weekly-by-age.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39d2d781-f46f-4ef3-9796-502c268a96dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "\n",
    "os.makedirs(\"cache\", exist_ok=True)\n",
    "\n",
    "\n",
    "def cached_fetch_json(url, key, skip_cache=False):\n",
    "    \"\"\"Download a URL and save the result in a cache\"\"\"\n",
    "    cache_path = f\"cache/{key}.json\"\n",
    "    # if in cache, return results\n",
    "    if os.path.exists(cache_path) and not skip_cache:\n",
    "        with open(cache_path) as f:\n",
    "            return json.load(f)\n",
    "\n",
    "    r = requests.get(url)\n",
    "    r.raise_for_status()\n",
    "    data = r.json()\n",
    "    if data:\n",
    "        with open(cache_path, \"w\") as f:\n",
    "            f.write(r.text)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a503a4e-d0d1-4338-b5b5-5e3c0f39a865",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "........................................................................................."
     ]
    },
    {
     "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>week</th>\n",
       "      <th>age</th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>0-9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>10-19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>20-29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>30-39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>40-49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>50-59</td>\n",
       "      <td>821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>60-69</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>70-79</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>80-89</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>90+</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>890 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          week    age  cases\n",
       "0   2020-02-17    0-9      0\n",
       "1   2020-02-17  10-19      0\n",
       "2   2020-02-17  20-29      1\n",
       "3   2020-02-17  30-39      0\n",
       "4   2020-02-17  40-49      0\n",
       "..         ...    ...    ...\n",
       "885 2021-11-01  50-59    821\n",
       "886 2021-11-01  60-69    522\n",
       "887 2021-11-01  70-79    368\n",
       "888 2021-11-01  80-89    170\n",
       "889 2021-11-01    90+     60\n",
       "\n",
       "[890 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "first_week = (2020, 8)\n",
    "\n",
    "# from https://www.fhi.no/sv/smittsomme-sykdommer/corona/dags--og-ukerapporter/dags--og-ukerapporter-om-koronavirus/\n",
    "chart_url = \"https://www.fhi.no/api/chartdata/api/90990\"\n",
    "\n",
    "\n",
    "def collect_data():\n",
    "    \"\"\"Collect weekly counts by age\"\"\"\n",
    "    today = datetime.date.today()\n",
    "    isotoday = today.isocalendar()\n",
    "    this_week = isotoday.week\n",
    "    this_year = today.year\n",
    "    year, week = first_week\n",
    "    all_data = []\n",
    "    weeks = []\n",
    "    rows = []\n",
    "    # don't collect this week, which is usually incomplete\n",
    "    end_week = this_week if isotoday.weekday > 4 else this_week - 1\n",
    "    while (year, week) < (this_year, this_week):\n",
    "        year_week = f\"{year}-{week:02}\"\n",
    "        #         print(f\"Fetching {year_week}\")\n",
    "        sys.stdout.write(\".\")\n",
    "        url = f\"{chart_url}?yearweek={year_week}\"\n",
    "        week_data = cached_fetch_json(\n",
    "            url,\n",
    "            key=year_week,\n",
    "            skip_cache=(year, week) > (this_year, this_week - 2),\n",
    "        )\n",
    "        if week_data:\n",
    "            # looks like\n",
    "            #             [\n",
    "            #                 ['Alder', 'Kvinner', 'Menn'],\n",
    "            #                 ['0-9', 4, 3],\n",
    "            #                 ['10-19', 8, 10],\n",
    "            week_int = year * 100 + week\n",
    "            for row in week_data[1:]:\n",
    "                # skip header\n",
    "                # each row is ('age', *counts)\n",
    "                # discard gender info\n",
    "                rows.append(\n",
    "                    (\n",
    "                        datetime.datetime.fromisocalendar(year, week, 1),\n",
    "                        row[0],\n",
    "                        sum(row[1:]),\n",
    "                    )\n",
    "                )\n",
    "        else:\n",
    "            print(f\"No data for {year}-{week}\", r, r.json())\n",
    "        week += 1\n",
    "        if week > 52:\n",
    "            # wraparound\n",
    "            week = 1\n",
    "            year += 1\n",
    "    return pd.DataFrame(rows, columns=[\"week\", \"age\", \"cases\"])\n",
    "\n",
    "\n",
    "df = collect_data()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1926fc-bd3c-4693-8a81-d84a0b3a29c9",
   "metadata": {},
   "source": [
    "Fetch demographic data for Norway's population by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e208a8ca-6c8a-4b47-97b1-0904d9491045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>contents</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-9 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>593533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10-19 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>644257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20-29 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>705307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30-39 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>739124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40-49 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>719591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50-59 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>714163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60-69 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>588519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70-79 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>450468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>80-89 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>190309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90-99 years</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>46098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age contents  year   value\n",
       "0    0-9 years  Persons  2021  593533\n",
       "1  10-19 years  Persons  2021  644257\n",
       "2  20-29 years  Persons  2021  705307\n",
       "3  30-39 years  Persons  2021  739124\n",
       "4  40-49 years  Persons  2021  719591\n",
       "5  50-59 years  Persons  2021  714163\n",
       "6  60-69 years  Persons  2021  588519\n",
       "7  70-79 years  Persons  2021  450468\n",
       "8  80-89 years  Persons  2021  190309\n",
       "9  90-99 years  Persons  2021   46098"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from https://www.ssb.no/en/statbank/table/07459/tableViewLayout1/\n",
    "ssb_pop_url = \"https://data.ssb.no/api/v0/en/table/07459/\"\n",
    "ssb_pop_query = {\n",
    "    \"query\": [\n",
    "        {\"code\": \"Region\", \"selection\": {\"filter\": \"vs:Landet\", \"values\": []}},\n",
    "        {\n",
    "            \"code\": \"Alder\",\n",
    "            \"selection\": {\n",
    "                \"filter\": \"agg:TiAarigGruppering\",\n",
    "                \"values\": [\n",
    "                    \"F00-09\",\n",
    "                    \"F10-19\",\n",
    "                    \"F20-29\",\n",
    "                    \"F30-39\",\n",
    "                    \"F40-49\",\n",
    "                    \"F50-59\",\n",
    "                    \"F60-69\",\n",
    "                    \"F70-79\",\n",
    "                    \"F80-89\",\n",
    "                    \"F90-99\",\n",
    "                    \"F100G10+\",\n",
    "                ],\n",
    "            },\n",
    "        },\n",
    "        {\"code\": \"Tid\", \"selection\": {\"filter\": \"item\", \"values\": [\"2021\"]}},\n",
    "    ],\n",
    "    \"response\": {\"format\": \"json-stat2\"},\n",
    "}\n",
    "\n",
    "r = requests.post(ssb_pop_url, data=json.dumps(ssb_pop_query))\n",
    "from pyjstat import pyjstat\n",
    "pop_by_age = pyjstat.Dataset.read(r.text).write('dataframe')\n",
    "pop_by_age\n",
    "\n",
    "# merge 90-99 and 100+ into 90+\n",
    "pop_by_age.loc[9, ['value']] += pop_by_age.loc[10, ['value']]\n",
    "pop_by_age = pop_by_age.drop(10)\n",
    "pop_by_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "366846a6-e76a-41f0-89b8-d8bba5fb7270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>contents</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>pop fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-9</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>593533</td>\n",
       "      <td>0.110089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10-19</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>644257</td>\n",
       "      <td>0.119498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20-29</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>705307</td>\n",
       "      <td>0.130822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30-39</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>739124</td>\n",
       "      <td>0.137094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40-49</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>719591</td>\n",
       "      <td>0.133471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50-59</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>714163</td>\n",
       "      <td>0.132464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60-69</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>588519</td>\n",
       "      <td>0.109159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70-79</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>450468</td>\n",
       "      <td>0.083554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>80-89</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>190309</td>\n",
       "      <td>0.035299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90+</td>\n",
       "      <td>Persons</td>\n",
       "      <td>2021</td>\n",
       "      <td>46098</td>\n",
       "      <td>0.008550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age contents  year  population  pop fraction\n",
       "0    0-9  Persons  2021      593533      0.110089\n",
       "1  10-19  Persons  2021      644257      0.119498\n",
       "2  20-29  Persons  2021      705307      0.130822\n",
       "3  30-39  Persons  2021      739124      0.137094\n",
       "4  40-49  Persons  2021      719591      0.133471\n",
       "5  50-59  Persons  2021      714163      0.132464\n",
       "6  60-69  Persons  2021      588519      0.109159\n",
       "7  70-79  Persons  2021      450468      0.083554\n",
       "8  80-89  Persons  2021      190309      0.035299\n",
       "9    90+  Persons  2021       46098      0.008550"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_by_age['age'] = pop_by_age.age.str.replace(\"-99\", \"+\").str.split().str[0]\n",
    "pop_by_age['pop fraction'] = pop_by_age.value / pop_by_age.value.sum()\n",
    "pop_by_age = pop_by_age.rename(columns={\"value\": \"population\"})\n",
    "pop_by_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb22a175-8f8c-4ef0-871a-4d6bf79b1ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0-9\n",
       "1      10-19\n",
       "2      20-29\n",
       "3      30-39\n",
       "4      40-49\n",
       "       ...  \n",
       "885    50-59\n",
       "886    60-69\n",
       "887    70-79\n",
       "888    80-89\n",
       "889      90+\n",
       "Name: age, Length: 890, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d20ac2de-3986-441d-8500-64dbe14967fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x444.984 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 380,
       "width": 595
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_by_age.set_index(\"age\")[\"pop fraction\"].cumsum().plot(style=\"-o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd43dcdb-b819-4db7-8f0e-02e95415af9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0-9\n",
       "1    10-19\n",
       "2    20-29\n",
       "3    30-39\n",
       "4    40-49\n",
       "5    50-59\n",
       "6    60-69\n",
       "7    70-79\n",
       "8    80-89\n",
       "9      90+\n",
       "Name: age, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_by_age.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "12e00d87-fde1-4c1e-99fc-a774b4e91dac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>age</th>\n",
       "      <th>cases</th>\n",
       "      <th>population</th>\n",
       "      <th>pop fraction</th>\n",
       "      <th>per100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>0-9</td>\n",
       "      <td>0</td>\n",
       "      <td>593533</td>\n",
       "      <td>0.110089</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>0-9</td>\n",
       "      <td>0</td>\n",
       "      <td>593533</td>\n",
       "      <td>0.110089</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0-9</td>\n",
       "      <td>7</td>\n",
       "      <td>593533</td>\n",
       "      <td>0.110089</td>\n",
       "      <td>1.179378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>0-9</td>\n",
       "      <td>9</td>\n",
       "      <td>593533</td>\n",
       "      <td>0.110089</td>\n",
       "      <td>1.516344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>0-9</td>\n",
       "      <td>11</td>\n",
       "      <td>593533</td>\n",
       "      <td>0.110089</td>\n",
       "      <td>1.853309</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>90+</td>\n",
       "      <td>17</td>\n",
       "      <td>46098</td>\n",
       "      <td>0.008550</td>\n",
       "      <td>36.877956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2021-10-11</td>\n",
       "      <td>90+</td>\n",
       "      <td>9</td>\n",
       "      <td>46098</td>\n",
       "      <td>0.008550</td>\n",
       "      <td>19.523624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>2021-10-18</td>\n",
       "      <td>90+</td>\n",
       "      <td>36</td>\n",
       "      <td>46098</td>\n",
       "      <td>0.008550</td>\n",
       "      <td>78.094494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>2021-10-25</td>\n",
       "      <td>90+</td>\n",
       "      <td>50</td>\n",
       "      <td>46098</td>\n",
       "      <td>0.008550</td>\n",
       "      <td>108.464575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>90+</td>\n",
       "      <td>60</td>\n",
       "      <td>46098</td>\n",
       "      <td>0.008550</td>\n",
       "      <td>130.157491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>890 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          week  age  cases  population  pop fraction     per100k\n",
       "0   2020-02-17  0-9      0      593533      0.110089    0.000000\n",
       "1   2020-02-24  0-9      0      593533      0.110089    0.000000\n",
       "2   2020-03-02  0-9      7      593533      0.110089    1.179378\n",
       "3   2020-03-09  0-9      9      593533      0.110089    1.516344\n",
       "4   2020-03-16  0-9     11      593533      0.110089    1.853309\n",
       "..         ...  ...    ...         ...           ...         ...\n",
       "885 2021-10-04  90+     17       46098      0.008550   36.877956\n",
       "886 2021-10-11  90+      9       46098      0.008550   19.523624\n",
       "887 2021-10-18  90+     36       46098      0.008550   78.094494\n",
       "888 2021-10-25  90+     50       46098      0.008550  108.464575\n",
       "889 2021-11-01  90+     60       46098      0.008550  130.157491\n",
       "\n",
       "[890 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.merge(pop_by_age[[\"age\", \"population\", \"pop fraction\"]], on=[\"age\"])\n",
    "df[\"per100k\"] = df.cases / (1e-5 * df.population)\n",
    "df"
   ]
  },
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   "execution_count": 23,
   "id": "7e215e4a-a050-409d-8405-1f48fd093cbc",
   "metadata": {},
   "outputs": [
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13.015749056358192}, {\"week\": \"2020-09-14T00:00:00\", \"age\": \"90+\", \"cases\": 0, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 0.0}, {\"week\": \"2020-09-21T00:00:00\", \"age\": \"90+\", \"cases\": 2, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 4.338583018786064}, {\"week\": \"2020-09-28T00:00:00\", \"age\": \"90+\", \"cases\": 4, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 8.677166037572128}, {\"week\": \"2020-10-05T00:00:00\", \"age\": \"90+\", \"cases\": 5, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 10.84645754696516}, {\"week\": \"2020-10-12T00:00:00\", \"age\": \"90+\", \"cases\": 2, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 4.338583018786064}, {\"week\": \"2020-10-19T00:00:00\", \"age\": \"90+\", \"cases\": 5, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 10.84645754696516}, {\"week\": 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\"2021-01-25T00:00:00\", \"age\": \"90+\", \"cases\": 10, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 21.69291509393032}, {\"week\": \"2021-02-01T00:00:00\", \"age\": \"90+\", \"cases\": 5, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 10.84645754696516}, {\"week\": \"2021-02-08T00:00:00\", \"age\": \"90+\", \"cases\": 4, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 8.677166037572128}, {\"week\": \"2021-02-15T00:00:00\", \"age\": \"90+\", \"cases\": 5, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 10.84645754696516}, {\"week\": \"2021-02-22T00:00:00\", \"age\": \"90+\", \"cases\": 6, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 13.015749056358192}, {\"week\": \"2021-03-01T00:00:00\", \"age\": \"90+\", \"cases\": 1, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 2.169291509393032}, {\"week\": \"2021-03-08T00:00:00\", \"age\": \"90+\", \"cases\": 11, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 23.86220660332335}, {\"week\": \"2021-03-15T00:00:00\", \"age\": \"90+\", \"cases\": 7, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 15.185040565751224}, {\"week\": \"2021-03-22T00:00:00\", \"age\": \"90+\", \"cases\": 4, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 8.677166037572128}, {\"week\": \"2021-03-29T00:00:00\", \"age\": \"90+\", \"cases\": 10, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 21.69291509393032}, {\"week\": \"2021-04-05T00:00:00\", \"age\": \"90+\", \"cases\": 7, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 15.185040565751224}, {\"week\": \"2021-04-12T00:00:00\", \"age\": \"90+\", \"cases\": 0, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 0.0}, {\"week\": \"2021-04-19T00:00:00\", \"age\": \"90+\", \"cases\": 7, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 15.185040565751224}, {\"week\": \"2021-04-26T00:00:00\", \"age\": \"90+\", \"cases\": 9, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 19.523623584537287}, {\"week\": \"2021-05-03T00:00:00\", \"age\": \"90+\", \"cases\": 0, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 0.0}, {\"week\": \"2021-05-10T00:00:00\", \"age\": \"90+\", \"cases\": 3, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 6.507874528179096}, {\"week\": \"2021-05-17T00:00:00\", \"age\": \"90+\", \"cases\": 0, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 0.0}, {\"week\": \"2021-05-24T00:00:00\", \"age\": \"90+\", \"cases\": 1, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 2.169291509393032}, {\"week\": \"2021-05-31T00:00:00\", \"age\": \"90+\", \"cases\": 0, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 0.0}, {\"week\": \"2021-06-07T00:00:00\", \"age\": \"90+\", \"cases\": 1, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 2.169291509393032}, {\"week\": \"2021-06-14T00:00:00\", \"age\": \"90+\", \"cases\": 8, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 17.354332075144256}, {\"week\": \"2021-06-21T00:00:00\", \"age\": \"90+\", \"cases\": 1, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 2.169291509393032}, {\"week\": \"2021-06-28T00:00:00\", \"age\": \"90+\", \"cases\": 1, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 2.169291509393032}, {\"week\": \"2021-07-05T00:00:00\", \"age\": \"90+\", \"cases\": 1, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 2.169291509393032}, {\"week\": \"2021-07-12T00:00:00\", \"age\": \"90+\", \"cases\": 1, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 2.169291509393032}, {\"week\": \"2021-07-19T00:00:00\", \"age\": \"90+\", \"cases\": 1, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 2.169291509393032}, {\"week\": \"2021-07-26T00:00:00\", \"age\": \"90+\", \"cases\": 5, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 10.84645754696516}, {\"week\": \"2021-08-02T00:00:00\", \"age\": \"90+\", \"cases\": 13, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 28.200789622109415}, {\"week\": \"2021-08-09T00:00:00\", \"age\": \"90+\", \"cases\": 7, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 15.185040565751224}, {\"week\": \"2021-08-16T00:00:00\", \"age\": \"90+\", \"cases\": 13, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 28.200789622109415}, {\"week\": \"2021-08-23T00:00:00\", \"age\": \"90+\", \"cases\": 17, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 36.87795565968155}, {\"week\": \"2021-08-30T00:00:00\", \"age\": \"90+\", \"cases\": 12, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 26.031498112716385}, {\"week\": \"2021-09-06T00:00:00\", \"age\": \"90+\", \"cases\": 14, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 30.37008113150245}, {\"week\": \"2021-09-13T00:00:00\", \"age\": \"90+\", \"cases\": 7, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 15.185040565751224}, {\"week\": \"2021-09-20T00:00:00\", \"age\": \"90+\", \"cases\": 9, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 19.523623584537287}, {\"week\": \"2021-09-27T00:00:00\", \"age\": \"90+\", \"cases\": 14, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 30.37008113150245}, {\"week\": \"2021-10-04T00:00:00\", \"age\": \"90+\", \"cases\": 17, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 36.87795565968155}, {\"week\": \"2021-10-11T00:00:00\", \"age\": \"90+\", \"cases\": 9, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 19.523623584537287}, {\"week\": \"2021-10-18T00:00:00\", \"age\": \"90+\", \"cases\": 36, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 78.09449433814915}, {\"week\": \"2021-10-25T00:00:00\", \"age\": \"90+\", \"cases\": 50, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 108.46457546965159}, {\"week\": \"2021-11-01T00:00:00\", \"age\": \"90+\", \"cases\": 60, \"population\": 46098, \"pop fraction\": 0.00855033294882988, \"per100k\": 130.15749056358192}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "\n",
    "selection = alt.selection_multi(fields=[\"age\"])\n",
    "color = alt.condition(selection, alt.Color(\"age:N\"), alt.value(\"lightgray\"))\n",
    "common = (\n",
    "    alt.Chart(df)\n",
    "    .transform_aggregate(count=\"sum(cases)\", per100=\"sum(per100k)\", pf=\"max(pop fraction)\", groupby=[\"age\", \"week\"])\n",
    "    .transform_joinaggregate(total=\"sum(count)\", groupby=[\"week\"])\n",
    "    .transform_calculate(frac=alt.datum.count / alt.datum.total)\n",
    "    # .transform_calculate(pf=alt.datum[\"pop fraction\"])\n",
    "\n",
    ")\n",
    "\n",
    "encodings = dict(\n",
    "    x=\"week\",\n",
    "    y=alt.Y(\"per100:Q\", title=\"Cases (per 100k)\"),\n",
    "    color=color,\n",
    "    tooltip=[\n",
    "        \"week\",\n",
    "        \"age\",\n",
    "        alt.Tooltip(\"total:Q\", title=\"Total weekly cases\"),\n",
    "        alt.Tooltip(\"count:Q\", title=\"Cases by age\"),\n",
    "        alt.Tooltip(\"per100:Q\", title=\"Cases by age (per 100k)\", format=\".0f\"),\n",
    "        alt.Tooltip(\"frac:Q\", title=\"Fraction of cases by age\", format=\".0%\"),\n",
    "        alt.Tooltip(\"pf:Q\", title=\"Fraction of population by age\", format=\".0%\"),\n",
    "\n",
    "    ],\n",
    "    order=alt.Order(\"age\", sort=\"ascending\"),\n",
    ")\n",
    "absolute = common.mark_line().encode(**encodings).interactive()\n",
    "\n",
    "\n",
    "total_encodings = {}\n",
    "total_encodings.update(encodings)\n",
    "total_encodings.update(dict(y=alt.Y(\"total:Q\", tooltip=[\"total:Q\"])))\n",
    "\n",
    "#absolute |= common.mark_line().encode(**total_encodings).interactive()\n",
    "encodings.update(dict(y=alt.Y(\"count:Q\", title=\"Fraction\", stack=\"normalize\")))\n",
    "fraction = common.mark_area().encode(**encodings)\n",
    "(absolute | fraction).add_selection(selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "468a2aa5-00f6-410a-ab74-19dd9fbd7ca2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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