Covid-19 cases in England

This analysis looks at the figures for Covid-19 cases in England. It’s a little dig into what this data can - and can’t - tell us. The source code is available on Github.

If you want to just skip to a general picture of Covid-19 cases across the UK, click here. If you want to look at a particular area, search in the page for the area name; if you can’t find it, that’s probably because cases aren’t that high there right now.

The data is from https://coronavirus.data.gov.uk/downloads/csv/coronavirus-cases_latest.csv and reports “lab-confirmed positive COVID-19 PCR test on or up to the specimen date”. The specimen date is the date that someone had the test taken, and it can take a number of days to process the test and to report the result. That has some implications about the numbers from more recent days, which we’ll come back to later. The official site has more details about the meaning of case numbers.

The most recent data shown here is from 2020-10-30. This data is quite big: it contains 113352 rows and 7 columns. That’s why we’re processing it in R rather than a Google Spreadsheet or Excel.

Let’s take a look at the first five lines.

Confirmed Covid-19 cases in England
AreaName	AreaCode	AreaType	Date	DailyCases	CumulativeCases	CumulativeRate
Adur	E07000223	ltla	2020-10-30	0	378	587.9
Adur	E07000223	ltla	2020-10-29	0	378	587.9
Adur	E07000223	ltla	2020-10-28	6	378	587.9
Adur	E07000223	ltla	2020-10-27	10	372	578.5
Adur	E07000223	ltla	2020-10-26	6	362	563.0

Sometimes this data seems to contain no information for the most recent date. This manifests itself in the data when the number of daily cases given for England is zero (which you wouldn’t expect unless we had really reached zero cases across the country, which is unlikely to happen any time soon). In these cases, we’re going to remove the data from the most recent date (all of which are zeros) because it is inaccurate and throws off the rest of the calculations.

As you can see, the data contains information about various different types of areas. Let’s get a list of the different types:

ltla
nation
region
utla

We’re going to do analysis at the most granular of these levels: on Lower tier local authorities (ltla), so we’ll filter the data down to rows that relate to that geography. We’ll also group by local authority, order with the most recent date first, select only the columns that actually have useful data in them, and have another look at the table.

Confirmed Covid-19 cases in lower tier local authorities
AreaName	AreaCode	Date	DailyCases	CumulativeCases	CumulativeRate
Adur	E07000223	2020-10-30	0	378	587.9
Allerdale	E07000026	2020-10-30	0	863	882.8
Amber Valley	E07000032	2020-10-30	1	1660	1295.4
Arun	E07000224	2020-10-30	0	792	492.7
Ashfield	E07000170	2020-10-30	0	2073	1620.6

Some of the data that we will look at is only available for regions, not at the local authority level. So we’ll use a lookup file from ONS to map the local authorities we have to regions.

Confirmed Covid-19 cases in lower tier local authorities
RegionName	RegionCode	AreaName	AreaCode	Date	DailyCases	CumulativeCases	CumulativeRate
South East	E12000008	Adur	E07000223	2020-10-30	0	378	587.9
North West	E12000002	Allerdale	E07000026	2020-10-30	0	863	882.8
East Midlands	E12000004	Amber Valley	E07000032	2020-10-30	1	1660	1295.4
South East	E12000008	Arun	E07000224	2020-10-30	0	792	492.7
East Midlands	E12000004	Ashfield	E07000170	2020-10-30	0	2073	1620.6

Examining cases in an area

Let’s pick an area and see whether we can plot the cumulative lab-confirmed cases for that area.

We’ll pick on Manchester. We can look at Manchester’s data in a table:

Confirmed Covid-19 cases in Manchester
RegionName	RegionCode	AreaName	AreaCode	Date	DailyCases	CumulativeCases	CumulativeRate
North West	E12000002	Manchester	E08000003	2020-10-30	43	20412	3692.1
North West	E12000002	Manchester	E08000003	2020-10-29	290	20369	3684.3
North West	E12000002	Manchester	E08000003	2020-10-28	371	20079	3631.9
North West	E12000002	Manchester	E08000003	2020-10-27	431	19708	3564.7
North West	E12000002	Manchester	E08000003	2020-10-26	467	19277	3486.8

Now let’s plot out the cumulative cases.

Cumulative cases don’t really help with understanding whether the number of cases are going up or down. To do that, we need to look at the daily lab-confirmed cases instead. This is what that shows:

And this isn’t great, for two reasons. First, it’s extremely spiky, especially around weekends when there is less testing. So we’ll calculate a seven day rolling average, including 3 days each side of the day in question.

Second, we need to be careful about the figures from the last week: lab-confirmed cases are dated according to the date of the specimen (when the test was done) rather than when the result comes in, and they can take time to both process and report. As results come in, the data is updated to increase the figures on previous dates. So figures from the last week or so tend to be underestimates of the eventual numbers. We’re going to indicate that on the graphs using an annotation.

Confirmed Covid-19 cases in Manchester
Date	DailyCases	AverageDailyCases
2020-10-30	43	283.7500
2020-10-29	290	320.4000
2020-10-28	371	311.1667
2020-10-27	431	305.5714
2020-10-26	467	356.0000
2020-10-25	265	373.0000
2020-10-24	272	384.5714
2020-10-23	396	394.5714
2020-10-22	409	399.0000
2020-10-21	452	398.8571

Indicating when restrictions are in place

I’ve created a Google spreadsheet of restrictions to make it easy for the graphs to indicate when restrictions are in place at different levels. Let’s take a look at this data.

Note that the data has missing EndDate values when the restrictions haven’t yet been lifted. To make the graphing work, we need to substitute any missing values with the most recent date.

Restrictions data
AreaCode	AreaName	StartDate	EndDate	LegislationRef	GuidanceRef	Tier
E07000129	Blaby	2020-07-04	2020-07-18	NA	https://www.gov.uk/guidance/leicester-lockdown-what-you-can-and-cannot-do	NA
E07000130	Charnwood	2020-07-04	2020-07-18	NA	https://www.gov.uk/guidance/leicester-lockdown-what-you-can-and-cannot-do	NA
E07000135	Oadby and Wigston	2020-07-04	2020-08-01	https://www.legislation.gov.uk/uksi/2020/685	https://www.gov.uk/guidance/leicester-lockdown-what-you-can-and-cannot-do	NA
E06000008	Blackburn with Darwen	2020-07-25	2020-08-01	https://www.legislation.gov.uk/uksi/2020/800	https://www.gov.uk/guidance/north-west-england-local-restrictions	NA
E06000032	Luton	2020-07-25	2020-08-01	https://www.legislation.gov.uk/uksi/2020/800	https://www.gov.uk/guidance/luton-local-restrictions	NA

Now, when we plot the cases in a particular area, we can find any restrictions in that area and also plot those, alongside the nation-wide lockdown that ran from 23rd March to 4th July. Here I’ve shown that in blue.

Here we can see that the national lockdown did have an effect in Manchester, but that the cases started rising again a couple of weeks before the local lockdown was put into effect. The local lockdown doesn’t seem to have had as big an effect on the number of cases.

Understanding testing levels

The graphs we’re creating here indicate underestimates due to the time it takes for new cases to filter through into reported data. Another thing that can cause an underestimate in the number of cases shown is the amount of testing that is going on and the degree to which people go and get testing (which they might not do for various reasons, such as if they think it will be unpleasant, time-consuming, or deprioritise their own testing in favour of others such as health and care workers). It would be good to be able to show where that’s likely to have been happening.

In the UK, the numbers of tests that are carried out are only available at the national level. We’ll load the data for England and take a look at it. (We’ll change the names of the columns because the data uses the pillar numbers which are a bit confusing if you’re not steeped in the data. The request I’m using to get this data doesn’t include surveillance data numbers - tests that are sent out randomly to get an understanding of prevalence across the country, rather than tests requested by people who have symptoms - because they only exist for the whole of the UK, not specifically for England.)

Number of lab-confirmed positive or negative COVID-19 test results, by pillar
Date	NewNHSTests	NewCommercialTests	NewAntibodyTests
2020-10-29	60969	211541	3266
2020-10-28	69337	179225	3338
2020-10-27	68334	152302	3214
2020-10-26	51702	135491	2489
2020-10-25	51245	202720	622

Let’s graph those tests in a stacked bar chart.

Again this data is very spikey so we’ll smooth it out.

Comparing tests to cases

One interesting thing to look at is the relative numbers of tests and lab-confirmed cases. Now this isn’t an exact science as the lab-confirmed cases are based on numbers of people while the tests are based on numbers of tests. The same person could receive lots of tests, and the number of tests each person receives could vary over time (for example as testing policy changes). More comparative measures would be the percentage of positive tests, or the number of people tested, but this data isn’t published (which may mean it doesn’t exist, or simply that it isn’t shared).

Since we’re dealing with data at a national scale, let’s join together the data we have for cases in England with this testing data to take a look at testing percentages. When doing this, we’ll sum together the NHS and commercial tests (but not the antibody tests, which measure when people have had Covid-19 rather than whether they currently have it) to see what population is being reached.

If we plot this data, we can see the scale of testing (in blue) is massive compared to the scale of cases (in orange).

We can work out the percentage of tests that are leading to cases each day, and plot that as follows.

The graph shows how the relationship between number of lab-confirmed cases (all of which will have had a test) and overall numbers of tests has changed dramatically over time. At the start, the number of tests was only about three times the number of cases; more recently, there have been more than 100 tests per positive case.

This is a bit of a misleading picture, however. The data about commercial tests isn’t represented in the data until 14th July, so we have to consider before and after that date separately. Testing data is also only released every week, on a Thursday, so we should ignore everything after last Thursday. Let’s narrow down the data to the period between those dates and take another look.

The proportion roughly doubled between mid and late August, but the proportion is still very low compared to that at the beginning of the UK epidemic. Given the proportions before the start of June, it’s likely that the number of cases before then were underestimates simply due to lower testing.

Comparing tests to capacity

An alternative mechanism for understanding the effect of testing is to use government figures for testing capacity. These are only available for the whole of the UK (when in fact capacity might be different in different nations and local authorities). Nevertheless, let’s look at what that data says.

Number and capacity of lab-confirmed positive or negative COVID-19 test results, by pillar
Date	NHSCapacity	CommercialCapacity	AntibodyCapacity	SurveillanceCapacity	NewNHSTests	NewCommercialTests	NewAntibodyTests	NewSurveillanceTests
2020-10-29	167879	301100	120000	13082	74031	243216	3266	57566
2020-10-28	161869	297200	120000	9543	81764	203833	3338	54840
2020-10-27	137574	295900	120000	7167	80017	176920	3214	50982
2020-10-26	134112	293200	120000	19511	63458	158141	2489	40256
2020-10-25	83164	281000	120000	14932	60363	230862	622	29888

We can calculate what percentage of capacity we’ve been operating on based on these figures, and then plot them. We’ll exclude surveillance data since the reported capacity is inaccurate (the data shows more tests being done than there is capacity for, for example). We’ll also only look at data since 14th July, since that’s when commercial testing data started being properly reported across the UK.

This shows in particular how reported commercial capacity was almost used up in mid August. The following graph shows overall NHS and commercial testing capacity since mid July.

The overall testing capacity isn’t maxed out, but remember these are figures for the whole of the UK. It’s likely (especially given recent news reports) that testing capacity is different in different parts of the country, and perhaps most stretched in places with the most cases. So we might expect figures since mid August to also be underestimates (compared to those from earlier in the summer).

Overall, though, it’s hard to draw solid conclusions from the available testing data about the degree to which lack of testing might be influencing the numbers of cases reported in the data.

Comparing local authorities

Let’s return now to the overall data for local authorities. When we start looking across local authorities, we need to bear in mind that different local authorities have different sizes. A certain absolute number of cases in a small local authority is more concerning than if that number of cases were present in a larger local authority, because it would mean a greater percentage of the population were affected.

So we need to calculate infection rates. To do that, we need population data from the ONS which is only available as an Excel file. I’ve cheated and downloaded and converted it to a CSV file locally so that I can use it, rather than try to load the Excel file from source.

Now we can join that data together with the data we have on cases, and from it calculate rates per 100,000 people. The table shows those areas with the highest current average daily rate (which, remember, are likely to be underestimates because recent case numbers are underestimates).

Confirmed Covid-19 case rates per 100,000
AreaName	Date	AverageDailyCases	All ages	AverageDailyRate
Oldham	2020-10-30	182.25	237110	76.86306
Blackburn with Darwen	2020-10-30	114.75	149696	76.65535
Wigan	2020-10-30	239.50	328662	72.87122
Salford	2020-10-30	180.25	258834	69.63923
Rochdale	2020-10-30	138.00	222412	62.04701
Tameside	2020-10-30	132.75	226493	58.61108
Bury	2020-10-30	111.75	190990	58.51092
Bolton	2020-10-30	166.00	287550	57.72909
Bradford	2020-10-30	309.50	539776	57.33860
Barnsley	2020-10-30	140.00	246866	56.71093

What kind of rate of new cases should we worry about? Well, in California, they have defined four levels of risk:

Widespread: more than 7 daily new cases / 100,000
Substantial: between 4-7 daily new cases / 100,000
Moderate: between 1-4 daily new cases / 100,000
Minimal: less than 1 daily new case / 100,000

As discussed, it is possible to reduce the number of new cases in the data by simply not testing as many people. In the UK, testing capacity is probably not limited in this way, but there isn’t enough granularity in the public testing figures to be able to tell. Regardless, the figures above give a rough indication of how concerned to be about the level of infection in an area.

We’ll try to plot these on a map. There’s a great tutorial for this which we’ll just follow. We’re using ultra generalised 2019 boundaries from ONS, which like the population data we’ve downloaded locally into a folder.

First we load the shape file and ensure that it knows what regions we’re using from it.

Then we filter the local authority data to the most recent date (2020-10-30) and retain only area code and the average daily rate. This is the data that’s relevant for the map.

Then we merge the map data into the shape data and plot the map.

This highlights some areas that have high rates of cases, but unless you’re great at UK geography you might struggle to name them. So we’ll move on to look at some individually in a bit.

Areas where there are or have been government lockdowns

First, we’ll have a look at what’s going on in the areas where there are or have been government lockdowns. These are listed in the restrictions table. We can cycle through them - here we’re sorting the areas based on the tier of the restriction, then by name - to take a look at what’s happening there.

Interesting areas to look at here are:

Northampton, where there was a local outbreak in the Greencore factories. The data shows a big spike in the middle of August, probably due to extensive local testing around those outbreaks.
Leicester, which shows a peak during June, when the majority of the rest of the country had come out the other side of the first wave.
Areas like Bolton and Salford, which were in a local lockdown from the beginning of August - probably because of their physical proximity to Manchester - despite their rate of infection being relatively low, but where case numbers have escalated significantly during this local lockdown, in the second half of August.

Current restrictions

Tier 3 restrictions

Current Tier 3 restrictions

Ashfield

Barnsley

Bassetlaw

Blackburn with Darwen

Blackpool

Bolton

Broxtowe

Burnley

Bury

Chorley

Doncaster

Fylde

Gedling

Halton

Hyndburn

Knowsley

Lancaster

Liverpool

Manchester

Mansfield

Newark and Sherwood

Nottingham

Oldham

Pendle

Preston

Ribble Valley

Rochdale

Rossendale

Rotherham

Rushcliffe

Salford

Sefton

Sheffield

South Ribble

St. Helens

Stockport

Tameside

Trafford

Warrington

West Lancashire

Wigan

Wirral

Wyre

Upcoming Tier 3 restrictions

Tier 2 restrictions

Current Tier 2 restrictions

Ashfield

Barking and Dagenham

Barnet

Barrow-in-Furness

Basildon

Bassetlaw

Bexley

Birmingham

Bradford

Braintree

Brent

Brentwood

Bromley

Broxtowe

Calderdale

Camden

Castle Point

Chelmsford

Cheshire East

Cheshire West and Chester

Chesterfield

Colchester

County Durham

Coventry

Croydon

Darlington

Ealing

Elmbridge

Enfield

Epping Forest

Erewash

Gateshead

Gedling

Greenwich

Hammersmith and Fulham

Haringey

Harlow

Harrow

Hartlepool

Havering

High Peak

Hillingdon

Hounslow

Islington

Kensington and Chelsea

Kingston upon Thames

Kirklees

Lambeth

Leeds

Leicester

Lewisham

Maldon

Mansfield

Merton

Middlesbrough

Newark and Sherwood

Newcastle upon Tyne

Newham

North East Derbyshire

North Tyneside

Northumberland

Nottingham

Oadby and Wigston

Redbridge

Redcar and Cleveland

Richmond upon Thames

Rochford

Rushcliffe

Sandwell

Slough

Solihull

South Tyneside

Southwark

Stockton-on-Tees

Stoke-on-Trent

Sunderland

Sutton

Tendring

Tower Hamlets

Uttlesford

Wakefield

Walsall

Waltham Forest

Wandsworth

Westminster

Wolverhampton

York

Upcoming Tier 2 restrictions

Amber Valley

Bolsover

Cannock Chase

Carlisle

Charnwood

Derby

Derbyshire Dales

Dudley

East Riding of Yorkshire

East Staffordshire

High Peak

Kingston upon Hull, City of

Lichfield

Luton

Newcastle-under-Lyme

North East Lincolnshire

North Lincolnshire

Oxford

South Derbyshire

South Staffordshire

Stafford

Staffordshire Moorlands

Tamworth

Telford and Wrekin

Previous restrictions

Blaby

Northampton

Identifying worrying areas

The areas we really care about are those where there’s a high daily rate, and particularly those where that rate is increasing. We’ll add a couple of columns that track the degree to which the average daily rate is increasing or decreasing.

Identifying trend in Manchester
Date	DailyRate	AverageDailyRate	DailyRateIncreasePercentage	AverageRateIncreasePercentage
2020-10-30	7.777766	51.32421	-0.13	-0.06
2020-10-29	52.454699	57.95340	0.03	-0.06
2020-10-28	67.105839	56.28329	0.02	-0.06
2020-10-27	77.958535	55.27123	-0.17	-0.05
2020-10-26	84.470153	64.39266	-0.05	-0.03

Now let’s narrow down to places that have seen concerning levels of cases over the last week.

Widespread infection

The most recent date is 2020-10-30 so one week ago is 2020-10-24. First let’s look at those who had widespread daily rates (over 7 cases / day / 100,000 population) at some point in the last week.

There are 304 areas where there is widespread infection, 159 of which aren’t currently in lockdown. Those are:

Bristol, City of
Blaby
Lincoln
North Warwickshire
Harrogate
South Gloucestershire
Warwick
Bath and North East Somerset
Selby
Bournemouth, Christchurch and Poole
Portsmouth
North West Leicestershire
Nuneaton and Bedworth
East Lindsey
Breckland
Bromsgrove
Hinckley and Bosworth
Hambleton
Broxbourne
Craven
Swale
Harborough
Redditch
South Holland
Great Yarmouth
Cambridge
Hackney and City of London
Richmondshire
Northampton
North Somerset
Brighton and Hove
Shropshire
Worcester
Hertsmere
Eden
West Lindsey
Thanet
Southampton
Runnymede
Thurrock
Copeland
East Northamptonshire
Plymouth
Boston
Watford
Rugby
Mole Valley
Swindon
North Kesteven
Eastleigh
Torbay
Windsor and Maidenhead
South Kesteven
Stratford-on-Avon
Wyre Forest
Scarborough
Wellingborough
Peterborough
Melton
Fareham
Cherwell
Winchester
Crawley
South Lakeland
Spelthorne
Exeter
Waverley
Epsom and Ewell
Wychavon
Woking
Reading
Rushmoor
Gosport
Malvern Hills
Chichester
Welwyn Hatfield
West Oxfordshire
Dartford
Sedgemoor
Guildford
Medway
Stevenage
South Northamptonshire
Milton Keynes
Allerdale
Three Rivers
Dorset
Norwich
Daventry
Havant
Test Valley
Gloucester
South Oxfordshire
Cotswold
Eastbourne
East Hertfordshire
Tandridge
Bedford
Ryedale
Reigate and Banstead
North Hertfordshire
Hart
East Devon
New Forest
Maidstone
Gravesham
Kettering
Dacorum
Stroud
Bracknell Forest
Wiltshire
King’s Lynn and West Norfolk
Cheltenham
Southend-on-Sea
Mid Sussex
West Berkshire
Babergh
Canterbury
Basingstoke and Deane
West Suffolk
Sevenoaks
Surrey Heath
South Hams
Ipswich
Forest of Dean
St Albans
Corby
Herefordshire, County of
Wokingham
Central Bedfordshire
Vale of White Horse
East Hampshire
Arun
Tonbridge and Malling
Lewes
Teignbridge
Rutland
Fenland
Dover
Worthing
North Devon
Adur
Tewkesbury
Horsham
Somerset West and Taunton
West Devon
Wealden
Folkestone and Hythe
Broadland
Mid Devon
South Cambridgeshire
Hastings
South Somerset
Tunbridge Wells
Ashford
Mendip
South Norfolk
Cornwall and Isles of Scilly
Mid Suffolk

Now we’ll create charts for those places where there is widespread infection that do not currently have additional restrictions (some of which might be duplicates of the graphs above, because they previously did have local restrictions):

Bristol, City of

Blaby

Lincoln

North Warwickshire

Harrogate

South Gloucestershire

Warwick

Bath and North East Somerset

Selby

Bournemouth, Christchurch and Poole

Portsmouth

North West Leicestershire

Nuneaton and Bedworth

East Lindsey

Breckland

Bromsgrove

Hinckley and Bosworth

Hambleton

Broxbourne

Craven

Swale

Harborough

Redditch

South Holland

Great Yarmouth

Cambridge

Hackney and City of London

Richmondshire

Northampton

North Somerset

Brighton and Hove

Shropshire

Worcester

Hertsmere

Eden

West Lindsey

Thanet

Southampton

Runnymede

Thurrock

Copeland

East Northamptonshire

Plymouth

Boston

Watford

Rugby

Mole Valley

Swindon

North Kesteven

Eastleigh

Torbay

Windsor and Maidenhead

South Kesteven

Stratford-on-Avon

Wyre Forest

Scarborough

Wellingborough

Peterborough

Melton

Fareham

Cherwell

Winchester

Crawley

South Lakeland

Spelthorne

Exeter

Waverley

Epsom and Ewell

Wychavon

Woking

Reading

Rushmoor

Gosport

Malvern Hills

Chichester

Welwyn Hatfield

West Oxfordshire

Dartford

Sedgemoor

Guildford

Medway

Stevenage

South Northamptonshire

Milton Keynes

Allerdale

Three Rivers

Dorset

Norwich

Daventry

Havant

Test Valley

Gloucester

South Oxfordshire

Cotswold

Eastbourne

East Hertfordshire

Tandridge

Bedford

Ryedale

Reigate and Banstead

North Hertfordshire

Hart

East Devon

New Forest

Maidstone

Gravesham

Kettering

Dacorum

Stroud

Bracknell Forest

Wiltshire

King’s Lynn and West Norfolk

Cheltenham

Southend-on-Sea

Mid Sussex

West Berkshire

Babergh

Canterbury

Basingstoke and Deane

West Suffolk

Sevenoaks

Surrey Heath

South Hams

Ipswich

Forest of Dean

St Albans

Corby

Herefordshire, County of

Wokingham

Central Bedfordshire

Vale of White Horse

East Hampshire

Arun

Tonbridge and Malling

Lewes

Teignbridge

Rutland

Fenland

Dover

Worthing

North Devon

Adur

Tewkesbury

Horsham

Somerset West and Taunton

West Devon

Wealden

Folkestone and Hythe

Broadland

Mid Devon

South Cambridgeshire

Hastings

South Somerset

Tunbridge Wells

Ashford

Mendip

South Norfolk

Cornwall and Isles of Scilly

Mid Suffolk

Significant and increasing infection

Finally, we’ll make a list of places where there’s been a significant infection (over 4 cases / day / 100,000 population) at some point over the last week and that are seeing increases in those rates.

Huntingdonshire

Again we’ll create charts for those places that do not currently have additional restrictions:

Huntingdonshire

Identifying areas that are the least worrying

Finally, for comparison, let’s have a look at the ten places with the lowest current average daily rates.

North Norfolk
Rother
Hastings
Cornwall and Isles of Scilly
East Cambridgeshire
Tunbridge Wells
Wealden
Torridge
Isle of Wight
Ashford

North Norfolk

## Warning: Removed 1 rows containing missing values (geom_text).

Rother

## Warning: Removed 1 rows containing missing values (geom_text).