Chronography of the �Covid-19 Pandemic
Page last
modified 1/7/2021
See also Medical
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 cases � The 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 pale green-highlighted
line is the latest ratio of deaths to total population (i.r. population per
single death)
The
countries on this spreadsheet are now ordered on a rolling basis,
1) Absolute
case numbers
2)Absolute
death numbers
3) Prevalency
ratio, population per case. Current listing end June 2021
4) Crude
Mortality rate, % of cases that have died. Next listing
due end July 2021
5)
Population per death.
Data is ordered
at all times from most to least severe.
Growth in Covid-19 cases, deaths, change on previous month, % and
absolute numbers (x = times, k = 000�s)
|
Month (last day) |
Cases |
Increase |
Deaths (>cases) |
Increase |
Crude Death Rate |
|
June 2021 |
1.07x |
11,508k |
1.10x |
357k |
2.17% |
|
May 2021 |
1.13x |
19,821k |
1.12x |
375k |
2.08% |
|
April� 2021 |
1.17x |
22,396k |
1.13x |
365k |
2.10% |
|
March 2021 |
1.13x |
14,436k |
1.11x |
278k |
2.19% |
|
February 2021 |
1.11x |
11,183k |
1.14x |
305k |
2.22% |
|
January 2021 |
1.24x |
19,824k |
1.23x |
414k |
2.16% |
|
December 2020 |
1.32x |
19,868k |
1.23x |
343k |
2.18% |
|
November 2020 |
1.38x |
17,295k |
1.23x |
273k |
2.32% |
|
October 2020 |
1.35x |
11,956k |
1.18x |
181k |
2.61% |
|
September 2020 |
1.34x |
8,588k |
1.19x |
164k |
2.99% |
|
August 2020 |
1.45x |
7,782k |
1.25x |
171k |
3.36% |
|
July 2020 |
1.68x |
6,999k |
1.33x |
168k |
3.89% |
|
June 2020 |
1.69x |
4,198k |
1.37x |
136k |
4.91% |
|
May 2020 |
1.88x |
2,857k |
1.60x |
138k |
6.06% |
|
April 2020 |
4.06x |
2,447k |
5.96x |
191k |
7.10% |
|
March 2020 |
9.54x |
716k |
13.49x |
36k |
4.84% |
|
February 2020 |
8.51x |
76k |
13.47x |
3k |
3.42% |
31/8/2021, Cases of Covid-19 reached 181,960,542
with 3,900,368
deaths (2.17%).
31/5/2021, Cases of Covid-19 reached 170,452,064,
with 3,543,601
deaths (2.08%).
30/4/2021, Cases of Covid-19 reached 150,630,922,
with 3,168,601
deaths (2.10%).
31/3/2021, Cases of Covid-19 reached 128,235,377,
with 2,804,036
deaths (2.19%).
28/2/2021, Cases of Covid-19 reached 113,799,097,
with 2,525,656
deaths (2.22%).
31/1/2021, Cases of Covid-19 reached 102,615,797,
with 2,220,787
deaths (2.16%). The vaccination rate
stood at, Israel, 25%; UAE, 17%; UK, 5%; USA, 4%; Swseden 1%; Russia 1%;
Germany 1%; France 1%; China 1%.
9/1/2021, By the end of the first
week in January, the UK had vaccinated 1.5% of the population, starting with
thse aged over 80, then the ovetr 70s. However Germany had vaccinated just
317,000 people, and France took until 4/1/2021 to vaccinate just 1,000 people.
Israel had vaccinated the highest proportion by now, 16% of its people. Russia
had vaccinated 1 million people. Japan would not begin vaccinations until early
February.
31/12/2020, Cases of
Covid-19 reached 82,792,115, with 1,806,478 deaths (2.18%)
30/11/2020, Cases of Covid-19 reached 62,924,259,
with 1,462,989
deaths (2.32%)
9/11/2020, Pfizer announced that it
had developed a reliable vaccine
against Covid-19. Other vaccines were also announced soon after. The first Covid-19
vaccinations were delivered in the UK on 8/12/2020
31/10/2020, Cases of Covid-19 reached 45,629,082,
with 1,189,515
deaths (2.61%)
30/9/2020, Cases of Covid-19 reached 33,673,556,
with 1,008,368
deaths (2.99%)
31/8/2020, Cases of Covid-19 reached 25,085,129,
with 843,945
deaths (3.36%)
31/7/2020, Cases of Covid-19 reached 17,303,253,
with 673,284
deaths (3.89%)
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%).
7/2/2020, In
China, Dr Li
Weinlang, aged 33, who blew the whistle on Covid-19, died of Covid
himself.
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,
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 earnings may well be below
Whites, but qualifications may help close this 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
