jasonreich/LFD.ipynb

## LFD.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from uk_covid19 import Cov19API\nimport pandas as pd",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "filters = [\n    'areaType=nation',\n    'areaName=England'\n]\nstructure = {\n    'date': 'date',\n    'pcrOnly': 'newCasesPCROnlyBySpecimenDate',\n    'lfdFirst': 'newCasesLFDConfirmedPCRBySpecimenDate',\n    'lfdOnly': 'newCasesLFDOnlyBySpecimenDate',\n    'lfdTests': 'newLFDTests'\n}\napi = Cov19API(filters=filters, structure=structure)\ndf = api.get_dataframe()\ndf['date'] = pd.to_datetime(df['date'])\ndf = df.set_index('date').iloc[::-1]['2021-01-01':]\ndf['Expected false positive'] = (df['lfdTests'] - df['lfdFirst'])* 0.0032\ndf['% LFD First'] = 100 * df['lfdFirst'].rolling(7).sum() / (df['lfdFirst'] + df['pcrOnly']).rolling(7).sum()\ndf",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            pcrOnly  lfdFirst  lfdOnly   lfdTests  Expected false positive  \\\ndate                                                                         \n2021-01-01    27770       297      166    15551.0                  48.8128   \n2021-01-02    52624      1123      481    48712.0                 152.2848   \n2021-01-03    47969      1138      454    53232.0                 166.7008   \n2021-01-04    66838      1721      632   112195.0                 353.5168   \n2021-01-05    55955      1532      630   104356.0                 329.0368   \n...             ...       ...      ...        ...                      ...   \n2021-03-30     2308       286      270   300298.0                 960.0384   \n2021-03-31     2008       551      576  1079656.0                3453.1360   \n2021-04-01     1915       309      484   712449.0                2278.8480   \n2021-04-02     1220       135      355   305303.0                 976.5376   \n2021-04-03      275        17      415   189339.0                 605.8304   \n\n            % LFD First  \ndate                     \n2021-01-01          NaN  \n2021-01-02          NaN  \n2021-01-03          NaN  \n2021-01-04          NaN  \n2021-01-05          NaN  \n...                 ...  \n2021-03-30    17.868212  \n2021-03-31    17.840127  \n2021-04-01    17.749390  \n2021-04-02    17.838242  \n2021-04-03    17.789775  \n\n[93 rows x 6 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>pcrOnly</th>\n      <th>lfdFirst</th>\n      <th>lfdOnly</th>\n      <th>lfdTests</th>\n      <th>Expected false positive</th>\n      <th>% LFD First</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-01-01</th>\n      <td>27770</td>\n      <td>297</td>\n      <td>166</td>\n      <td>15551.0</td>\n      <td>48.8128</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2021-01-02</th>\n      <td>52624</td>\n      <td>1123</td>\n      <td>481</td>\n      <td>48712.0</td>\n      <td>152.2848</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2021-01-03</th>\n      <td>47969</td>\n      <td>1138</td>\n      <td>454</td>\n      <td>53232.0</td>\n      <td>166.7008</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2021-01-04</th>\n      <td>66838</td>\n      <td>1721</td>\n      <td>632</td>\n      <td>112195.0</td>\n      <td>353.5168</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2021-01-05</th>\n      <td>55955</td>\n      <td>1532</td>\n      <td>630</td>\n      <td>104356.0</td>\n      <td>329.0368</td>\n      <td>NaN</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>2021-03-30</th>\n      <td>2308</td>\n      <td>286</td>\n      <td>270</td>\n      <td>300298.0</td>\n      <td>960.0384</td>\n      <td>17.868212</td>\n    </tr>\n    <tr>\n      <th>2021-03-31</th>\n      <td>2008</td>\n      <td>551</td>\n      <td>576</td>\n      <td>1079656.0</td>\n      <td>3453.1360</td>\n      <td>17.840127</td>\n    </tr>\n    <tr>\n      <th>2021-04-01</th>\n      <td>1915</td>\n      <td>309</td>\n      <td>484</td>\n      <td>712449.0</td>\n      <td>2278.8480</td>\n      <td>17.749390</td>\n    </tr>\n    <tr>\n      <th>2021-04-02</th>\n      <td>1220</td>\n      <td>135</td>\n      <td>355</td>\n      <td>305303.0</td>\n      <td>976.5376</td>\n      <td>17.838242</td>\n    </tr>\n    <tr>\n      <th>2021-04-03</th>\n      <td>275</td>\n      <td>17</td>\n      <td>415</td>\n      <td>189339.0</td>\n      <td>605.8304</td>\n      <td>17.789775</td>\n    </tr>\n  </tbody>\n</table>\n<p>93 rows × 6 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df['% LFD First'].plot.line(ylabel='% of positive PCR tests that were found with positive LFDs')",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='date', ylabel='% of positive PCR tests that were found with positive LFDs'>"
          },
          "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['% LFD First'].max(), df['% LFD First'].idxmax())",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "(19.27135577578943, Timestamp('2021-03-28 00:00:00'))"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "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"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Positive LFDs finding positive PCRs in England",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}