Events – Covid-19 Pandemic

Page last modified 7/7/2020

 

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See below for UK-related data, maps, correlations, on Covid-19

 

Global spreadsheet of Covid-19 prevalence rates and deaths by country

 

Global Covid-19 figures by country – trends over time in cases and fatalities Source, John Hopkins https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Graphic of Covid-19 casesThe Economist, 7/3/2020, p.81.

Spreadsheet of Covid19 numbers here

Notes on this spreadeheet.  Each day comprises two lines; cases and deaths. The top line, cases, also gives the ratio of population to total cases. The bottom line, deaths, gives the total deaths and deaths as % of cases. For all lines, colour highlighting shows the evolution of cases as ratio population, and deaths as % cases. The brown figures at the start of the grey (end of month) totals are the amount the cases, deaths, have multiplied by over the previous month.

Cartographic visualisation of covid19 fatalities. This is what a map would look like with a town or city missing whose population is equivalent to total global covid19 deaths so far. (latest – Toulon, France, 11/7/2020; 579,000)

 

Month-on-month growth in Covid-19 cases, deaths

Month

Cases

Increase

Deaths

Increase

To 7 July

1.13x

1,324k

1.06x

33k

June 2020

1.69x

4,198k

1.37x

136k

May 2020

1.88x

2,857k

1.60x

138k

April 2020

4.06x

2,447k

5.96x

191k

March 2020

9.54x

716k

13.49x

36k

February 2020

8.51x

76k

13.47x

3k

 

7/7/2020, Cases of Covid-19 reached 11,626,759, with 538,190 deaths (4.63%)

30/6/2020, Cases of Covid-19 reached 10,302,867, with 505,518 deaths (4.91%)

31/5/2020, Cases of Covid-19 reached 6,104,980, with 369,078 deaths (6.06%)

30/4/2020, Cases of Covid-19 reached 3,247,648, with 230,615 deaths (7.10%)

6/4/2020, The UK Prime Minister, Boris Johnson, was admitted to intensive care with Covid-19.

31/3/2020, Cases of Covid-19 reached 800,149, with 38,714 deaths (4.84%)

24/3/2020, Britain implemented unprecedented lockdown measures to try and curb the growth in Covid19 cases. People were only allowed to leave home to go shopping, travel to essential work, or to exercise. All non-essential retailing had to close, including sports shops and hairdressers. Religious gatherings were halted, and no more than 2 people could gather unless they lived in the same household. The measures would be in place initially for three weeks.

21/3/2020, Cases of Covid-19 reached 303,001, with 12,944 deaths (4.27%).


20/3/2020, Cases of Covid-19 reached 250,856, with 10,389 deaths (4.14%). The UK took the unprecedented step of ordering all pubs, restaurants and cafes to close after today, although they could still offer a takeaway service, to curb gatherings of people in close proximity. Nightclubs, theatres, betting shops, museums and art galleries, cinemas and gyms also had to close. Meanwhile France and Italy had much more draconian sanctions, banning all travel apart from essential journeys to buy food or go to work, and walking only allowed within 2 km of home, with no cycling allowed. Beaches in southern France were closed, as were parks. California also had a lockdown, and most air travel ceased, as many national birders closed. The Olympic Games due for July and August 2020 in Japan were likely to be cancelled, but this move met with resistance from both athletes and the Japanese Government.


10/3/2020, Italy woke up to severe restrictions on all but essential travel across the entire country, as coronavirus cases took off there, especially in the wealthy North. Worldwide, Covid-19 cases reached 114,457, with 4,026 deaths (3.52%).

28/2/2020, Coronavirus cases now reached 83,878, with 2,869 deaths (3.42%).

31/1/2020, In China 9,692 cases of coronavirus have been confirmed, with a further 121 cases across over 20 countries outside China (total, 9,813). There have been 213 deaths (2.17%) so far, all in China.

30/1/2020, Cases of coronavirus worldwide now stood at 7,811, with 170 deaths (2.18%). Of these, 7,711 cases and all deaths were in China.

27/1/2020, Cases of coronavirus now stood at 4,585 (4,515 in China). There had been 106 deaths (2.31%), all in China. Other cases included 2 in Canada, 5 in the USA, 1 in Germany, 3 in France, 5 in Australia, 4 in Japan, and others across SE Asia.

25/1/2020, An epidemic of coronavirus, causing a flu-like illness, had now infected 2,010 people. Of these, 1,975 were in China, many around the city of Wuhan where the epidemic began. There were also cases in the USA, France, Australia and several SE Asian countries. 56 people had died, all in China.

23/1/2020, The Chinese city of Wuhan was put on ‘lockdown’, with all public transport suspended, after the coronavirus, causing a flu-like illness,  appeared there.

 

UK maps of Covid-19 deaths

 

Maps of Covid-19 fatalities, selected areas of the UK 1 March to 17 April 2020. Source,

https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsinvolvingcovid19bylocalareasanddeprivation/deathsoccurringbetween1marchand17april

Areas have been selected to cover the areas with highest Covid-19 fatalities over this period, with adjacent rural areas. 3,001 MSOAs mapped here –

Note 1) an MSOA is a statistical area covering around 5,000 – 15,000 people. They are nested inside Local Authority areas, and drawn so as to be as socially-homogenous as possible.

Note 2) Although currently-available MSOA socio-economic data may he several years old, against current Covid-19 figures, it can be assumed that the relative positions of MSOAs, in terms of social indicator rankings, will not have greatly changed as the UK population has grown through migration etc, so the correlations analysed below are still meaningful.

Map One, Greater London, Slough, SE Kent and E Sussex, Bournemouth, Hertfordshire, south Bedfordshire

Map Two, Greater Liverpool, Greater Manchester, Welsh Valleys, Swansea to Newport, east Lincolnshire, Midlands (Leicester and Derby to Coventry, Greater Birmingham, Stoke on Trent and Telford)

 

Brief discussion points from the Covid-19 maps

1) Rural areas generally have lower deaths than medium-sized towns, which in turn have seen fewer detahs than large cities. Probably due to physical proximity of people, and poverty-related factors such as overcrowded households. Even less-affluent rural areas such as East Lincolnshire, north-east Kent and the south Wales Valleys have mortality rates below the big cities.

2) Within the major cities, less affluent areas such as Southall, Edmonton and Newham in London, and Sandwell in Bormingham, have higher mortality rates than some better-off areas such as Richmond (London) or outer Stockport (Manchester). This may be related to overcrowding and BAME ethnicity; also more residents of these areas working in jobs (often low-paid) that cannot be done from home, and involve close contact with many others, e.g. leisure, hospitality and health). Of course these factors, ethnicity, overcrowding and low pay, are all interrelated

3) Notwithstanding (2), there is also a tendency for some peripheral wealthier (i.e. higher house-price) suburban areas to have higher Covid-19 deaths. These areas include outer NW and SE London, and wesetern Wolverhampton. This may be due to older people being more vulnerable to Covid-19 deaths. The inflated nature of the UK housing market means housing in these costlier areas may only be affordable to older people (who have owned houses since long ago when house prices were cheaper, or who have had more time to build up housing capital).

4) Superimposed on the above factors, there are ‘free-standing’ MSOAs with anomalously-high Covid-19 deaths. This may be due to local OAP care home clusters of Covid-19 fatalities. An example is NWAshford in Kent. This MSOA does indeed contain an OAP care home. However care homes are present in many MSOAs – more likely, the outer leafier ones, because a) there are more of the larger properties with grounds available for such homes there, and b) OAP homes may shun deprived areas as that might repel potential residents and their families. It would be difficult to ascertain if all these ‘islands’ of high Covid-19 deaths atre due to care home clusters without contacting all care homes, and assuming they will all be candid about their Covid-19 mortalities.

5) Alternatively, as the Covid-19 fatality figure is a discrete variable, and that many of the MSOA figures are low numbers, and many urban MSOAs cover only small areas, much of the data could be random noise. People may catch Covid-19 in a variety of locales, from work, friends or whilst shopping, in MSOAs far from their home (which is the recorded location of a Covid-19 fatality). Additionally there has been well-publicised under-recording of Covid-19 deaths; where a comorbidity exists this may be given as cause of death, even if the coronavirus hastened their death. In this case the correlations data examined below will be purely random.

 

UK correlations, Covid-19 deaths by MSOA and socio-economic / demographic factors

Source. Nomis, https://www.nomisweb.co.uk/query/construct/submit.asp?forward=yes&menuopt=201&subcomp

 

Six socio-economic / demographic factors were chosen, as indiecative of factors linked to higher risk if Covid-10 deaths, e.g. age, deprivation, ethnicity, overcrowding, poor health. The correlations to Covid-19 deaths are given below:-

 

Overall (all 3,001 MSOAs mapped)

1) % of population that is White (NOMIS indicator CT0010, self-reported ethnicity = English, Irish, Scottish, Welsh,  other White) to Covid-19 deaths,

-0.3217

2) % of population aged 65 and over to Covid-19 deaths

-0.0879

3) % of population self-reporting as having bad or very bad health to Covid-19 deaths

+0.0539

4) % of population not deprived in any of four ways listed on NOMIS (Indicator QS119EW) to Covid-19 deaths

-0.1692

5) % of households that are ‘overcrowded’. i.e. (NOMIS indicator LC4104 EW) households that have one or more rooms fewer than are deemed necessary by UK standards, to Covid-19 deaths

+0.2191

6) % of population that has Level-4 qualifications or above (NOMIS DC5106EW), i.e. HNC, university bachekor’s Degreen, Masters (level 3 equates to A or AS Levels), to Covid-19 deaths

+0.0051

 

Cross correlations

Covid deaths

% White h/h

% over 65

% bad hlth

% hh not dep

% hh overcrowd

Level 4 Qls

 

 

-0.3217

-0.0879

+0.0539

-0.1692

+0.2191

+0.0051

Covid death

 

 

+0.6684

+0.1567

+0.3902

-0.8347

-0.0195

% White

 

 

 

+0.0931

+0.3122

-0.7236

+0.1424

% over 65

 

 

 

 

-0.7343

-0.0594

-0.7034

% bad hlth

 

 

 

 

 

-0.4499

0.8129

% not deprv

 

 

 

 

 

 

-0.0500

% overcrowd

 

 

 

 

 

 

 

Level 4 Qual

 

Brief discussion points from the above correlation figures

1) The correlation values are all very low (insignificant). This may be due to two risk factors working ‘against each other’ – old age and deprivation. As noted above (Map discussion point 3) higher Covid-19 fatality rates seem to occur in both deprived inner urban areas and the more affluent (expensive-housing) outer urban areas.

2) Although not significant, the age indicator went the opposite way to what one might expect, being (very slightly) negative. Prima-facie this implies that older people,thosed agted 65+,  are slightly less vulnerable to Covid-19. As noted, this may be a confounding effect due to the younger population being more BAME and less affluent.

This theory is supported by the correlation figures below;

2)a) % of population aged 65+ to % households not deprived, +0.3122

2)b) % of population aged 65+ to % population White, +0.6684

3) The correlation between % population White and households that are overcrowded was strongly negative, at

 -0.8347; most households with too few rooms per household numbers are non-White. Many non-White households are multi-generational, as opposed to the 1 or 2 generation White households. This multi-generational overcrowding has been identified as a risk factor for spreading Covid-19, because of more close proximity between family members and perhaps becasuse 3 geneerations means different family members will travel to sdeveral different social venues (schools, work, old-age centres) and then mix together at home. Overcrowding also may associated with poverty (but some more affluent non-White households may also count as ‘overcrowded); the correlation between households not deprived and those overcrowded was -0.4499.

 

Correlations as ‘social transects’

A form of partial correlation, where the extent to which the relationship between two cariables is controlled by a third, can be achieved by converting the ‘overall’ (all-3001-MSOA) correlations into ‘social transects’. These are analogous to geographical transects, for example, taking a metro line trip across a city from north to south and exiting at every alternate stop tonote the socio-economic conditions prevailing in that area.

 

By taking e.g. the correlation between Covid-19 deaths and % White population, and shaving off the 10% MSOAs with the highest % over 65 year olds, recalculating, shaving off the next 10% highest % over 65s, and so on down to the last 10% (which will by definition be the cohort of MSOAs with the youngest population) one can obtain a series of correlations suggesting how the relationship between Covid-19 mortality and ethnicity changes as one transects from older-age areas to more youthful areas. The analysis can of course then be done the other way, shaving off progressively 10% MSOAs with the lowest % over 65, creating a trabsect towards ever-older areas.

 

This methodology can be used to investigate the relationship/correlation between any two variables A and B as one transects the entire sample by a 3rd factor C. In fact C can equally be one of the initial two factors A or B. One could investigate the ‘narrative’ of the correlation between e.g. Covid-19 deaths and % White population, produced by a transect of MSOA selections from higher % White areas to lower % White areas. Or, as above, by a 3rd factor such as age.

 

This ‘narrative’ (akin to the socio-geographical ‘narrative’ an observer might tell after she had transected a city by alternate Metro stops, from outer affluent areas through to inner city areas) now comprises 19 correlation figures; the 10% lowest factor C census areas, the 20% lowest factor C areas, the lowest 30%......(overall 100% figure)…..highest 30%, highest 20%, highest 10%.

 

This social transect can reveal much more than a single figure can about possible relationships between socio-economic factors. For example in a study of obesity in Birmingham, UK (Food access diet and health in the UK: an empirical study of Birmingham’, British Food Journal, Vol. 114, Issue 4, (2012), pp. 598-616, https://www.emerald.com/insight/content/doi/10.1108/00070701211219577/full/html?skipTracking=true ) it was found that the expected positive correlation between % population obese and several indeces of deprivation was slanted from more positive in wealthier areas (as indicated by e.g. less unempoloyment, less overcrowding) to actually negative in moredeprived areas. In other words, 1) deprivation did, overall as expected, imply more obesity 2) In wealthier areas, this relationship was more pronounced, likely because the poor in wealthier areas faced a more expsnive slelection of foodstores, Sainsbury and Waitrose rather than Asda and Iceland, and their living costs (accommodation rentals) may be higher, further reducing the funds available for purchasing healthier (often more expensive – see e.g. https://www.hsph.harvard.edu/news/press-releases/healthy-vs-unhealthy-diet-costs-1-50-more/  3) In less affluent areas the positive correlation between obesity and deprivation was lower and as one transected to the poorest areas the correlation sign actually reversed and became negative – being more affluent, or being employed, in a poorer area could make you MORE prone to obesity. This may have been due to i) the unemployed having more time to access cheaper street markets and more time and energy to cook fresh produce, whilst ii) the employed in these less-affluent areas would still have been on lowish wages, and their work hours may have precluded them from accessing street markets and pushed them more towards easier microwave or takeaway meals at the end of a hard working day.

 

Covid-19 social transect correlation results

The 19 partial correlation figures for each set of indicators (A, B, C) described above are presented in a standard format as shown below. The expected ‘wealthier’ is the factor expected to be associated with lower Covid-19 deaths

 

100%

figure

Minus

wealthier

-10%

-20%

-30%

-40%

-50%

-60%

-70%

-80%

-90%

 

Minus

poorer

-10%

-20%

-30%

-40%

-50%

-60%

-70%

-80%

-90%

 

Additionally, each cell is spectrally colour-coded according to the correlation value obtained, from +0.0000 to +0.9999

0.0000 – 0.0999

0.1000 – 0.1999

0.2000 – 0.2999

0.3000 – 0.3999

0.4000 – 0.4999

0.5000 – 0.5999

0.6000 – 0.6999

0.7000 – 0.7999

0.8000 – 0.8999

0.9000 – 0,9999

 

1) Transecting across non-White to White districts, the correlations between Covid-19 deaths and % househiolds not deprived did not in fact change very much

100%

-0.1962

Minus

most White

-10%

-0.1781

-20%

-0.1674

-30%

-0.1610

-40%

-0.1580

-50%

-0.1559

-60%

-0.1429

-70%

-0.1143

-80%

-0.1416

-90%

-0.0382

 

Minus

least White

-10%

-0.0820

-20%

-0.0560

-30%

-0.0657

-40%

-0.0727

-50%

-0.0781

-60%

-0.0871

-70%

-0.1141

-80%

-0.1335

-90%

-0.0833

 

2) Transecting across deprived to non-deprived districts, the correlations between Covid-19 deaths and % people White suggests that the mortality gap between White and non-White is wider in poorer areas, but im wealthier sreas the death rate is more equal. Being poor and BAME is especially risky; in wealthier areas, being White is less ‘protective’ against Covid-19.

100%

-0.3217

Minus

least depr.

-10%

-0.3232

-20%

-0.3258

-30%

-0.3343

-40%

-0.3396

-50%

-0.3395

-60%

-0.3543

-70%

-0.3433

-80%

-0.3333

-90%

-0.3689

 

Minus

most depr.

-10%

-0.2804

-20%

-0.2773

-30%

-0.2659

-40%

-0.2415

-50%

-0.2548

-60%

-0.2278

-70%

-0.1714

-80%

-0.1612

-90%

-0.2457

 

3) Transecting across non-White to White districts, the correlations between Covid-19 deaths and % people White also showed a slight tendency to be higher in less White areas. As in (2) above, being BAME in a BAME area is (a little) riskier) than being BAME in a White area (which may be wealthier than non-White areas).

100%

-0.3217

Minus

most White

-10%

-0.3131

-20%

-0.3041

-30%

-0.2898

-40%

-0.2626

-50%

-0.2752

-60%

-0.2537

-70%

-0.2379

-80%

-0.2495

-90%

-0.1614

 

Minus

least White

-10%

-0.2169

-20%

-0.1908

-30%

-0.1343

-40%

-0.1157

-50%

-0.1323

-60%

-0.0391

-70%

-0.0469

-80%

-0.0464

 

-90%

-0.0280

 

4) Transecting across deprived to non-deprived districts, the correlations between Covid-19 deaths and % households deprived suggests that living a deprived household in a more deprived area is slightly riskier for Covid-19 than living in a deprived household in a more affluent area. This result is in line with (2) above, but is statistically less strong.

100%

-0.1962

Minus

least depr.

-10%

-0.1725

-20%

-0.1428

-30%

-0.1337

-40%

-0.1554

-50%

0.1661

-60%

-0.1610

-70%

-0.1689

-80%

-0.1521

-90%

-0.0760

 

Minus

most depr.

-10%

-0.1181

-20%

-0.0911

-30%

-0.0834

-40%

-0.0771

-50%

-0.0761

-60%

-0.0648

-70%

-0.0143

-80%

-0.0355

-90%

-0.0246

 

5) Transecting areas by % households overcrowded, the correlation between % people White and Covid-19 deaths was stronger in areas with more overcrowded households, less so in (more affluent?) areas with less overcrowded households. This may correspond with (2) and (3) above; being BAME in an area with more overcrowded households appears to be relatively (to Whites) riskier than being BAME in an area with less overcrowding.

100%

-0.3217

Minus

Least o/c

-10%

-0.3167

-20%

-0.3090

-30%

-0.2964

-40%

-0.2898

-50%

-0.2864

-60%

-0.3000

-70%

-0.3232

-80%

-0.3413

-90%

-0.4042

 

Minus

Most o/c

-10%

-0.2828

-20%

-0.2769

-30%

-0.2235

-40%

-0.1804

-50%

-0.1834

-60%

-0.1803

-70%

-0.1550

-80%

0.2106

-90%

-0.2088

 

6) However transecting areas by % White, the correlation between Covid-19 deaths and % housholds overcrowded was very minor – and actually turned very slightly negative in the most BAME areas. This would suggest that, contrary to (5), being overcrowded in a BAME area is not a risk factor. Essentially there was no statistically-significant relationship here; perhaps the older-age of Whites in non-overcrowded (more affluent) househoild areas was confounding the issue. However there was no significant change in correlations between Covid-19 deaths and overcrowding when transecting for age 65+.

100%

+0.2191

Minus

most White

-10%

+0.2032

-20%

+0.1824

-30%

+0.1564

-40%

+0.1180

-50%

+0.0985

-60%

+0.0442

-70%

-0.0074

-80%

-0.0252

-90%

-0.0103

 

Minus

least White

-10%

+0.1189

-20%

+0.0784

-30%

+0.0293

-40%

+0.0307

-50%

+0.0505

-60%

+0.0256

-70%

+0.0818

-80%

+0.1216

-90%

+0.0446

 

7) Transecting by age %65+, the correlation between Covid-19 fatalities and bad health reduces and actually becomes very slightly negative for the oldest-cohort of MSOAs; it increases for the youngest cohorts. This, strangely, implies that being in poor health heightens the risk of dying oc Covid-19 most for youngest people, whilst the oldest are unaffected or even very slightly protected by being in poor health! This medical paradox may well be explained by the high correlation between bad health and households being (not) deprived, at -0.7343. The anti-Covid-19 effect of younger age is perhaps being outweighed by a deprivation and young-age effect, amplified by the BAME population being younger, and more deprived, than Whites.

100%

+0.0539

Minus

youngest

-10%

+0.0413

-20%

+0.0268

-30%

+0.0218

-40%

+0.0272

-50%

+0.0353

-60%

+0.0172

-70%

+0.0195

-80%

-0.0201

-90%

-0.1660

 

Minus

oldest

-10%

+0.0727

-20%

+0.0654

-30%

+0.0683

-40%

+0.0849

-50%

+0.0871

-60%

+0.1237

-70%

+0.1979

-80%

+0.2695

-90%

+0.3204

 

8) Transecting by ethnicity, the correlation between Covid-19 fatalities and bad health does not significantly change as one moves from high % BAME to high % White areas; unlike moving between older age and youngerareas (7). In the light of (7) above, this tends to add emphasis to a deprivation effect on enhancing Covid-19 deaths, rather than mainly  a BAME effect.

100%

+0.0539

Minus

most White

-10%

+0.0852

-20%

+0.0860

-30%

+0.0945

-40%

+0.1151

-50%

+0.1232

-60%

+0.1334

-70%

+0.1219

-80%

+0.1568

-90%

+0.0669

 

Minus

least White

-10%

+0.0547

-20%

++0.0552

-30%

+0.0831

-40%

+0.0943

-50%

+0.1063

-60%

+0.1316

-70%

+0.1479

-80%

+0.1599

-90%

+0.1167

 

9) Transecting by age %65+, the correlation between Covid-19 fatalities and Qualifications Level 4 is rather minimal for all age cohorts of MSOA, but there is a slight pattern of such qualifications being more protective against Covid-19 deaths to older people, and actually appears to make younger people more vulnerable. Possession of such qualifications increases earnings, but more so over time; gaining such qualifications may in the short run reduce material wealth, as earnings are foregone for study. Therefore again an economic effect of increased wealth protecting against Covid-19 is suggested.

100%

+0.0051

Minus

youngest

-10%

+0.0230

-20%

+0.0464

-30%

+0.0545

-40%

+0.0580

-50%

+0.0460

-60%

+0.0678

-70%

+0.0625

-80%

+0.0734

-90%

+0.1792

 

Minus

oldest

-10%

-0.0027

-20%

+0.0040

-30%

+0.0003

-40%

-0.0160

-50%

-0.0170

-60%

-0.0541

-70%

-0.0915

-80%

-0.1516

-90%

-0.1734

 

10) Transecting by ethnicity, the correlation between Covid-19 fatalities and Qualifications level 4  As with (9) a slight effect only, but a small tendency for possession of these qualifiactions to protect against Covid-19 more in the case of more BAME cohorts of MSOAs. Since BAME earninsg may well be below Whites, but qualifications may help close thos gap, possibly an economic effect here too of higher earnings reducing Covid-19 deaths.

100%

+0.0051

Minus

most White

-10%

+0.0016

-20%

+0.0087

-30%

+0.0049

-40%

-0.0104

-50%

-0.0170

-60%

-0.0384

-70%

-0.0226

-80%

-0.0481

-90%

+0.0187

 

Minus

least White

-10%

+0.0426

-20%

+0.0389

-30%

+0.0054

-40%

-0.0115

-50%

-0.0246

-60%

-0.0363

-70%

-0.0697

-80%

-0.1127

-90%

-0.0725

 

The mapping and statistical analysis of the UK’s Covid-19 pandemic has been funded by a grant from BAL, De Montfort University, Leicester

https://www.dmu.ac.uk/study/business-and-law/business-and-law.aspx

 

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