United States Model updates for September 17, 2020 covid.healthdata.org Institute for Health Metrics and Evaluation Cases have stayed constant but deaths have been steadily declining in the last week. Our forecasts of cumulative deaths by January 1 have declined to 378,000, down from 415,000 as of the last projection on September 10. Even if herd immunity were to occur at a low level, such as 40% (the level seen in Mexico City now) with cumulative infections, by January 1, the US epidemic would only be less than half over. Current situation • Confirmed cases remain relative constant since the end of August (Figure 1). • Daily deaths have been steadily declining, reaching 650 per day in the last week, although COVID-19 remains the second leading cause of death in the nation (Figure 2 and Table 1). • Effective R based on the combined analysis of cases, hospitalizations, and deaths remains above 1 in Nebraska, Kansas, Tennessee, and New Jersey (Figure 3). • Given widespread discussion of the possibility of herd immunity at low levels of cumulative infection in the US, we note that New Jersey has already reached a cumulative percentage infected of 25%, still well below the levels of greater than 40% seen in Mexico City, Ecuador, and parts of Brazil (Figure 4). Trends in key drivers of transmission (mobility, mask use, testing, and seasonality) • In the last week, the only change in state-level mandates was an easing of educational restrictions in West Virginia and removal of a mask mandate in Alaska (Table 2). • The longer-term trend in mobility has remained quite constant in the last week. Some cell phone data suggest a substantial decline over the Labor Day weekend, similar to the decline seen on the July 4 th holiday. We do not expect this decline to be sustained in the next week so it has not been included in this analysis • There is slightly less of a decline in mask use as compared to one week prior (Figure 9). Projections • Our forecast of cumulative deaths by January 1 has declined from 415,000 on September 10 to 378,320 today. This decline is driven by steeper than expected declines seen in deaths in several states. In our model, these declines push out further into January and February the expected seasonal surge, reducing the number of deaths expected by January 1. We expect over 3,000 deaths per day by the end of December. • Between now and January 1 we expect approximately 180,000 deaths. Increasing mask use to 95% can save nearly 115,000 lives, reducing that expected number of deaths by 62.7%. Model updates • With each re-estimation of the regression coefficients over the last 3 months for predicting b(t), the transmission parameter, the coefficient on testing per capita has tended to get closer to 0. In many of the 1,000 models, the coefficient is now 0. This declining role of testing in reducing transmission seen empirically may have several explanations. First, many tests are being conducted but results are not being returned fast enough to impact transmission. Second, since most testing is still in symptomatic individuals, testing per capita may be poorly correlated with actual testing of contacts that may have a larger impact on reducing transmission. Third, when the epidemic starts to increase, testing of symptomatic individuals increases and vice versa. • Given considerable public discussion of the role of herd immunity in explaining peaks and subsequent declines in the daily death and case rates, we have explored the implied total death rate for each state based on the infection-fatality rate (IFR) and different assumptions about the level of cumulative infection that will be associated with herd immunity. The natural experiment of the Charles de Gaulle aircraft carrier suggests that up to 70% of individuals can get infected in a situation of near-random mixing. But various theories, including the
Transcript
United States Model updates for September 17, 2020
covid.healthdata.org Institute for Health Metrics and Evaluation
Cases have stayed constant but deaths have been steadily declining in the last week. Our forecasts of cumulative deaths by January 1 have declined to 378,000, down from 415,000 as of the last projection on September 10. Even if herd immunity were to occur at a low level, such as 40% (the level seen in Mexico City now) with cumulative infections, by January 1, the US epidemic would only be less than half over.
Current situation
• Confirmed cases remain relative constant since the end of August (Figure 1). • Daily deaths have been steadily declining, reaching 650 per day in the last week, although COVID-19 remains the
second leading cause of death in the nation (Figure 2 and Table 1). • Effective R based on the combined analysis of cases, hospitalizations, and deaths remains above 1 in Nebraska,
Kansas, Tennessee, and New Jersey (Figure 3). • Given widespread discussion of the possibility of herd immunity at low levels of cumulative infection in the US,
we note that New Jersey has already reached a cumulative percentage infected of 25%, still well below the levels of greater than 40% seen in Mexico City, Ecuador, and parts of Brazil (Figure 4).
Trends in key drivers of transmission (mobility, mask use, testing, and seasonality)
• In the last week, the only change in state-level mandates was an easing of educational restrictions in West Virginia and removal of a mask mandate in Alaska (Table 2).
• The longer-term trend in mobility has remained quite constant in the last week. Some cell phone data suggest a substantial decline over the Labor Day weekend, similar to the decline seen on the July 4th holiday. We do not expect this decline to be sustained in the next week so it has not been included in this analysis
• There is slightly less of a decline in mask use as compared to one week prior (Figure 9).
Projections
• Our forecast of cumulative deaths by January 1 has declined from 415,000 on September 10 to 378,320 today. This decline is driven by steeper than expected declines seen in deaths in several states. In our model, these declines push out further into January and February the expected seasonal surge, reducing the number of deaths expected by January 1. We expect over 3,000 deaths per day by the end of December.
• Between now and January 1 we expect approximately 180,000 deaths. Increasing mask use to 95% can save nearly 115,000 lives, reducing that expected number of deaths by 62.7%.
Model updates
• With each re-estimation of the regression coefficients over the last 3 months for predicting b(t), the transmission parameter, the coefficient on testing per capita has tended to get closer to 0. In many of the 1,000 models, the coefficient is now 0. This declining role of testing in reducing transmission seen empirically may have several explanations. First, many tests are being conducted but results are not being returned fast enough to impact transmission. Second, since most testing is still in symptomatic individuals, testing per capita may be poorly correlated with actual testing of contacts that may have a larger impact on reducing transmission. Third, when the epidemic starts to increase, testing of symptomatic individuals increases and vice versa.
• Given considerable public discussion of the role of herd immunity in explaining peaks and subsequent declines in the daily death and case rates, we have explored the implied total death rate for each state based on the infection-fatality rate (IFR) and different assumptions about the level of cumulative infection that will be associated with herd immunity. The natural experiment of the Charles de Gaulle aircraft carrier suggests that up to 70% of individuals can get infected in a situation of near-random mixing. But various theories, including the
United States Model updates for September 17, 2020
covid.healthdata.org Institute for Health Metrics and Evaluation
role of super-spreaders, non-random mixing in less dense populations, non-overlapping social networks, and some prior coronavirus immunity, have led to the belief that herd immunity may take place at much lower levels of cumulative infection.
• In social media, there are unsupported claims that herd immunity may occur at levels below 20% cumulative infection. However, in the US, New Jersey is already at 25%, and multiple locations in Latin America are now at over 40%. Our IFR based on the analysis of seroprevalence data and herd immunity at 40% cumulative infection would suggest the US will eventually see 815,000 deaths; with herd immunity at 50% cumulative infection, the figure would be 1,018,000 deaths, and at 65% it would be 1,324,000 deaths. Scale-up of a vaccine or improved treatments could substantially reduce these estimates. These calculations only serve to suggest that the epidemic in the US is far from complete and, even in the optimistic scenario of herd immunity kicking in at 40% cumulative infection, is less than halfway through the epidemic on January 1.
IHME wishes to warmly acknowledge the support of these and others who have made our COVID-19 estimation efforts possible. Thank you.
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.
COVID-19 Results Briefing: United States of America
Institute for Health Metrics and Evaluation (IHME)
September 17, 2020
This briefing contains summary information on the latest projections from the IHME model on COVID-19 inUnited States of America. The model was run on September 16, 2020.
Model updates
Updates to the model this week include additional data on deaths, cases, and updates on covariates.
covid19.healthdata.org 1 Institute for Health Metrics and Evaluation
United States of America CURRENT SITUATION
Current situation
Figure 1. Reported daily COVID-19 cases
0
20,000
40,000
60,000
80,000
Feb Mar Apr May Jun Jul Aug SepMonth
Cou
nt
Daily cases
covid19.healthdata.org 2 Institute for Health Metrics and Evaluation
United States of America CURRENT SITUATION
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 10,724 1COVID-19 4,551 2Tracheal, bronchus, and lung cancer 3,965 3Chronic obstructive pulmonary disease 3,766 4Stroke 3,643 5Alzheimer’s disease and other dementias 2,768 6Chronic kidney disease 2,057 7Colon and rectum cancer 1,616 8Lower respiratory infections 1,575 9Diabetes mellitus 1,495 10
Figure 2a. Reported daily COVID-19 deaths and smoothed trend estimate
0
1,000
2,000
Feb Mar Apr May Jun Jul Aug Sep
Dai
ly d
eath
s
covid19.healthdata.org 3 Institute for Health Metrics and Evaluation
United States of America CURRENT SITUATION
Figure 2b. Estimated cumulative deaths by age group
Figure 3. Mean effective R on September 03, 2020. The estimate of effective R is based on the combinedanalysis of deaths, case reporting and hospitalizations where available. Current reported cases reflect infections11-13 days prior so estimates of effective R can only be made for the recent past. Effective R less than 1means that transmission should decline all other things being held the same.
<0.79
0.79−0.83
0.84−0.89
0.9−0.94
0.95−0.99
1−1.04
1.05−1.09
1.1−1.15
1.16−1.2
>=1.21
covid19.healthdata.org 4 Institute for Health Metrics and Evaluation
United States of America CURRENT SITUATION
Figure 4. Estimated percent infected with COVID-19 on September 14, 2020
<1
1−3.9
4−6.9
7−9.9
10−13.4
13.5−16.4
16.5−19.4
19.5−22.4
22.5−25.4
>=25.5
Figure 5. Percent of COVID-19 infections detected. This is estimated as the ratio of reported COVID-19cases to estimated COVID-19 infections based on the SEIR model.
0
20
40
Feb Mar Apr May Jun Jul Aug Sep
Per
cent
of i
nfec
tions
det
ecte
d
Republic of Korea Italy United Kingdom United States of America
covid19.healthdata.org 5 Institute for Health Metrics and Evaluation
United States of America CURRENT SITUATION
Figure 6. Daily COVID-19 death rate per 1 million on September 14, 2020
<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
covid19.healthdata.org 6 Institute for Health Metrics and Evaluation
United States of America CRITICAL DRIVERS
Critical drivers
Table 2. Current mandate implementation
All
gath
erin
gs r
estr
icte
d
All
none
ssen
tial b
usin
esse
s cl
osed
Any
bus
ines
ses
rest
ricte
d
Mas
k us
e
Sch
ool c
losu
re
Sta
y ho
me
orde
r
Trav
el li
mits
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 No mandate
covid19.healthdata.org 7 Institute for Health Metrics and Evaluation
United States of America CRITICAL DRIVERS
Figure 7. Total number of social distancing mandates (not 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
Feb Mar Apr May Jun Jul Aug Sep
# of mandates
0
1
2
3
4
5
6
7
Mandate imposition timing
covid19.healthdata.org 8 Institute for Health Metrics and Evaluation
United States of America CRITICAL DRIVERS
Figure 8a. Trend in mobility as measured through smartphone app use compared to January 2020 baseline
−80
−60
−40
−20
0
Jan Feb Mar Apr May Jun Jul Aug Sep
Per
cent
red
uctio
n fr
om a
vera
ge m
obili
ty
Republic of Korea Italy United Kingdom United States of America
Figure 8b. Mobility level as measured through smartphone app use compared to January 2020 baseline(percent)
=<−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
covid19.healthdata.org 9 Institute for Health Metrics and Evaluation
United States of America CRITICAL DRIVERS
Figure 9a. Trend in the proportion of the population reporting always wearing a mask when leaving home
0
25
50
75
Jan Feb Mar Apr May Jun Jul Aug Sep
Per
cent
of p
opul
atio
n
Republic of Korea Italy United Kingdom United States of America
Figure 9b. Proportion of the population reporting always wearing a mask when leaving home on September14, 2020
<30%
30 to 34%
35 to 39%
40 to 44%
45 to 49%
50 to 54%
55 to 59%
60 to 64%
65 to 69%
>=70
covid19.healthdata.org 10 Institute for Health Metrics and Evaluation
United States of America CRITICAL DRIVERS
Figure 10a. Trend in COVID-19 diagnostic tests per 100,000 people
0
50
100
150
200
250
Jan Feb Mar Apr May Jun Jul Aug Sep
Test
per
100
,000
pop
ulat
ion
Republic of Korea Italy United Kingdom United States of America
Figure 10b. COVID-19 diagnostic tests per 100,000 people on September 09, 2020
<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
covid19.healthdata.org 11 Institute for Health Metrics and Evaluation
United States of America CRITICAL DRIVERS
Figure 11. Increase in the risk of death due to pneumonia on February 1 compared to August 1
<−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%
covid19.healthdata.org 12 Institute for Health Metrics and Evaluation
United States of America PROJECTIONS AND SCENARIOS
Projections and scenarios
We produce three scenarios when projecting COVID-19. The reference scenario is our forecast of what wethink is most likely to happen. We assume that if the daily mortality rate from COVID-19 reaches 8 permillion, social distancing (SD) mandates will be re-imposed. The mandate easing scenario is what wouldhappen if governments continue to ease social distancing mandates with no re-imposition. The universal maskmandate scenario is what would happen if mask use increased immediately to 95% and social distancingmandates were re-imposed at 8 deaths per million.
Figure 12. Cumulative COVID-19 deaths until January 01, 2021 for three scenarios.
0
100,000
200,000
300,000
400,000
0
50
100
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
Cum
ulat
ive
deat
hsC
umulative deaths per 100,000
Continued SD mandate easing
Reference scenario
Universal mask use
Fig 13. Daily COVID-19 deaths until January 01, 2021 for three scenarios.
0
2,000
4,000
6,000
8,000
0.0
0.5
1.0
1.5
2.0
2.5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
Dai
ly d
eath
sD
aily deaths per 100,000
Continued SD mandate easing
Reference scenario
Universal mask use
covid19.healthdata.org 13 Institute for Health Metrics and Evaluation
United States of America PROJECTIONS AND SCENARIOS
Fig 14. Daily COVID-19 infections until January 01, 2021 for three scenarios.
0
500,000
1,000,000
1,500,000
0
100
200
300
400
500
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
Dai
ly in
fect
ions
Daily infections per 100,000
Continued SD mandate easing
Reference scenario
Universal mask use
Fig 15. Month of assumed mandate re-implementation. (Month when daily death rate passes 8 per million,when reference scenario model assumes mandates will be re-imposed.)
September
October
November
DecemberNo mandates before Jan 1
covid19.healthdata.org 14 Institute for Health Metrics and Evaluation
United States of America PROJECTIONS AND SCENARIOS
Figure 16. Forecasted percent infected with COVID-19 on January 01, 2021
<1
1−3.9
4−6.9
7−9.9
10−13.4
13.5−16.4
16.5−19.4
19.5−22.4
22.5−25.4
>=25.5
Figure 17. Daily COVID-19 deaths per million forecasted on January 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
covid19.healthdata.org 15 Institute for Health Metrics and Evaluation
United States of America PROJECTIONS AND SCENARIOS
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 557,600 1COVID-19 378,321 2Tracheal, bronchus, and lung cancer 206,200 3Chronic obstructive pulmonary disease 195,800 4Stroke 189,500 5Alzheimer’s disease and other dementias 143,900 6Chronic kidney disease 107,000 7Colon and rectum cancer 84,000 8Lower respiratory infections 81,900 9Diabetes mellitus 77,700 10
Mask data source: Premise; Facebook Global symptom survey (This research is based on survey resultsfrom University of Maryland Social Data Science Center); Kaiser Family Foundation; YouGov COVID-19Behaviour Tracker survey
A note of thanks:
We would like to extend a special thanks to the Pan American Health Organization (PAHO) for keydata sources; our partners and collaborators in Argentina, Brazil, Bolivia, Chile, Colombia, Cuba, theDominican Republic, Ecuador, Egypt, Honduras, Israel, Japan, Malaysia, Mexico, Moldova, Panama, Peru,the Philippines, Russia, Serbia, South Korea, Turkey, and Ukraine for their support and expert advice; andto the tireless data collection and collation efforts of individuals and institutions throughout the world.
In addition, we wish to express our gratitude for efforts to collect social distancing policy information inLatin America to University of Miami Institute for Advanced Study of the Americas (Felicia Knaul, MichaelTouchton), with data published here: http://observcovid.miami.edu/; Fundación Mexicana para la Salud(Héctor Arreola-Ornelas) with support from the GDS Services International: Tómatelo a Pecho A.C.; andCentro de Investigaciones en Ciencias de la Salud, Universidad Anáhuac (Héctor Arreola-Ornelas); Lab onResearch, Ethics, Aging and Community-Health at Tufts University (REACH Lab) and the University ofMiami Institute for Advanced Study of the Americas (Thalia Porteny).
Further, IHME is grateful to the Microsoft AI for Health program for their support in hosting our COVID-19data visualizations on the Azure Cloud. We would like to also extend a warm thank you to the many otherswho have made our COVID-19 estimation efforts possible.
covid19.healthdata.org 16 Institute for Health Metrics and Evaluation
