{"id":2859,"date":"2020-09-27T17:55:11","date_gmt":"2020-09-27T16:55:11","guid":{"rendered":"https:\/\/marriott-stats.com\/nigels-blog\/?p=2859"},"modified":"2020-10-18T21:38:23","modified_gmt":"2020-10-18T20:38:23","slug":"covid19-cases-latest-data-england","status":"publish","type":"post","link":"https:\/\/marriott-stats.com\/nigels-blog\/covid19-cases-latest-data-england\/","title":{"rendered":"COVID19 Cases #1 – Latest Data and Trends for England"},"content":{"rendered":"

Last updated on 27th September 2020 but downloadable spreadsheet in section 3a was updated on 19th October 2020.\u00a0 I will update the post when I get the time!<\/em><\/p>\n

The latest data for COVID19 (Coronavirus) cases in England as of Saturday 26th September 2020 shows the number of people testing positive for COVID19 is up 60% from a week ago but this masks extreme regional disparities that make the national trend meaningless.\u00a0 The North is in the grip of a second wave unlike the South which is not.\u00a0 Unless recent trends in the North abate, the scenario of 50k positive tests per day by the end of October recently postulated by the Chief Scientific Officer remains feasible.<\/p>\n

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If you are familiar with the 5 data sources I use in this blog, you can skip to my thoughts at the end of this post.\u00a0 I first go through each source to explain what they are and what they are showing in the last week before I pull the whole thing together into a commentary.<\/p>\n

I plan to update this post every Sunday.\u00a0 You can follow me on Twitter<\/a> to be told when I have made updates.<\/p>\n

The 5 time series for COVID19 Cases in England<\/span><\/strong><\/h4>\n

Each time series is denoted with a 4 letter code which I will use throughout.\u00a0 Clicking on the 4 letter code will take you to the source data.\u00a0 I have only extracted data for England from these sources but some also cover Scotland, Wales & Northern Ireland.<\/p>\n

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  1. PHEs<\/span><\/a>\u00a0– Public Health England Positive Tests for COVID19 by Date of Specimen – <\/span><\/b>Daily number of positive tests for COVID19 from either an NHS\/PHE laboratory (Pillar 1) or a commercial laboratory (Pillar 2)<\/span><\/span>.\u00a0 Data is published daily.<\/span><\/li>\n
  2. DHSCp<\/strong><\/span><\/a>\u00a0– Department of Health & Social Care<\/span><\/strong> COVID19 Tests by Date of Processing<\/strong><\/span> – Daily number of tests (pillar 1 & 2) for the United Kingdom along with % of tests giving a positive result.\u00a0 Publication of daily data though is due to cease as of 20th August and in future will only be published as weekly data.<\/li>\n
  3. Local Authority Data<\/a><\/strong><\/span> – PHEs & DHSC data broken down by local authorit<\/strong>y<\/span>.\u00a0 Clicking the link downloads a spreadsheet I’ve created which allows you to choose a selection of local authorities to include in a number of charts.\u00a0 You can also find PHE’s watchlist of local areas of concern here<\/a>.<\/li>\n
  4. NHSt<\/a> – Number of Positive COVID19 cases transferred to NHS Track and Trace Service<\/span><\/strong> – Weekly number of positive cases from the PHEs time series above transferred to NHS T&T.\u00a0 This is then broken down into cases where the NHS were able to obtain the contact details of those who came into contact with the infected person and those cases where this wasn’t achieved.<\/li>\n
  5. ONSe<\/a>\u00a0<\/span><\/strong>– ONS COVID19 Household Infection Survey <\/strong><\/span>– Using a random sample of households in England who are tested every week, the % of people infected with COVID19 is estimated on a fortnightly basis.\u00a0 Sample excludes institutional settings such as Hospitals & Care Homes.\u00a0 Data is published weekly on a Friday.<\/span><\/span><\/li>\n<\/ol>\n

     <\/p>\n

    1 – PHEs – Public Health England COVID19 Positive Tests<\/strong><\/span><\/h4>\n

    As of 26th September 2020, a total of 373,717 tests in England gave a positive result for the SARS-COV-2 virus which causes the disease COVID19.\u00a0 The breakdown by the date the specimen was taken is shown in the chart below as bars.<\/p>\n

    \"\"<\/p>\n

    The chart format is identical to the PHEr series plotted in section 1 of my post “Latest Trends for COVID19 deaths in England<\/em><\/a>“.\u00a0 The extrapolated number of positive tests is based on the last 3 weeks of data and the method I use is the same as explained in that link.\u00a0 Here, I will display the same calculation using a different chart format as below which uses a vertical log scale to show two things; the natural logarithm of the cumulative number of positive tests (diamonds) and the difference between the logarithm of the cumulative number and the logarithm of the cumulative number 7 days previously (small circles).\u00a0 Mathematically the second line is the same as the natural logarithm of the dashed black line in the chart above.<\/p>\n

    \"\"<\/p>\n

    By fitting a straight line through the small circles, I can use it to extrapolate the log of the geometric trend into the future.\u00a0 It is apparent though that a straight line fit is not possible for the whole timeframe.\u00a0 From the end of March to the beginning of June, a straight line fit is apparent as shown by the red dashed line.\u00a0 From 6th June the geometric trend turned upwards but at a slow rate and a new straight line fit as shown by the solid blue line is apparent.\u00a0 Then on 31st August, the sharp upward spike occurred as shown by the dashed gold line.\u00a0 My extrapolation model is based on the last 3 weeks of data so as to avoid overreacting to the last few days but in my comments at the end, I will explore other extrapolations.<\/p>\n

    Unlike PHEr which was based on the date the death was registered, PHEs is based on the date the specimen was taken.\u00a0 Given that it might take a few days for the specimen to be sent to a laboratory, be tested and then the results to be communicated to PHE, this means that the data for the most recent dates are always being revised, sometimes by a considerable amount.\u00a0 Typically it is the last 4 days that see the largest changes so these days are not included in the extrapolation and are shaded in the first chart above.<\/p>\n

    However, that doesn’t mean I have to ignore the last 4 days.\u00a0 I have been tracking on a weekly basis the extent to which the last 4 days are revised a week after they were first published.\u00a0 The extent is fairly obvious from the chart below.<\/p>\n

    \"\"<\/p>\n

    Each line is coded with a V number.\u00a0 This refers to the version number of the spreadsheet you can download in section 3a. The chart shows a weekly pattern to the data that has been fairly consistent.\u00a0 You can see that the biggest differences between the latest data and what is restated a week later are for the last 4 days hence why I exclude these from the underlying trend and extrapolation of that trend.\u00a0 Over the last few weeks, I have been experimenting with ways of estimating what the last 4 days will be in a weeks time and what I have learned is that the last 2 days are very hard to predict but the 3rd & 4th day are easier to predict.\u00a0 As yet, I have not worked out the best way to present these estimates in this blog post but I use them to track whether the revisions over the next few days are in line with my expectations.<\/p>\n

     <\/p>\n

    2. DHSCp – Dept Health & Social Care Number of COVID19 Tests<\/strong><\/span><\/h4>\n

    What I have plotted in the charts in section 1 is the sum of “Pillar 1” and “Pillar 2” testing data as explained in the detailed notes about the data supplied by PHE<\/a>.\u00a0 The DHSC (Department of Health & Social Care) provides further details<\/a> but briefly the difference is this –<\/p>\n

      \n
    1. Pillar 1<\/strong> are tests carried out in NHS\/PHE laboratories focusing on key workers and those with a clinical need.<\/li>\n
    2. Pillar 2<\/strong> are tests carried out by commercial laboratories which tend to focus on the wider community.<\/li>\n<\/ol>\n

      DHSC compile this data for all 4 nations and publish the data by the date the test was processed rather than by date of specimen.\u00a0 They report both the total number of tests undertaken and the number of tests with positive result for COVID19.\u00a0 The chart below shows both the number of tests processed by pillar and the overall % of tests that were positive.\u00a0 Data is for the whole of the UK rather than England only.<\/p>\n

      \"\"<\/p>\n

      You can see that the Pillar 2 testing process only got up to speed at the beginning of May.\u00a0 Total number of tests then averaged 80k per day until the end of June whereupon the numbers rose considerably and continue to increase with the latest 7 day CMA at 230k tests per day.<\/p>\n

      Since the number of tests is now on the rise, it is possible that any increase in the number of positive tests could simply be due to more tests.\u00a0 Therefore, the above chart also shows the percentage of tests that give a positive result (known as Positivity<\/strong>) as black diamonds with a black line showing the 7 day CMA.\u00a0 This was broadly stable in the range 0.6%-0.7% for a number of weeks up to the end of August but has almost quadrupled in the last 3 weeks to 2.3%.<\/p>\n

       <\/p>\n

      3a – Regional Data<\/strong><\/span><\/h4>\n

      \"\"For the 7 days to 22nd September, a total of 30,014 positive tests were recorded across England which is up by almost 60% on the previous 7-day period.\u00a0 PHEs can be broken down by regions and local authorities.\u00a0 At the regional level, this table shows a significant regional divide in England with the 3 Northern regions and West Midlands recording levels higher than that seen in the spring whilst the South remains well below the peaks recorded in Q2.<\/p>\n

      I am recommending that all organisations vulnerable to local lockdowns keep a close eye on local case count.\u00a0 The government has given local authorities the power to reimpose lockdowns<\/a> in their areas like those that have occurred already in Leicester<\/a>, Blackburn<\/a>, Oldham<\/a>\u00a0and Preston<\/a>.\u00a0 To help you analyse your own area, I created a spreadsheet which recreates the PHEs charts from section 1 for any area you wish.\u00a0 Click on the link below to download the file and please read the HELP sheet to get started.<\/p>\n

      COVID19 Infections Tracker – Local Areas P1 & P2 – v2.16<\/a><\/strong><\/p>\n

      Purely for archive purposes, the links to previous versions are here – v1.00<\/a>, v1.01<\/a>, v2.02<\/a>, v2.03<\/a>, v2.04<\/a>, v2.05<\/a>, v2.06<\/a>, v2.07<\/a>, v2.08<\/a>, v2.09<\/a>, v2.11<\/a>, v2.12<\/a>\u00a0<\/em><\/strong>.\u00a0 They are mostly 7 days apart.\u00a0 Version 1s contain pillar 1 data only whilst version 2s contain pillar 1 & 2 data.<\/p>\n

      As well as PHEs data, my spreadsheet also includes charts showing the number of positive tests per 100,000 population by week supplied by DHSC.\u00a0 This provides a useful variant on the daily charts shown before.\u00a0 At a regional level, DHSC also provide data showing the % of COVID19 tests giving a positive result (known as Positivity<\/strong>) by pillars 1 & 2 and my spreadsheet includes the chart format below.\u00a0 Here I have shown all 9 regions together as a matrix.\u00a0 A new feature of these charts are the light green bars which are regional estimates of positivity made by the Office of National Statistics (ONS) which I talk about in section 4.\u00a0 The reason I’ve added this to the chart is that the pillar 2 data is not collected from a sample designed in accordance with statistical principles whereas the ONS data is so collected.\u00a0 What this matrix of charts is showing is that for now concerns about the statistical reliability of pillar 2 numbers are overblown.<\/p>\n

      \"\"<\/p>\n

      The obvious conclusion from the regional positivity matrix is that the regional divide between North & South is extraordinary and means that national trends are unreliable.\u00a0 Inferences must be made from regional & local data instead.<\/p>\n

      3b – Local Authority Watchlist<\/strong><\/span><\/h4>\n

      PHE publishes a local authority watchlist of areas that it is concerned about.\u00a0 I recommend you click on this link<\/a> as it includes additional data which is not included in my spreadsheet including number of tests by local authority and the breakdown of number of positive tests by age.\u00a0 Where necessary I will comment on this watchlist but I’ve also created my own watchlist based on 5 criteria:-<\/p>\n

        \n
      1. Number of positive tests per 100K population for latest week<\/li>\n
      2. How that figure for the latest week compares with the highest weekly figure seen in Q2 2020.<\/li>\n
      3. The % growth in number of positive tests over the last 3 weeks<\/li>\n
      4. The % growth in number of positive tests over the last week<\/li>\n
      5. The number of positive tests for the latest week<\/li>\n<\/ol>\n

        I can only do this for the 150 Upper Tier Local Authorities which are unitary authorities, county councils and metropolitan district and London borough councils.\u00a0 Each UTLA has been ranked on each of the 5 measures and I have arbitrarily then taken an average of the 5 ranks with the 1st rank given twice as much weight.\u00a0 This is the figure (AVG column) I have sorted the UTLAs on in the table below and next to it is the range of the 5 ranks used in that calculation.\u00a0 Given that the maximum rank is 150, this can be used to place these numbers in context.\u00a0 30 UTLAs are shown who together make up the top 20% of UTLAs of concern.<\/p>\n

        \"\"<\/p>\n

        One problem when interpreting PHEs at a local level is putting the data into context.\u00a0 I would much rather have positivity data at a local level but that is not available as a data download.\u00a0 So to provide some context for PHEs, I’ve created a new chart format below which is based on the DHSC number of positive tests per 100K population which is available weekly for the 150 UTLAs.\u00a0 On this chart, I’ve plotted Bolton (as the area where the recent surge started) data against the distribution seen each week using a box plot format<\/a>, a format that I’ve also explained in more depth in this twitter thread<\/a>.\u00a0 Bolton has dropped to number 25 in my watchlist and the recent pause looks like it might be genuine.\u00a0 It is still however the UTLA with the highest number of positive tests per 100k population.<\/p>\n

        \"\"<\/p>\n

        A few points to bear in mind when analysing local authority data:-<\/p>\n