_selection(\n",
    "            scales\n",
    "        ).add_selection(\n",
    "            selection\n",
    "        ).properties(\n",
    "            width=chart_width,\n",
    "            height=chart_height\n",
    "        )\n",
    "        return(\n",
    "        ( lines + shades)\n",
    "        .configure_title(fontSize=20)\n",
    "        .configure_axis(labelFontSize=15,titleFontSize=18)\n",
    "        )\n",
    "    \n",
    "    if levels == True:\n",
    "        ylabel = \"Infected\"\n",
    "        xlabel = \"Cases\"\n",
    "    else :\n",
    "        ylabel = \"Per Million Infected\"\n",
    "        xlabel = \"Per Million Cases\"\n",
    "        \n",
    "    if type1 == \"scatter\":\n",
    "        base = alt.Chart(source, title = \"COVID-19 Cases VS Infected\").encode(\n",
    "            x = alt.X('cases:Q', title = xlabel),\n",
    "            y = alt.Y(\"infected:Q\",title = ylabel),\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",
    "        \n",
    "        scatter = base.mark_point().add_selection(\n",
    "            scales\n",
    "        ).add_selection(\n",
    "            selection\n",
    "        ).properties(\n",
    "            width=chart_width,\n",
    "            height=chart_height\n",
    "        )\n",
    "\n",
    "        line_45 = alt.Chart(source).encode(\n",
    "            x = \"cases:Q\",\n",
    "            y = alt.Y(\"45_line:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"infected\"])))),\n",
    "        ).mark_line(color=\"grey\", strokeDash=[3,3])\n",
    "        \n",
    "        return(\n",
    "        (scatter + line_45)\n",
    "        .configure_title(fontSize=20)\n",
    "        .configure_axis(labelFontSize=15,titleFontSize=18)\n",
    "        )"
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   "source": [
    "#hide \n",
    "# Get data on deaths D_t\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_deaths\"})\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",
    "#hide\n",
    "# Get data and clean it\n",
    "data_cases = 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",
    "\n",
    "data_cases = data_cases.drop(columns=[\"Lat\", \"Long\"])\n",
    "data_cases = data_cases.melt(id_vars= [\"Province/State\", \"Country/Region\"])\n",
    "data_cases = pd.DataFrame(data_cases.groupby(['Country/Region', \"variable\"]).sum())\n",
    "data_cases.reset_index(inplace=True)  \n",
    "data_cases = data_cases.rename(columns={\"Country/Region\": \"location\", \"variable\": \"date\", \"value\": \"total_cases\"})\n",
    "data_cases['date'] =pd.to_datetime(data_cases.date)\n",
    "data_cases = data_cases.sort_values(by = \"date\")\n",
    "data_cases.loc[data_cases.location == \"US\",\"location\"] = \"United States\"\n",
    "data_cases.loc[data_cases.location == \"Korea, South\",\"location\"] = \"South Korea\"\n",
    "# Add countries\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\",\"South Korea\", \"Japan\", \n",
    "             \"Brazil\",\"Iran\", \"India\", \"Switzerland\", \"Canada\", \"Australia\"]\n",
    "\n",
    "data_final = pd.merge(data,\n",
    "                 data_cases\n",
    "                 )\n",
    "data_final[\"CFR\"] = data_final[\"total_deaths\"]/data_final[\"total_cases\"]\n",
    "\n",
    "\n",
    "data_final[\"total_infected\"] = np.NaN\n",
    "data_final = data_final.sort_values(by = ['location', 'date'])\n",
    "data_final = data_final.reset_index(drop = True)\n",
    "\n",
    "\n",
    "for j in countries:\n",
    "    for i in data_final[\"date\"].unique()[0:-8]:\n",
    "        data_final.loc[(data_final.date == i) & (data_final.location == j), \"total_infected\"] = data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"total_deaths\"].iloc[0]/data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"CFR\"].iloc[0]\n",
    "        \n",
    "\n",
    "# Estimate growth rate of infected, g        \n",
    "data_final['infected_g'] = np.log(data_final['total_infected'])\n",
    "data_final['infected_g'] = data_final['infected_g'].diff() \n",
    "\n",
    "# Estimate number of infected given g\n",
    "today = data_final.date.iloc[-1]\n",
    "for j in countries:\n",
    "    for i in range(7,-1,-1):\n",
    "        data_final.loc[(data_final.location == j) & (data_final.date == today - timedelta(i)), \"total_infected\"] = data_final.loc[data_final.location == j, \"total_infected\"].iloc[-i-2]*(1+data_final.loc[data_final.location == j, \"infected_g\"][-12:-8].aggregate(func = \"mean\"))\n",
    "    \n",
    "\n",
    "\n",
    "# Upper Bound\n",
    "data_final[\"total_infected_upper\"] = np.NaN\n",
    "data_final = data_final.sort_values(by = ['location', 'date'])\n",
    "data_final = data_final.reset_index(drop = True)\n",
    "for j in countries:\n",
    "    for i in data_final[\"date\"].unique()[0:-8]:\n",
    "        data_final.loc[(data_final.date == i) & (data_final.location == j), \"total_infected_upper\"] = data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"total_deaths\"].iloc[0]/(data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"CFR\"].iloc[0]*0.7)\n",
    "# Estimate growth rate of infected, g        \n",
    "data_final['infected_g'] = np.log(data_final['total_infected_upper'])\n",
    "data_final['infected_g'] = data_final['infected_g'].diff() \n",
    "# Estimate number of infected given g \n",
    "today = data_final.date.iloc[-1]\n",
    "for j in countries:\n",
    "    for i in range(7,-1,-1):\n",
    "        data_final.loc[(data_final.location == j) & (data_final.date == today - timedelta(i)), \"total_infected_upper\"] = data_final.loc[data_final.location == j, \"total_infected_upper\"].iloc[-i-2]*(1+data_final.loc[data_final.location == j, \"infected_g\"][-12:-8].aggregate(func = \"mean\"))\n",
    "\n",
    "# Lower Bound\n",
    "data_final[\"total_infected_lower\"] = np.NaN\n",
    "data_final = data_final.sort_values(by = ['location', 'date'])\n",
    "data_final = data_final.reset_index(drop = True)\n",
    "for j in countries:\n",
    "    for i in data_final[\"date\"].unique()[0:-8]:\n",
    "        data_final.loc[(data_final.date == i) & (data_final.location == j), \"total_infected_lower\"] = data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"total_deaths\"].iloc[0]/(data_final.loc[(data_final.date == i + np.timedelta64(8, 'D')) & (data_final.location == j), \"CFR\"].iloc[0]*1.3)\n",
    "# Estimate growth rate of infected, g        \n",
    "data_final['infected_g'] = np.log(data_final['total_infected_lower'])\n",
    "data_final['infected_g'] = data_final['infected_g'].diff() \n",
    "# Estimate number of infected given g \n",
    "today = data_final.date.iloc[-1]\n",
    "for j in countries:\n",
    "    for i in range(7,-1,-1):\n",
    "        data_final.loc[(data_final.location == j) & (data_final.date == today - timedelta(i)), \"total_infected_lower\"] = data_final.loc[data_final.location == j, \"total_infected_lower\"].iloc[-i-2]*(1+data_final.loc[data_final.location == j, \"infected_g\"][-12:-8].aggregate(func = \"mean\"))\n",
    "data_final.loc[data_final.total_infected_lower < data_final.total_cases, \"total_infected_lower\"] = data_final.loc[data_final.total_infected_lower < data_final.total_cases, \"total_cases\"]\n",
    "\n",
    "\n",
    "data_pc = data_final[['location', 'date', 'total_infected', 'total_infected_lower', 'total_infected_upper']].copy()\n",
    "\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\",\"South Korea\", \"Japan\", \n",
    "             \"Brazil\",\"Iran\"]\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_infected\"] = data_pc.loc[data_pc.location == i,\"total_infected\"]\n",
    "\n",
    "# Get each country time series\n",
    "filter1 = data_pc[\"total_infected\"] > 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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   "source": [
    "## Estimated Infected Population By Country\n",
    "\n",
    "with respect to days since outbreak\n",
    "\n",
    "> Tip: Click (Shift+ for multiple) on countries in the legend to filter the visualization. "
   ]
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\"Japan\", \"date\": \"2020-03-27T00:00:00\", \"total_infected\": 2445.3408948322044, \"total_infected_lower\": 1881.031457563237, \"total_infected_upper\": 3493.3441354745773, \"n_days\": 51, \"infected\": 2445.3408948322044}, {\"index\": 1499, \"location\": \"Brazil\", \"date\": \"2020-03-09T00:00:00\", \"total_infected\": 321.0, \"total_infected_lower\": 246.9230769230769, \"total_infected_upper\": 458.57142857142856, \"n_days\": 0, \"infected\": 321.0}, {\"index\": 1500, \"location\": \"Brazil\", \"date\": \"2020-03-10T00:00:00\", \"total_infected\": 372.0, \"total_infected_lower\": 286.15384615384613, \"total_infected_upper\": 531.4285714285714, \"n_days\": 1, \"infected\": 372.0}, {\"index\": 1501, \"location\": \"Brazil\", \"date\": \"2020-03-11T00:00:00\", \"total_infected\": 621.0, \"total_infected_lower\": 477.69230769230774, \"total_infected_upper\": 887.1428571428572, \"n_days\": 2, \"infected\": 621.0}, {\"index\": 1502, \"location\": \"Brazil\", \"date\": \"2020-03-12T00:00:00\", \"total_infected\": 793.0, \"total_infected_lower\": 610.0, \"total_infected_upper\": 1132.857142857143, \"n_days\": 3, \"infected\": 793.0}, {\"index\": 1503, \"location\": \"Brazil\", \"date\": \"2020-03-13T00:00:00\", \"total_infected\": 1021.0, \"total_infected_lower\": 785.3846153846154, \"total_infected_upper\": 1458.5714285714287, \"n_days\": 4, \"infected\": 1021.0}, {\"index\": 1504, \"location\": \"Brazil\", \"date\": \"2020-03-14T00:00:00\", \"total_infected\": 1546.0, \"total_infected_lower\": 1189.230769230769, \"total_infected_upper\": 2208.5714285714284, \"n_days\": 5, \"infected\": 1546.0}, {\"index\": 1505, \"location\": \"Brazil\", \"date\": \"2020-03-15T00:00:00\", \"total_infected\": 1924.0, \"total_infected_lower\": 1480.0, \"total_infected_upper\": 2748.5714285714284, \"n_days\": 6, \"infected\": 1924.0}, {\"index\": 1506, \"location\": \"Brazil\", \"date\": \"2020-03-16T00:00:00\", \"total_infected\": 2247.0, \"total_infected_lower\": 1728.4615384615383, \"total_infected_upper\": 3210.0, \"n_days\": 7, \"infected\": 2247.0}, {\"index\": 1507, \"location\": \"Brazil\", \"date\": \"2020-03-17T00:00:00\", \"total_infected\": 2554.0, \"total_infected_lower\": 1964.6153846153845, \"total_infected_upper\": 3648.5714285714284, \"n_days\": 8, \"infected\": 2554.0}, {\"index\": 1508, \"location\": \"Brazil\", \"date\": \"2020-03-18T00:00:00\", \"total_infected\": 2985.0, \"total_infected_lower\": 2296.1538461538457, \"total_infected_upper\": 4264.285714285714, \"n_days\": 9, \"infected\": 2985.0}, {\"index\": 1509, \"location\": \"Brazil\", \"date\": \"2020-03-19T00:00:00\", \"total_infected\": 3417.0, \"total_infected_lower\": 2628.4615384615386, \"total_infected_upper\": 4881.428571428572, \"n_days\": 10, \"infected\": 3417.0}, {\"index\": 1510, \"location\": \"Brazil\", \"date\": \"2020-03-20T00:00:00\", \"total_infected\": 3907.6441433949744, \"total_infected_lower\": 3005.880110303827, \"total_infected_upper\": 5582.348776278536, \"n_days\": 11, \"infected\": 3907.6441433949744}, {\"index\": 1511, \"location\": \"Brazil\", \"date\": \"2020-03-21T00:00:00\", \"total_infected\": 4468.739464854856, \"total_infected_lower\": 3437.4918960421983, \"total_infected_upper\": 6383.913521221226, \"n_days\": 12, \"infected\": 4468.739464854856}, {\"index\": 1512, \"location\": \"Brazil\", \"date\": \"2020-03-22T00:00:00\", \"total_infected\": 5110.401989522408, \"total_infected_lower\": 3931.078453478778, \"total_infected_upper\": 7300.574270746302, \"n_days\": 13, \"infected\": 5110.401989522408}, {\"index\": 1513, \"location\": \"Brazil\", \"date\": \"2020-03-23T00:00:00\", \"total_infected\": 5844.200294044853, \"total_infected_lower\": 4495.538687726814, \"total_infected_upper\": 8348.857562921226, \"n_days\": 14, \"infected\": 5844.200294044853}, {\"index\": 1514, \"location\": \"Brazil\", \"date\": \"2020-03-24T00:00:00\", \"total_infected\": 6683.364077217312, \"total_infected_lower\": 5141.049290167169, \"total_infected_upper\": 9547.662967453314, \"n_days\": 15, \"infected\": 6683.364077217312}, {\"index\": 1515, \"location\": \"Brazil\", \"date\": \"2020-03-25T00:00:00\", \"total_infected\": 7643.022679108746, \"total_infected_lower\": 5879.248214699043, \"total_infected_upper\": 10918.603827298224, \"n_days\": 16, \"infected\": 7643.022679108746}, {\"index\": 1516, \"location\": \"Brazil\", \"date\": \"2020-03-26T00:00:00\", \"total_infected\": 8740.47784894769, \"total_infected_lower\": 6723.444499190541, \"total_infected_upper\": 12486.396927068148, \"n_days\": 17, \"infected\": 8740.47784894769}, {\"index\": 1517, \"location\": \"Brazil\", \"date\": \"2020-03-27T00:00:00\", \"total_infected\": 9995.515679518274, \"total_infected_lower\": 7688.85821501407, \"total_infected_upper\": 14279.308113597559, \"n_days\": 18, \"infected\": 9995.515679518274}, {\"index\": 5168, \"location\": \"Iran\", \"date\": \"2020-02-11T00:00:00\", \"total_infected\": 2.0, \"total_infected_lower\": 1.5384615384615383, \"total_infected_upper\": 2.857142857142857, \"n_days\": 0, \"infected\": 2.0}, {\"index\": 5169, \"location\": \"Iran\", \"date\": \"2020-02-12T00:00:00\", \"total_infected\": 5.0, \"total_infected_lower\": 3.846153846153846, \"total_infected_upper\": 7.142857142857143, \"n_days\": 1, \"infected\": 5.0}, {\"index\": 5170, \"location\": \"Iran\", \"date\": \"2020-02-13T00:00:00\", \"total_infected\": 18.0, \"total_infected_lower\": 13.846153846153847, \"total_infected_upper\": 25.71428571428572, \"n_days\": 2, \"infected\": 18.0}, {\"index\": 5171, \"location\": \"Iran\", \"date\": \"2020-02-14T00:00:00\", \"total_infected\": 28.0, \"total_infected_lower\": 21.538461538461537, \"total_infected_upper\": 40.0, \"n_days\": 3, \"infected\": 28.0}, {\"index\": 5172, \"location\": \"Iran\", \"date\": \"2020-02-15T00:00:00\", \"total_infected\": 43.0, \"total_infected_lower\": 33.07692307692308, \"total_infected_upper\": 61.42857142857143, \"n_days\": 4, \"infected\": 43.0}, {\"index\": 5173, \"location\": \"Iran\", \"date\": \"2020-02-16T00:00:00\", \"total_infected\": 61.0, \"total_infected_lower\": 46.92307692307693, \"total_infected_upper\": 87.14285714285715, \"n_days\": 5, \"infected\": 61.0}, {\"index\": 5174, \"location\": \"Iran\", \"date\": \"2020-02-17T00:00:00\", \"total_infected\": 95.0, \"total_infected_lower\": 73.07692307692308, \"total_infected_upper\": 135.71428571428572, \"n_days\": 6, \"infected\": 95.0}, {\"index\": 5175, \"location\": \"Iran\", \"date\": \"2020-02-18T00:00:00\", \"total_infected\": 139.0, \"total_infected_lower\": 106.9230769230769, \"total_infected_upper\": 198.57142857142856, \"n_days\": 7, \"infected\": 139.0}, {\"index\": 5176, \"location\": \"Iran\", \"date\": \"2020-02-19T00:00:00\", \"total_infected\": 245.0, \"total_infected_lower\": 188.46153846153848, \"total_infected_upper\": 350.00000000000006, \"n_days\": 8, \"infected\": 245.0}, {\"index\": 5177, \"location\": \"Iran\", \"date\": \"2020-02-20T00:00:00\", \"total_infected\": 388.0, \"total_infected_lower\": 298.46153846153845, \"total_infected_upper\": 554.2857142857143, \"n_days\": 9, \"infected\": 388.0}, {\"index\": 5178, \"location\": \"Iran\", \"date\": \"2020-02-21T00:00:00\", \"total_infected\": 593.0, \"total_infected_lower\": 456.15384615384613, \"total_infected_upper\": 847.1428571428571, \"n_days\": 10, \"infected\": 593.0}, {\"index\": 5179, \"location\": \"Iran\", \"date\": \"2020-02-22T00:00:00\", \"total_infected\": 978.0, \"total_infected_lower\": 752.3076923076923, \"total_infected_upper\": 1397.142857142857, \"n_days\": 11, \"infected\": 978.0}, {\"index\": 5180, \"location\": \"Iran\", \"date\": \"2020-02-23T00:00:00\", \"total_infected\": 1501.0, \"total_infected_lower\": 1154.6153846153845, \"total_infected_upper\": 2144.285714285714, \"n_days\": 12, \"infected\": 1501.0}, {\"index\": 5181, \"location\": \"Iran\", \"date\": \"2020-02-24T00:00:00\", \"total_infected\": 2336.0, \"total_infected_lower\": 1796.923076923077, \"total_infected_upper\": 3337.1428571428573, \"n_days\": 13, \"infected\": 2336.0}, {\"index\": 5182, \"location\": \"Iran\", \"date\": \"2020-02-25T00:00:00\", \"total_infected\": 2922.0000000000005, \"total_infected_lower\": 2247.692307692308, \"total_infected_upper\": 4174.285714285716, \"n_days\": 14, \"infected\": 2922.0000000000005}, {\"index\": 5183, \"location\": \"Iran\", \"date\": \"2020-02-26T00:00:00\", \"total_infected\": 3513.0, \"total_infected_lower\": 2702.3076923076924, \"total_infected_upper\": 5018.571428571429, \"n_days\": 15, \"infected\": 3513.0}, {\"index\": 5184, \"location\": \"Iran\", \"date\": \"2020-02-27T00:00:00\", \"total_infected\": 4747.0, \"total_infected_lower\": 3651.538461538461, \"total_infected_upper\": 6781.428571428572, \"n_days\": 16, \"infected\": 4747.0}, {\"index\": 5185, \"location\": \"Iran\", \"date\": \"2020-02-28T00:00:00\", \"total_infected\": 5823.0, \"total_infected_lower\": 4479.230769230769, \"total_infected_upper\": 8318.571428571428, \"n_days\": 17, \"infected\": 5823.0}, {\"index\": 5186, \"location\": \"Iran\", \"date\": \"2020-02-29T00:00:00\", \"total_infected\": 6566.0, \"total_infected_lower\": 5050.7692307692305, \"total_infected_upper\": 9380.0, \"n_days\": 18, \"infected\": 6566.0}, {\"index\": 5187, \"location\": \"Iran\", \"date\": \"2020-03-01T00:00:00\", \"total_infected\": 7161.000000000001, \"total_infected_lower\": 5508.461538461539, \"total_infected_upper\": 10230.000000000002, \"n_days\": 19, \"infected\": 7161.000000000001}, {\"index\": 5188, \"location\": \"Iran\", \"date\": \"2020-03-02T00:00:00\", \"total_infected\": 8042.000000000001, \"total_infected_lower\": 6186.153846153847, \"total_infected_upper\": 11488.57142857143, \"n_days\": 20, \"infected\": 8042.000000000001}, {\"index\": 5189, \"location\": \"Iran\", \"date\": \"2020-03-03T00:00:00\", \"total_infected\": 9000.0, \"total_infected_lower\": 6923.076923076924, \"total_infected_upper\": 12857.142857142859, \"n_days\": 21, \"infected\": 9000.0}, {\"index\": 5190, \"location\": \"Iran\", \"date\": \"2020-03-04T00:00:00\", \"total_infected\": 10075.0, \"total_infected_lower\": 7750.0, \"total_infected_upper\": 14392.857142857145, \"n_days\": 22, \"infected\": 10075.0}, {\"index\": 5191, \"location\": \"Iran\", \"date\": \"2020-03-05T00:00:00\", \"total_infected\": 11364.0, \"total_infected_lower\": 8741.538461538461, \"total_infected_upper\": 16234.285714285714, \"n_days\": 23, \"infected\": 11364.0}, {\"index\": 5192, \"location\": \"Iran\", \"date\": \"2020-03-06T00:00:00\", \"total_infected\": 12729.0, \"total_infected_lower\": 9791.538461538461, \"total_infected_upper\": 18184.285714285714, \"n_days\": 24, \"infected\": 12729.0}, {\"index\": 5193, \"location\": \"Iran\", \"date\": \"2020-03-07T00:00:00\", \"total_infected\": 13938.0, \"total_infected_lower\": 10721.538461538463, \"total_infected_upper\": 19911.428571428572, \"n_days\": 25, \"infected\": 13938.0}, {\"index\": 5194, \"location\": \"Iran\", \"date\": \"2020-03-08T00:00:00\", \"total_infected\": 14991.0, \"total_infected_lower\": 11531.538461538461, \"total_infected_upper\": 21415.714285714286, \"n_days\": 26, \"infected\": 14991.0}, {\"index\": 5195, \"location\": \"Iran\", \"date\": \"2020-03-09T00:00:00\", \"total_infected\": 16169.0, \"total_infected_lower\": 12437.692307692307, \"total_infected_upper\": 23098.57142857143, \"n_days\": 27, \"infected\": 16169.0}, {\"index\": 5196, \"location\": \"Iran\", \"date\": \"2020-03-10T00:00:00\", \"total_infected\": 17361.0, \"total_infected_lower\": 13354.615384615383, \"total_infected_upper\": 24801.428571428572, \"n_days\": 28, \"infected\": 17361.0}, {\"index\": 5197, \"location\": \"Iran\", \"date\": \"2020-03-11T00:00:00\", \"total_infected\": 18407.0, \"total_infected_lower\": 14159.230769230768, \"total_infected_upper\": 26295.714285714283, \"n_days\": 29, \"infected\": 18407.0}, {\"index\": 5198, \"location\": \"Iran\", \"date\": \"2020-03-12T00:00:00\", \"total_infected\": 19644.0, \"total_infected_lower\": 15110.76923076923, \"total_infected_upper\": 28062.85714285715, \"n_days\": 30, \"infected\": 19644.0}, {\"index\": 5199, \"location\": \"Iran\", \"date\": \"2020-03-13T00:00:00\", \"total_infected\": 20610.0, \"total_infected_lower\": 15853.846153846152, \"total_infected_upper\": 29442.857142857145, \"n_days\": 31, \"infected\": 20610.0}, {\"index\": 5200, \"location\": \"Iran\", \"date\": \"2020-03-14T00:00:00\", \"total_infected\": 21638.0, \"total_infected_lower\": 16644.615384615387, \"total_infected_upper\": 30911.428571428576, \"n_days\": 32, \"infected\": 21638.0}, {\"index\": 5201, \"location\": \"Iran\", \"date\": \"2020-03-15T00:00:00\", \"total_infected\": 23049.0, \"total_infected_lower\": 17730.0, \"total_infected_upper\": 32927.142857142855, \"n_days\": 33, \"infected\": 23049.0}, {\"index\": 5202, \"location\": \"Iran\", \"date\": \"2020-03-16T00:00:00\", \"total_infected\": 24811.0, \"total_infected_lower\": 19085.384615384613, \"total_infected_upper\": 35444.28571428572, \"n_days\": 34, \"infected\": 24811.0}, {\"index\": 5203, \"location\": \"Iran\", \"date\": \"2020-03-17T00:00:00\", \"total_infected\": 27017.0, \"total_infected_lower\": 20782.30769230769, \"total_infected_upper\": 38595.71428571429, \"n_days\": 35, \"infected\": 27017.0}, {\"index\": 5204, \"location\": \"Iran\", \"date\": \"2020-03-18T00:00:00\", \"total_infected\": 29406.0, \"total_infected_lower\": 22620.0, \"total_infected_upper\": 42008.571428571435, \"n_days\": 36, \"infected\": 29406.0}, {\"index\": 5205, \"location\": \"Iran\", \"date\": \"2020-03-19T00:00:00\", \"total_infected\": 32331.999999999996, \"total_infected_lower\": 24870.769230769227, \"total_infected_upper\": 46188.57142857143, \"n_days\": 37, \"infected\": 32331.999999999996}, {\"index\": 5206, \"location\": \"Iran\", \"date\": \"2020-03-20T00:00:00\", \"total_infected\": 35067.570649131325, \"total_infected_lower\": 26975.05434548562, \"total_infected_upper\": 50096.52949875902, \"n_days\": 38, \"infected\": 35067.570649131325}, {\"index\": 5207, \"location\": \"Iran\", \"date\": \"2020-03-21T00:00:00\", \"total_infected\": 38034.59455746062, \"total_infected_lower\": 29257.380428815835, \"total_infected_upper\": 54335.13508208656, \"n_days\": 39, \"infected\": 38034.59455746062}, {\"index\": 5208, \"location\": \"Iran\", \"date\": \"2020-03-22T00:00:00\", \"total_infected\": 41252.6546998273, \"total_infected_lower\": 31732.811307559416, \"total_infected_upper\": 58932.363856896074, \"n_days\": 40, \"infected\": 41252.6546998273}, {\"index\": 5209, \"location\": \"Iran\", \"date\": \"2020-03-23T00:00:00\", \"total_infected\": 44742.99094242277, \"total_infected_lower\": 34417.68534032515, \"total_infected_upper\": 63918.558489175295, \"n_days\": 41, \"infected\": 44742.99094242277}, {\"index\": 5210, \"location\": \"Iran\", \"date\": \"2020-03-24T00:00:00\", \"total_infected\": 48528.640230324556, \"total_infected_lower\": 37329.723254095734, \"total_infected_upper\": 69326.62890046352, \"n_days\": 42, \"infected\": 48528.640230324556}, {\"index\": 5211, \"location\": \"Iran\", \"date\": \"2020-03-25T00:00:00\", \"total_infected\": 52634.58863612468, \"total_infected_lower\": 40488.14510471119, \"total_infected_upper\": 75192.26948017794, \"n_days\": 43, \"infected\": 52634.58863612468}, {\"index\": 5212, \"location\": \"Iran\", \"date\": \"2020-03-26T00:00:00\", \"total_infected\": 57087.936273205094, \"total_infected_lower\": 43913.79713323456, \"total_infected_upper\": 81554.19467600706, \"n_days\": 44, \"infected\": 57087.936273205094}, {\"index\": 5213, \"location\": \"Iran\", \"date\": \"2020-03-27T00:00:00\", \"total_infected\": 61918.07616212194, \"total_infected_lower\": 47629.28935547826, \"total_infected_upper\": 88454.39451731679, \"n_days\": 45, \"infected\": 61918.07616212194}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Plot estimated absolute number of infected\n",
    "plot1 = plot(data_countries_pc, \"line\", True)\n",
    "plot1.save(\"../images/covid-estimate-infections.png\")\n",
    "plot1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014093,
     "end_time": "2020-03-28T20:36:02.115688",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.101595",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Latest Country Estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "papermill": {
     "duration": 0.0569,
     "end_time": "2020-03-28T20:36:02.187399",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.130499",
     "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>Estimated Infected</th>\n",
       "      <th>Lower Bound Estimates</th>\n",
       "      <th>Upper Bound Estimates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></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>9,996</td>\n",
       "      <td>7,689</td>\n",
       "      <td>14,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>82,700</td>\n",
       "      <td>81,897</td>\n",
       "      <td>118,143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>86,737</td>\n",
       "      <td>66,720</td>\n",
       "      <td>123,909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>145,131</td>\n",
       "      <td>111,639</td>\n",
       "      <td>207,330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>61,918</td>\n",
       "      <td>47,629</td>\n",
       "      <td>88,454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>154,952</td>\n",
       "      <td>119,193</td>\n",
       "      <td>221,359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2,445</td>\n",
       "      <td>1,881</td>\n",
       "      <td>3,493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>16,274</td>\n",
       "      <td>12,518</td>\n",
       "      <td>23,248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>1,467</td>\n",
       "      <td>1,129</td>\n",
       "      <td>2,096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>10,117</td>\n",
       "      <td>9,332</td>\n",
       "      <td>14,452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>210,367</td>\n",
       "      <td>161,821</td>\n",
       "      <td>300,524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>61,825</td>\n",
       "      <td>47,558</td>\n",
       "      <td>88,322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>467,805</td>\n",
       "      <td>359,850</td>\n",
       "      <td>668,293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Date Estimated Infected Lower Bound Estimates  \\\n",
       "Country                                                              \n",
       "Brazil         2020-03-27              9,996                 7,689   \n",
       "China          2020-03-27             82,700                81,897   \n",
       "France         2020-03-27             86,737                66,720   \n",
       "Germany        2020-03-27            145,131               111,639   \n",
       "Iran           2020-03-27             61,918                47,629   \n",
       "Italy          2020-03-27            154,952               119,193   \n",
       "Japan          2020-03-27              2,445                 1,881   \n",
       "Portugal       2020-03-27             16,274                12,518   \n",
       "Singapore      2020-03-27              1,467                 1,129   \n",
       "South Korea    2020-03-27             10,117                 9,332   \n",
       "Spain          2020-03-27            210,367               161,821   \n",
       "United Kingdom 2020-03-27             61,825                47,558   \n",
       "United States  2020-03-27            467,805               359,850   \n",
       "\n",
       "               Upper Bound Estimates  \n",
       "Country                               \n",
       "Brazil                        14,279  \n",
       "China                        118,143  \n",
       "France                       123,909  \n",
       "Germany                      207,330  \n",
       "Iran                          88,454  \n",
       "Italy                        221,359  \n",
       "Japan                          3,493  \n",
       "Portugal                      23,248  \n",
       "Singapore                      2,096  \n",
       "South Korea                   14,452  \n",
       "Spain                        300,524  \n",
       "United Kingdom                88,322  \n",
       "United States                668,293  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input    \n",
    "label = 'Estimated Infected'\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}'\n",
    "temp.columns = ['Country', 'Date', metric_name, \"Lower Bound Estimates\", \"Upper Bound Estimates\"]\n",
    "temp.loc[:, \"Estimated Infected\"] = temp.loc[:, \"Estimated Infected\"].round(0).map('{:,.0f}'.format) \n",
    "temp.loc[:, \"Lower Bound Estimates\"] = temp.loc[:, \"Lower Bound Estimates\"].round(0).map('{:,.0f}'.format) \n",
    "temp.loc[:, \"Upper Bound Estimates\"] = temp.loc[:, \"Upper Bound Estimates\"].round(0).map('{:,.0f}'.format) \n",
    "temp.groupby('Country').last()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014772,
     "end_time": "2020-03-28T20:36:02.217221",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.202449",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Infected vs. number of confirmed cases\n",
    "> Allows you to compare how countries have been tracking the true number of infected people. The smaller deviation from the dashed line (45 degree line) the better job at tracking the true number of infected people."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01499,
     "end_time": "2020-03-28T20:36:02.246796",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.231806",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> Tip: Click (Shift+ for multiple) on countries in the legend to filter the visualization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "papermill": {
     "duration": 0.271825,
     "end_time": "2020-03-28T20:36:02.534251",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.262426",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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\"total_infected\": 9995.515679518274, \"n_days\": 18, \"cases\": 3417, \"infected\": 9995.515679518274, \"45_line\": 3417}, {\"index\": 5168, \"location\": \"Iran\", \"date\": \"2020-02-11T00:00:00\", \"total_cases\": 0, \"total_infected\": 2.0, \"n_days\": 0, \"cases\": 0, \"infected\": 2.0, \"45_line\": 0}, {\"index\": 5169, \"location\": \"Iran\", \"date\": \"2020-02-12T00:00:00\", \"total_cases\": 0, \"total_infected\": 5.0, \"n_days\": 1, \"cases\": 0, \"infected\": 5.0, \"45_line\": 0}, {\"index\": 5170, \"location\": \"Iran\", \"date\": \"2020-02-13T00:00:00\", \"total_cases\": 0, \"total_infected\": 18.0, \"n_days\": 2, \"cases\": 0, \"infected\": 18.0, \"45_line\": 0}, {\"index\": 5171, \"location\": \"Iran\", \"date\": \"2020-02-14T00:00:00\", \"total_cases\": 0, \"total_infected\": 28.0, \"n_days\": 3, \"cases\": 0, \"infected\": 28.0, \"45_line\": 0}, {\"index\": 5172, \"location\": \"Iran\", \"date\": \"2020-02-15T00:00:00\", \"total_cases\": 0, \"total_infected\": 43.0, \"n_days\": 4, \"cases\": 0, \"infected\": 43.0, \"45_line\": 0}, {\"index\": 5173, \"location\": \"Iran\", \"date\": \"2020-02-16T00:00:00\", \"total_cases\": 0, \"total_infected\": 61.0, \"n_days\": 5, \"cases\": 0, \"infected\": 61.0, \"45_line\": 0}, {\"index\": 5174, \"location\": \"Iran\", \"date\": \"2020-02-17T00:00:00\", \"total_cases\": 0, \"total_infected\": 95.0, \"n_days\": 6, \"cases\": 0, \"infected\": 95.0, \"45_line\": 0}, {\"index\": 5175, \"location\": \"Iran\", \"date\": \"2020-02-18T00:00:00\", \"total_cases\": 0, \"total_infected\": 139.0, \"n_days\": 7, \"cases\": 0, \"infected\": 139.0, \"45_line\": 0}, {\"index\": 5176, \"location\": \"Iran\", \"date\": \"2020-02-19T00:00:00\", \"total_cases\": 2, \"total_infected\": 245.0, \"n_days\": 8, \"cases\": 2, \"infected\": 245.0, \"45_line\": 2}, {\"index\": 5177, \"location\": \"Iran\", \"date\": \"2020-02-20T00:00:00\", \"total_cases\": 5, \"total_infected\": 388.0, \"n_days\": 9, \"cases\": 5, \"infected\": 388.0, \"45_line\": 5}, {\"index\": 5178, \"location\": \"Iran\", \"date\": \"2020-02-21T00:00:00\", \"total_cases\": 18, \"total_infected\": 593.0, \"n_days\": 10, \"cases\": 18, \"infected\": 593.0, \"45_line\": 18}, {\"index\": 5179, \"location\": \"Iran\", \"date\": \"2020-02-22T00:00:00\", \"total_cases\": 28, \"total_infected\": 978.0, \"n_days\": 11, \"cases\": 28, \"infected\": 978.0, \"45_line\": 28}, {\"index\": 5180, \"location\": \"Iran\", \"date\": \"2020-02-23T00:00:00\", \"total_cases\": 43, \"total_infected\": 1501.0, \"n_days\": 12, \"cases\": 43, \"infected\": 1501.0, \"45_line\": 43}, {\"index\": 5181, \"location\": \"Iran\", \"date\": \"2020-02-24T00:00:00\", \"total_cases\": 61, \"total_infected\": 2336.0, \"n_days\": 13, \"cases\": 61, \"infected\": 2336.0, \"45_line\": 61}, {\"index\": 5182, \"location\": \"Iran\", \"date\": \"2020-02-25T00:00:00\", \"total_cases\": 95, \"total_infected\": 2922.0000000000005, \"n_days\": 14, \"cases\": 95, \"infected\": 2922.0000000000005, \"45_line\": 95}, {\"index\": 5183, \"location\": \"Iran\", \"date\": \"2020-02-26T00:00:00\", \"total_cases\": 139, \"total_infected\": 3513.0, \"n_days\": 15, \"cases\": 139, \"infected\": 3513.0, \"45_line\": 139}, {\"index\": 5184, \"location\": \"Iran\", \"date\": \"2020-02-27T00:00:00\", \"total_cases\": 245, \"total_infected\": 4747.0, \"n_days\": 16, \"cases\": 245, \"infected\": 4747.0, \"45_line\": 245}, {\"index\": 5185, \"location\": \"Iran\", \"date\": \"2020-02-28T00:00:00\", \"total_cases\": 388, \"total_infected\": 5823.0, \"n_days\": 17, \"cases\": 388, \"infected\": 5823.0, \"45_line\": 388}, {\"index\": 5186, \"location\": \"Iran\", \"date\": \"2020-02-29T00:00:00\", \"total_cases\": 593, \"total_infected\": 6566.0, \"n_days\": 18, \"cases\": 593, \"infected\": 6566.0, \"45_line\": 593}, {\"index\": 5187, \"location\": \"Iran\", \"date\": \"2020-03-01T00:00:00\", \"total_cases\": 978, \"total_infected\": 7161.000000000001, \"n_days\": 19, \"cases\": 978, \"infected\": 7161.000000000001, \"45_line\": 978}, {\"index\": 5188, \"location\": \"Iran\", \"date\": \"2020-03-02T00:00:00\", \"total_cases\": 1501, \"total_infected\": 8042.000000000001, \"n_days\": 20, \"cases\": 1501, \"infected\": 8042.000000000001, \"45_line\": 1501}, {\"index\": 5189, \"location\": \"Iran\", \"date\": \"2020-03-03T00:00:00\", \"total_cases\": 2336, \"total_infected\": 9000.0, \"n_days\": 21, \"cases\": 2336, \"infected\": 9000.0, \"45_line\": 2336}, {\"index\": 5190, \"location\": \"Iran\", \"date\": \"2020-03-04T00:00:00\", \"total_cases\": 2922, \"total_infected\": 10075.0, \"n_days\": 22, \"cases\": 2922, \"infected\": 10075.0, \"45_line\": 2922}, {\"index\": 5191, \"location\": \"Iran\", \"date\": \"2020-03-05T00:00:00\", \"total_cases\": 3513, \"total_infected\": 11364.0, \"n_days\": 23, \"cases\": 3513, \"infected\": 11364.0, \"45_line\": 3513}, {\"index\": 5192, \"location\": \"Iran\", \"date\": \"2020-03-06T00:00:00\", \"total_cases\": 4747, \"total_infected\": 12729.0, \"n_days\": 24, \"cases\": 4747, \"infected\": 12729.0, \"45_line\": 4747}, {\"index\": 5193, \"location\": \"Iran\", \"date\": \"2020-03-07T00:00:00\", \"total_cases\": 5823, \"total_infected\": 13938.0, \"n_days\": 25, \"cases\": 5823, \"infected\": 13938.0, \"45_line\": 5823}, {\"index\": 5194, \"location\": \"Iran\", \"date\": \"2020-03-08T00:00:00\", \"total_cases\": 6566, \"total_infected\": 14991.0, \"n_days\": 26, \"cases\": 6566, \"infected\": 14991.0, \"45_line\": 6566}, {\"index\": 5195, \"location\": \"Iran\", \"date\": \"2020-03-09T00:00:00\", \"total_cases\": 7161, \"total_infected\": 16169.0, \"n_days\": 27, \"cases\": 7161, \"infected\": 16169.0, \"45_line\": 7161}, {\"index\": 5196, \"location\": \"Iran\", \"date\": \"2020-03-10T00:00:00\", \"total_cases\": 8042, \"total_infected\": 17361.0, \"n_days\": 28, \"cases\": 8042, \"infected\": 17361.0, \"45_line\": 8042}, {\"index\": 5197, \"location\": \"Iran\", \"date\": \"2020-03-11T00:00:00\", \"total_cases\": 9000, \"total_infected\": 18407.0, \"n_days\": 29, \"cases\": 9000, \"infected\": 18407.0, \"45_line\": 9000}, {\"index\": 5198, \"location\": \"Iran\", \"date\": \"2020-03-12T00:00:00\", \"total_cases\": 10075, \"total_infected\": 19644.0, \"n_days\": 30, \"cases\": 10075, \"infected\": 19644.0, \"45_line\": 10075}, {\"index\": 5199, \"location\": \"Iran\", \"date\": \"2020-03-13T00:00:00\", \"total_cases\": 11364, \"total_infected\": 20610.0, \"n_days\": 31, \"cases\": 11364, \"infected\": 20610.0, \"45_line\": 11364}, {\"index\": 5200, \"location\": \"Iran\", \"date\": \"2020-03-14T00:00:00\", \"total_cases\": 12729, \"total_infected\": 21638.0, \"n_days\": 32, \"cases\": 12729, \"infected\": 21638.0, \"45_line\": 12729}, {\"index\": 5201, \"location\": \"Iran\", \"date\": \"2020-03-15T00:00:00\", \"total_cases\": 13938, \"total_infected\": 23049.0, \"n_days\": 33, \"cases\": 13938, \"infected\": 23049.0, \"45_line\": 13938}, {\"index\": 5202, \"location\": \"Iran\", \"date\": \"2020-03-16T00:00:00\", \"total_cases\": 14991, \"total_infected\": 24811.0, \"n_days\": 34, \"cases\": 14991, \"infected\": 24811.0, \"45_line\": 14991}, {\"index\": 5203, \"location\": \"Iran\", \"date\": \"2020-03-17T00:00:00\", \"total_cases\": 16169, \"total_infected\": 27017.0, \"n_days\": 35, \"cases\": 16169, \"infected\": 27017.0, \"45_line\": 16169}, {\"index\": 5204, \"location\": \"Iran\", \"date\": \"2020-03-18T00:00:00\", \"total_cases\": 17361, \"total_infected\": 29406.0, \"n_days\": 36, \"cases\": 17361, \"infected\": 29406.0, \"45_line\": 17361}, {\"index\": 5205, \"location\": \"Iran\", \"date\": \"2020-03-19T00:00:00\", \"total_cases\": 18407, \"total_infected\": 32331.999999999996, \"n_days\": 37, \"cases\": 18407, \"infected\": 32331.999999999996, \"45_line\": 18407}, {\"index\": 5206, \"location\": \"Iran\", \"date\": \"2020-03-20T00:00:00\", \"total_cases\": 19644, \"total_infected\": 35067.570649131325, \"n_days\": 38, \"cases\": 19644, \"infected\": 35067.570649131325, \"45_line\": 19644}, {\"index\": 5207, \"location\": \"Iran\", \"date\": \"2020-03-21T00:00:00\", \"total_cases\": 20610, \"total_infected\": 38034.59455746062, \"n_days\": 39, \"cases\": 20610, \"infected\": 38034.59455746062, \"45_line\": 20610}, {\"index\": 5208, \"location\": \"Iran\", \"date\": \"2020-03-22T00:00:00\", \"total_cases\": 21638, \"total_infected\": 41252.6546998273, \"n_days\": 40, \"cases\": 21638, \"infected\": 41252.6546998273, \"45_line\": 21638}, {\"index\": 5209, \"location\": \"Iran\", \"date\": \"2020-03-23T00:00:00\", \"total_cases\": 23049, \"total_infected\": 44742.99094242277, \"n_days\": 41, \"cases\": 23049, \"infected\": 44742.99094242277, \"45_line\": 23049}, {\"index\": 5210, \"location\": \"Iran\", \"date\": \"2020-03-24T00:00:00\", \"total_cases\": 24811, \"total_infected\": 48528.640230324556, \"n_days\": 42, \"cases\": 24811, \"infected\": 48528.640230324556, \"45_line\": 24811}, {\"index\": 5211, \"location\": \"Iran\", \"date\": \"2020-03-25T00:00:00\", \"total_cases\": 27017, \"total_infected\": 52634.58863612468, \"n_days\": 43, \"cases\": 27017, \"infected\": 52634.58863612468, \"45_line\": 27017}, {\"index\": 5212, \"location\": \"Iran\", \"date\": \"2020-03-26T00:00:00\", \"total_cases\": 29406, \"total_infected\": 57087.936273205094, \"n_days\": 44, \"cases\": 29406, \"infected\": 57087.936273205094, \"45_line\": 29406}, {\"index\": 5213, \"location\": \"Iran\", \"date\": \"2020-03-27T00:00:00\", \"total_cases\": 32332, \"total_infected\": 61918.07616212194, \"n_days\": 45, \"cases\": 32332, \"infected\": 61918.07616212194, \"45_line\": 32332}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Plot it using Altair\n",
    "data_pc = data_final[['location', 'date', 'total_cases', 'total_infected']].copy()\n",
    "\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\",\"South Korea\", \"Japan\", \n",
    "             \"Brazil\",\"Iran\"]\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_infected\"] = data_pc.loc[data_pc.location == i,\"total_infected\"]\n",
    "    data_pc.loc[data_pc.location == i,\"total_cases\"] = data_pc.loc[data_pc.location == i,\"total_cases\"]\n",
    "    # get each country time series\n",
    "filter1 = data_pc[\"total_infected\"] > 1\n",
    "for i in countries:\n",
    "    filter_country = data_pc[\"location\"]== i\n",
    "    data_countries_pc.append(data_pc[filter_country & filter1])\n",
    "\n",
    "\n",
    "plot(data_countries_pc, \"scatter\", True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017149,
     "end_time": "2020-03-28T20:36:02.568626",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.551477",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Latest Observed vs. Estimate of Infected Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "papermill": {
     "duration": 0.048578,
     "end_time": "2020-03-28T20:36:02.634036",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.585458",
     "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>Observed Cases</th>\n",
       "      <th>Estimated Infected</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>3,417</td>\n",
       "      <td>9,996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>81,897</td>\n",
       "      <td>82,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>33,402</td>\n",
       "      <td>86,737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>50,871</td>\n",
       "      <td>145,131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>32,332</td>\n",
       "      <td>61,918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>86,498</td>\n",
       "      <td>154,952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>1,468</td>\n",
       "      <td>2,445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>4,268</td>\n",
       "      <td>16,274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>732</td>\n",
       "      <td>1,467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>9,332</td>\n",
       "      <td>10,117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>65,719</td>\n",
       "      <td>210,367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>14,745</td>\n",
       "      <td>61,825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>101,657</td>\n",
       "      <td>467,805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Date Observed Cases Estimated Infected\n",
       "Country                                                    \n",
       "Brazil         2020-03-27          3,417              9,996\n",
       "China          2020-03-27         81,897             82,700\n",
       "France         2020-03-27         33,402             86,737\n",
       "Germany        2020-03-27         50,871            145,131\n",
       "Iran           2020-03-27         32,332             61,918\n",
       "Italy          2020-03-27         86,498            154,952\n",
       "Japan          2020-03-27          1,468              2,445\n",
       "Portugal       2020-03-27          4,268             16,274\n",
       "Singapore      2020-03-27            732              1,467\n",
       "South Korea    2020-03-27          9,332             10,117\n",
       "Spain          2020-03-27         65,719            210,367\n",
       "United Kingdom 2020-03-27         14,745             61,825\n",
       "United States  2020-03-27        101,657            467,805"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "label1 = 'Observed Cases'\n",
    "label2 = 'Estimated Infected'\n",
    "temp = pd.concat([x.copy() for x in data_countries_pc]).loc[lambda x: x.date >= '3/1/2020']\n",
    "\n",
    "metric_name1 = f'{label1}'\n",
    "metric_name2 = f'{label2}'\n",
    "temp.columns = ['Country', 'Date', metric_name1, metric_name2]\n",
    "# temp.loc[:, 'month'] = temp.date.dt.strftime('%Y-%m')\n",
    "temp.loc[:, \"Observed Cases\"] = temp.loc[:, \"Observed Cases\"].round(0).map('{:,.0f}'.format)\n",
    "temp.loc[:, \"Estimated Infected\"] = temp.loc[:, \"Estimated Infected\"].round(0).map('{:,.0f}'.format)\n",
    "temp.groupby('Country').last()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017036,
     "end_time": "2020-03-28T20:36:02.667889",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.650853",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Methodology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017373,
     "end_time": "2020-03-28T20:36:02.702197",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.684824",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We argue that the number of infected in the past can be inferred using today's number of deaths and average fatality rate from confirmed cases in the following way:\n",
    "\n",
    "{%raw%}$$I_{t-j} = \\frac{D_t}{{CFR}_t}$${%endraw%}\n",
    "\n",
    "where {% raw %}$I_t${% endraw %} = number of infected, {% raw %}$D_t${% endraw %} = number of deaths, and {% raw %}${CFR}_t ${% endraw %} = case fatality rate = {% raw %}$\\frac{D}{C}${% endraw %}. The {% raw %}$j${% endraw %} depends on the average number of days that covid patients die after having the first symptoms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.016553,
     "end_time": "2020-03-28T20:36:02.736407",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.719854",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Assumption 1**: The case fatality rate is a good proxy for the fatality rate of the infected population\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01731,
     "end_time": "2020-03-28T20:36:02.770951",
     "exception": false,
     "start_time": "2020-03-28T20:36:02.753641",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then, in order to estimate the current number of infected {% raw %}$I_t${% endraw %} we need to estimate its growth rate from {% raw %}$t-j${% endraw %} to {% raw %}$t${% endraw %}.\n",
    "\n",
    "{% raw %}$$I_t = (1+\\hat{g})^j I_{t-j}$${% endraw %}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "**Assumption 2**: The growth rate of infected $\\hat{g}$ is an unbiased estimate of $g$ .\n",
    "\n",
    "For now we estimate $g$ using the average growth rate since having the first infected person."
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    "**Assumption 3**: It takes on average 8 days to die after having the first symptoms."
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   "source": [
    "This analysis was conducted by [Joao B. Duarte](https://www.jbduarte.com). 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)\n"
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