COVID-19ResultsBriefingNebraska COVID-19 RESULTS BRIEFING COVID-19ResultsBriefing Nebraska...
Transcript of COVID-19ResultsBriefingNebraska COVID-19 RESULTS BRIEFING COVID-19ResultsBriefing Nebraska...
Nebraska COVID-19 RESULTS BRIEFING
COVID-19 Results Briefing
Nebraska
January 28, 2021
This document contains summary information on the latest projections from the IHME model on COVID-19in Nebraska. The model was run on January 27, 2021 with data through January 25, 2021.
Current situation
• Daily reported cases in the last week decreased to 700 per day on average compared to 900 the weekbefore (Figure 1).
• Daily deaths in the last week decreased to 10 per day on average compared to 10 the week before(Figure 2). This makes COVID-19 the number 2 cause of death in Nebraska this week (Table 1).
• Effective R, computed using cases, hospitalizations, and deaths, is greater than 1 in 1 states (Figure 3).The Effective R in Nebraska on January 14 was 0.87.
• We estimated that 27% of people in Nebraska have been infected as of January 25 (Figure 4).• The daily death rate is greater than 4 per million in 44 states (Figure 5).
Trends in drivers of transmission
• In the last week, new mandates have been imposed in Rhode Island. Mandates have been lifted inCalifornia (Table 2).
• Mobility last week was 20% lower than the pre-COVID-19 baseline (Figure 7). Mobility was nearbaseline (within 10%) in Wyoming. Mobility was lower than 30% of baseline in in 20 states.
• As of January 25 we estimated that 70% of people always wore a mask when leaving their homecompared to 70% last week (Figure 8). Mask use was lower than 50% in no states.
• There were 106 diagnostic tests per 100,000 people on January 25 (Figure 9).• In Nebraska 57% of people say they would accept a vaccine for COVID-19 and 20.6% say they are
unsure if they would accept one. The fraction of the population who are open to receiving a COVID-19vaccine ranges from 69% in South Dakota to 85% in District of Columbia (Figure 11).
• We expect that 1 million people will be vaccinated by May 1 (Figure 12). With faster scale-up, thenumber vaccinated could reach 1 million people.
Projections
• In our reference scenario, which represents what we think is most likely to happen, our model projects2,000 cumulative deaths on May 1, 2021. This represents 0 additional deaths from January 25 to May 1(Figure 13). Daily deaths will peak at 10 on February 4, 2021 (Figure 14).
• By May 1, 2021, we project that 100 lives will be saved by the projected vaccine rollout.• If universal mask coverage (95%) were attained in the next week, our model projects 0 fewer
cumulative deaths compared to the reference scenario on May 1, 2021 (Figure 13).• In the rapid spread of variants scenario daily deaths would remain above 0 on May 1, 2021.
Cumulative deaths on May 1, 2021 would be 2,000 (Figure 13).• Under our worst case scenario, our model projects 2,000 cumulative deaths on May 1, 2021 (Figure
13).• We estimate that 43.9% of people will be immune on May 1, 2021 (Figure 14).• The reference scenario assumes that 5 states will re-impose mandates by May 1, 2021 (Figure 17).• Figure 20 compares our reference scenario forecasts to other publicly archived models. Forecasts are
widely divergent.
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• At some point from January through May 1, 41 states will have high or extreme stress on hospital beds(Figure 21). At some point from January through May 1, 44 states will have high or extreme stress onICU capacity (Figure 22).
Model updates
In order to capture the impact of variants and the potential impact of further spread of new variants, wehave made changes to our scenarios. We now show results for four scenarios when projecting COVID-19.
The reference scenario is our forecast of what we think is most likely to happen and makes the followingassumptions: 1) Vaccines will continue to be distributed at the expected pace. 2) Governments adapt theirresponse by re-imposing social distancing mandates for six weeks whenever daily deaths reach eight permillion, unless a location has already spent at least seven of the last 14 days with daily deaths above thisrate and not yet re-imposed social distancing mandates, in which case mandates are re-imposed when dailydeaths reach 15 per million. 3) Variant B.1.1.7 (first identified in the UK) continues to spread in locationswhere 100 or more isolates have been detected to date. 4) Mask use stays at current levels.
The rapid variant spread scenario shares assumptions with the reference scenario and in addition, weassume that variant B.1.351 (first identified in South Africa) spreads to everywhere in the world, startingFebruary 1, 2021. Variant B.1.351 spreads at the observed rate that B.1.1.7 spread in London. The variantis assumed to increase the infection-fatality ratio by 29% and transmissibility by 35%. This scenario alsoassumes that those vaccinated are less effectively protected against variant B.1.351: Pfizer, Moderna, Janssen,and Novavax clinical effectiveness is reduced by 20%; all other vaccines’ clinical effectiveness is reduced by50%.
The worst case scenario makes the same assumptions as the rapid variant spread scenario and also assumesthat mobility moves towards pre-COVID-19 levels as vaccination rates increase.
The universal masks scenario makes all the same assumptions as the reference scenario and also assumes95% mask usage adopted in public in every location.
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In summary, here are the assumptions in each of the four scenarios:
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More details on each of the assumptions
• How do the new variants scale up over time?
In locations with more than 100 B.1.1.7 variants sequenced, we have included the further scale-up of thevariant. Based on studies reported in England, we assume that B.1.1.7 is 35% more transmissible and theinfection-fatality ratio is 29% higher than wild variants.
For B.1.351, we assume that the scale-up of the proportion of cases due to the new variant will follow thetrajectory that has been well documented in London and other English locations for B.1.1.7. We assume thatthe variant is 35% more transmissible and the infection-fatality ratio is 29% higher. In the rapid variantscenario and the worst case scenario, we assume that B.1.351 will be introduced in all locations on February1. With our assumptions of infectiousness, we find that all locations reach 80% of infections due to B.1.351by May 19th.
• How effective are the vaccines against the new variants?
This scenario assumes that those vaccinated are less effectively protected against variant B.1.351: Pfizer,Moderna, Janssen, and Novavax clinical effectiveness is reduced by 20%; all other vaccines clinical effectivenessis reduced by 50%.
• How do we forecast increases in mobility in the worst case scenario?
We have modified our mobility forecasts to reflect that as the coverage of vaccination increases, there willlikely be fewer mandates in place. We reflect this in our model that forecasts mandates by building in anassumption that as vaccine coverage increases, the probability that mandates will stay in place decreases.Specifically, we do this by applying scalar that ranges from 1 when vaccine coverage a month ago was zero to0.5 when vaccine coverage a month ago was 75%. This scalar is multiplied by the location-specific projectionsof the percent of mandates that are in place. As data emerges in places with high levels of vaccination, wewill modify this assumption in future iterations of the model.
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Current situation
Figure 1. Reported daily COVID-19 cases
0
1,000
2,000
3,000
Mar 20 Apr 20 May 20 Jun 20 Jul 20 Aug 20 Sep 20 Oct 20 Nov 20 Dec 20 Jan 21 Feb 21Month
Cou
nt
Daily cases
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Table 1. Ranking of COVID-19 among the leading causes of mortality this week, assuming uniform deathsof non-COVID causes throughout the year
Cause name Weekly deaths RankingIschemic heart disease 54 1COVID-19 39 2Chronic obstructive pulmonary disease 26 3Tracheal, bronchus, and lung cancer 22 4Stroke 22 5Alzheimer’s disease and other dementias 16 6Chronic kidney disease 12 7Colon and rectum cancer 10 8Lower respiratory infections 9 9Diabetes mellitus 8 10
Figure 2a. Reported daily COVID-19 deaths
0.00
10.00
20.00
30.00
40.00
50.00
Mar 20 Apr 20 May 20 Jun 20 Jul 20 Aug 20 Sep 20 Oct 20 Nov 20 Dec 20 Jan 21 Feb 21
Dai
ly d
eath
s
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Figure 2b. Estimated cumulative deaths by age group
0
5
10
15
<5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99Age group
Sha
re o
f cum
ulat
ive
deat
hs, %
Figure 3. Mean effective R on January 14, 2021. The estimate of effective R is based on the combinedanalysis of deaths, case reporting, and hospitalizations where available. Current reported cases reflectinfections 11-13 days prior, so estimates of effective R can only be made for the recent past. Effective R lessthan 1 means that transmission should decline, all other things being held the same.
<0.84
0.84−0.87
0.88−0.91
0.92−0.95
0.96−0.99
1−1.03
1.04−1.07
1.08−1.11
1.12−1.15
>=1.16
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Figure 4. Estimated percent of the population infected with COVID-19 on January 25, 2021
<5
5−9.9
10−14.9
15−19.9
20−24.9
25−29.9
>=30
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Figure 5. Daily COVID-19 death rate per 1 million on January 25, 2021
<1
1 to 1.9
2 to 2.9
3 to 3.9
4 to 4.9
5 to 5.9
6 to 6.9
7 to 7.9
>=8
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Critical drivers
Table 2. Current mandate implementation
Prim
ary
scho
ol c
losu
re
Sec
onda
ry s
choo
l clo
sure
Hig
her
scho
ol c
losu
re
Bor
ders
clo
sed
to a
ny n
on−
resi
dent
Bor
ders
clo
sed
to a
ll no
n−re
side
nts
Indi
vidu
al m
ovem
ents
res
tric
ted
Cur
few
for
busi
ness
es
Indi
vidu
al c
urfe
w
Gat
herin
g lim
it: 6
indo
or, 1
0 ou
tdoo
r
Gat
herin
g lim
it: 1
0 in
door
, 25
outd
oor
Gat
herin
g lim
it: 2
5 in
door
, 50
outd
oor
Gat
herin
g lim
it: 5
0 in
door
, 100
out
door
Gat
herin
g lim
it: 1
00 in
door
, 250
out
door
Res
taur
ants
clo
sed
Bar
s cl
osed
Res
taur
ants
/ ba
rs c
lose
d
Res
taur
ants
/ ba
rs c
urbs
ide
only
Gym
s, p
ools
, oth
er le
isur
e cl
osed
Non
−es
sent
ial r
etai
l clo
sed
Non
−es
sent
ial r
etai
l cur
bsid
e on
ly
Non
−es
sent
ial w
orkp
lace
s cl
osed
Sta
y ho
me
orde
r
Sta
y ho
me
fine
Mas
k m
anda
te
Mas
k m
anda
te fi
ne
WyomingWisconsin
West VirginiaWashington
VirginiaVermont
UtahTexas
TennesseeSouth Dakota
South CarolinaRhode IslandPennsylvania
OregonOklahoma
OhioNorth Dakota
North CarolinaNew York
New MexicoNew Jersey
New HampshireNevada
NebraskaMontanaMissouri
MississippiMinnesota
MichiganMassachusetts
MarylandMaine
LouisianaKentucky
KansasIowa
IndianaIllinoisIdaho
HawaiiGeorgiaFlorida
District of ColumbiaDelaware
ConnecticutColoradoCaliforniaArkansas
ArizonaAlaska
Alabama
Mandate in place
Mandate in place (implemented this week)
Mandate in place (update from previous reporting)
No mandate
No mandate (lifted this week)
No mandate (update from previous reporting)
*Not all locations are measured at the subnational level.
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Figure 6. Total number of social distancing mandates (including mask use)
WyomingWisconsin
West VirginiaWashington
VirginiaVermont
UtahTexas
TennesseeSouth Dakota
South CarolinaRhode IslandPennsylvania
OregonOklahoma
OhioNorth Dakota
North CarolinaNew York
New MexicoNew Jersey
New HampshireNevada
NebraskaMontanaMissouri
MississippiMinnesota
MichiganMassachusetts
MarylandMaine
LouisianaKentucky
KansasIowa
IndianaIllinoisIdaho
HawaiiGeorgiaFlorida
District of ColumbiaDelaware
ConnecticutColoradoCaliforniaArkansas
ArizonaAlaska
Alabama
Mar
20
Apr 2
0
May
20
Jun
20
Jul 2
0
Aug 2
0
Sep 2
0
Oct 20
Nov 2
0
Dec 2
0
Jan
21
Feb
21
# of mandates
0
1−5
6−10
11−15
16−20
20−25
Mandate imposition timing
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Figure 7a. Trend in mobility as measured through smartphone app use compared to January 2020 baseline
−50
−25
0
Jan 20 Feb 20 Mar 20 Apr 20 May 20 Jun 20 Jul 20 Aug 20 Sep 20 Oct 20 Nov 20 Dec 20 Jan 21 Feb 21
Per
cent
red
uctio
n fr
om a
vera
ge m
obili
ty
California Florida Nebraska New York Texas
Figure 7b. Mobility level as measured through smartphone app use compared to January 2020 baseline(percent) on January 25, 2021
=<−50
−49 to −45
−44 to −40
−39 to −35
−34 to −30
−29 to −25
−24 to −20
−19 to −15
−14 to −10
>−10
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Figure 7c. Trend in visits to restaurants as measured through cell phone data compared to 2019 average
WYWI
WVWAVAVTUTTXTNSDSCRIPA
OROKOHNDNCNYNMNJNHNVNEMTMOMSMNMI
MAMDMELAKYKSIAINILIDHI
GAFLDCDECTCOCAARAZAKAL
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
% of 2019 mean
>=120
100−119
80−99
60−79
40−59
20−39
<20
Figure 7d. Trend in visits to bars as measured through cell phone data compared to 2019 average
WYWI
WVWAVAVTUTTXTNSDSCRIPA
OROKOHNDNCNYNMNJNHNVNEMTMOMSMNMI
MAMDMELAKYKSIAINILIDHI
GAFLDCDECTCOCAARAZAKAL
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
% of 2019 mean
>=120
100−119
80−99
60−79
40−59
20−39
<20
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Figure 7e. Trend in visits to elementary & secondary schools as measured through cell phone data comparedto 2019 average
WYWI
WVWAVAVTUTTXTNSDSCRIPA
OROKOHNDNCNYNMNJNHNVNEMTMOMSMNMI
MAMDMELAKYKSIAINILIDHI
GAFLDCDECTCOCAARAZAKAL
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
% of 2019 mean
>=120
100−119
80−99
60−79
40−59
20−39
<20
Figure 7f. Trend in visits to department stores as measured through cell phone data compared to 2019average
WYWI
WVWAVAVTUTTXTNSDSCRIPA
OROKOHNDNCNYNMNJNHNVNEMTMOMSMNMI
MAMDMELAKYKSIAINILIDHI
GAFLDCDECTCOCAARAZAKAL
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
% of 2019 mean
>=120
100−119
80−99
60−79
40−59
20−39
<20
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Figure 8a. Trend in the proportion of the population reporting always wearing a mask when leaving home
0
20
40
60
80
Jan 20 Feb 20 Mar 20 Apr 20 May 20 Jun 20 Jul 20 Aug 20 Sep 20 Oct 20 Nov 20 Dec 20 Jan 21 Feb 21
Per
cent
of p
opul
atio
n
California Florida Nebraska New York Texas
Figure 8b. Proportion of the population reporting always wearing a mask when leaving home on January25, 2021
<50%
50 to 54%
55 to 59%
60 to 64%
65 to 69%
70 to 74%
75 to 79%
80 to 84%
85 to 89%
>=90%
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Figure 9a. Trend in COVID-19 diagnostic tests per 100,000 people
0
300
600
900
1200
Jan 20 Feb 20 Mar 20 Apr 20 May 20 Jun 20 Jul 20 Aug 20 Sep 20 Oct 20 Nov 20 Dec 20 Jan 21 Feb 21
Test
per
100
,000
pop
ulat
ion
California Florida Nebraska New York Texas
Figure 9b. COVID-19 diagnostic tests per 100,000 people on January 20, 2021
<5
5 to 9.9
10 to 24.9
25 to 49
50 to 149
150 to 249
250 to 349
350 to 449
450 to 499
>=500
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Figure 10. Increase in the risk of death due to pneumonia on February 1 2020 compared to August 1 2020
<−80%
−80 to −61%
−60 to −41%
−40 to −21%
−20 to −1%
0 to 19%
20 to 39%
40 to 59%
60 to 79%
>=80%
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Figure 11. This figure shows the estimated proportion of the adult (18+) population that is open toreceiving a COVID-19 vaccine based on Facebook survey responses (yes and unsure).
<50%
50−59%
60−69%
70−74%
75−79%
80−84%
>85%
Figure 12. The number of people who receive any vaccine and those who are immune, accounting for efficacy,loss to follow up for two-dose vaccines, partial immunity after one dose, and immunity after two doses.
0.00
300,000.00
600,000.00
900,000.00
0
20
40
60
Dec 2
0
Jan
21
Feb
21
Mar
21
Apr 2
1
May
21
Peo
ple
Percent of adult population
Reference rollout Rapid rollout
Solid lines represent the total vaccine doses, dashed lines represent effective vaccination
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Projections and scenarios
We produce four scenarios when projecting COVID-19. The reference scenario is our forecast of what wethink is most likely to happen. Vaccines are distributed at the expected pace. Governments adapt theirresponse by re-imposing social distancing mandates for 6 weeks whenever daily deaths reach 8 per million,unless a location has already spent at least 7 of the last 14 days with daily deaths above this rate and not yetre-imposed social distancing mandates, in which case mandates are re-imposed when daily deaths reach 15per million. Variant B.1.1.7 (first identified in the UK) continues to spread in locations where 100 or moreisolates have been detected to date.
The rapid variant spread scenario shares assumptions with reference but variant B.1.351 (first identified inSouth Africa) spreads to everywhere in the world, starting Feb. 1, 2021. Variant B.1.351 spreads at theobserved rate that B.1.1.7 spread in London. The variant is assumed to increase the infection-fatality rateby 29% and transmissibility by 25%. This scenario also assumes that those vaccinated are less effectivelyprotected against variant B.1.351: Pfizer, Moderna, Janssen, and Novavax clinical effectiveness is reducedby 20%; all other vaccines clinical effectiveness is reduced by 50%. Governments adapt their response byre-imposing social distancing mandates for 6 weeks whenever daily deaths reach 8 per million, unless a locationhas already spent at least 7 of the last 14 days with daily deaths above this rate and not yet re-imposedsocial distancing mandates, in which case mandates are re-imposed when daily deaths reach 15 per million.Variant B.1.1.7 (first identified in the UK) continues to spread in locations where 100 or more isolates havebeen detected to date.
The worst case scenario makes the same assumptions as the rapid variant spread scenario but also assumedthat in those that are vaccinated mobility moves towards pre-COVID-19 levels.
The universal masks scenario makes all the same assumptions as the reference scenario but also assumes 95%mask usage adopted in public in every location.
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Figure 13. Cumulative COVID-19 deaths until May 01, 2021 for four scenarios
0
500
1,000
1,500
2,000
0
30
60
90
120
Oct 20 Nov 20 Dec 20 Jan 21 Feb 21 Mar 21 Apr 21 May 21
Cum
ulat
ive
deat
hsC
umulative deaths per 100,000
Reference scenario
Rapid Variant Spread
Universal mask use
Worst case
Figure 14. Daily COVID-19 deaths until May 01, 2021 for four scenarios
0
10
20
0.0
0.5
1.0
Feb 20 Apr 20 Jun 20 Aug 20 Oct 20 Dec 20 Feb 21 Apr 21
Dai
ly d
eath
sD
aily deaths per 100,000
Reference scenario
Rapid Variant Spread
Universal mask use
Worst case
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Figure 15. Daily COVID-19 infections until May 01, 2021 for four scenarios
0
1,000
2,000
3,000
4,000
0
50
100
150
200
Feb 20 Apr 20 Jun 20 Aug 20 Oct 20 Dec 20 Feb 21 Apr 21
Dai
ly in
fect
ions
Daily infections per 100,000
Reference scenario
Rapid Variant Spread
Universal mask use
Worst case
Figure 16. Estimated percentage immune based on cumulative infections and vaccinations. We assume thatvaccine impact on transmission is 50% of the vaccine effectiveness for severe disease
0
250,000
500,000
750,000
1,000,000
0
10
20
30
40
50
Oct 20 Nov 20 Dec 20 Jan 21 Feb 21 Mar 21 Apr 21 May 21
Peo
ple
imm
une
Percent im
mune
Reference scenario
Rapid variant spread
Universal mask use
Worst case
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Figure 17. Month of assumed mandate re-implementation. We assume that governments adapt theirresponse by re-imposing social distancing mandates for 6 weeks whenever daily deaths reach 8 per million,unless a location has already spent at least 7 of the last 14 days with daily deaths above this rate and not yetre-imposed social distancing mandates, in which case mandates are assumed to be re-imposed when dailydeaths reach 15 per million.
January 2021
February 2021
March 2021
April 2021No mandates before May 1 2021
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Figure 18. Forecasted percent infected with COVID-19 on May 01, 2021
<5
5−9.9
10−14.9
15−19.9
20−24.9
25−29.9
30−34.9
>=35
Figure 19. Daily COVID-19 deaths per million forecasted on May 01, 2021 in the reference scenario
<1
1 to 1.9
2 to 2.9
3 to 3.9
4 to 4.9
5 to 5.9
6 to 6.9
7 to 7.9
>=8
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Figure 20. Comparison of reference model projections with other COVID modeling groups. For thiscomparison, we are including projections of daily COVID-19 deaths from other modeling groups when available:Delphi from the Massachussets Institute of Technology (Delphi; https://www.covidanalytics.io/home),Imperial College London (Imperial; https://www.covidsim.org), The Los Alamos National Laboratory (LANL;https://covid-19.bsvgateway.org/), and the SI-KJalpha model from the University of Southern California(SIKJalpha; https://github.com/scc-usc/ReCOVER-COVID-19). Daily deaths from other modeling groupsare smoothed to remove inconsistencies with rounding. Regional values are aggregates from availble locationsin that region.
0.00
2.50
5.00
7.50
Feb 21 Mar 21 Apr 21 May 21Date
Dai
ly d
eath
s
Models
IHME
Delphi
LANL
SIKJalpha
NA
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Figure 21. The estimated inpatient hospital usage is shown over time. The percent of hospital beds occupiedby COVID-19 patients is color coded based on observed quantiles of the maximum proportion of beds occupiedby COVID-19 patients. Less than 5% is considered low stress, 5-9% is considered moderate stress, 10-19% isconsidered high stress, and greater than 20% is considered extreme stress.
WyomingWisconsin
West VirginiaWashington, DC
WashingtonVirginia
VermontUtah
TexasTennessee
South DakotaSouth Carolina
Rhode IslandPennsylvania
OregonOklahoma
OhioNorth Dakota
North CarolinaNew York
New MexicoNew Jersey
New HampshireNevada
NebraskaMontanaMissouri
MississippiMinnesota
MichiganMassachusetts
MarylandMaine
LouisianaKentucky
KansasIowa
IndianaIllinoisIdaho
HawaiiGeorgiaFlorida
DelawareConnecticut
ColoradoCaliforniaArkansas
ArizonaAlaska
Alabama
Apr 20 Jun 20 Aug 20 Oct 20 Dec 20 Feb 21 Apr 21
Stress level
Low
Moderate
High
Extreme
All hospital beds
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Figure 22. The estimated intensive care unit (ICU) usage is shown over time. The percent of ICU bedsoccupied by COVID-19 patients is color coded based on observed quantiles of the maximum proportion ofICU beds occupied by COVID-19 patients. Less than 10% is considered low stress, 10-29% is consideredmoderate stress, 30-59% is considered high stress, and greater than 60% is considered extreme stress.
WyomingWisconsin
West VirginiaWashington, DC
WashingtonVirginia
VermontUtah
TexasTennessee
South DakotaSouth Carolina
Rhode IslandPennsylvania
OregonOklahoma
OhioNorth Dakota
North CarolinaNew York
New MexicoNew Jersey
New HampshireNevada
NebraskaMontanaMissouri
MississippiMinnesota
MichiganMassachusetts
MarylandMaine
LouisianaKentucky
KansasIowa
IndianaIllinoisIdaho
HawaiiGeorgiaFlorida
DelawareConnecticut
ColoradoCaliforniaArkansas
ArizonaAlaska
Alabama
Apr 20 Jun 20 Aug 20 Oct 20 Dec 20 Feb 21 Apr 21
Stress level
Low
Moderate
High
Extreme
Intensive care unit beds
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Table 3. Ranking of COVID-19 among the leading causes of mortality in the full year 2020. Deaths fromCOVID-19 are projections of cumulative deaths on Jan 1, 2021 from the reference scenario. Deaths fromother causes are from the Global Burden of Disease study 2019 (rounded to the nearest 100).
Cause name Annual deaths RankingIschemic heart disease 2,800 1COVID-19 1,629 2Chronic obstructive pulmonary disease 1,300 3Tracheal, bronchus, and lung cancer 1,200 4Stroke 1,100 5Alzheimer’s disease and other dementias 800 6Chronic kidney disease 600 7Colon and rectum cancer 500 8Lower respiratory infections 500 9Diabetes mellitus 400 10
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More information
Data sources:
Mask use data sources include PREMISE; Facebook Global symptom survey (This research is based onsurvey results from University of Maryland Social Data Science Center) and the Facebook United Statessymptom survey (in collaboration with Carnegie Mellon University); Kaiser Family Foundation; YouGovCOVID-19 Behaviour Tracker survey.
Vaccine hesitancy data are from the COVID-19 Beliefs, Behaviors, and Norms Study, a survey conducted onFacebook by the Massachusetts Institute of Technology (https://covidsurvey.mit.edu/).
Data on vaccine candidates, stages of development, manufacturing capacity, and pre-purchasing agreementsare primarily from Linksbridge and supplemented by Duke University.
A note of thanks:
We wish to warmly acknowledge the support of these and others who have made our covid-19 estimationefforts possible.
More information:
For all COVID-19 resources at IHME, visit http://www.healthdata.org/covid.
Questions? Requests? Feedback? Please contact us at https://www.healthdata.org/covid/contact-us.
covid19.healthdata.org 28 Institute for Health Metrics and Evaluation