jasonreich/Age Disaggregated .ipynb

## Age Disaggregated .ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nfrom uk_covid19 import Cov19API\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "england_only = [\n    \"areaType=nation\",\n    \"areaName=England\"\n]\ncases = {\n    \"date\": \"date\",\n    \"newCases\": \"newCasesBySpecimenDateAgeDemographics\"\n}\napi_e = Cov19API(filters=england_only, structure=cases)\ndf_e = api_e.get_dataframe()\ndf = df_e['newCases'].apply(pd.Series)\ncategories = df.iloc[0].map(lambda x: x['age'])",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = df_e['date'].to_frame().join(df.rename(categories, axis=1).applymap(lambda x: x['rollingRate']))\ndf['date'] = pd.to_datetime(df['date'])\ndf = df.set_index('date')\ndf = df[::-1]\n# df = df[-90:]\ndf['Days'] = range(0, len(df))\ndf",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "            00_04  05_09  10_14  15_19  20_24  25_29  30_34  35_39  40_44  \\\ndate                                                                        \n2020-01-30    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n2020-01-31    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n2020-02-01    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n2020-02-02    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n2020-02-03    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n...           ...    ...    ...    ...    ...    ...    ...    ...    ...   \n2021-05-28   11.9   31.4   72.3   68.9   50.3   44.9   43.8   41.2   37.8   \n2021-05-29   12.1   33.2   73.9   72.5   53.7   48.0   46.2   43.9   39.8   \n2021-05-30   12.3   35.0   72.7   75.2   57.9   51.2   48.5   45.9   40.2   \n2021-05-31   12.1   33.0   71.1   78.9   62.5   53.7   50.2   46.7   40.2   \n2021-06-01   12.8   32.8   70.3   85.5   74.3   60.7   55.7   50.5   41.9   \n\n            45_49  ...  65_69  70_74  75_79  80_84  85_89  90+  unassigned  \\\ndate               ...                                                       \n2020-01-30    0.0  ...    0.0    0.0    0.0    0.0    0.0  0.0        None   \n2020-01-31    0.0  ...    0.0    0.0    0.0    0.0    0.0  0.0        None   \n2020-02-01    0.0  ...    0.0    0.0    0.0    0.0    0.0  0.0        None   \n2020-02-02    0.0  ...    0.0    0.0    0.0    0.0    0.0  0.0        None   \n2020-02-03    0.0  ...    0.0    0.0    0.0    0.0    0.0  0.0        None   \n...           ...  ...    ...    ...    ...    ...    ...  ...         ...   \n2021-05-28   26.3  ...    7.4    5.3    5.3    5.1    5.3  4.6        None   \n2021-05-29   28.4  ...    7.3    5.0    5.4    4.9    6.4  4.4        None   \n2021-05-30   28.9  ...    7.7    5.4    5.7    5.5    6.7  4.4        None   \n2021-05-31   29.3  ...    7.9    5.9    6.0    4.9    7.4  4.3        None   \n2021-06-01   32.0  ...    8.5    6.6    5.7    5.4    8.2  5.2        None   \n\n            60+  00_59 Days  \ndate                         \n2020-01-30  0.0    0.0    0  \n2020-01-31  0.0    0.0    1  \n2020-02-01  0.0    0.0    2  \n2020-02-02  0.0    0.0    3  \n2020-02-03  0.0    0.0    4  \n...         ...    ...  ...  \n2021-05-28  7.6   38.4  484  \n2021-05-29  7.7   40.3  485  \n2021-05-30  8.1   41.8  486  \n2021-05-31  8.1   42.8  487  \n2021-06-01  8.6   46.4  488  \n\n[489 rows x 23 columns]",
            "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>00_04</th>\n      <th>05_09</th>\n      <th>10_14</th>\n      <th>15_19</th>\n      <th>20_24</th>\n      <th>25_29</th>\n      <th>30_34</th>\n      <th>35_39</th>\n      <th>40_44</th>\n      <th>45_49</th>\n      <th>...</th>\n      <th>65_69</th>\n      <th>70_74</th>\n      <th>75_79</th>\n      <th>80_84</th>\n      <th>85_89</th>\n      <th>90+</th>\n      <th>unassigned</th>\n      <th>60+</th>\n      <th>00_59</th>\n      <th>Days</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-30</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2020-01-31</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2020-02-01</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2020-02-02</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2020-02-03</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2021-05-28</th>\n      <td>11.9</td>\n      <td>31.4</td>\n      <td>72.3</td>\n      <td>68.9</td>\n      <td>50.3</td>\n      <td>44.9</td>\n      <td>43.8</td>\n      <td>41.2</td>\n      <td>37.8</td>\n      <td>26.3</td>\n      <td>...</td>\n      <td>7.4</td>\n      <td>5.3</td>\n      <td>5.3</td>\n      <td>5.1</td>\n      <td>5.3</td>\n      <td>4.6</td>\n      <td>None</td>\n      <td>7.6</td>\n      <td>38.4</td>\n      <td>484</td>\n    </tr>\n    <tr>\n      <th>2021-05-29</th>\n      <td>12.1</td>\n      <td>33.2</td>\n      <td>73.9</td>\n      <td>72.5</td>\n      <td>53.7</td>\n      <td>48.0</td>\n      <td>46.2</td>\n      <td>43.9</td>\n      <td>39.8</td>\n      <td>28.4</td>\n      <td>...</td>\n      <td>7.3</td>\n      <td>5.0</td>\n      <td>5.4</td>\n      <td>4.9</td>\n      <td>6.4</td>\n      <td>4.4</td>\n      <td>None</td>\n      <td>7.7</td>\n      <td>40.3</td>\n      <td>485</td>\n    </tr>\n    <tr>\n      <th>2021-05-30</th>\n      <td>12.3</td>\n      <td>35.0</td>\n      <td>72.7</td>\n      <td>75.2</td>\n      <td>57.9</td>\n      <td>51.2</td>\n      <td>48.5</td>\n      <td>45.9</td>\n      <td>40.2</td>\n      <td>28.9</td>\n      <td>...</td>\n      <td>7.7</td>\n      <td>5.4</td>\n      <td>5.7</td>\n      <td>5.5</td>\n      <td>6.7</td>\n      <td>4.4</td>\n      <td>None</td>\n      <td>8.1</td>\n      <td>41.8</td>\n      <td>486</td>\n    </tr>\n    <tr>\n      <th>2021-05-31</th>\n      <td>12.1</td>\n      <td>33.0</td>\n      <td>71.1</td>\n      <td>78.9</td>\n      <td>62.5</td>\n      <td>53.7</td>\n      <td>50.2</td>\n      <td>46.7</td>\n      <td>40.2</td>\n      <td>29.3</td>\n      <td>...</td>\n      <td>7.9</td>\n      <td>5.9</td>\n      <td>6.0</td>\n      <td>4.9</td>\n      <td>7.4</td>\n      <td>4.3</td>\n      <td>None</td>\n      <td>8.1</td>\n      <td>42.8</td>\n      <td>487</td>\n    </tr>\n    <tr>\n      <th>2021-06-01</th>\n      <td>12.8</td>\n      <td>32.8</td>\n      <td>70.3</td>\n      <td>85.5</td>\n      <td>74.3</td>\n      <td>60.7</td>\n      <td>55.7</td>\n      <td>50.5</td>\n      <td>41.9</td>\n      <td>32.0</td>\n      <td>...</td>\n      <td>8.5</td>\n      <td>6.6</td>\n      <td>5.7</td>\n      <td>5.4</td>\n      <td>8.2</td>\n      <td>5.2</td>\n      <td>None</td>\n      <td>8.6</td>\n      <td>46.4</td>\n      <td>488</td>\n    </tr>\n  </tbody>\n</table>\n<p>489 rows × 23 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "series = ['20_24', '40_44', '60_64', '80_84']\ndf[-90:][series].plot.line(logy=True, ylabel=\"7-day cases per 100,000\", style='.-')",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='date', ylabel='7-day cases per 100,000'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_window = df[-7:]\n# Model as: cases = a * b ^ day\n#         : ln cases = ln a + day * ln b\nlog_params = np.polyfit(df_window['Days'], np.log(df_window[series]), 1)\n# Model as: 2 * a * b ^ 0 = a * b ^ d\n#         : 2 = b ^ d\n#         : ln 2 = d * ln b\n#.        : ln 2 / ln b = d\ndoubling_rates = (np.log(2) / log_params[0]).round()\nprint(series)\nprint(f'Doubling rates: {doubling_rates}')\n# Using CDC estimate of serial interval\nr_numbers = np.exp(log_params[0] * 3.96)\nprint(f'R Numbers: {r_numbers}')",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": "['20_24', '40_44', '60_64', '80_84']\nDoubling rates: [ 8. 19. 38. 47.]\nR Numbers: [1.41026145 1.1577434  1.07522237 1.06000058]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/a997cc77cf84ab452bd45b5f3b839d4d"
    },
    "gist": {
      "id": "a997cc77cf84ab452bd45b5f3b839d4d",
      "data": {
        "description": "Age Disaggregated ",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.2+",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}